fbpx
Wikipedia

Artificial intelligence

January 04, 2024

"AI" redirects here. For other uses, see AI (disambiguation), Artificial intelligence (disambiguation), and Intelligent agent.

Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.

AI technology is widely used throughout industry, government, and science. Some high-profile applications are: advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Google Assistant, Siri, and Alexa), self-driving cars (e.g., Waymo), generative and creative tools (ChatGPT and AI art), and superhuman play and analysis in strategy games (such as chess and Go).[1]

Alan Turing was the first person to carry out substantial research in the field that he called Machine Intelligence.[2] Artificial intelligence was founded as an academic discipline in 1956.[3] The field went through multiple cycles of optimism[4][5] followed by disappointment and loss of funding.[6][7] Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques,[8] and after 2017 with the transformer architecture.[9] This led to the AI spring of the 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence.[10]

The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.[a] General intelligence (the ability to complete any task performable by a human) is among the field's long-term goals.[11]

To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.[b] AI also draws upon psychology, linguistics, philosophy, neuroscience and other fields.[12]

Goals

The general problem of simulating (or creating) intelligence has been broken into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.[a]

Reasoning, problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[13] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[14]

Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": they became exponentially slower as the problems grew larger.[15] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[16] Accurate and efficient reasoning is an unsolved problem.

Knowledge representation

 
An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.

Knowledge representation and knowledge engineering[17] allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,[18] scene interpretation,[19] clinical decision support,[20] knowledge discovery (mining "interesting" and actionable inferences from large databases),[21] and other areas.[22]

A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.[23] Knowledge bases need to represent things such as: objects, properties, categories and relations between objects;[24] situations, events, states and time;[25] causes and effects;[26] knowledge about knowledge (what we know about what other people know);[27] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[28] and many other aspects and domains of knowledge.

Among the most difficult problems in knowledge representation are: the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous);[29] and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally).[16] There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.[c]

Planning and decision making

An "agent" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen.[d][32] In automated planning, the agent has a specific goal.[33] In automated decision making, the agent has preferences – there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision making agent assigns a number to each situation (called the "utility") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.[34]

In classical planning, the agent knows exactly what the effect of any action will be.[35] In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.[36]

In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning) or the agent can seek information to improve its preferences.[37] Information value theory can be used to weigh the value of exploratory or experimental actions.[38] The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain what the outcome will be.

A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way, and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g. by iteration), be heuristic, or it can be learned.[39]

Game theory describes rational behavior of multiple interacting agents, and is used in AI programs that make decisions that involve other agents.[40]

Learning

Machine learning is the study of programs that can improve their performance on a given task automatically.[41] It has been a part of AI from the beginning.[e]

There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[44] Supervised learning requires a human to label the input data first, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).[45]

In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".[46] Transfer learning is when the knowledge gained from one problem is applied to a new problem.[47] Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.[48]

Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[49]

Natural language processing

Natural language processing (NLP)[50] allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.[51]

Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation[f] unless restricted to small domains called "micro-worlds" (due to the common sense knowledge problem[29]). Margaret Masterman believed that it was meaning, and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure.

Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning),[52] transformers (a deep learning architecture using an attention mechanism),[53] and others.[54] In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text,[55][56] and by 2023 these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.[57]

Perception

Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.[58]

The field includes speech recognition,[59] image classification,[60] facial recognition, object recognition,[61] and robotic perception.[62]

Social intelligence

 
Kismet, a robot head which was made in the 1990s; a machine that can recognize and simulate emotions.[63]

Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood.[64] For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.

However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents.[65] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.[66]

General intelligence

A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.[11]

Techniques

AI research uses a wide variety of techniques to accomplish the goals above.[b]

Search and optimization

AI can solve many problems by intelligently searching through many possible solutions.[67] There are two very different kinds of search used in AI: state space search and local search.

State space search

State space search searches through a tree of possible states to try to find a goal state.[68] For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[69]

Simple exhaustive searches[70] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes.[15] "Heuristics" or "rules of thumb" can help to prioritize choices that are more likely to reach a goal.[71]

Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position.[72]

Local search

 
Illustration of gradient descent for 3 different starting points. Two parameters (represented by the plan coordinates) are adjusted in order to minimize the loss function (the height).

Local search uses mathematical optimization to find a numeric solution to a problem. It begins with some form of a guess and then refines the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. This process is called stochastic gradient descent.[73]

Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses).[74]

Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[75]

Neural networks and statistical classifiers (discussed below), also use a form of local search, where the "landscape" to be searched is formed by learning.

Logic

Formal Logic is used for reasoning and knowledge representation.[76] Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies")[77] and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as "Every X is a Y" and "There are some Xs that are Ys").[78]

Logical inference (or deduction) is the process of proving a new statement (conclusion) from other statements that are already known to be true (the premises).[79] A logical knowledge base also handles queries and assertions as a special case of inference.[80] An inference rule describes what is a valid step in a proof. The most general inference rule is resolution.[81] Inference can be reduced to performing a search to find a path that leads from premises to conclusions, where each step is the application of an inference rule.[82] Inference performed this way is intractable except for short proofs in restricted domains. No efficient, powerful and general method has been discovered.

Fuzzy logic assigns a "degree of truth" between 0 and 1 and handles uncertainty and probabilistic situations.[83]Non-monotonic logics are designed to handle default reasoning.[28] Other specialized versions of logic have been developed to describe many complex domains (see knowledge representation above).

Probabilistic methods for uncertain reasoning

 
Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.

Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.[84]

Bayesian networks[85] are a very general tool that can be used for many problems, including reasoning (using the Bayesian inference algorithm),[g][87] learning (using the expectation-maximization algorithm),[h][89] planning (using decision networks)[90] and perception (using dynamic Bayesian networks).[91]

Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[91]

Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[92] and information value theory.[93] These tools include models such as Markov decision processes,[94] dynamic decision networks,[91] game theory and mechanism design.[95]

Classifiers and statistical learning methods

The simplest AI applications can be divided into two types: classifiers (e.g. "if shiny then diamond"), on one hand, and controllers (e.g. "if diamond then pick up"), on the other hand. Classifiers[96] are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an "observation") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[45]

There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm.[97] K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s.[98] The naive Bayes classifier is reportedly the "most widely used learner"[99] at Google, due in part to its scalability.[100]Neural networks are also used as classifiers.[101]

Artificial neural networks

 
A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.

An Artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns, once trained it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses it’s specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.[101]

Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm.[102] Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.[103]

In feedforward neural networks the signal passes in only one direction.[104]Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks.[105]Perceptrons[106] use only a single layer of neurons, deep learning[107] uses multiple layers. Convolutional neural networks strengthen the connection between neurons that are "close" to each other – this is especially important in image processing, where a local set of neurons must identify an "edge" before the network can identify an object.[108]

Deep learning

 

Deep learning[107] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[109]

Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification[110] and others. The reason that deep learning performs so well in so many applications is not known as of 2023.[111] The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s)[i] but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.[j]

GPT

Generative pre-trained transformers (GPT) are large language models that are based on the semantic relationships between words in sentences (natural language processing). Text-based GPT models are pre-trained on a large corpus of text which can be from the internet. The pre-training consists in predicting the next token (a token being usually a word, subword, or punctuation). Throughout this pre-training, GPT models accumulate knowledge about the world, and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful and harmless, usually with a technique called reinforcement learning from human feedback (RLHF). Current GPT models are still prone to generating falsehoods called "hallucinations", although this can be reduced with RLHF and quality data. They are used in chatbots which allow you to ask a question or request a task in simple text.[120][121]

Current models and services include: Bard, ChatGPT, Claude, Copilot and LLaMA.[122] Multimodal GPT models can process different types of data (modalities) such as images, videos, sound and text.[123]

Specialized hardware and software

Main articles: Programming languages for artificial intelligence and Hardware for artificial intelligence

In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software, had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training.[124] Historically, specialized languages, such as Lisp, Prolog, Python and others, had been used.

Applications

Main article: Applications of artificial intelligence

AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple's Face ID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used by Facebook, Apple's iPhoto and TikTok).

Health and Medicine

Main article: Artificial intelligence in healthcare

The application of AI in medicine and medical research has the potential to increase patient care and quality of life.[125] Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.

For medical research, AI is an important tool for processing and integrating Big Data. This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication.[126] It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research, such as cardiovascular research which typically receives a disproportionately less funding that areas such as cancer research, relative to the morbidity and mortality of these diseases.[126] New AI tools can deepen our understanding of biomedically relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein.[127]

Games

Main article: Game artificial intelligence

Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Then it defeated Ke Jie in 2017, who at the time continuously held the world No. 1 ranking for two years. Other programs handle imperfect-information games; such as for poker at a superhuman level, Pluribus and Cepheus. DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own. In 2021 an AI agent competed in a Playstation Gran Turismo competition, winning against four of the world’s best Gran Turismo drivers using deep reinforcement learning.[128]

Military

Main article: Military artificial intelligence

Various countries are deploying AI military applications. The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams. AI was incorporated into military operations in Iraq and Syria.

Generative AI

Main article: Generative artificial intelligence
 
Vincent van Gogh in watercolour created by generative AI software

In the early 2020s, generative AI gained widespread prominence. ChatGPT, based on GPT-3, and other large language models, were tried by 14% of Americans adults. The increasing realism and ease-of-use of AI-based text-to-image generators such as Midjourney, DALL-E, and Stable Diffusion sparked a trend of viral AI-generated photos. Widespread attention was gained by a fake photo of Pope Francis wearing a white puffer coat, the fictional arrest of Donald Trump, and a hoax of an attack on the Pentagon, as well as the usage in professional creative arts.

Industry Specific Tasks

There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported they had incorporated "AI" in some offerings or processes. A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management.

In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.

Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.

Ethics

AI, like any powerful technology, has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of Deep Mind hopes to "solve intelligence, and then use that to solve everything else".[129] However, as the use of AI has become widespread, several unintended consequences and risks have been identified.[130]

Anyone looking to use machine learning as part of real-world, in-production systems needs to factor ethics into their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that are inherently unexplainable in deep learning.[131]

Risks and harm

Privacy and copyright

Further information: Information privacy and Artificial intelligence and copyright

Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright.

Technology companies collect a wide range of data from their users, including online activity, geolocation data, video and audio.[132] For example, in order to build speech recognition algorithms, Amazon have recorded millions of private conversations and allowed temps to listen to and transcribe some of them.[133] Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.[134]

AI developers argue that this is the only way to deliver valuable applications. and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy.[135] Since 2016, some privacy experts, such as Cynthia Dwork, began to view privacy in terms of fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'.".[136]

Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under a rationale of "fair use". Also website owners who do not wish to have their copyrighted content be AI indexed or ‘scraped’ can add code to their site, as you would, if you did not want your website to be indexed by a search engine which is currently available to certain services such as OpenAI. Experts disagree about how well, and under what circumstances, this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work".[137] In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI.[138][139]

Misinformation

See also: YouTube § Moderation and offensive content

YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation.[140] This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government.[141] The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem.

In 2022, generative AI began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda.[142] AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.[143]

Algorithmic bias and fairness

Main articles: Algorithmic bias and Fairness (machine learning)

Machine learning applications will be biased if they learn from biased data.[144] The developers may not be aware that the bias exists.[145] Bias can be introduced by the way training data is selected and by the way a model is deployed.[146][144] If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination.[147]Fairness in machine learning is the study of how to prevent the harm caused by algorithmic bias. It has become serious area of academic study within AI. Researchers have discovered it is not always possible to define "fairness" in a way that satisfies all stakeholders.[148]

On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people,[149] a problem called "sample size disparity".[150] Google "fixed" this problem by preventing the system from labelling anything as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.[151]

COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend.[152] In 2017, several researchers[k] showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.[154]

A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender".[155] Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."[156]

Criticism of COMPAS highlighted a deeper problem with the misuse of AI. Machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. Unfortunately, if an application then uses these predictions as recommendations, some of these "recommendations" will likely be racist.[157] Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is necessarily descriptive and not proscriptive.[l]

Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.[150]

At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) the Association for Computing Machinery, in Seoul, South Korea, presented and published findings recommending that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.[159]

Lack of transparency

See also: Explainable AI, Algorithmic transparency, and Right to explanation
 
Lidar testing vehicle for autonomous driving

Many AI systems are so complex that their designers cannot explain how they reach their decisions.[160] Particularly with deep neural networks, in which there are a large amount of non-linear relationships between inputs and outputs. But some popular explainability techniques exist.[161]

There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to show the scale.[162] Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading.[163]

People who have been harmed by an algorithm's decision have a right to an explanation. Doctors, for example, are required to clearly and completely explain the reasoning behind any decision they make.[clarification needed][164] Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists.[m] Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used.[165]

DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try and solve these problems.[166]

There are several potential solutions to the transparency problem. SHAP helps visualise the contribution of each feature to the output.[167] LIME can locally approximate a model with a simpler, interpretable model.[168] Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned.[169] Deconvolution, DeepDream and other generative methods can allow developers to see what different layers of a deep network have learned and produce output that can suggest what the network is learning.[170]

Conflict, surveillance and weaponized AI

Main articles: Lethal autonomous weapon, Artificial intelligence arms race, and AI safety

A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision.[n] By 2015, over fifty countries were reported to be researching battlefield robots.[172] These weapons are considered especially dangerous for several reasons: if they kill an innocent person it is not clear who should be held accountable, it is unlikely they will reliably choose targets, and, if produced at scale, they are potentially weapons of mass destruction.[173] In 2014, 30 nations (including China) supported a ban on autonomous weapons under the United Nations' Convention on Certain Conventional Weapons, however the United States and others disagreed.[174]

AI provides a number of tools that are particularly useful for authoritarian governments: smart spyware, face recognition and voice recognition allow widespread surveillance; such surveillance allows machine learning to classify potential enemies of the state and can prevent them from hiding; recommendation systems can precisely target propaganda and misinformation for maximum effect; deepfakes and generative AI aid in producing misinformation; advanced AI can make authoritarian centralized decision making more competitive with liberal and decentralized systems such as markets.[175]

AI facial recognition systems are used for mass surveillance, notably in China.[176][177] In 2019, Bengaluru, India deployed AI-managed traffic signals. This system uses cameras to monitor traffic density and adjust signal timing based on the interval needed to clear traffic.[178] Terrorists, criminals and rogue states can use weaponized AI such as advanced digital warfare and lethal autonomous weapons. Machine-learning AI is also able to design tens of thousands of toxic molecules in a matter of hours.[179]

Technological unemployment

Main articles: Workplace impact of artificial intelligence and Technological unemployment

From the early days of the development of artificial intelligence there have been arguments, for example those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement.[180]

Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.[181]

In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.[182] A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed.[183] Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk".[o][185] The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies.[181]

Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".[186] Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.[187]

In April 2023, it was reported that 70% of the jobs for Chinese video game illlustrators had been eliminated by generative artificial intelligence.[188][189]

Existential risk

Main article: Existential risk from artificial general intelligence

It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, "spell the end of the human race".[190] This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character.[p] These sci-fi scenarios are misleading in several ways.

First, AI does not require human-like "sentience" to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a paperclip factory manager).[192] Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead."[193] In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is "fundamentally on our side".[194]

Second, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are made of language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.[195]

The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.[196] Personalities such as Stephen Hawking, Bill Gates, and Elon Musk have expressed concern about existential risk from AI.[197] In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine.[198] However, after 2016, the study of current and future risks and possible solutions became a serious area of research.[199] AI pioneers including Fei-Fei Li, Geoffrey Hinton, Yoshua Bengio, Cynthia Breazeal, Rana el Kaliouby, Demis Hassabis, Joy Buolamwini, and Sam Altman have expressed concerns about the risks of AI. In 2023, many leading AI experts issued the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".[200]

Other researchers, however, spoke in favor of a less dystopian view. AI pioneer Juergen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier."[201] While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors."[202][203] Andrew Ng also argued that "it’s a mistake to fall for the doomsday hype on AI — and that regulators who do will only benefit vested interests."[204] Yann LeCun "scoffs at his peers’ dystopian scenarios of supercharged misinformation and even, eventually, human extinction."[205]

Limiting AI

Possible options for limiting AI include: using Embedded Ethics or Constitutional AI where companies or governments can add a policy, restricting high levels of compute power in training, restricting the ability to rewrite its own code base, restrict certain AI techniques but not in the training phase, open-source (transparency) vs proprietary (could be more restricted), backup model with redundancy, restricting security, privacy and copyright, restricting or controlling the memory, real-time monitoring, risk analysis, emergency shut-off, rigorous simulation and testing, model certification, assess known vulnerabilities, restrict the training material, restrict access to the internet, issue terms of use.

Ethical machines and alignment

Main articles: Machine ethics, AI safety, Friendly artificial intelligence, Artificial moral agents, and Human Compatible

Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk.[206]

Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas.[207] The field of machine ethics is also called computational morality,[207] and was founded at an AAAI symposium in 2005.[208]

Other approaches include Wendell Wallach's "artificial moral agents"[209] and Stuart J. Russell's three principles for developing provably beneficial machines.[210]

Frameworks

Artificial Intelligence projects can have their ethical permissibility tested while designing, developing, and implementing an AI system. An AI framework such as the Care and Act Framework containing the SUM values – developed by the Alan Turing Institute tests projects in four main areas:[211][212]

  • RESPECT the dignity of individual people
  • CONNECT with other people sincerely, openly and inclusively
  • CARE for the wellbeing of everyone
  • PROTECT social values, justice and the public interest

Other developments in ethical frameworks include those decided upon during the Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others;[213] however, these principles do not go without their criticisms, especially regards to the people chosen contributes to these frameworks.[214]

Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers.[215]

Regulation

Main articles: Regulation of artificial intelligence, Regulation of algorithms, and AI safety
 
The first global AI Safety Summit was held in 2023 with a declaration calling for international co-operation

The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms.[216] The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.[217] According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone.[218][219] Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.[220] Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, US and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia.[220] The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology.[220] Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI.[221] In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.[222] In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics.[223]

In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks".[218] A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.[224] In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".[225][226]

In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks.[227] 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence.[228][229]

History

Main article: History of artificial intelligence
For a chronological guide, see Timeline of artificial intelligence.

The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate both mathematical deduction and formal reasoning, which is known as the Church–Turing thesis.[230] This, along with concurrent discoveries in cybernetics and information theory, led researchers to consider the possibility of building an "electronic brain".[q][232]

Alan Turing was thinking about machine intelligence at least as early as 1941, when he circulated a paper on machine intelligence which could be the earliest paper in the field of AI – though it is now lost.[2] The first available paper generally recognized as "AI" was McCullouch and Pitts design for Turing-complete "artificial neurons" in 1943 – the first mathematical model of a neural network.[233] The paper was influenced by Turing's earlier paper 'On Computable Numbers' from 1936 using similar two-state boolean 'neurons', but was the first to apply it to neuronal function.[2]

The term 'Machine Intelligence' was used by Alan Turing during his life which was later often referred to as 'Artificial Intelligence' after his death in 1954. In 1950 Turing published the best known of his papers 'Computing Machinery and Intelligence', the paper introduced his concept of what is now known as the Turing test to the general public. Then followed three radio broadcasts on AI by Turing, the lectures: 'Intelligent Machinery, A Heretical Theory’, ‘Can Digital Computers Think’? and the panel discussion ‘Can Automatic Calculating Machines be Said to Think’. By 1956 computer intelligence had been actively pursued for more than a decade in Britain; the earliest AI programmes were written there in 1951–1952.[2]

In 1951, using a Ferranti Mark 1 computer of the University of Manchester, checkers and chess programs were wrote where you could play against the computer.[234] The field of American AI research was founded at a workshop at Dartmouth College in 1956.[r][3] The attendees became the leaders of AI research in the 1960s.[s] They and their students produced programs that the press described as "astonishing":[t] computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.[u][4] Artificial Intelligence laboratories were set up at a number of British and US Universities in the latter 1950s and early 1960s.[2]

They had, however, underestimated the difficulty of the problem.[v] Both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill[239] and ongoing pressure from the U.S. Congress to fund more productive projects. Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether.[240] The "AI winter", a period when obtaining funding for AI projects was difficult, followed.[6]

In the early 1980s, AI research was revived by the commercial success of expert systems,[241] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.[5] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.[7]

Many researchers began to doubt that the current practices would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition.[242] A number of researchers began to look into "sub-symbolic" approaches.[243] Robotics researchers, such as Rodney Brooks, rejected "representation" in general and focussed directly on engineering machines that move and survive.[w] Judea Pearl, Lofti Zadeh and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic.[84][248] But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others.[249] In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.[250]

AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics).[251] By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".[252]

Several academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.[11]

Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.[8] For many specific tasks, other methods were abandoned.[x] Deep learning's success was based on both hardware improvements (faster computers,[254] graphics processing units, cloud computing[255]) and access to large amounts of data[256] (including curated datasets,[255] such as ImageNet).

Deep learning's success led to an enormous increase in interest and funding in AI.[y] The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019,[220] and WIPO reported that AI was the most prolific emerging technology in terms of the number of patent applications and granted patents.[257] According to 'AI Impacts', about $50 billion annually was invested in "AI" around 2022 in the US alone and about 20% of new US Computer Science PhD graduates have specialized in "AI";[258] about 800,000 "AI"-related US job openings existed in 2022.[259] The large majority of the advances have occurred within the United States, with its companies, universities, and research labs leading artificial intelligence research.[10]

In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study.[199]

Philosophy

Main article: Philosophy of artificial intelligence

Defining artificial intelligence

Main articles: Turing test, Intelligent agent, Dartmouth workshop, and Synthetic intelligence

Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?"[260] He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".[260] He devised the Turing test, which measures the ability of a machine to simulate human conversation.[261] Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people[z] but "it is usual to have a polite convention that everyone thinks"[262]

Russell and Norvig agree with Turing that AI must be defined in terms of "acting" and not "thinking".[263] However, they are critical that the test compares machines to people. "Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons.'"[264] AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".[265]

McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world."[266] Another AI founder, Marvin Minsky similarly defines it as "the ability to solve hard problems".[267] These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible.

Another definition has been adopted by Google,[268] a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.

Evaluating approaches to AI

No established unifying theory or paradigm has guided AI research for most of its history.[aa] The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, soft and narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers.

Symbolic AI and its limits

Symbolic AI (or "GOFAI")[270] simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."[271]

However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.[272] Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge.[273] Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.[ab][16]

The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence,[275][276] in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neuro-symbolic artificial intelligence attempts to bridge the two approaches.

Neat vs. scruffy

Main article: Neats and scruffies

"Neats" hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s,[277] but eventually was seen as irrelevant. Modern AI has elements of both.

Soft vs. hard computing

Main article: Soft computing

Finding a provably correct or optimal solution is intractable for many important problems.[15] Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks.

Narrow vs. general AI

Main article: Artificial general intelligence

AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals.[278][279] General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The experimental sub-field of artificial general intelligence studies this area exclusively.

Machine consciousness, sentience and mind

Main articles: Philosophy of artificial intelligence and Artificial consciousness

The philosophy of mind does not know whether a machine can have a mind, consciousness and mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on."[280] However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction.

Consciousness

Main articles: Hard problem of consciousness and Theory of mind

David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.[281] The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). Human information processing is easy to explain, however, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to know what red looks like.[282]

Computationalism and functionalism

Main articles: Computational theory of mind, Functionalism (philosophy of mind), and Chinese room

Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind–body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.[283]

Philosopher John Searle characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."[ac] Searle counters this assertion with his Chinese room argument, which attempts to show that, even if a machine perfectly simulates human behavior, there is still no reason to suppose it also has a mind.[287]

Robot rights

Main article: Robot rights

If a machine has a mind and subjective experience, then it may also have sentience (the ability to feel), and if so it could also suffer; it has been argued that this could entitle it to certain rights.[288] Any hypothetical robot rights would lie on a spectrum with animal rights and human rights.[289] This issue has been considered in fiction for centuries,[290] and is now being considered by, for example, California's Institute for the Future; however, critics argue that the discussion is premature.[291]

Future

Superintelligence and the singularity

A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.[279]

If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to what I. J. Good called an "intelligence explosion" and Vernor Vinge called a "singularity".[292]

However, technologies cannot improve exponentially indefinitely, and typically follow an S-shaped curve, slowing when they reach the physical limits of what the technology can do.[293]

Transhumanism

Robot designer Hans Moravec, cyberneticist Kevin Warwick, and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.[294]

Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998.[295]

In fiction

Main article: Artificial intelligence in fiction
 
The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots".

Thought-capable artificial beings have appeared as storytelling devices since antiquity,[296] and have been a persistent theme in science fiction.[297]

A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.[298]

Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;[299] while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.[300]

Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.[301]

See also

Explanatory notes

  1. ^ a b This list of intelligent traits is based on the topics covered by the major AI textbooks, including: Russell & Norvig (2021), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and Nilsson (1998)
  2. ^ a b This list of tools is based on the topics covered by the major AI textbooks, including: Russell & Norvig (2021), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and Nilsson (1998)
  3. ^ It is among the reasons that expert systems proved to be inefficient for capturing knowledge.[30][31]
  4. ^ "Rational agent" is general term used in economics, philosophy and theoretical artificial intelligence. It can refer to anything that directs its behavior to accomplish goals, such as a person, an animal, a corporation, a nation, or, in the case of AI, a computer program.
  5. ^ Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".[42] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[43]
  6. ^ See AI winter § Machine translation and the ALPAC report of 1966
  7. ^ Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.[86]
  8. ^ Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown latent variables.[88]
  9. ^ Some form of deep neural networks (without a specific learning algorithm) were described by: Alan Turing (1948);[112]Frank Rosenblatt(1957);[112]Karl Steinbuch and Roger David Joseph (1961).[113] Deep or recurrent networks that learned (or used gradient descent) were developed by: Ernst Ising and Wilhelm Lenz (1925);[114]Oliver Selfridge (1959);[113]Alexey Ivakhnenko and Valentin Lapa (1965);[114]Kaoru Nakano (1977);[115]Shun-Ichi Amari (1972);[115]John Joseph Hopfield (1982).[115] Backpropagation was independently discovered by: Henry J. Kelley (1960);[112]Arthur E. Bryson (1962);[112]Stuart Dreyfus (1962);[112]Arthur E. Bryson and Yu-Chi Ho (1969);[112]Seppo Linnainmaa (1970);[116]Paul Werbos (1974).[112] In fact, backpropagation and gradient descent are straight forward applications of Gottfried Leibniz' chain rule in calculus (1676),[117] and is essentially identical (for one layer) to the method of least squares, developed independently by Johann Carl Friedrich Gauss (1795) and Adrien-Marie Legendre (1805).[118] There are probably many others, yet to be discovered by historians of science.
  10. ^ Geoffrey Hinton said, of his work on neural networks in the 1990s, "our labeled datasets were thousands of times too small. [And] our computers were millions of times too slow"[119]
  11. ^ Including Jon Kleinberg (Cornell), Sendhil Mullainathan (University of Chicago), Cynthia Chouldechova (Carnegie Mellon) and Sam Corbett-Davis (Stanford)[153]
  12. ^ Moritz Hardt (a director at the Max Planck Institute for Intelligent Systems) argues that machine learning "is fundamentally the wrong tool for a lot of domains, where you're trying to design interventions and mechanisms that change the world."[158]
  13. ^ When the law was passed in 2018, it still contained a form of this provision.
  14. ^ This is the United Nations' definition, and includes things like land mines as well.[171]
  15. ^ See table 4; 9% is both the OECD average and the US average.[184]
  16. ^ Sometimes called a "robopocalypse".[191]
  17. ^ "Electronic brain" was the term used by the press around this time.[231]
  18. ^ Daniel Crevier wrote, "the conference is generally recognized as the official birthdate of the new science."[235] Russell and Norvig called the conference "the inception of artificial intelligence."[233]
  19. ^ Russell and Norvig wrote "for the next 20 years the field would be dominated by these people and their students."[236]
  20. ^ Russell and Norvig wrote "it was astonishing whenever a computer did anything kind of smartish".[237]
  21. ^ The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU.
  22. ^ Russell and Norvig write: "in almost all cases, these early systems failed on more difficult problems"[238]
  23. ^ Embodied approaches to AI[244] were championed by Hans Moravec[245] and Rodney Brooks[246] and went by many names: Nouvelle AI.[246] Developmental robotics,[247]
  24. ^ Matteo Wong wrote in The Atlantic: "Whereas for decades, computer-science fields such as natural-language processing, computer vision, and robotics used extremely different methods, now they all use a programming method called "deep learning." As a result, their code and approaches have become more similar, and their models are easier to integrate into one another."[253]
  25. ^ Jack Clark wrote in Bloomberg: "After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever," and noted that the number of software projects that use machine learning at Google increased from a "sporadic usage" in 2012 to more than 2,700 projects in 2015.[255]
  26. ^ See Problem of other minds
  27. ^ Nils Nilsson wrote in 1983: "Simply put, there is wide disagreement in the field about what AI is all about."[269]
  28. ^ Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."[274]
  29. ^ Searle presented this definition of "Strong AI" in 1999.[284] Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states."[285] Strong AI is defined similarly by Russell and Norvig: "Stong AI – the assertion that machines that do so are actually thinking (as opposed to simulating thinking)."[286]

References

  1. ^ Google (2016).
  2. ^ a b c d e Copeland, J., ed. (2004). The Essential Turing: the ideas that gave birth to the computer age. Oxford, England: Clarendon Press. ISBN 0-19-825079-7.
  3. ^ a b Dartmouth workshop: The proposal:
  4. ^ a b Successful programs the 1960s:
  5. ^ a b Funding initiatives in the early 1980s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US):
  6. ^ a b First AI Winter, Lighthill report, Mansfield Amendment
  7. ^ a b Second AI Winter:
  8. ^ a b Deep learning revolution, AlexNet:
  9. ^ Toews (2023).
  10. ^ a b Frank (2023).
  11. ^ a b c Artificial general intelligence: Proposal for the modern version: Warnings of overspecialization in AI from leading researchers:
  12. ^ Russell & Norvig (2021, §1.2).
  13. ^ Problem solving, puzzle solving, game playing and deduction:
  14. ^ Uncertain reasoning:
  15. ^ a b c Intractability and efficiency and the combinatorial explosion:
  16. ^ a b c Psychological evidence of the prevalence sub-symbolic reasoning and knowledge:
  17. ^ Knowledge representation and knowledge engineering:
  18. ^ Smoliar & Zhang (1994).
  19. ^ Neumann & Möller (2008).
  20. ^ Kuperman, Reichley & Bailey (2006).
  21. ^ McGarry (2005).
  22. ^ Bertini, Del Bimbo & Torniai (2006).
  23. ^ Russell & Norvig (2021), pp. 272.
  24. ^ Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts):
  25. ^ Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem):
  26. ^ Causal calculus:
  27. ^ Representing knowledge about knowledge: Belief calculus, modal logics:
  28. ^ a b Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription, closed world assumption, abduction: (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning").
  29. ^ a b Breadth of commonsense knowledge:
  30. ^ Newquist (1994), p. 296.
  31. ^ Crevier (1993), pp. 204–208.
  32. ^ Russell & Norvig (2021), p. 528.
  33. ^ Automated planning:
  34. ^ Automated decision making, Decision theory:
  35. ^ Classical planning:
  36. ^ Sensorless or "conformant" planning, contingent planning, replanning (a.k.a online planning):
  37. ^ Uncertain preferences: Inverse reinforcement learning:
  38. ^ Information value theory:
  39. ^ Markov decision process:
  40. ^ Game theory and multi-agent decision theory:
  41. ^ Learning:
  42. ^ Turing (1950).
  43. ^ Solomonoff (1956).
  44. ^ Unsupervised learning:
  45. ^ a b Supervised learning:
  46. ^ Reinforcement learning:
  47. ^ Transfer learning:
  48. ^ "Artificial Intelligence (AI): What Is AI and How Does It Work? | Built In". builtin.com. Retrieved 30 October 2023.
  49. ^ Computational learning theory:
  50. ^ Natural language processing (NLP):
  51. ^ Subproblems of NLP:
  52. ^ Russell & Norvig (2021), p. 856–858.
  53. ^ Dickson (2022).
  54. ^ Modern statistical and deep learning approaches to NLP:
  55. ^ Vincent (2019).
  56. ^ Russell & Norvig (2021), p. 875–878.
  57. ^ Bushwick (2023).
  58. ^ Computer vision:
  59. ^ Russell & Norvig (2021), pp. 849–850.
  60. ^ Russell & Norvig (2021), pp. 895–899.
  61. ^ Russell & Norvig (2021), pp. 899–901.
  62. ^ Russell & Norvig (2021), pp. 931–938.
  63. ^ MIT AIL (2014).
  64. ^ Affective computing:
  65. ^ Waddell (2018).
  66. ^ Poria et al. (2017).
  67. ^ Search algorithms:
  68. ^ State space search:
  69. ^ Russell & Norvig (2021), §11.2.
  70. ^ Uninformed searches (breadth first search, depth-first search and general state space search):
  71. ^ Heuristic or informed searches (e.g., greedy best first and A*):
  72. ^ Adversarial search:
  73. ^ Local or "optimization" search:
  74. ^ Evolutionary computation:
  75. ^ Merkle & Middendorf (2013).
  76. ^ Logic:
  77. ^ Propositional logic:
  78. ^ First-order logic and features such as equality:
  79. ^ Logical inference:
  80. ^ Russell & Norvig (2021), §8.3.1.
  81. ^ Resolution and unification:
  82. ^ Forward chaining, backward chaining, Horn clauses, and logical deduction as search:
  83. ^ Fuzzy logic:
  84. ^ a b Stochastic methods for uncertain reasoning:
  85. ^ Bayesian networks:
  86. ^ Domingos (2015), chapter 6.
  87. ^ Bayesian inference algorithm:
  88. ^ Domingos (2015), p. 210.
  89. ^ Bayesian learning and the expectation-maximization algorithm:
  90. ^ Bayesian decision theory and Bayesian decision networks:
  91. ^ a b c Stochastic temporal models: Hidden Markov model: Kalman filters: Dynamic Bayesian networks:
  92. ^ decision theory and decision analysis:
  93. ^ Information value theory:
  94. ^ Markov decision processes and dynamic decision networks:
  95. ^ Game theory and mechanism design:
  96. ^ Statistical learning methods and classifiers:
  97. ^ Decision trees:
  98. ^ Non-parameteric learning models such as K-nearest neighbor and support vector machines:
  99. ^ Domingos (2015), p. 152.
  100. ^ Naive Bayes classifier:
  101. ^ a b Neural networks:
  102. ^ Gradient calculation in computational graphs, backpropagation, automatic differentiation:
  103. ^ Universal approximation theorem: The theorem:
  104. ^ Feedforward neural networks:
  105. ^ Recurrent neural networks:
  106. ^ Perceptrons:
  107. ^ a b Deep learning:
  108. ^ Convolutional neural networks:
  109. ^ Deng & Yu (2014), pp. 199–200.
  110. ^ Ciresan, Meier & Schmidhuber (2012).
  111. ^ Russell & Norvig (2021), p. 751.
  112. ^ a b c d e f g Russell & Norvig (2021), p. 785.
  113. ^ a b Schmidhuber (2022), §5.
  114. ^ a b Schmidhuber (2022), §6.
  115. ^ a b c Schmidhuber (2022), §7.
  116. ^ Schmidhuber (2022), §8.
  117. ^ Schmidhuber (2022), §2.
  118. ^ Schmidhuber (2022), §3.
  119. ^ Quoted in Christian (2020, p. 22)
  120. ^ Smith (2023).
  121. ^ "Explained: Generative AI". 9 November 2023.
  122. ^ "AI Writing and Content Creation Tools". MIT Sloan Teaching & Learning Technologies. Retrieved 25 December 2023.
  123. ^ Marmouyet (2023).
  124. ^ Kobielus (2019).
  125. ^ Davenport, T; Kalakota, R (June 2019). "The potential for artificial intelligence in healthcare". Future Healthc J. 6 (2): 94–98. doi:10.7861/futurehosp.6-2-94. PMC 6616181. PMID 31363513.
  126. ^ a b Bax, Monique; Thorpe, Jordan; Romanov, Valentin (December 2023). "The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence". Frontiers in Sensors. 4. doi:10.3389/fsens.2023.1294721. ISSN 2673-5067.
  127. ^ Jumper, J; Evans, R; Pritzel, A (2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
  128. ^ Wurman, P.R.; Barrett, S.; Kawamoto, K. (2022). "Outracing champion Gran Turismo drivers with deep reinforcement learning". Nature 602. 602: 223–228. doi:10.1038/s41586-021-04357-7.
  129. ^ Simonite (2016).
  130. ^ Russell & Norvig (2021), p. 987.
  131. ^ Laskowski (2023).
  132. ^ GAO (2022).
  133. ^ Valinsky (2019).
  134. ^ Russell & Norvig (2021), p. 991.
  135. ^ Russell & Norvig (2021), p. 991–992.
  136. ^ Christian (2020), p. 63.
  137. ^ Vincent (2022).
  138. ^ Reisner (2023).
  139. ^ Alter & Harris (2023).
  140. ^ Nicas (2018).
  141. ^ "Trust and Distrust in America". 22 July 2019.
  142. ^ Williams (2023).
  143. ^ Taylor & Hern (2023).
  144. ^ a b Rose (2023).
  145. ^ CNA (2019).
  146. ^ Goffrey (2008), p. 17.
  147. ^ Berdahl et al. (2023); Goffrey (2008, p. 17); Rose (2023); Russell & Norvig (2021, p. 995)
  148. ^ Algorithmic bias and Fairness (machine learning):
  149. ^ Christian (2020), p. 25.
  150. ^ a b Russell & Norvig (2021), p. 995.
  151. ^ Grant & Hill (2023).
  152. ^ Larson & Angwin (2016).
  153. ^ Christian (2020), p. 67–70.
  154. ^ Christian (2020, pp. 67–70); Russell & Norvig (2021, pp. 993–994)
  155. ^ Russell & Norvig (2021, p. 995); Lipartito (2011, p. 36); Goodman & Flaxman (2017, p. 6); Christian (2020, pp. 39–40, 65)
  156. ^ Quoted in Christian (2020, p. 65).
  157. ^ Russell & Norvig (2021, p. 994); Christian (2020, pp. 40, 80–81)
  158. ^ Quoted in Christian (2020, p. 80)
  159. ^ Dockrill (2022).
  160. ^ Sample (2017).
  161. ^ "Black Box AI". 16 June 2023.
  162. ^ Christian (2020), p. 110.
  163. ^ Christian (2020), pp. 88–91.
  164. ^ Christian (2020, p. 83); Russell & Norvig (2021, p. 997)
  165. ^ Christian (2020), p. 91.
  166. ^ Christian (2020), p. 83.
  167. ^ Verma (2021).
  168. ^ Rothman (2020).
  169. ^ Christian (2020), p. 105-108.
  170. ^ Christian (2020), pp. 108–112.
  171. ^ Russell & Norvig (2021), p. 989.
  172. ^ Robitzski (2018); Sainato (2015)
  173. ^ Russell & Norvig (2021), p. 987-990.
  174. ^ Russell & Norvig (2021), p. 988.
  175. ^ Harari (2018).
  176. ^ Buckley, Chris; Mozur, Paul (22 May 2019). "How China Uses High-Tech Surveillance to Subdue Minorities". The New York Times.
  177. ^ . 3 May 2019. Archived from the original on 7 March 2021. Retrieved 14 September 2020.
  178. ^ "AI traffic signals to be installed in Bengaluru soon". NextBigWhat. 24 September 2019. Retrieved 1 October 2019.
  179. ^ Urbina et al. (2022).
  180. ^ Tarnoff, Ben (4 August 2023). "Lessons from Eliza". The Guardian Weekly. pp. 34–9.
  181. ^ a b E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2022) 51(3) Industrial Law Journal 511–559 27 May 2023 at the Wayback Machine
  182. ^ Ford & Colvin (2015);McGaughey (2022)
  183. ^ IGM Chicago (2017).
  184. ^ Arntz, Gregory & Zierahn (2016), p. 33.
  185. ^ Lohr (2017); Frey & Osborne (2017); Arntz, Gregory & Zierahn (2016, p. 33)
  186. ^ Morgenstern (2015).
  187. ^ Mahdawi (2017); Thompson (2014)
  188. ^ Zhou, Viola (11 April 2023). "AI is already taking video game illustrators' jobs in China". Rest of World. Retrieved 17 August 2023.
  189. ^ Carter, Justin (11 April 2023). "China's game art industry reportedly decimated by growing AI use". Game Developer. Retrieved 17 August 2023.
  190. ^ Cellan-Jones (2014).
  191. ^ Russell & Norvig 2021, p. 1001.
  192. ^ Bostrom (2014).
  193. ^ Russell (2019).
  194. ^ Bostrom (2014); Müller & Bostrom (2014); Bostrom (2015)
  195. ^ Harari (2023).
  196. ^ Müller & Bostrom (2014).
  197. ^ Leaders' concerns about the existential risks of AI around 2015:
  198. ^ Arguments that AI is not an imminent risk:
  199. ^ a b Christian (2020), pp. 67, 73.
  200. ^ Valance (2023).
  201. ^ Taylor, Josh (7 May 2023). "Rise of artificial intelligence is inevitable but should not be feared, 'father of AI' says". The Guardian. Retrieved 26 May 2023.
  202. ^ Colton, Emma (7 May 2023). "'Father of AI' says tech fears misplaced: 'You cannot stop it'". Fox News. Retrieved 26 May 2023.
  203. ^ Jones, Hessie (23 May 2023). "Juergen Schmidhuber, Renowned 'Father Of Modern AI,' Says His Life's Work Won't Lead To Dystopia". Forbes. Retrieved 26 May 2023.
  204. ^ McMorrow, Ryan (19 December 2023). "Andrew Ng: 'Do we think the world is better off with more or less intelligence?'". Financial Times. Retrieved 30 December 2023.
  205. ^ Levy, Steven (22 December 2023). "How Not to Be Stupid About AI, With Yann LeCun". Wired. Retrieved 30 December 2023.
  206. ^ Yudkowsky (2008).
  207. ^ a b Anderson & Anderson (2011).
  208. ^ AAAI (2014).
  209. ^ Wallach (2010).
  210. ^ Russell (2019), p. 173.
  211. ^ Alan Turing Institute (2019). "Understanding artificial intelligence ethics and safety" (PDF).
  212. ^ Alan Turing Institute (2023). "AI Ethics and Governance in Practice" (PDF).
  213. ^ Floridi, Luciano; Cowls, Josh (23 June 2019). "A Unified Framework of Five Principles for AI in Society". Harvard Data Science Review. 1 (1). doi:10.1162/99608f92.8cd550d1. S2CID 198775713.
  214. ^ Buruk, Banu; Ekmekci, Perihan Elif; Arda, Berna (1 September 2020). "A critical perspective on guidelines for responsible and trustworthy artificial intelligence". Medicine, Health Care and Philosophy. 23 (3): 387–399. doi:10.1007/s11019-020-09948-1. ISSN 1572-8633. PMID 32236794. S2CID 214766800.
  215. ^ Kamila, Manoj Kumar; Jasrotia, Sahil Singh (1 January 2023). "Ethical issues in the development of artificial intelligence: recognizing the risks". International Journal of Ethics and Systems. ahead-of-print (ahead-of-print). doi:10.1108/IJOES-05-2023-0107. ISSN 2514-9369. S2CID 259614124.
  216. ^ Regulation of AI to mitigate risks:
  217. ^ a b Vincent (2023).
  218. ^ Stanford University (2023).
  219. ^ a b c d UNESCO (2021).
  220. ^ Kissinger (2021).
  221. ^ Altman, Brockman & Sutskever (2023).
  222. ^ VOA News (25 October 2023). "UN Announces Advisory Body on Artificial Intelligence".
  223. ^ Edwards (2023).
  224. ^ Kasperowicz (2023).
  225. ^ Fox News (2023).
  226. ^ Milmo, Dan (3 November 2023). "Hope or Horror? The great AI debate dividing its pioneers". The Guardian Weekly. pp. 10–12.
  227. ^ . GOV.UK. 1 November 2023. Archived from the original on 1 November 2023. Retrieved 2 November 2023.
  228. ^ "Countries agree to safe and responsible development of frontier AI in landmark Bletchley Declaration". GOV.UK (Press release). from the original on 1 November 2023. Retrieved 1 November 2023.
  229. ^ Berlinski (2000).
  230. ^ "Google books ngram".
  231. ^ AI's immediate precursors:
  232. ^ a b Russell & Norvig (2021), p. 17.
  233. ^ See "A Brief History of Computing" at AlanTuring.net.
  234. ^ Crevier (1993), pp. 47–49.
  235. ^ Russell & Norvig (2003), p. 17.
  236. ^ Russell & Norvig (2003), p. 18.
  237. ^ Russell & Norvig (2021), p. 21.
  238. ^ Lighthill (1973).
  239. ^ Russell & Norvig (2021), p. 22.
  240. ^ Expert systems:
  241. ^ Russell & Norvig (2021), p. 24.
  242. ^ Nilsson (1998), p. 7.
  243. ^ McCorduck (2004), pp. 454–462.
  244. ^ Moravec (1988).
  245. ^ a b Brooks (1990).
  246. ^ Developmental robotics:
  247. ^ Russell & Norvig (2021), p. 25.
  248. ^
  249. ^ Russell & Norvig (2021), p. 26.
  250. ^ Formal and narrow methods adopted in the 1990s:
  251. ^ AI widely used in the late 1990s:
  252. ^ Wong (2023).
  253. ^ Moore's Law and AI:
  254. ^ a b c Clark (2015b).
  255. ^ Big data:
  256. ^ "Intellectual Property and Frontier Technologies". WIPO. from the original on 2 April 2022. Retrieved 30 March 2022.
  257. ^ DiFeliciantonio (2023).
  258. ^ Goswami (2023).
  259. ^ a b Turing (1950), p. 1.
  260. ^ Turing's original publication of the Turing test in "Computing machinery and intelligence": Historical influence and philosophical implications:
  261. ^ Turing (1950), Under "The Argument from Consciousness".
  262. ^ Russell & Norvig (2021), chpt. 2.
  263. ^ Russell & Norvig (2021), p. 3.
  264. ^ Maker (2006).
  265. ^ McCarthy (1999).
  266. ^ Minsky (1986).
  267. ^ "What Is Artificial Intelligence (AI)?". Google Cloud Platform. from the original on 31 July 2023. Retrieved 16 October 2023.
  268. ^ Nilsson (1983), p. 10.
  269. ^ Haugeland (1985), pp. 112–117.
  270. ^ Physical symbol system hypothesis: Historical significance:
  271. ^ Moravec's paradox:
  272. ^ Dreyfus' critique of AI: Historical significance and philosophical implications:
  273. ^ Crevier (1993), p. 125.
  274. ^ Langley (2011).
  275. ^ Katz (2012).
  276. ^ Neats vs. scruffies, the historic debate: A classic example of the "scruffy" approach to intelligence: A modern example of neat AI and its aspirations in the 21st century:
  277. ^ Pennachin & Goertzel (2007).
  278. ^ a b Roberts (2016).
  279. ^ Russell & Norvig (2021), p. 986.
  280. ^ Chalmers (1995).
  281. ^ Dennett (1991).
  282. ^ Horst (2005).
  283. ^ Searle (1999).
  284. ^ Searle (1980), p. 1.
  285. ^ Russell & Norvig (2021), p. 9817.
  286. ^ Searle's Chinese room argument: Discussion:
  287. ^ Robot rights:
  288. ^ Evans (2015).
  289. ^ McCorduck (2004), pp. 19–25.
  290. ^ Henderson (2007).
  291. ^ The Intelligence explosion and technological singularity: I. J. Good's "intelligence explosion" Vernor Vinge's "singularity"
  292. ^ Russell & Norvig (2021), p. 1005.
  293. ^ Transhumanism:
  294. ^ AI as evolution:
  295. ^ AI in myth:
  296. ^ McCorduck (2004), pp. 340–400.
  297. ^ Buttazzo (2001).
  298. ^ Anderson (2008).
  299. ^ McCauley (2007).
  300. ^ Galvan (1997).

AI textbooks

The two most widely used textbooks in 2023. (See the Open Syllabus).

These were the four of the most widely used AI textbooks in 2008:

Later editions.

  • Poole, David; Mackworth, Alan (2017). Artificial Intelligence: Foundations of Computational Agents (2nd ed.). Cambridge University Press. ISBN 978-1-107-19539-4. from the original on 7 December 2017. Retrieved 6 December 2017.

History of AI

Other sources

  • AI & ML in Fusion
  • AI & ML in Fusion, video lecture 2 July 2023 at the Wayback Machine
  • "AlphaGo – Google DeepMind". from the original on 10 March 2016.
  • Alter, Alexandra; Harris, Elizabeth A. (20 September 2023), "Franzen, Grisham and Other Prominent Authors Sue OpenAI", The New York Times
  • Altman, Sam; Brockman, Greg; Sutskever, Ilya (22 May 2023). "Governance of Superintelligence". openai.com. from the original on 27 May 2023. Retrieved 27 May 2023.
  • Anderson, Susan Leigh (2008). "Asimov's "three laws of robotics" and machine metaethics". AI & Society. 22 (4): 477–493. doi:10.1007/s00146-007-0094-5. S2CID 1809459.
  • Anderson, Michael; Anderson, Susan Leigh (2011). Machine Ethics. Cambridge University Press.
  • Arntz, Melanie; Gregory, Terry; Zierahn, Ulrich (2016), "The risk of automation for jobs in OECD countries: A comparative analysis", OECD Social, Employment, and Migration Working Papers 189
  • Asada, M.; Hosoda, K.; Kuniyoshi, Y.; Ishiguro, H.; Inui, T.; Yoshikawa, Y.; Ogino, M.; Yoshida, C. (2009). "Cognitive developmental robotics: a survey". IEEE Transactions on Autonomous Mental Development. 1 (1): 12–34. doi:10.1109/tamd.2009.2021702. S2CID 10168773.
  • "Ask the AI experts: What's driving today's progress in AI?". McKinsey & Company. from the original on 13 April 2018. Retrieved 13 April 2018.
  • Barfield, Woodrow; Pagallo, Ugo (2018). Research handbook on the law of artificial intelligence. Cheltenham, UK: Edward Elgar Publishing. ISBN 978-1-78643-904-8. OCLC 1039480085.
  • Beal, J.; Winston, Patrick (2009), "The New Frontier of Human-Level Artificial Intelligence", IEEE Intelligent Systems, 24: 21–24, doi:10.1109/MIS.2009.75, hdl:1721.1/52357, S2CID 32437713
  • Berdahl, Carl Thomas; Baker, Lawrence; Mann, Sean; Osoba, Osonde; Girosi, Federico (7 February 2023). "Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review". JMIR AI. 2: e42936. doi:10.2196/42936. ISSN 2817-1705. S2CID 256681439. from the original on 21 February 2023. Retrieved 21 February 2023.
  • Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. from the original on 26 July 2020. Retrieved 22 August 2020.
  • Berryhill, Jamie; Heang, Kévin Kok; Clogher, Rob; McBride, Keegan (2019). Hello, World: Artificial Intelligence and its Use in the Public Sector (PDF). Paris: OECD Observatory of Public Sector Innovation. (PDF) from the original on 20 December 2019. Retrieved 9 August 2020.
  • Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682.
  • Bostrom, Nick (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
  • Bostrom, Nick (2015). "What happens when our computers get smarter than we are?". TED (conference). from the original on 25 July 2020. Retrieved 30 January 2020.
  • Brooks, Rodney (10 November 2014). . Archived from the original on 12 November 2014.
  • Brooks, Rodney (1990). "Elephants Don't Play Chess" (PDF). Robotics and Autonomous Systems. 6 (1–2): 3–15. CiteSeerX 10.1.1.588.7539. doi:10.1016/S0921-8890(05)80025-9. (PDF) from the original on 9 August 2007.
  • Buiten, Miriam C (2019). "Towards Intelligent Regulation of Artificial Intelligence". European Journal of Risk Regulation. 10 (1): 41–59. doi:10.1017/err.2019.8. ISSN 1867-299X.
  • Bushwick, Sophie (16 March 2023), "What the New GPT-4 AI Can Do", Scientific American
  • Butler, Samuel (13 June 1863). "Darwin among the Machines". Letters to the Editor. The Press. Christchurch, New Zealand. from the original on 19 September 2008. Retrieved 16 October 2014 – via Victoria University of Wellington.
  • Buttazzo, G. (July 2001). "Artificial consciousness: Utopia or real possibility?". Computer. 34 (7): 24–30. doi:10.1109/2.933500.
  • Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]". IEEE Computational Intelligence Magazine. 9 (2): 48–57. doi:10.1109/MCI.2014.2307227. S2CID 206451986.
  • Cellan-Jones, Rory (2 December 2014). "Stephen Hawking warns artificial intelligence could end mankind". BBC News. from the original on 30 October 2015. Retrieved 30 October 2015.
  • Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. from the original on 8 March 2005. Retrieved 11 October 2018.
  • Christian, Brian (2020). The Alignment Problem: Machine learning and human values. W. W. Norton & Company. ISBN 978-0-393-86833-3. OCLC 1233266753.
  • Ciresan, D.; Meier, U.; Schmidhuber, J. (2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3642–3649. arXiv:1202.2745. doi:10.1109/cvpr.2012.6248110. ISBN 978-1-4673-1228-8. S2CID 2161592.
  • Clark, Jack (2015b). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg.com. from the original on 23 November 2016. Retrieved 23 November 2016.
  • CNA (12 January 2019). "Commentary: Bad news. Artificial intelligence is biased". CNA. from the original on 12 January 2019. Retrieved 19 June 2020.
  • Cybenko, G. (1988). Continuous valued neural networks with two hidden layers are sufficient (Report). Department of Computer Science, Tufts University.
  • Deng, L.; Yu, D. (2014). "Deep Learning: Methods and Applications" (PDF). Foundations and Trends in Signal Processing. 7 (3–4): 1–199. doi:10.1561/2000000039. (PDF) from the original on 14 March 2016. Retrieved 18 October 2014.
  • Dennett, Daniel (1991). Consciousness Explained. The Penguin Press. ISBN 978-0-7139-9037-9.
  • DiFeliciantonio, Chase (3 April 2023). "AI has already changed the world. This report shows how". San Francisco Chronicle. from the original on 19 June 2023. Retrieved 19 June 2023.
  • Dickson, Ben (2 May 2022). "Machine learning: What is the transformer architecture?". TechTalks. Retrieved 22 November 2023.
  • Dockrill, Peter (27 June 2022), , Science Alert, archived from the original on 27 June 2022
  • Domingos, Pedro (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0465065707.
  • Dreyfus, Hubert (1972). What Computers Can't Do. New York: MIT Press. ISBN 978-0-06-011082-6.
  • Dreyfus, Hubert; Dreyfus, Stuart (1986). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Oxford: Blackwell. ISBN 978-0-02-908060-3. from the original on 26 July 2020. Retrieved 22 August 2020.
  • Dyson, George (1998). Darwin among the Machines. Allan Lane Science. ISBN 978-0-7382-0030-9. from the original on 26 July 2020. Retrieved 22 August 2020.
  • Edelson, Edward (1991). The Nervous System. New York: Chelsea House. ISBN 978-0-7910-0464-7. from the original on 26 July 2020. Retrieved 18 November 2019.
  • Edwards, Benj (17 May 2023). "Poll: AI poses risk to humanity, according to majority of Americans". Ars Technica. from the original on 19 June 2023. Retrieved 19 June 2023.
  • Evans, Woody (2015). "Posthuman Rights: Dimensions of Transhuman Worlds". Teknokultura. 12 (2). doi:10.5209/rev_TK.2015.v12.n2.49072.
  • Fearn, Nicholas (2007). The Latest Answers to the Oldest Questions: A Philosophical Adventure with the World's Greatest Thinkers. New York: Grove Press. ISBN 978-0-8021-1839-4.
  • Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. from the original on 16 June 2018. Retrieved 13 January 2018.
  • Fox News (2023). "Fox News Poll" (PDF). Fox News. (PDF) from the original on 12 May 2023. Retrieved 19 June 2023.
  • Frank, Michael (22 September 2023). "US Leadership in Artificial Intelligence Can Shape the 21st Century Global Order". The Diplomat. Retrieved 8 December 2023. Instead, the United States has developed a new area of dominance that the rest of the world views with a mixture of awe, envy, and resentment: artificial intelligence... From AI models and research to cloud computing and venture capital, U.S. companies, universities, and research labs – and their affiliates in allied countries – appear to have an enormous lead in both developing cutting-edge AI and commercializing it. The value of U.S. venture capital investments in AI start-ups exceeds that of the rest of the world combined.
  • Frey, Carl Benedikt; Osborne, Michael A (1 January 2017). "The future of employment: How susceptible are jobs to computerisation?". Technological Forecasting and Social Change. 114: 254–280. CiteSeerX 10.1.1.395.416. doi:10.1016/j.techfore.2016.08.019. ISSN 0040-1625.
  • "From not working to neural networking". The Economist. 2016. from the original on 31 December 2016. Retrieved 26 April 2018.
  • Galvan, Jill (1 January 1997). "Entering the Posthuman Collective in Philip K. Dick's "Do Androids Dream of Electric Sheep?"". Science Fiction Studies. 24 (3): 413–429. JSTOR 4240644.
  • Geist, Edward Moore (9 August 2015). "Is artificial intelligence really an existential threat to humanity?". Bulletin of the Atomic Scientists. from the original on 30 October 2015. Retrieved 30 October 2015.
  • Gertner, Jon (18 July 2023). "Wikipedia's Moment of Truth – Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process? + comment". The New York Times. from the original on 18 July 2023. Retrieved 19 July 2023.
  • Gibbs, Samuel (27 October 2014). "Elon Musk: artificial intelligence is our biggest existential threat". The Guardian. from the original on 30 October 2015. Retrieved 30 October 2015.
  • Goffrey, Andrew (2008). "Algorithm". In Fuller, Matthew (ed.). Software studies: a lexicon. Cambridge, Mass.: MIT Press. pp. 15–20. ISBN 978-1-4356-4787-9.
  • Goldman, Sharon (14 September 2022). "10 years later, deep learning 'revolution' rages on, say AI pioneers Hinton, LeCun and Li". VentureBeat. Retrieved 8 December 2023.
  • Good, I. J. (1965), Speculations Concerning the First Ultraintelligent Machine
  • Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016), , MIT Press., archived from the original on 16 April 2016, retrieved 12 November 2017
  • Goodman, Bryce; Flaxman, Seth (2017). "EU regulations on algorithmic decision-making and a 'right to explanation'". AI Magazine. 38 (3): 50. arXiv:1606.08813. doi:10.1609/aimag.v38i3.2741. S2CID 7373959.
  • Government Accountability Office (13 September 2022). Consumer Data: Increasing Use Poses Risks to Privacy. gao.gov (Report).
  • Grant, Nico; Hill, Kashmir (22 May 2023). "Google's Photo App Still Can't Find Gorillas. And Neither Can Apple's". The New York Times.
  • Goswami, Rohan (5 April 2023). "Here's where the A.I. jobs are". CNBC. from the original on 19 June 2023. Retrieved 19 June 2023.
  • Harari, Yuval Noah (October 2018). "Why Technology Favors Tyranny". The Atlantic. from the original on 25 September 2021. Retrieved 23 September 2021.
  • Harari, Yuval Noah (2023). "AI and the future of humanity". YouTube.
  • Haugeland, John (1985). Artificial Intelligence: The Very Idea. Cambridge, Mass.: MIT Press. ISBN 978-0-262-08153-5.
  • Henderson, Mark (24 April 2007). "Human rights for robots? We're getting carried away". The Times Online. London. from the original on 31 May 2014. Retrieved 31 May 2014.
  • Hinton, G.; Deng, L.; Yu, D.; Dahl, G.; Mohamed, A.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T.; Kingsbury, B. (2012). "Deep Neural Networks for Acoustic Modeling in Speech Recognition – The shared views of four research groups". IEEE Signal Processing Magazine. 29 (6): 82–97. Bibcode:2012ISPM...29...82H. doi:10.1109/msp.2012.2205597. S2CID 206485943.
  • Holley, Peter (28 January 2015). "Bill Gates on dangers of artificial intelligence: 'I don't understand why some people are not concerned'". The Washington Post. ISSN 0190-8286. from the original on 30 October 2015. Retrieved 30 October 2015.
  • Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert (1989). Multilayer Feedforward Networks are Universal Approximators (PDF). Neural Networks. Vol. 2. Pergamon Press. pp. 359–366.
  • Horst, Steven (2005). "The Computational Theory of Mind". The Stanford Encyclopedia of Philosophy. from the original on 6 March 2016. Retrieved 7 March 2016.
  • Howe, J. (November 1994). "Artificial Intelligence at Edinburgh University: a Perspective". from the original on 15 May 2007. Retrieved 30 August 2007.
  • IGM Chicago (30 June 2017). "Robots and Artificial Intelligence". www.igmchicago.org. from the original on 1 May 2019. Retrieved 3 July 2019.
  • Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science. 16 (2): 170–184. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. S2CID 59298502.
  • Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218.
  • Kahneman, Daniel (2011). Thinking, Fast and Slow. Macmillan. ISBN 978-1-4299-6935-2. from the original on 15 March 2023. Retrieved 8 April 2012.
  • Kahneman, Daniel; Slovic, D.; Tversky, Amos (1982). "Judgment under uncertainty: Heuristics and biases". Science. New York: Cambridge University Press. 185 (4157): 1124–1131. Bibcode:1974Sci...185.1124T. doi:10.1126/science.185.4157.1124. ISBN 978-0-521-28414-1. PMID 17835457. S2CID 143452957.
  • Kasperowicz, Peter (1 May 2023). "Regulate AI? GOP much more skeptical than Dems that government can do it right: poll". Fox News. from the original on 19 June 2023. Retrieved 19 June 2023.
  • Katz, Yarden (1 November 2012). "Noam Chomsky on Where Artificial Intelligence Went Wrong". The Atlantic. from the original on 28 February 2019. Retrieved 26 October 2014.
  • "Kismet". MIT Artificial Intelligence Laboratory, Humanoid Robotics Group. from the original on 17 October 2014. Retrieved 25 October 2014.
  • Kissinger, Henry (1 November 2021). "The Challenge of Being Human in the Age of AI". The Wall Street Journal. from the original on 4 November 2021. Retrieved 4 November 2021.
  • Kobielus, James (27 November 2019). "GPUs Continue to Dominate the AI Accelerator Market for Now". InformationWeek. from the original on 19 October 2021. Retrieved 11 June 2020.
  • Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160.
  • Kurzweil, Ray (2005). The Singularity is Near. Penguin Books. ISBN 978-0-670-03384-3.
  • Langley, Pat (2011). "The changing science of machine learning". Machine Learning. 82 (3): 275–279. doi:10.1007/s10994-011-5242-y.
  • Larson, Jeff; Angwin, Julia (23 May 2016). "How We Analyzed the COMPAS Recidivism Algorithm". ProPublica. from the original on 29 April 2019. Retrieved 19 June 2020.
  • Laskowski, Nicole (November 2023). "What is Artificial Intelligence and How Does AI Work? TechTarget". Enterprise AI. Retrieved 30 October 2023.
  • Law Library of Congress (U.S.). Global Legal Research Directorate, issuing body. (2019). Regulation of artificial intelligence in selected jurisdictions. LCCN 2019668143. OCLC 1110727808.
  • Lee, Timothy B. (22 August 2014). "Will artificial intelligence destroy humanity? Here are 5 reasons not to worry". Vox. from the original on 30 October 2015. Retrieved 30 October 2015.
  • Lenat, Douglas; Guha, R. V. (1989). Building Large Knowledge-Based Systems. Addison-Wesley. ISBN 978-0-201-51752-1.
  • Lighthill, James (1973). "Artificial Intelligence: A General Survey". Artificial Intelligence: a paper symposium. Science Research Council.
  • Lipartito, Kenneth (6 January 2011), The Narrative and the Algorithm: Genres of Credit Reporting from the Nineteenth Century to Today (PDF) (Unpublished manuscript), doi:10.2139/ssrn.1736283, S2CID 166742927, archived (PDF) from the original on 9 October 2022
  • Lohr, Steve (2017). "Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says". The New York Times. from the original on 14 January 2018. Retrieved 13 January 2018.
  • Lungarella, M.; Metta, G.; Pfeifer, R.; Sandini, G. (2003). "Developmental robotics: a survey". Connection Science. 15 (4): 151–190. CiteSeerX 10.1.1.83.7615. doi:10.1080/09540090310001655110. S2CID 1452734.
  • . aaai.org. Archived from the original on 29 November 2014.
  • Madrigal, Alexis C. (27 February 2015). "The case against killer robots, from a guy actually working on artificial intelligence". Fusion.net. from the original on 4 February 2016. Retrieved 31 January 2016.
  • Mahdawi, Arwa (26 June 2017). "What jobs will still be around in 20 years? Read this to prepare your future". The Guardian. from the original on 14 January 2018. Retrieved 13 January 2018.
  • Maker, Meg Houston (2006). . Dartmouth College. Archived from the original on 3 January 2007. Retrieved 16 October 2008.
  • Marmouyet, Françoise (15 December 2023). "Google's Gemini: is the new AI model really better than ChatGPT?". The Conversation. Retrieved 25 December 2023.
  • Minsky, Marvin (1986), The Society of Mind, Simon and Schuster
  • Maschafilm (2010). "Content: Plug & Pray Film – Artificial Intelligence – Robots". plugandpray-film.de. from the original on 12 February 2016.
  • McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude (1955). . Archived from the original on 26 August 2007. Retrieved 30 August 2007.
  • McCarthy, John (2007), "From Here to Human-Level AI", Artificial Intelligence: 171
  • McCarthy, John (1999), What is AI?, from the original on 4 December 2022, retrieved 4 December 2022
  • McCauley, Lee (2007). "AI armageddon and the three laws of robotics". Ethics and Information Technology. 9 (2): 153–164. CiteSeerX 10.1.1.85.8904. doi:10.1007/s10676-007-9138-2. S2CID 37272949.
  • McGarry, Ken (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656.
  • McGaughey, E (2022), Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy, p. 51(3) Industrial Law Journal 511–559, doi:10.2139/ssrn.3044448, S2CID 219336439, SSRN 3044448, from the original on 31 January 2021, retrieved 27 May 2023
  • Merkle, Daniel; Middendorf, Martin (2013). "Swarm Intelligence". In Burke, Edmund K.; Kendall, Graham (eds.). Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer Science & Business Media. ISBN 978-1-4614-6940-7.
  • Moravec, Hans (1988). Mind Children. Harvard University Press. ISBN 978-0-674-57616-2. from the original on 26 July 2020. Retrieved 18 November 2019.
  • Morgenstern, Michael (9 May 2015). "Automation and anxiety". The Economist. from the original on 12 January 2018. Retrieved 13 January 2018.
  • Müller, Vincent C.; Bostrom, Nick (2014). "Future Progress in Artificial Intelligence: A Poll Among Experts" (PDF). AI Matters. 1 (1): 9–11. doi:10.1145/2639475.2639478. S2CID 8510016. (PDF) from the original on 15 January 2016.
  • Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. S2CID 10767011.
  • Nilsson, Nils (1995), "Eyes on the Prize", AI Magazine, vol. 16, pp. 9–17
  • Newell, Allen; Simon, H. A. (1976). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3): 113–126. doi:10.1145/360018.360022.
  • Nicas, Jack (7 February 2018). "How YouTube Drives People to the Internet's Darkest Corners". The Wall Street Journal. ISSN 0099-9660. Retrieved 16 June 2018.
  • Nilsson, Nils (1983). "Artificial Intelligence Prepares for 2001" (PDF). AI Magazine. 1 (1). (PDF) from the original on 17 August 2020. Retrieved 22 August 2020. Presidential Address to the Association for the Advancement of Artificial Intelligence.
  • NRC (United States National Research Council) (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press.
  • Omohundro, Steve (2008). The Nature of Self-Improving Artificial Intelligence. presented and distributed at the 2007 Singularity Summit, San Francisco, CA.
  • Oudeyer, P-Y. (2010). "On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development" (PDF). IEEE Transactions on Autonomous Mental Development. 2 (1): 2–16. doi:10.1109/tamd.2009.2039057. S2CID 6362217. (PDF) from the original on 3 October 2018. Retrieved 4 June 2013.
  • Pennachin, C.; Goertzel, B. (2007). "Contemporary Approaches to Artificial General Intelligence". Artificial General Intelligence. Cognitive Technologies. Berlin, Heidelberg: Springer. pp. 1–30. doi:10.1007/978-3-540-68677-4_1. ISBN 978-3-540-23733-4.
  • Pinker, Steven (2007) [1994], The Language Instinct, Perennial Modern Classics, Harper, ISBN 978-0-06-133646-1
  • Poria, Soujanya; Cambria, Erik; Bajpai, Rajiv; Hussain, Amir (September 2017). "A review of affective computing: From unimodal analysis to multimodal fusion". Information Fusion. 37: 98–125. doi:10.1016/j.inffus.2017.02.003. hdl:1893/25490. S2CID 205433041. from the original on 23 March 2023. Retrieved 27 April 2021.
  • Rawlinson, Kevin (29 January 2015). "Microsoft's Bill Gates insists AI is a threat". BBC News. from the original on 29 January 2015. Retrieved 30 January 2015.
  • Reisner, Alex (19 August 2023), "Revealed: The Authors Whose Pirated Books are Powering Generative AI", The Atlantic
  • Roberts, Jacob (2016). . Distillations. Vol. 2, no. 2. pp. 14–23. Archived from the original on 19 August 2018. Retrieved 20 March 2018.
  • Robitzski, Dan (5 September 2018). "Five experts share what scares them the most about AI". from the original on 8 December 2019. Retrieved 8 December 2019.
  • "Robots could demand legal rights". BBC News. 21 December 2006. from the original on 15 October 2019. Retrieved 3 February 2011.
  • Rose, Steve (11 July 2023). "AI Utopia or dystopia?". The Guardian Weekly. pp. 42–43.
  • Russell, Stuart (2019). Human Compatible: Artificial Intelligence and the Problem of Control. United States: Viking. ISBN 978-0-525-55861-3. OCLC 1083694322.
  • Sainato, Michael (19 August 2015). "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. from the original on 30 October 2015. Retrieved 30 October 2015.
  • Sample, Ian (5 November 2017). "Computer says no: why making AIs fair, accountable and transparent is crucial". The Guardian. Retrieved 30 January 2018.
  • Rothman, Denis (7 October 2020). "Exploring LIME Explanations and the Mathematics Behind It". Codemotion. Retrieved 25 November 2023.
  • Scassellati, Brian (2002). "Theory of mind for a humanoid robot". Autonomous Robots. 12 (1): 13–24. doi:10.1023/A:1013298507114. S2CID 1979315.
  • Schmidhuber, J. (2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828. doi:10.1016/j.neunet.2014.09.003. PMID 25462637. S2CID 11715509.
  • Schmidhuber, Jürgen (2022). "Annotated History of Modern AI and Deep Learning".
  • Schulz, Hannes; Behnke, Sven (1 November 2012). "Deep Learning". KI – Künstliche Intelligenz. 26 (4): 357–363. doi:10.1007/s13218-012-0198-z. ISSN 1610-1987. S2CID 220523562.
  • Searle, John (1980). "Minds, Brains and Programs" (PDF). Behavioral and Brain Sciences. 3 (3): 417–457. doi:10.1017/S0140525X00005756. S2CID 55303721. (PDF) from the original on 17 March 2019. Retrieved 22 August 2020.
  • Searle, John (1999). Mind, language and society. New York: Basic Books. ISBN 978-0-465-04521-1. OCLC 231867665. from the original on 26 July 2020. Retrieved 22 August 2020.
  • Simonite, Tom (31 March 2016). "How Google Plans to Solve Artificial Intelligence". MIT Technology Review.
  • Smith, Craig S. (15 March 2023). "ChatGPT-4 Creator Ilya Sutskever on AI Hallucinations and AI Democracy". Forbes. Retrieved 25 December 2023.
  • Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE MultiMedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913.
  • Solomonoff, Ray (1956). An Inductive Inference Machine (PDF). Dartmouth Summer Research Conference on Artificial Intelligence. (PDF) from the original on 26 April 2011. Retrieved 22 March 2011 – via std.com, pdf scanned copy of the original. Later published as
    Solomonoff, Ray (1957). "An Inductive Inference Machine". IRE Convention Record. Vol. Section on Information Theory, part 2. pp. 56–62.
  • Stanford University (2023). "Artificial Intelligence Index Report 2023/Chapter 6: Policy and Governance" (PDF). AI Index. (PDF) from the original on 19 June 2023. Retrieved 19 June 2023.
  • Tao, Jianhua; Tan, Tieniu (2005). Affective Computing and Intelligent Interaction. Affective Computing: A Review. Lecture Notes in Computer Science. Vol. 3784. Springer. pp. 981–995. doi:10.1007/11573548. ISBN 978-3-540-29621-8.
  • Taylor, Josh; Hern, Alex (2 May 2023). "'Godfather of AI' Geoffrey Hinton quits Google and warns over dangers of misinformation". The Guardian.
  • Thompson, Derek (23 January 2014). "What Jobs Will the Robots Take?". The Atlantic. from the original on 24 April 2018. Retrieved 24 April 2018.
  • Thro, Ellen (1993). Robotics: The Marriage of Computers and Machines. New York: Facts on File. ISBN 978-0-8160-2628-9. from the original on 26 July 2020. Retrieved 22 August 2020.
  • Toews, Rob (3 September 2023). "Transformers Revolutionized AI. What Will Replace Them?". Forbes. Retrieved 8 December 2023.
  • Turing, Alan (October 1950), "Computing Machinery and Intelligence", Mind, LIX (236): 433–460, doi:10.1093/mind/LIX.236.433, ISSN 0026-4423
  • UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 2021. ISBN 978-92-3-100450-6. from the original on 18 June 2022. Retrieved 18 September 2021.
  • Urbina, Fabio; Lentzos, Filippa; Invernizzi, Cédric; Ekins, Sean (7 March 2022). "Dual use of artificial-intelligence-powered drug discovery". Nature Machine Intelligence. 4 (3): 189–191. doi:10.1038/s42256-022-00465-9. PMC 9544280. PMID 36211133. S2CID 247302391.
  • Valance, Christ (30 May 2023). "Artificial intelligence could lead to extinction, experts warn". BBC News. from the original on 17 June 2023. Retrieved 18 June 2023.
  • Valinsky, Jordan (11 April 2019), "Amazon reportedly employs thousands of people to listen to your Alexa conversations", CNN.com
  • Verma, Yugesh (25 December 2021). "A Complete Guide to SHAP – SHAPley Additive exPlanations for Practitioners". Analytics India Magazine. Retrieved 25 November 2023.
  • Vincent, James (7 November 2019). "OpenAI has published the text-generating AI it said was too dangerous to share". The Verge. from the original on 11 June 2020. Retrieved 11 June 2020.
  • Vincent, James (15 November 2022). "The scary truth about AI copyright is nobody knows what will happen next". The Verge. from the original on 19 June 2023. Retrieved 19 June 2023.
  • Vincent, James (3 April 2023). "AI is entering an era of corporate control". The Verge. from the original on 19 June 2023. Retrieved 19 June 2023.
  • Vinge, Vernor (1993). . Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace: 11. Bibcode:1993vise.nasa...11V. Archived from the original on 1 January 2007. Retrieved 14 November 2011.
  • Waddell, Kaveh (2018). "Chatbots Have Entered the Uncanny Valley". The Atlantic. from the original on 24 April 2018. Retrieved 24 April 2018.
  • Wallach, Wendell (2010). Moral Machines. Oxford University Press.
  • Wason, P. C.; Shapiro, D. (1966). "Reasoning". In Foss, B. M. (ed.). New horizons in psychology. Harmondsworth: Penguin. from the original on 26 July 2020. Retrieved 18 November 2019.
  • Weng, J.; McClelland; Pentland, A.; Sporns, O.; Stockman, I.; Sur, M.; Thelen, E. (2001). "Autonomous mental development by robots and animals"
January 04, 2024
artificial, intelligence, redirects, here, other, uses, disambiguation, disambiguation, intelligent, agent, intelligence, machines, software, opposed, intelligence, humans, animals, field, study, computer, science, that, develops, studies, intelligent, machine. AI redirects here For other uses see AI disambiguation Artificial intelligence disambiguation and Intelligent agent Artificial intelligence AI is the intelligence of machines or software as opposed to the intelligence of humans or animals It is a field of study in computer science that develops and studies intelligent machines Such machines may be called AIs AI technology is widely used throughout industry government and science Some high profile applications are advanced web search engines e g Google Search recommendation systems used by YouTube Amazon and Netflix understanding human speech such as Google Assistant Siri and Alexa self driving cars e g Waymo generative and creative tools ChatGPT and AI art and superhuman play and analysis in strategy games such as chess and Go 1 Alan Turing was the first person to carry out substantial research in the field that he called Machine Intelligence 2 Artificial intelligence was founded as an academic discipline in 1956 3 The field went through multiple cycles of optimism 4 5 followed by disappointment and loss of funding 6 7 Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques 8 and after 2017 with the transformer architecture 9 This led to the AI spring of the 2020s with companies universities and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence 10 The various sub fields of AI research are centered around particular goals and the use of particular tools The traditional goals of AI research include reasoning knowledge representation planning learning natural language processing perception and support for robotics a General intelligence the ability to complete any task performable by a human is among the field s long term goals 11 To solve these problems AI researchers have adapted and integrated a wide range of problem solving techniques including search and mathematical optimization formal logic artificial neural networks and methods based on statistics operations research and economics b AI also draws upon psychology linguistics philosophy neuroscience and other fields 12 Contents 1 Goals 1 1 Reasoning problem solving 1 2 Knowledge representation 1 3 Planning and decision making 1 4 Learning 1 5 Natural language processing 1 6 Perception 1 7 Social intelligence 1 8 General intelligence 2 Techniques 2 1 Search and optimization 2 1 1 State space search 2 1 2 Local search 2 2 Logic 2 3 Probabilistic methods for uncertain reasoning 2 4 Classifiers and statistical learning methods 2 5 Artificial neural networks 2 6 Deep learning 2 7 GPT 2 8 Specialized hardware and software 3 Applications 3 1 Health and Medicine 3 2 Games 3 3 Military 3 4 Generative AI 3 5 Industry Specific Tasks 4 Ethics 4 1 Risks and harm 4 1 1 Privacy and copyright 4 1 2 Misinformation 4 1 3 Algorithmic bias and fairness 4 1 4 Lack of transparency 4 1 5 Conflict surveillance and weaponized AI 4 1 6 Technological unemployment 4 1 7 Existential risk 4 1 8 Limiting AI 4 2 Ethical machines and alignment 4 3 Frameworks 4 4 Regulation 5 History 6 Philosophy 6 1 Defining artificial intelligence 6 2 Evaluating approaches to AI 6 2 1 Symbolic AI and its limits 6 2 2 Neat vs scruffy 6 2 3 Soft vs hard computing 6 2 4 Narrow vs general AI 6 3 Machine consciousness sentience and mind 6 3 1 Consciousness 6 3 2 Computationalism and functionalism 6 3 3 Robot rights 7 Future 7 1 Superintelligence and the singularity 7 2 Transhumanism 8 In fiction 9 See also 10 Explanatory notes 11 References 11 1 AI textbooks 11 2 History of AI 11 3 Other sources 12 Further reading 13 External linksGoalsThe general problem of simulating or creating intelligence has been broken into sub problems These consist of particular traits or capabilities that researchers expect an intelligent system to display The traits described below have received the most attention and cover the scope of AI research a Reasoning problem solving Early researchers developed algorithms that imitated step by step reasoning that humans use when they solve puzzles or make logical deductions 13 By the late 1980s and 1990s methods were developed for dealing with uncertain or incomplete information employing concepts from probability and economics 14 Many of these algorithms are insufficient for solving large reasoning problems because they experience a combinatorial explosion they became exponentially slower as the problems grew larger 15 Even humans rarely use the step by step deduction that early AI research could model They solve most of their problems using fast intuitive judgments 16 Accurate and efficient reasoning is an unsolved problem Knowledge representation nbsp An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts Knowledge representation and knowledge engineering 17 allow AI programs to answer questions intelligently and make deductions about real world facts Formal knowledge representations are used in content based indexing and retrieval 18 scene interpretation 19 clinical decision support 20 knowledge discovery mining interesting and actionable inferences from large databases 21 and other areas 22 A knowledge base is a body of knowledge represented in a form that can be used by a program An ontology is the set of objects relations concepts and properties used by a particular domain of knowledge 23 Knowledge bases need to represent things such as objects properties categories and relations between objects 24 situations events states and time 25 causes and effects 26 knowledge about knowledge what we know about what other people know 27 default reasoning things that humans assume are true until they are told differently and will remain true even when other facts are changing 28 and many other aspects and domains of knowledge Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge the set of atomic facts that the average person knows is enormous 29 and the sub symbolic form of most commonsense knowledge much of what people know is not represented as facts or statements that they could express verbally 16 There is also the difficulty of knowledge acquisition the problem of obtaining knowledge for AI applications c Planning and decision making An agent is anything that perceives and takes actions in the world A rational agent has goals or preferences and takes actions to make them happen d 32 In automated planning the agent has a specific goal 33 In automated decision making the agent has preferences there are some situations it would prefer to be in and some situations it is trying to avoid The decision making agent assigns a number to each situation called the utility that measures how much the agent prefers it For each possible action it can calculate the expected utility the utility of all possible outcomes of the action weighted by the probability that the outcome will occur It can then choose the action with the maximum expected utility 34 In classical planning the agent knows exactly what the effect of any action will be 35 In most real world problems however the agent may not be certain about the situation they are in it is unknown or unobservable and it may not know for certain what will happen after each possible action it is not deterministic It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked 36 In some problems the agent s preferences may be uncertain especially if there are other agents or humans involved These can be learned e g with inverse reinforcement learning or the agent can seek information to improve its preferences 37 Information value theory can be used to weigh the value of exploratory or experimental actions 38 The space of possible future actions and situations is typically intractably large so the agents must take actions and evaluate situations while being uncertain what the outcome will be A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action A policy associates a decision with each possible state The policy could be calculated e g by iteration be heuristic or it can be learned 39 Game theory describes rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents 40 Learning Machine learning is the study of programs that can improve their performance on a given task automatically 41 It has been a part of AI from the beginning e There are several kinds of machine learning Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance 44 Supervised learning requires a human to label the input data first and comes in two main varieties classification where the program must learn to predict what category the input belongs in and regression where the program must deduce a numeric function based on numeric input 45 In reinforcement learning the agent is rewarded for good responses and punished for bad ones The agent learns to choose responses that are classified as good 46 Transfer learning is when the knowledge gained from one problem is applied to a new problem 47 Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning 48 Computational learning theory can assess learners by computational complexity by sample complexity how much data is required or by other notions of optimization 49 Natural language processing Natural language processing NLP 50 allows programs to read write and communicate in human languages such as English Specific problems include speech recognition speech synthesis machine translation information extraction information retrieval and question answering 51 Early work based on Noam Chomsky s generative grammar and semantic networks had difficulty with word sense disambiguation f unless restricted to small domains called micro worlds due to the common sense knowledge problem 29 Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages and that thesauri and not dictionaries should be the basis of computational language structure Modern deep learning techniques for NLP include word embedding representing words typically as vectors encoding their meaning 52 transformers a deep learning architecture using an attention mechanism 53 and others 54 In 2019 generative pre trained transformer or GPT language models began to generate coherent text 55 56 and by 2023 these models were able to get human level scores on the bar exam SAT test GRE test and many other real world applications 57 Perception Machine perception is the ability to use input from sensors such as cameras microphones wireless signals active lidar sonar radar and tactile sensors to deduce aspects of the world Computer vision is the ability to analyze visual input 58 The field includes speech recognition 59 image classification 60 facial recognition object recognition 61 and robotic perception 62 Social intelligence nbsp Kismet a robot head which was made in the 1990s a machine that can recognize and simulate emotions 63 Affective computing is an interdisciplinary umbrella that comprises systems that recognize interpret process or simulate human feeling emotion and mood 64 For example some virtual assistants are programmed to speak conversationally or even to banter humorously it makes them appear more sensitive to the emotional dynamics of human interaction or to otherwise facilitate human computer interaction However this tends to give naive users an unrealistic conception of the intelligence of existing computer agents 65 Moderate successes related to affective computing include textual sentiment analysis and more recently multimodal sentiment analysis wherein AI classifies the affects displayed by a videotaped subject 66 General intelligence A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence 11 TechniquesAI research uses a wide variety of techniques to accomplish the goals above b Search and optimization AI can solve many problems by intelligently searching through many possible solutions 67 There are two very different kinds of search used in AI state space search and local search State space search State space search searches through a tree of possible states to try to find a goal state 68 For example planning algorithms search through trees of goals and subgoals attempting to find a path to a target goal a process called means ends analysis 69 Simple exhaustive searches 70 are rarely sufficient for most real world problems the search space the number of places to search quickly grows to astronomical numbers The result is a search that is too slow or never completes 15 Heuristics or rules of thumb can help to prioritize choices that are more likely to reach a goal 71 Adversarial search is used for game playing programs such as chess or Go It searches through a tree of possible moves and counter moves looking for a winning position 72 Local search nbsp Illustration of gradient descent for 3 different starting points Two parameters represented by the plan coordinates are adjusted in order to minimize the loss function the height Local search uses mathematical optimization to find a numeric solution to a problem It begins with some form of a guess and then refines the guess incrementally until no more refinements can be made These algorithms can be visualized as blind hill climbing we begin the search at a random point on the landscape and then by jumps or steps we keep moving our guess uphill until we reach the top This process is called stochastic gradient descent 73 Evolutionary computation uses a form of optimization search For example they may begin with a population of organisms the guesses and then allow them to mutate and recombine selecting only the fittest to survive each generation refining the guesses 74 Distributed search processes can coordinate via swarm intelligence algorithms Two popular swarm algorithms used in search are particle swarm optimization inspired by bird flocking and ant colony optimization inspired by ant trails 75 Neural networks and statistical classifiers discussed below also use a form of local search where the landscape to be searched is formed by learning Logic Formal Logic is used for reasoning and knowledge representation 76 Formal logic comes in two main forms propositional logic which operates on statements that are true or false and uses logical connectives such as and or not and implies 77 and predicate logic which also operates on objects predicates and relations and uses quantifiers such as Every X is a Y and There are some Xs that are Ys 78 Logical inference or deduction is the process of proving a new statement conclusion from other statements that are already known to be true the premises 79 A logical knowledge base also handles queries and assertions as a special case of inference 80 An inference rule describes what is a valid step in a proof The most general inference rule is resolution 81 Inference can be reduced to performing a search to find a path that leads from premises to conclusions where each step is the application of an inference rule 82 Inference performed this way is intractable except for short proofs in restricted domains No efficient powerful and general method has been discovered Fuzzy logic assigns a degree of truth between 0 and 1 and handles uncertainty and probabilistic situations 83 Non monotonic logics are designed to handle default reasoning 28 Other specialized versions of logic have been developed to describe many complex domains see knowledge representation above Probabilistic methods for uncertain reasoning nbsp Expectation maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption Many problems in AI including in reasoning planning learning perception and robotics require the agent to operate with incomplete or uncertain information AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics 84 Bayesian networks 85 are a very general tool that can be used for many problems including reasoning using the Bayesian inference algorithm g 87 learning using the expectation maximization algorithm h 89 planning using decision networks 90 and perception using dynamic Bayesian networks 91 Probabilistic algorithms can also be used for filtering prediction smoothing and finding explanations for streams of data helping perception systems to analyze processes that occur over time e g hidden Markov models or Kalman filters 91 Precise mathematical tools have been developed that analyze how an agent can make choices and plan using decision theory decision analysis 92 and information value theory 93 These tools include models such as Markov decision processes 94 dynamic decision networks 91 game theory and mechanism design 95 Classifiers and statistical learning methods The simplest AI applications can be divided into two types classifiers e g if shiny then diamond on one hand and controllers e g if diamond then pick up on the other hand Classifiers 96 are functions that use pattern matching to determine the closest match They can be fine tuned based on chosen examples using supervised learning Each pattern also called an observation is labeled with a certain predefined class All the observations combined with their class labels are known as a data set When a new observation is received that observation is classified based on previous experience 45 There are many kinds of classifiers in use The decision tree is the simplest and most widely used symbolic machine learning algorithm 97 K nearest neighbor algorithm was the most widely used analogical AI until the mid 1990s and Kernel methods such as the support vector machine SVM displaced k nearest neighbor in the 1990s 98 The naive Bayes classifier is reportedly the most widely used learner 99 at Google due in part to its scalability 100 Neural networks are also used as classifiers 101 Artificial neural networks nbsp A neural network is an interconnected group of nodes akin to the vast network of neurons in the human brain An Artificial neural network is based on a collection of nodes also known as artificial neurons which loosely model the neurons in a biological brain It is trained to recognise patterns once trained it can recognise those patterns in fresh data There is an input at least one hidden layer of nodes and an output Each node applies a function and once the weight crosses it s specified threshold the data is transmitted to the next layer A network is typically called a deep neural network if it has at least 2 hidden layers 101 Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training The most common training technique is the backpropagation algorithm 102 Neural networks learn to model complex relationships between inputs and outputs and find patterns in data In theory a neural network can learn any function 103 In feedforward neural networks the signal passes in only one direction 104 Recurrent neural networks feed the output signal back into the input which allows short term memories of previous input events Long short term memory is the most successful network architecture for recurrent networks 105 Perceptrons 106 use only a single layer of neurons deep learning 107 uses multiple layers Convolutional neural networks strengthen the connection between neurons that are close to each other this is especially important in image processing where a local set of neurons must identify an edge before the network can identify an object 108 Deep learning nbsp Deep learning 107 uses several layers of neurons between the network s inputs and outputs The multiple layers can progressively extract higher level features from the raw input For example in image processing lower layers may identify edges while higher layers may identify the concepts relevant to a human such as digits or letters or faces 109 Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence including computer vision speech recognition natural language processing image classification 110 and others The reason that deep learning performs so well in so many applications is not known as of 2023 111 The sudden success of deep learning in 2012 2015 did not occur because of some new discovery or theoretical breakthrough deep neural networks and backpropagation had been described by many people as far back as the 1950s i but because of two factors the incredible increase in computer power including the hundred fold increase in speed by switching to GPUs and the availability of vast amounts of training data especially the giant curated datasets used for benchmark testing such as ImageNet j GPT Generative pre trained transformers GPT are large language models that are based on the semantic relationships between words in sentences natural language processing Text based GPT models are pre trained on a large corpus of text which can be from the internet The pre training consists in predicting the next token a token being usually a word subword or punctuation Throughout this pre training GPT models accumulate knowledge about the world and can then generate human like text by repeatedly predicting the next token Typically a subsequent training phase makes the model more truthful useful and harmless usually with a technique called reinforcement learning from human feedback RLHF Current GPT models are still prone to generating falsehoods called hallucinations although this can be reduced with RLHF and quality data They are used in chatbots which allow you to ask a question or request a task in simple text 120 121 Current models and services include Bard ChatGPT Claude Copilot and LLaMA 122 Multimodal GPT models can process different types of data modalities such as images videos sound and text 123 Specialized hardware and software Main articles Programming languages for artificial intelligence and Hardware for artificial intelligence In the late 2010s graphics processing units GPUs that were increasingly designed with AI specific enhancements and used with specialized TensorFlow software had replaced previously used central processing unit CPUs as the dominant means for large scale commercial and academic machine learning models training 124 Historically specialized languages such as Lisp Prolog Python and others had been used ApplicationsMain article Applications of artificial intelligenceAI and machine learning technology is used in most of the essential applications of the 2020s including search engines such as Google Search targeting online advertisements recommendation systems offered by Netflix YouTube or Amazon driving internet traffic targeted advertising AdSense Facebook virtual assistants such as Siri or Alexa autonomous vehicles including drones ADAS and self driving cars automatic language translation Microsoft Translator Google Translate facial recognition Apple s Face ID or Microsoft s DeepFace and Google s FaceNet and image labeling used by Facebook Apple s iPhoto and TikTok Health and Medicine Main article Artificial intelligence in healthcare The application of AI in medicine and medical research has the potential to increase patient care and quality of life 125 Through the lens of the Hippocratic Oath medical professionals are ethically compelled to use AI if applications can more accurately diagnose and treat patients For medical research AI is an important tool for processing and integrating Big Data This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication 126 It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research such as cardiovascular research which typically receives a disproportionately less funding that areas such as cancer research relative to the morbidity and mortality of these diseases 126 New AI tools can deepen our understanding of biomedically relevant pathways For example AlphaFold 2 2021 demonstrated the ability to approximate in hours rather than months the 3D structure of a protein 127 Games Main article Game artificial intelligence Game playing programs have been used since the 1950s to demonstrate and test AI s most advanced techniques Deep Blue became the first computer chess playing system to beat a reigning world chess champion Garry Kasparov on 11 May 1997 In 2011 in a Jeopardy quiz show exhibition match IBM s question answering system Watson defeated the two greatest Jeopardy champions Brad Rutter and Ken Jennings by a significant margin In March 2016 AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol becoming the first computer Go playing system to beat a professional Go player without handicaps Then it defeated Ke Jie in 2017 who at the time continuously held the world No 1 ranking for two years Other programs handle imperfect information games such as for poker at a superhuman level Pluribus and Cepheus DeepMind in the 2010s developed a generalized artificial intelligence that could learn many diverse Atari games on its own In 2021 an AI agent competed in a Playstation Gran Turismo competition winning against four of the world s best Gran Turismo drivers using deep reinforcement learning 128 Military Main article Military artificial intelligence Various countries are deploying AI military applications The main applications enhance command and control communications sensors integration and interoperability Research is targeting intelligence collection and analysis logistics cyber operations information operations and semiautonomous and autonomous vehicles AI technologies enable coordination of sensors and effectors threat detection and identification marking of enemy positions target acquisition coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams AI was incorporated into military operations in Iraq and Syria Generative AI Main article Generative artificial intelligence nbsp Vincent van Gogh in watercolour created by generative AI softwareIn the early 2020s generative AI gained widespread prominence ChatGPT based on GPT 3 and other large language models were tried by 14 of Americans adults The increasing realism and ease of use of AI based text to image generators such as Midjourney DALL E and Stable Diffusion sparked a trend of viral AI generated photos Widespread attention was gained by a fake photo of Pope Francis wearing a white puffer coat the fictional arrest of Donald Trump and a hoax of an attack on the Pentagon as well as the usage in professional creative arts Industry Specific Tasks There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions In a 2017 survey one in five companies reported they had incorporated AI in some offerings or processes A few examples are energy storage medical diagnosis military logistics applications that predict the result of judicial decisions foreign policy or supply chain management In agriculture AI has helped farmers identify areas that need irrigation fertilization pesticide treatments or increasing yield Agronomists use AI to conduct research and development AI has been used to predict the ripening time for crops such as tomatoes monitor soil moisture operate agricultural robots conduct predictive analytics classify livestock pig call emotions automate greenhouses detect diseases and pests and save water Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications mainly for classification regression clustering forecasting generation discovery and the development of new scientific insights for example for discovering exoplanets forecasting solar activity and distinguishing between signals and instrumental effects in gravitational wave astronomy It could also be used for activities in space such as space exploration including analysis of data from space missions real time science decisions of spacecraft space debris avoidance and more autonomous operation EthicsAI like any powerful technology has potential benefits and potential risks AI may be able to advance science and find solutions for serious problems Demis Hassabis of Deep Mind hopes to solve intelligence and then use that to solve everything else 129 However as the use of AI has become widespread several unintended consequences and risks have been identified 130 Anyone looking to use machine learning as part of real world in production systems needs to factor ethics into their AI training processes and strive to avoid bias This is especially true when using AI algorithms that are inherently unexplainable in deep learning 131 Risks and harm Privacy and copyright Further information Information privacy and Artificial intelligence and copyright Machine learning algorithms require large amounts of data The techniques used to acquire this data have raised concerns about privacy surveillance and copyright Technology companies collect a wide range of data from their users including online activity geolocation data video and audio 132 For example in order to build speech recognition algorithms Amazon have recorded millions of private conversations and allowed temps to listen to and transcribe some of them 133 Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy 134 AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data such as data aggregation de identification and differential privacy 135 Since 2016 some privacy experts such as Cynthia Dwork began to view privacy in terms of fairness Brian Christian wrote that experts have pivoted from the question of what they know to the question of what they re doing with it 136 Generative AI is often trained on unlicensed copyrighted works including in domains such as images or computer code the output is then used under a rationale of fair use Also website owners who do not wish to have their copyrighted content be AI indexed or scraped can add code to their site as you would if you did not want your website to be indexed by a search engine which is currently available to certain services such as OpenAI Experts disagree about how well and under what circumstances this rationale will hold up in courts of law relevant factors may include the purpose and character of the use of the copyrighted work and the effect upon the potential market for the copyrighted work 137 In 2023 leading authors including John Grisham and Jonathan Franzen sued AI companies for using their work to train generative AI 138 139 Misinformation See also YouTube Moderation and offensive content YouTube Facebook and others use recommender systems to guide users to more content These AI programs were given the goal of maximizing user engagement that is the only goal was to keep people watching The AI learned that users tended to choose misinformation conspiracy theories and extreme partisan content and to keep them watching the AI recommended more of it Users also tended to watch more content on the same subject so the AI led people into filter bubbles where they received multiple versions of the same misinformation 140 This convinced many users that the misinformation was true and ultimately undermined trust in institutions the media and the government 141 The AI program had correctly learned to maximize its goal but the result was harmful to society After the U S election in 2016 major technology companies took steps to mitigate the problem In 2022 generative AI began to create images audio video and text that are indistinguishable from real photographs recordings films or human writing It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda 142 AI pioneer Geoffrey Hinton expressed concern about AI enabling authoritarian leaders to manipulate their electorates on a large scale among other risks 143 Algorithmic bias and fairness Main articles Algorithmic bias and Fairness machine learning Machine learning applications will be biased if they learn from biased data 144 The developers may not be aware that the bias exists 145 Bias can be introduced by the way training data is selected and by the way a model is deployed 146 144 If a biased algorithm is used to make decisions that can seriously harm people as it can in medicine finance recruitment housing or policing then the algorithm may cause discrimination 147 Fairness in machine learning is the study of how to prevent the harm caused by algorithmic bias It has become serious area of academic study within AI Researchers have discovered it is not always possible to define fairness in a way that satisfies all stakeholders 148 On June 28 2015 Google Photos s new image labeling feature mistakenly identified Jacky Alcine and a friend as gorillas because they were black The system was trained on a dataset that contained very few images of black people 149 a problem called sample size disparity 150 Google fixed this problem by preventing the system from labelling anything as a gorilla Eight years later in 2023 Google Photos still could not identify a gorilla and neither could similar products from Apple Facebook Microsoft and Amazon 151 COMPAS is a commercial program widely used by U S courts to assess the likelihood of a defendant becoming a recidivist In 2016 Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias despite the fact that the program was not told the races of the defendants Although the error rate for both whites and blacks was calibrated equal at exactly 61 the errors for each race were different the system consistently overestimated the chance that a black person would re offend and would underestimate the chance that a white person would not re offend 152 In 2017 several researchers k showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re offense were different for whites and blacks in the data 154 A program can make biased decisions even if the data does not explicitly mention a problematic feature such as race or gender The feature will correlate with other features like address shopping history or first name and the program will make the same decisions based on these features as it would on race or gender 155 Moritz Hardt said the most robust fact in this research area is that fairness through blindness doesn t work 156 Criticism of COMPAS highlighted a deeper problem with the misuse of AI Machine learning models are designed to make predictions that are only valid if we assume that the future will resemble the past If they are trained on data that includes the results of racist decisions in the past machine learning models must predict that racist decisions will be made in the future Unfortunately if an application then uses these predictions as recommendations some of these recommendations will likely be racist 157 Thus machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past It is necessarily descriptive and not proscriptive l Bias and unfairness may go undetected because the developers are overwhelmingly white and male among AI engineers about 4 are black and 20 are women 150 At its 2022 Conference on Fairness Accountability and Transparency ACM FAccT 2022 the Association for Computing Machinery in Seoul South Korea presented and published findings recommending that until AI and robotics systems are demonstrated to be free of bias mistakes they are unsafe and the use of self learning neural networks trained on vast unregulated sources of flawed internet data should be curtailed 159 Lack of transparency See also Explainable AI Algorithmic transparency and Right to explanation nbsp Lidar testing vehicle for autonomous drivingMany AI systems are so complex that their designers cannot explain how they reach their decisions 160 Particularly with deep neural networks in which there are a large amount of non linear relationships between inputs and outputs But some popular explainability techniques exist 161 There have been many cases where a machine learning program passed rigorous tests but nevertheless learned something different than what the programmers intended For example a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as cancerous because pictures of malignancies typically include a ruler to show the scale 162 Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at low risk of dying from pneumonia Having asthma is actually a severe risk factor but since the patients having asthma would usually get much more medical care they were relatively unlikely to die according to the training data The correlation between asthma and low risk of dying from pneumonia was real but misleading 163 People who have been harmed by an algorithm s decision have a right to an explanation Doctors for example are required to clearly and completely explain the reasoning behind any decision they make clarification needed 164 Early drafts of the European Union s General Data Protection Regulation in 2016 included an explicit statement that this right exists m Industry experts noted that this is an unsolved problem with no solution in sight Regulators argued that nevertheless the harm is real if the problem has no solution the tools should not be used 165 DARPA established the XAI Explainable Artificial Intelligence program in 2014 to try and solve these problems 166 There are several potential solutions to the transparency problem SHAP helps visualise the contribution of each feature to the output 167 LIME can locally approximate a model with a simpler interpretable model 168 Multitask learning provides a large number of outputs in addition to the target classification These other outputs can help developers deduce what the network has learned 169 Deconvolution DeepDream and other generative methods can allow developers to see what different layers of a deep network have learned and produce output that can suggest what the network is learning 170 Conflict surveillance and weaponized AI Main articles Lethal autonomous weapon Artificial intelligence arms race and AI safety A lethal autonomous weapon is a machine that locates selects and engages human targets without human supervision n By 2015 over fifty countries were reported to be researching battlefield robots 172 These weapons are considered especially dangerous for several reasons if they kill an innocent person it is not clear who should be held accountable it is unlikely they will reliably choose targets and if produced at scale they are potentially weapons of mass destruction 173 In 2014 30 nations including China supported a ban on autonomous weapons under the United Nations Convention on Certain Conventional Weapons however the United States and others disagreed 174 AI provides a number of tools that are particularly useful for authoritarian governments smart spyware face recognition and voice recognition allow widespread surveillance such surveillance allows machine learning to classify potential enemies of the state and can prevent them from hiding recommendation systems can precisely target propaganda and misinformation for maximum effect deepfakes and generative AI aid in producing misinformation advanced AI can make authoritarian centralized decision making more competitive with liberal and decentralized systems such as markets 175 AI facial recognition systems are used for mass surveillance notably in China 176 177 In 2019 Bengaluru India deployed AI managed traffic signals This system uses cameras to monitor traffic density and adjust signal timing based on the interval needed to clear traffic 178 Terrorists criminals and rogue states can use weaponized AI such as advanced digital warfare and lethal autonomous weapons Machine learning AI is also able to design tens of thousands of toxic molecules in a matter of hours 179 Technological unemployment Main articles Workplace impact of artificial intelligence and Technological unemployment From the early days of the development of artificial intelligence there have been arguments for example those put forward by Joseph Weizenbaum about whether tasks that can be done by computers actually should be done by them given the difference between computers and humans and between quantitative calculation and qualitative value based judgement 180 Economists have frequently highlighted the risks of redundancies from AI and speculated about unemployment if there is no adequate social policy for full employment 181 In the past technology has tended to increase rather than reduce total employment but economists acknowledge that we re in uncharted territory with AI 182 A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long term unemployment but they generally agree that it could be a net benefit if productivity gains are redistributed 183 Risk estimates vary for example in the 2010s Michael Osborne and Carl Benedikt Frey estimated 47 of U S jobs are at high risk of potential automation while an OECD report classified only 9 of U S jobs as high risk o 185 The methodology of speculating about future employment levels has been criticised as lacking evidential foundation and for implying that technology rather than social policy creates unemployment as opposed to redundancies 181 Unlike previous waves of automation many middle class jobs may be eliminated by artificial intelligence The Economist stated in 2015 that the worry that AI could do to white collar jobs what steam power did to blue collar ones during the Industrial Revolution is worth taking seriously 186 Jobs at extreme risk range from paralegals to fast food cooks while job demand is likely to increase for care related professions ranging from personal healthcare to the clergy 187 In April 2023 it was reported that 70 of the jobs for Chinese video game illlustrators had been eliminated by generative artificial intelligence 188 189 Existential risk Main article Existential risk from artificial general intelligence It has been argued AI will become so powerful that humanity may irreversibly lose control of it This could as physicist Stephen Hawking stated spell the end of the human race 190 This scenario has been common in science fiction when a computer or robot suddenly develops a human like self awareness or sentience or consciousness and becomes a malevolent character p These sci fi scenarios are misleading in several ways First AI does not require human like sentience to be an existential risk Modern AI programs are given specific goals and use learning and intelligence to achieve them Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI it may choose to destroy humanity to achieve it he used the example of a paperclip factory manager 192 Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged reasoning that you can t fetch the coffee if you re dead 193 In order to be safe for humanity a superintelligence would have to be genuinely aligned with humanity s morality and values so that it is fundamentally on our side 194 Second Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk The essential parts of civilization are not physical Things like ideologies law government money and the economy are made of language they exist because there are stories that billions of people believe The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything even to take actions that are destructive 195 The opinions amongst experts and industry insiders are mixed with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI 196 Personalities such as Stephen Hawking Bill Gates and Elon Musk have expressed concern about existential risk from AI 197 In the early 2010s experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine 198 However after 2016 the study of current and future risks and possible solutions became a serious area of research 199 AI pioneers including Fei Fei Li Geoffrey Hinton Yoshua Bengio Cynthia Breazeal Rana el Kaliouby Demis Hassabis Joy Buolamwini and Sam Altman have expressed concerns about the risks of AI In 2023 many leading AI experts issued the joint statement that Mitigating the risk of extinction from AI should be a global priority alongside other societal scale risks such as pandemics and nuclear war 200 Other researchers however spoke in favor of a less dystopian view AI pioneer Juergen Schmidhuber did not sign the joint statement emphasising that in 95 of all cases AI research is about making human lives longer and healthier and easier 201 While the tools that are now being used to improve lives can also be used by bad actors they can also be used against the bad actors 202 203 Andrew Ng also argued that it s a mistake to fall for the doomsday hype on AI and that regulators who do will only benefit vested interests 204 Yann LeCun scoffs at his peers dystopian scenarios of supercharged misinformation and even eventually human extinction 205 Limiting AI Possible options for limiting AI include using Embedded Ethics or Constitutional AI where companies or governments can add a policy restricting high levels of compute power in training restricting the ability to rewrite its own code base restrict certain AI techniques but not in the training phase open source transparency vs proprietary could be more restricted backup model with redundancy restricting security privacy and copyright restricting or controlling the memory real time monitoring risk analysis emergency shut off rigorous simulation and testing model certification assess known vulnerabilities restrict the training material restrict access to the internet issue terms of use Ethical machines and alignment Main articles Machine ethics AI safety Friendly artificial intelligence Artificial moral agents and Human Compatible Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans Eliezer Yudkowsky who coined the term argues that developing friendly AI should be a higher research priority it may require a large investment and it must be completed before AI becomes an existential risk 206 Machines with intelligence have the potential to use their intelligence to make ethical decisions The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas 207 The field of machine ethics is also called computational morality 207 and was founded at an AAAI symposium in 2005 208 Other approaches include Wendell Wallach s artificial moral agents 209 and Stuart J Russell s three principles for developing provably beneficial machines 210 Frameworks Artificial Intelligence projects can have their ethical permissibility tested while designing developing and implementing an AI system An AI framework such as the Care and Act Framework containing the SUM values developed by the Alan Turing Institute tests projects in four main areas 211 212 RESPECT the dignity of individual people CONNECT with other people sincerely openly and inclusively CARE for the wellbeing of everyone PROTECT social values justice and the public interestOther developments in ethical frameworks include those decided upon during the Asilomar Conference the Montreal Declaration for Responsible AI and the IEEE s Ethics of Autonomous Systems initiative among others 213 however these principles do not go without their criticisms especially regards to the people chosen contributes to these frameworks 214 Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design development and implementation and collaboration between job roles such as data scientists product managers data engineers domain experts and delivery managers 215 Regulation Main articles Regulation of artificial intelligence Regulation of algorithms and AI safety nbsp The first global AI Safety Summit was held in 2023 with a declaration calling for international co operationThe regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence AI it is therefore related to the broader regulation of algorithms 216 The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally 217 According to AI Index at Stanford the annual number of AI related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone 218 219 Between 2016 and 2020 more than 30 countries adopted dedicated strategies for AI 220 Most EU member states had released national AI strategies as had Canada China India Japan Mauritius the Russian Federation Saudi Arabia United Arab Emirates US and Vietnam Others were in the process of elaborating their own AI strategy including Bangladesh Malaysia and Tunisia 220 The Global Partnership on Artificial Intelligence was launched in June 2020 stating a need for AI to be developed in accordance with human rights and democratic values to ensure public confidence and trust in the technology 220 Henry Kissinger Eric Schmidt and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI 221 In 2023 OpenAI leaders published recommendations for the governance of superintelligence which they believe may happen in less than 10 years 222 In 2023 the United Nations also launched an advisory body to provide recommendations on AI governance the body comprises technology company executives governments officials and academics 223 In a 2022 Ipsos survey attitudes towards AI varied greatly by country 78 of Chinese citizens but only 35 of Americans agreed that products and services using AI have more benefits than drawbacks 218 A 2023 Reuters Ipsos poll found that 61 of Americans agree and 22 disagree that AI poses risks to humanity 224 In a 2023 Fox News poll 35 of Americans thought it very important and an additional 41 thought it somewhat important for the federal government to regulate AI versus 13 responding not very important and 8 responding not at all important 225 226 In November 2023 the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks 227 28 countries including the United States China and the European Union issued a declaration at the start of the summit calling for international co operation to manage the challenges and risks of artificial intelligence 228 229 HistoryMain article History of artificial intelligence For a chronological guide see Timeline of artificial intelligence The study of mechanical or formal reasoning began with philosophers and mathematicians in antiquity The study of logic led directly to Alan Turing s theory of computation which suggested that a machine by shuffling symbols as simple as 0 and 1 could simulate both mathematical deduction and formal reasoning which is known as the Church Turing thesis 230 This along with concurrent discoveries in cybernetics and information theory led researchers to consider the possibility of building an electronic brain q 232 Alan Turing was thinking about machine intelligence at least as early as 1941 when he circulated a paper on machine intelligence which could be the earliest paper in the field of AI though it is now lost 2 The first available paper generally recognized as AI was McCullouch and Pitts design for Turing complete artificial neurons in 1943 the first mathematical model of a neural network 233 The paper was influenced by Turing s earlier paper On Computable Numbers from 1936 using similar two state boolean neurons but was the first to apply it to neuronal function 2 The term Machine Intelligence was used by Alan Turing during his life which was later often referred to as Artificial Intelligence after his death in 1954 In 1950 Turing published the best known of his papers Computing Machinery and Intelligence the paper introduced his concept of what is now known as the Turing test to the general public Then followed three radio broadcasts on AI by Turing the lectures Intelligent Machinery A Heretical Theory Can Digital Computers Think and the panel discussion Can Automatic Calculating Machines be Said to Think By 1956 computer intelligence had been actively pursued for more than a decade in Britain the earliest AI programmes were written there in 1951 1952 2 In 1951 using a Ferranti Mark 1 computer of the University of Manchester checkers and chess programs were wrote where you could play against the computer 234 The field of American AI research was founded at a workshop at Dartmouth College in 1956 r 3 The attendees became the leaders of AI research in the 1960s s They and their students produced programs that the press described as astonishing t computers were learning checkers strategies solving word problems in algebra proving logical theorems and speaking English u 4 Artificial Intelligence laboratories were set up at a number of British and US Universities in the latter 1950s and early 1960s 2 They had however underestimated the difficulty of the problem v Both the U S and British governments cut off exploratory research in response to the criticism of Sir James Lighthill 239 and ongoing pressure from the U S Congress to fund more productive projects Minsky s and Papert s book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real world tasks thus discrediting the approach altogether 240 The AI winter a period when obtaining funding for AI projects was difficult followed 6 In the early 1980s AI research was revived by the commercial success of expert systems 241 a form of AI program that simulated the knowledge and analytical skills of human experts By 1985 the market for AI had reached over a billion dollars At the same time Japan s fifth generation computer project inspired the U S and British governments to restore funding for academic research 5 However beginning with the collapse of the Lisp Machine market in 1987 AI once again fell into disrepute and a second longer lasting winter began 7 Many researchers began to doubt that the current practices would be able to imitate all the processes of human cognition especially perception robotics learning and pattern recognition 242 A number of researchers began to look into sub symbolic approaches 243 Robotics researchers such as Rodney Brooks rejected representation in general and focussed directly on engineering machines that move and survive w Judea Pearl Lofti Zadeh and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic 84 248 But the most important development was the revival of connectionism including neural network research by Geoffrey Hinton and others 249 In 1990 Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits the first of many successful applications of neural networks 250 AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems This narrow and formal focus allowed researchers to produce verifiable results and collaborate with other fields such as statistics economics and mathematics 251 By 2000 solutions developed by AI researchers were being widely used although in the 1990s they were rarely described as artificial intelligence 252 Several academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile fully intelligent machines Beginning around 2002 they founded the subfield of artificial general intelligence or AGI which had several well funded institutions by the 2010s 11 Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field 8 For many specific tasks other methods were abandoned x Deep learning s success was based on both hardware improvements faster computers 254 graphics processing units cloud computing 255 and access to large amounts of data 256 including curated datasets 255 such as ImageNet Deep learning s success led to an enormous increase in interest and funding in AI y The amount of machine learning research measured by total publications increased by 50 in the years 2015 2019 220 and WIPO reported that AI was the most prolific emerging technology in terms of the number of patent applications and granted patents 257 According to AI Impacts about 50 billion annually was invested in AI around 2022 in the US alone and about 20 of new US Computer Science PhD graduates have specialized in AI 258 about 800 000 AI related US job openings existed in 2022 259 The large majority of the advances have occurred within the United States with its companies universities and research labs leading artificial intelligence research 10 In 2016 issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences publications vastly increased funding became available and many researchers re focussed their careers on these issues The alignment problem became a serious field of academic study 199 PhilosophyMain article Philosophy of artificial intelligence Defining artificial intelligence Main articles Turing test Intelligent agent Dartmouth workshop and Synthetic intelligence Alan Turing wrote in 1950 I propose to consider the question can machines think 260 He advised changing the question from whether a machine thinks to whether or not it is possible for machinery to show intelligent behaviour 260 He devised the Turing test which measures the ability of a machine to simulate human conversation 261 Since we can only observe the behavior of the machine it does not matter if it is actually thinking or literally has a mind Turing notes that we can not determine these things about other people z but it is usual to have a polite convention that everyone thinks 262 Russell and Norvig agree with Turing that AI must be defined in terms of acting and not thinking 263 However they are critical that the test compares machines to people Aeronautical engineering texts they wrote do not define the goal of their field as making machines that fly so exactly like pigeons that they can fool other pigeons 264 AI founder John McCarthy agreed writing that Artificial intelligence is not by definition simulation of human intelligence 265 McCarthy defines intelligence as the computational part of the ability to achieve goals in the world 266 Another AI founder Marvin Minsky similarly defines it as the ability to solve hard problems 267 These definitions view intelligence in terms of well defined problems with well defined solutions where both the difficulty of the problem and the performance of the program are direct measures of the intelligence of the machine and no other philosophical discussion is required or may not even be possible Another definition has been adopted by Google 268 a major practitioner in the field of AI This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence similar to the way it is defined in biological intelligence Evaluating approaches to AI No established unifying theory or paradigm has guided AI research for most of its history aa The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches so much so that some sources especially in the business world use the term artificial intelligence to mean machine learning with neural networks This approach is mostly sub symbolic soft and narrow see below Critics argue that these questions may have to be revisited by future generations of AI researchers Symbolic AI and its limits Symbolic AI or GOFAI 270 simulated the high level conscious reasoning that people use when they solve puzzles express legal reasoning and do mathematics They were highly successful at intelligent tasks such as algebra or IQ tests In the 1960s Newell and Simon proposed the physical symbol systems hypothesis A physical symbol system has the necessary and sufficient means of general intelligent action 271 However the symbolic approach failed on many tasks that humans solve easily such as learning recognizing an object or commonsense reasoning Moravec s paradox is the discovery that high level intelligent tasks were easy for AI but low level instinctive tasks were extremely difficult 272 Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a feel for the situation rather than explicit symbolic knowledge 273 Although his arguments had been ridiculed and ignored when they were first presented eventually AI research came to agree with him ab 16 The issue is not resolved sub symbolic reasoning can make many of the same inscrutable mistakes that human intuition does such as algorithmic bias Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence 275 276 in part because sub symbolic AI is a move away from explainable AI it can be difficult or impossible to understand why a modern statistical AI program made a particular decision The emerging field of neuro symbolic artificial intelligence attempts to bridge the two approaches Neat vs scruffy Main article Neats and scruffies Neats hope that intelligent behavior is described using simple elegant principles such as logic optimization or neural networks Scruffies expect that it necessarily requires solving a large number of unrelated problems Neats defend their programs with theoretical rigor scruffies rely mainly on incremental testing to see if they work This issue was actively discussed in the 1970s and 1980s 277 but eventually was seen as irrelevant Modern AI has elements of both Soft vs hard computing Main article Soft computing Finding a provably correct or optimal solution is intractable for many important problems 15 Soft computing is a set of techniques including genetic algorithms fuzzy logic and neural networks that are tolerant of imprecision uncertainty partial truth and approximation Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks Narrow vs general AI Main article Artificial general intelligence AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible narrow AI in hopes these solutions will lead indirectly to the field s long term goals 278 279 General intelligence is difficult to define and difficult to measure and modern AI has had more verifiable successes by focusing on specific problems with specific solutions The experimental sub field of artificial general intelligence studies this area exclusively Machine consciousness sentience and mind Main articles Philosophy of artificial intelligence and Artificial consciousness The philosophy of mind does not know whether a machine can have a mind consciousness and mental states in the same sense that human beings do This issue considers the internal experiences of the machine rather than its external behavior Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field to build machines that can solve problems using intelligence Russell and Norvig add that t he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on 280 However the question has become central to the philosophy of mind It is also typically the central question at issue in artificial intelligence in fiction Consciousness Main articles Hard problem of consciousness and Theory of mind David Chalmers identified two problems in understanding the mind which he named the hard and easy problems of consciousness 281 The easy problem is understanding how the brain processes signals makes plans and controls behavior The hard problem is explaining how this feels or why it should feel like anything at all assuming we are right in thinking that it truly does feel like something Dennett s consciousness illusionism says this is an illusion Human information processing is easy to explain however human subjective experience is difficult to explain For example it is easy to imagine a color blind person who has learned to identify which objects in their field of view are red but it is not clear what would be required for the person to know what red looks like 282 Computationalism and functionalism Main articles Computational theory of mind Functionalism philosophy of mind and Chinese room Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind body problem This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam 283 Philosopher John Searle characterized this position as strong AI The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds ac Searle counters this assertion with his Chinese room argument which attempts to show that even if a machine perfectly simulates human behavior there is still no reason to suppose it also has a mind 287 Robot rights Main article Robot rights If a machine has a mind and subjective experience then it may also have sentience the ability to feel and if so it could also suffer it has been argued that this could entitle it to certain rights 288 Any hypothetical robot rights would lie on a spectrum with animal rights and human rights 289 This issue has been considered in fiction for centuries 290 and is now being considered by for example California s Institute for the Future however critics argue that the discussion is premature 291 FutureSuperintelligence and the singularity A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind 279 If research into artificial general intelligence produced sufficiently intelligent software it might be able to reprogram and improve itself The improved software would be even better at improving itself leading to what I J Good called an intelligence explosion and Vernor Vinge called a singularity 292 However technologies cannot improve exponentially indefinitely and typically follow an S shaped curve slowing when they reach the physical limits of what the technology can do 293 Transhumanism Robot designer Hans Moravec cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either This idea called transhumanism has roots in Aldous Huxley and Robert Ettinger 294 Edward Fredkin argues that artificial intelligence is the next stage in evolution an idea first proposed by Samuel Butler s Darwin among the Machines as far back as 1863 and expanded upon by George Dyson in his book of the same name in 1998 295 In fictionMain article Artificial intelligence in fiction nbsp The word robot itself was coined by Karel Capek in his 1921 play R U R the title standing for Rossum s Universal Robots Thought capable artificial beings have appeared as storytelling devices since antiquity 296 and have been a persistent theme in science fiction 297 A common trope in these works began with Mary Shelley s Frankenstein where a human creation becomes a threat to its masters This includes such works as Arthur C Clarke s and Stanley Kubrick s 2001 A Space Odyssey both 1968 with HAL 9000 the murderous computer in charge of the Discovery One spaceship as well as The Terminator 1984 and The Matrix 1999 In contrast the rare loyal robots such as Gort from The Day the Earth Stood Still 1951 and Bishop from Aliens 1986 are less prominent in popular culture 298 Isaac Asimov introduced the Three Laws of Robotics in many books and stories most notably the Multivac series about a super intelligent computer of the same name Asimov s laws are often brought up during lay discussions of machine ethics 299 while almost all artificial intelligence researchers are familiar with Asimov s laws through popular culture they generally consider the laws useless for many reasons one of which is their ambiguity 300 Several works use AI to force us to confront the fundamental question of what makes us human showing us artificial beings that have the ability to feel and thus to suffer This appears in Karel Capek s R U R the films A I Artificial Intelligence and Ex Machina as well as the novel Do Androids Dream of Electric Sheep by Philip K Dick Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence 301 See alsoAI effect Artificial intelligence detection software Software to detect AI generated contentPages displaying short descriptions of redirect targets Artificial intelligence in healthcare Overview of the use of artificial intelligence in healthcare Behavior selection algorithm Algorithm that selects actions for intelligent agents Business process automation Technology enabled automation of complex business processes Case based reasoning Process of solving new problems based on the solutions of similar past problems Emergent algorithm Algorithm exhibiting emergent behavior Female gendering of AI technologies Glossary of artificial intelligence List of definitions of terms and concepts commonly used in the study of artificial intelligence Robotic process automation Form of business process automation technology Super Artificial Narrow Intelligence Weak artificial intelligence Form of artificial intelligence Wetware computer Computer composed of organic materialExplanatory notes a b This list of intelligent traits is based on the topics covered by the major AI textbooks including Russell amp Norvig 2021 Luger amp Stubblefield 2004 Poole Mackworth amp Goebel 1998 and Nilsson 1998 a b This list of tools is based on the topics covered by the major AI textbooks including Russell amp Norvig 2021 Luger amp Stubblefield 2004 Poole Mackworth amp Goebel 1998 and Nilsson 1998 It is among the reasons that expert systems proved to be inefficient for capturing knowledge 30 31 Rational agent is general term used in economics philosophy and theoretical artificial intelligence It can refer to anything that directs its behavior to accomplish goals such as a person an animal a corporation a nation or in the case of AI a computer program Alan Turing discussed the centrality of learning as early as 1950 in his classic paper Computing Machinery and Intelligence 42 In 1956 at the original Dartmouth AI summer conference Ray Solomonoff wrote a report on unsupervised probabilistic machine learning An Inductive Inference Machine 43 See AI winter Machine translation and the ALPAC report of 1966 Compared with symbolic logic formal Bayesian inference is computationally expensive For inference to be tractable most observations must be conditionally independent of one another AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve 86 Expectation maximization one of the most popular algorithms in machine learning allows clustering in the presence of unknown latent variables 88 Some form of deep neural networks without a specific learning algorithm were described by Alan Turing 1948 112 Frank Rosenblatt 1957 112 Karl Steinbuch and Roger David Joseph 1961 113 Deep or recurrent networks that learned or used gradient descent were developed by Ernst Ising and Wilhelm Lenz 1925 114 Oliver Selfridge 1959 113 Alexey Ivakhnenko and Valentin Lapa 1965 114 Kaoru Nakano 1977 115 Shun Ichi Amari 1972 115 John Joseph Hopfield 1982 115 Backpropagation was independently discovered by Henry J Kelley 1960 112 Arthur E Bryson 1962 112 Stuart Dreyfus 1962 112 Arthur E Bryson and Yu Chi Ho 1969 112 Seppo Linnainmaa 1970 116 Paul Werbos 1974 112 In fact backpropagation and gradient descent are straight forward applications of Gottfried Leibniz chain rule in calculus 1676 117 and is essentially identical for one layer to the method of least squares developed independently by Johann Carl Friedrich Gauss 1795 and Adrien Marie Legendre 1805 118 There are probably many others yet to be discovered by historians of science Geoffrey Hinton said of his work on neural networks in the 1990s our labeled datasets were thousands of times too small And our computers were millions of times too slow 119 Including Jon Kleinberg Cornell Sendhil Mullainathan University of Chicago Cynthia Chouldechova Carnegie Mellon and Sam Corbett Davis Stanford 153 Moritz Hardt a director at the Max Planck Institute for Intelligent Systems argues that machine learning is fundamentally the wrong tool for a lot of domains where you re trying to design interventions and mechanisms that change the world 158 When the law was passed in 2018 it still contained a form of this provision This is the United Nations definition and includes things like land mines as well 171 See table 4 9 is both the OECD average and the US average 184 Sometimes called a robopocalypse 191 Electronic brain was the term used by the press around this time 231 Daniel Crevier wrote the conference is generally recognized as the official birthdate of the new science 235 Russell and Norvig called the conference the inception of artificial intelligence 233 Russell and Norvig wrote for the next 20 years the field would be dominated by these people and their students 236 Russell and Norvig wrote it was astonishing whenever a computer did anything kind of smartish 237 The programs described are Arthur Samuel s checkers program for the IBM 701 Daniel Bobrow s STUDENT Newell and Simon s Logic Theorist and Terry Winograd s SHRDLU Russell and Norvig write in almost all cases these early systems failed on more difficult problems 238 Embodied approaches to AI 244 were championed by Hans Moravec 245 and Rodney Brooks 246 and went by many names Nouvelle AI 246 Developmental robotics 247 Matteo Wong wrote in The Atlantic Whereas for decades computer science fields such as natural language processing computer vision and robotics used extremely different methods now they all use a programming method called deep learning As a result their code and approaches have become more similar and their models are easier to integrate into one another 253 Jack Clark wrote in Bloomberg After a half decade of quiet breakthroughs in artificial intelligence 2015 has been a landmark year Computers are smarter and learning faster than ever and noted that the number of software projects that use machine learning at Google increased from a sporadic usage in 2012 to more than 2 700 projects in 2015 255 See Problem of other minds Nils Nilsson wrote in 1983 Simply put there is wide disagreement in the field about what AI is all about 269 Daniel Crevier wrote that time has proven the accuracy and perceptiveness of some of Dreyfus s comments Had he formulated them less aggressively constructive actions they suggested might have been taken much earlier 274 Searle presented this definition of Strong AI in 1999 284 Searle s original formulation was The appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states 285 Strong AI is defined similarly by Russell and Norvig Stong AI the assertion that machines that do so are actually thinking as opposed to simulating thinking 286 References Google 2016 a b c d e Copeland J ed 2004 The Essential Turing the ideas that gave birth to the computer age Oxford England Clarendon Press ISBN 0 19 825079 7 a b Dartmouth workshop Russell amp Norvig 2021 p 18 McCorduck 2004 pp 111 136 NRC 1999 pp 200 201 The proposal McCarthy et al 1955 a b Successful programs the 1960s McCorduck 2004 pp 243 252 Crevier 1993 pp 52 107 Moravec 1988 p 9 Russell amp Norvig 2021 pp 19 21 a b Funding initiatives in the early 1980s Fifth Generation Project Japan Alvey UK Microelectronics and Computer Technology Corporation US Strategic Computing Initiative US McCorduck 2004 pp 426 441 Crevier 1993 pp 161 162 197 203 211 240 Russell amp Norvig 2021 p 23 NRC 1999 pp 210 211 Newquist 1994 pp 235 248 a b First AI Winter Lighthill report Mansfield Amendment Crevier 1993 pp 115 117 Russell amp Norvig 2021 pp 21 22 NRC 1999 pp 212 213 Howe 1994 Newquist 1994 pp 189 201 a b Second AI Winter Russell amp Norvig 2021 p 24 McCorduck 2004 pp 430 435 Crevier 1993 pp 209 210 NRC 1999 pp 214 216 Newquist 1994 pp 301 318 a b Deep learning revolution AlexNet Goldman 2022 Russell amp Norvig 2021 p 26 McKinsey 2018 Toews 2023 a b Frank 2023 a b c Artificial general intelligence Russell amp Norvig 2021 pp 32 33 1020 1021 Proposal for the modern version Pennachin amp Goertzel 2007 Warnings of overspecialization in AI from leading researchers Nilsson 1995 McCarthy 2007 Beal amp Winston 2009 Russell amp Norvig 2021 1 2 Problem solving puzzle solving game playing and deduction Russell amp Norvig 2021 chpt 3 5 Russell amp Norvig 2021 chpt 6 constraint satisfaction Poole Mackworth amp Goebel 1998 chpt 2 3 7 9 Luger amp Stubblefield 2004 chpt 3 4 6 8 Nilsson 1998 chpt 7 12 Uncertain reasoning Russell amp Norvig 2021 chpt 12 18 Poole Mackworth amp Goebel 1998 pp 345 395 Luger amp Stubblefield 2004 pp 333 381 Nilsson 1998 chpt 7 12 a b c Intractability and efficiency and the combinatorial explosion Russell amp Norvig 2021 p 21 a b c Psychological evidence of the prevalence sub symbolic reasoning and knowledge Kahneman 2011 Dreyfus amp Dreyfus 1986 Wason amp Shapiro 1966 Kahneman Slovic amp Tversky 1982 Knowledge representation and knowledge engineering Russell amp Norvig 2021 chpt 10 Poole Mackworth amp Goebel 1998 pp 23 46 69 81 169 233 235 277 281 298 319 345 Luger amp Stubblefield 2004 pp 227 243 Nilsson 1998 chpt 17 1 17 4 18 Smoliar amp Zhang 1994 Neumann amp Moller 2008 Kuperman Reichley amp Bailey 2006 McGarry 2005 Bertini Del Bimbo amp Torniai 2006 Russell amp Norvig 2021 pp 272 Representing categories and relations Semantic networks description logics inheritance including frames and scripts Russell amp Norvig 2021 10 2 amp 10 5 Poole Mackworth amp Goebel 1998 pp 174 177 Luger amp Stubblefield 2004 pp 248 258 Nilsson 1998 chpt 18 3 Representing events and time Situation calculus event calculus fluent calculus including solving the frame problem Russell amp Norvig 2021 10 3 Poole Mackworth amp Goebel 1998 pp 281 298 Nilsson 1998 chpt 18 2 Causal calculus Poole Mackworth amp Goebel 1998 pp 335 337 Representing knowledge about knowledge Belief calculus modal logics Russell amp Norvig 2021 10 4 Poole Mackworth amp Goebel 1998 pp 275 277 a b Default reasoning Frame problem default logic non monotonic logics circumscription closed world assumption abduction Russell amp Norvig 2021 10 6 Poole Mackworth amp Goebel 1998 pp 248 256 323 335 Luger amp Stubblefield 2004 pp 335 363 Nilsson 1998 18 3 3 Poole et al places abduction under default reasoning Luger et al places this under uncertain reasoning a b Breadth of commonsense knowledge Lenat amp Guha 1989 Introduction Crevier 1993 pp 113 114 Moravec 1988 p 13 Russell amp Norvig 2021 pp 241 385 982 qualification problem Newquist 1994 p 296 Crevier 1993 pp 204 208 Russell amp Norvig 2021 p 528 Automated planning Russell amp Norvig 2021 chpt 11 Automated decision making Decision theory Russell amp Norvig 2021 chpt 16 18 Classical planning Russell amp Norvig 2021 Section 11 2 Sensorless or conformant planning contingent planning replanning a k a online planning Russell amp Norvig 2021 Section 11 5 Uncertain preferences Russell amp Norvig 2021 Section 16 7 Inverse reinforcement learning Russell amp Norvig 2021 Section 22 6 Information value theory Russell amp Norvig 2021 Section 16 6 Markov decision process Russell amp Norvig 2021 chpt 17 Game theory and multi agent decision theory Russell amp Norvig 2021 chpt 18 Learning Russell amp Norvig 2021 chpt 19 22 Poole Mackworth amp Goebel 1998 pp 397 438 Luger amp Stubblefield 2004 pp 385 542 Nilsson 1998 chpt 3 3 10 3 17 5 20 Turing 1950 Solomonoff 1956 Unsupervised learning Russell amp Norvig 2021 pp 653 definition Russell amp Norvig 2021 pp 738 740 cluster analysis Russell amp Norvig 2021 pp 846 860 word embedding a b Supervised learning Russell amp Norvig 2021 19 2 Definition Russell amp Norvig 2021 Chpt 19 20 Techniques Reinforcement learning Russell amp Norvig 2021 chpt 22 Luger amp Stubblefield 2004 pp 442 449 Transfer learning Russell amp Norvig 2021 pp 281 The Economist 2016 Artificial Intelligence AI What Is AI and How Does It Work Built In builtin com Retrieved 30 October 2023 Computational learning theory Russell amp Norvig 2021 pp 672 674 Jordan amp Mitchell 2015 Natural language processing NLP Russell amp Norvig 2021 chpt 23 24 Poole Mackworth amp Goebel 1998 pp 91 104 Luger amp Stubblefield 2004 pp 591 632 Subproblems of NLP Russell amp Norvig 2021 pp 849 850 Russell amp Norvig 2021 p 856 858 Dickson 2022 Modern statistical and deep learning approaches to NLP Russell amp Norvig 2021 chpt 24 Cambria amp White 2014 Vincent 2019 Russell amp Norvig 2021 p 875 878 Bushwick 2023 Computer vision Russell amp Norvig 2021 chpt 25 Nilsson 1998 chpt 6 Russell amp Norvig 2021 pp 849 850 Russell amp Norvig 2021 pp 895 899 Russell amp Norvig 2021 pp 899 901 Russell amp Norvig 2021 pp 931 938 MIT AIL 2014 Affective computing Thro 1993 Edelson 1991 Tao amp Tan 2005 Scassellati 2002 Waddell 2018 Poria et al 2017 Search algorithms Russell amp Norvig 2021 Chpt 3 5 Poole Mackworth amp Goebel 1998 pp 113 163 Luger amp Stubblefield 2004 pp 79 164 193 219 Nilsson 1998 chpt 7 12 State space search Russell amp Norvig 2021 chpt 3 Russell amp Norvig 2021 11 2 Uninformed searches breadth first search depth first search and general state space search Russell amp Norvig 2021 3 4 Poole Mackworth amp Goebel 1998 pp 113 132 Luger amp Stubblefield 2004 pp 79 121 Nilsson 1998 chpt 8 Heuristic or informed searches e g greedy best first and A Russell amp Norvig 2021 s 3 5 Poole Mackworth amp Goebel 1998 pp 132 147 Poole amp Mackworth 2017 3 6 Luger amp Stubblefield 2004 pp 133 150 Adversarial search Russell amp Norvig 2021 chpt 5 Local or optimization search Russell amp Norvig 2021 chpt 4 Evolutionary computation Russell amp Norvig 2021 4 1 2 Merkle amp Middendorf 2013 Logic Russell amp Norvig 2021 chpt 6 9 Luger amp Stubblefield 2004 pp 35 77 Nilsson 1998 chpt 13 16 Propositional logic Russell amp Norvig 2021 chpt 6 Luger amp Stubblefield 2004 pp 45 50 Nilsson 1998 chpt 13 First order logic and features such as equality Russell amp Norvig 2021 chpt 7 Poole Mackworth amp Goebel 1998 pp 268 275 Luger amp Stubblefield 2004 pp 50 62 Nilsson 1998 chpt 15 Logical inference Russell amp Norvig 2021 chpt 10 Russell amp Norvig 2021 8 3 1 Resolution and unification Russell amp Norvig 2021 7 5 2 9 2 9 5 Forward chaining backward chaining Horn clauses and logical deduction as search Russell amp Norvig 2021 9 3 9 4 Poole Mackworth amp Goebel 1998 pp 46 52 Luger amp Stubblefield 2004 pp 62 73 Nilsson 1998 chpt 4 2 7 2 Fuzzy logic Russell amp Norvig 2021 pp 214 255 459 Scientific American 1999 a b Stochastic methods for uncertain reasoning Russell amp Norvig 2021 Chpt 12 18 and 20 Poole Mackworth amp Goebel 1998 pp 345 395 Luger amp Stubblefield 2004 pp 165 191 333 381 Nilsson 1998 chpt 19 Bayesian networks Russell amp Norvig 2021 12 5 12 6 13 4 13 5 14 3 14 5 16 5 20 2 20 3 Poole Mackworth amp Goebel 1998 pp 361 381 Luger amp Stubblefield 2004 pp 182 190 363 379 Nilsson 1998 chpt 19 3 4 Domingos 2015 chapter 6 Bayesian inference algorithm Russell amp Norvig 2021 13 3 13 5 Poole Mackworth amp Goebel 1998 pp 361 381 Luger amp Stubblefield 2004 pp 363 379 Nilsson 1998 chpt 19 4 amp 7 Domingos 2015 p 210 Bayesian learning and the expectation maximization algorithm Russell amp Norvig 2021 Chpt 20 Poole Mackworth amp Goebel 1998 pp 424 433 Nilsson 1998 chpt 20 Domingos 2015 p 210 Bayesian decision theory and Bayesian decision networks Russell amp Norvig 2021 16 5 a b c Stochastic temporal models Russell amp Norvig 2021 Chpt 14 Hidden Markov model Russell amp Norvig 2021 14 3 Kalman filters Russell amp Norvig 2021 14 4 Dynamic Bayesian networks Russell amp Norvig 2021 14 5 decision theory and decision analysis Russell amp Norvig 2021 Chpt 16 18 Poole Mackworth amp Goebel 1998 pp 381 394 Information value theory Russell amp Norvig 2021 16 6 Markov decision processes and dynamic decision networks Russell amp Norvig 2021 chpt 17 Game theory and mechanism design Russell amp Norvig 2021 chpt 18 Statistical learning methods and classifiers Russell amp Norvig 2021 chpt 20 Decision trees Russell amp Norvig 2021 19 3 Domingos 2015 p 88 Non parameteric learning models such as K nearest neighbor and support vector machines Russell amp Norvig 2021 19 7 Domingos 2015 p 187 k nearest neighbor Domingos 2015 p 88 kernel methods Domingos 2015 p 152 Naive Bayes classifier Russell amp Norvig 2021 12 6 Domingos 2015 p 152 a b Neural networks Russell amp Norvig 2021 Chpt 21 Domingos 2015 Chapter 4 Gradient calculation in computational graphs backpropagation automatic differentiation Russell amp Norvig 2021 21 2 Luger amp Stubblefield 2004 pp 467 474 Nilsson 1998 chpt 3 3 Universal approximation theorem Russell amp Norvig 2021 p 752 The theorem Cybenko 1988 Hornik Stinchcombe amp White 1989 Feedforward neural networks Russell amp Norvig 2021 21 1 Recurrent neural networks Russell amp Norvig 2021 21 6 Perceptrons Russell amp Norvig 2021 pp 21 22 683 22 a b Deep learning Russell amp Norvig 2021 Chpt 21 Goodfellow Bengio amp Courville 2016 Hinton et al 2016 Schmidhuber 2015 Convolutional neural networks Russell amp Norvig 2021 21 3 Deng amp Yu 2014 pp 199 200 Ciresan Meier amp Schmidhuber 2012 Russell amp Norvig 2021 p 751 a b c d e f g Russell amp Norvig 2021 p 785 a b Schmidhuber 2022 5 a b Schmidhuber 2022 6 a b c Schmidhuber 2022 7 Schmidhuber 2022 8 Schmidhuber 2022 2 Schmidhuber 2022 3 Quoted in Christian 2020 p 22 Smith 2023 Explained Generative AI 9 November 2023 AI Writing and Content Creation Tools MIT Sloan Teaching amp Learning Technologies Retrieved 25 December 2023 Marmouyet 2023 Kobielus 2019 Davenport T Kalakota R June 2019 The potential for artificial intelligence in healthcare Future Healthc J 6 2 94 98 doi 10 7861 futurehosp 6 2 94 PMC 6616181 PMID 31363513 a b Bax Monique Thorpe Jordan Romanov Valentin December 2023 The future of personalized cardiovascular medicine demands 3D and 4D printing stem cells and artificial intelligence Frontiers in Sensors 4 doi 10 3389 fsens 2023 1294721 ISSN 2673 5067 Jumper J Evans R Pritzel A 2021 Highly accurate protein structure prediction with AlphaFold Nature 596 7873 583 589 Bibcode 2021Natur 596 583J doi 10 1038 s41586 021 03819 2 PMC 8371605 PMID 34265844 Wurman P R Barrett S Kawamoto K 2022 Outracing champion Gran Turismo drivers with deep reinforcement learning Nature 602 602 223 228 doi 10 1038 s41586 021 04357 7 Simonite 2016 Russell amp Norvig 2021 p 987 Laskowski 2023 GAO 2022 Valinsky 2019 Russell amp Norvig 2021 p 991 Russell amp Norvig 2021 p 991 992 Christian 2020 p 63 Vincent 2022 Reisner 2023 Alter amp Harris 2023 Nicas 2018 Trust and Distrust in America 22 July 2019 Williams 2023 Taylor amp Hern 2023 a b Rose 2023 CNA 2019 Goffrey 2008 p 17 Berdahl et al 2023 Goffrey 2008 p 17 Rose 2023 Russell amp Norvig 2021 p 995 Algorithmic bias and Fairness machine learning Russell amp Norvig 2021 section 27 3 3 Christian 2020 Fairness Christian 2020 p 25 a b Russell amp Norvig 2021 p 995 Grant amp Hill 2023 Larson amp Angwin 2016 Christian 2020 p 67 70 Christian 2020 pp 67 70 Russell amp Norvig 2021 pp 993 994 Russell amp Norvig 2021 p 995 Lipartito 2011 p 36 Goodman amp Flaxman 2017 p 6 Christian 2020 pp 39 40 65 Quoted in Christian 2020 p 65 Russell amp Norvig 2021 p 994 Christian 2020 pp 40 80 81 Quoted in Christian 2020 p 80 Dockrill 2022 Sample 2017 Black Box AI 16 June 2023 Christian 2020 p 110 Christian 2020 pp 88 91 Christian 2020 p 83 Russell amp Norvig 2021 p 997 Christian 2020 p 91 Christian 2020 p 83 Verma 2021 Rothman 2020 Christian 2020 p 105 108 Christian 2020 pp 108 112 Russell amp Norvig 2021 p 989 Robitzski 2018 Sainato 2015 Russell amp Norvig 2021 p 987 990 Russell amp Norvig 2021 p 988 Harari 2018 Buckley Chris Mozur Paul 22 May 2019 How China Uses High Tech Surveillance to Subdue Minorities The New York Times Security lapse exposed a Chinese smart city surveillance system 3 May 2019 Archived from the original on 7 March 2021 Retrieved 14 September 2020 AI traffic signals to be installed in Bengaluru soon NextBigWhat 24 September 2019 Retrieved 1 October 2019 Urbina et al 2022 Tarnoff Ben 4 August 2023 Lessons from Eliza The Guardian Weekly pp 34 9 a b E McGaughey Will Robots Automate Your Job Away Full Employment Basic Income and Economic Democracy 2022 51 3 Industrial Law Journal 511 559 Archived 27 May 2023 at the Wayback Machine Ford amp Colvin 2015 McGaughey 2022 IGM Chicago 2017 Arntz Gregory amp Zierahn 2016 p 33 Lohr 2017 Frey amp Osborne 2017 Arntz Gregory amp Zierahn 2016 p 33 Morgenstern 2015 Mahdawi 2017 Thompson 2014 Zhou Viola 11 April 2023 AI is already taking video game illustrators jobs in China Rest of World Retrieved 17 August 2023 Carter Justin 11 April 2023 China s game art industry reportedly decimated by growing AI use Game Developer Retrieved 17 August 2023 Cellan Jones 2014 Russell amp Norvig 2021 p 1001 Bostrom 2014 Russell 2019 Bostrom 2014 Muller amp Bostrom 2014 Bostrom 2015 Harari 2023 Muller amp Bostrom 2014 Leaders concerns about the existential risks of AI around 2015 Rawlinson 2015 Holley 2015 Gibbs 2014 Sainato 2015 Arguments that AI is not an imminent risk Brooks 2014 Geist 2015 Madrigal 2015 Lee 2014 a b Christian 2020 pp 67 73 Valance 2023 Taylor Josh 7 May 2023 Rise of artificial intelligence is inevitable but should not be feared father of AI says The Guardian Retrieved 26 May 2023 Colton Emma 7 May 2023 Father of AI says tech fears misplaced You cannot stop it Fox News Retrieved 26 May 2023 Jones Hessie 23 May 2023 Juergen Schmidhuber Renowned Father Of Modern AI Says His Life s Work Won t Lead To Dystopia Forbes Retrieved 26 May 2023 McMorrow Ryan 19 December 2023 Andrew Ng Do we think the world is better off with more or less intelligence Financial Times Retrieved 30 December 2023 Levy Steven 22 December 2023 How Not to Be Stupid About AI With Yann LeCun Wired Retrieved 30 December 2023 Yudkowsky 2008 a b Anderson amp Anderson 2011 AAAI 2014 Wallach 2010 Russell 2019 p 173 Alan Turing Institute 2019 Understanding artificial intelligence ethics and safety PDF Alan Turing Institute 2023 AI Ethics and Governance in Practice PDF Floridi Luciano Cowls Josh 23 June 2019 A Unified Framework of Five Principles for AI in Society Harvard Data Science Review 1 1 doi 10 1162 99608f92 8cd550d1 S2CID 198775713 Buruk Banu Ekmekci Perihan Elif Arda Berna 1 September 2020 A critical perspective on guidelines for responsible and trustworthy artificial intelligence Medicine Health Care and Philosophy 23 3 387 399 doi 10 1007 s11019 020 09948 1 ISSN 1572 8633 PMID 32236794 S2CID 214766800 Kamila Manoj Kumar Jasrotia Sahil Singh 1 January 2023 Ethical issues in the development of artificial intelligence recognizing the risks International Journal of Ethics and Systems ahead of print ahead of print doi 10 1108 IJOES 05 2023 0107 ISSN 2514 9369 S2CID 259614124 Regulation of AI to mitigate risks Berryhill et al 2019 Barfield amp Pagallo 2018 Iphofen amp Kritikos 2019 Wirtz Weyerer amp Geyer 2018 Buiten 2019 Law Library of Congress U S Global Legal Research Directorate 2019 a b Vincent 2023 Stanford University 2023 a b c d UNESCO 2021 Kissinger 2021 Altman Brockman amp Sutskever 2023 VOA News 25 October 2023 UN Announces Advisory Body on Artificial Intelligence Edwards 2023 Kasperowicz 2023 Fox News 2023 Milmo Dan 3 November 2023 Hope or Horror The great AI debate dividing its pioneers The Guardian Weekly pp 10 12 The Bletchley Declaration by Countries Attending the AI Safety Summit 1 2 November 2023 GOV UK 1 November 2023 Archived from the original on 1 November 2023 Retrieved 2 November 2023 Countries agree to safe and responsible development of frontier AI in landmark Bletchley Declaration GOV UK Press release Archived from the original on 1 November 2023 Retrieved 1 November 2023 Berlinski 2000 Google books ngram AI s immediate precursors McCorduck 2004 pp 51 107 Crevier 1993 pp 27 32 Russell amp Norvig 2021 pp 8 17 Moravec 1988 p 3 a b Russell amp Norvig 2021 p 17 See A Brief History of Computing at AlanTuring net Crevier 1993 pp 47 49 Russell amp Norvig 2003 p 17 Russell amp Norvig 2003 p 18 Russell amp Norvig 2021 p 21 Lighthill 1973 Russell amp Norvig 2021 p 22 Expert systems Russell amp Norvig 2021 pp 23 292 Luger amp Stubblefield 2004 pp 227 331 Nilsson 1998 chpt 17 4 McCorduck 2004 pp 327 335 434 435 Crevier 1993 pp 145 62 197 203 Newquist 1994 pp 155 183 Russell amp Norvig 2021 p 24 Nilsson 1998 p 7 McCorduck 2004 pp 454 462 Moravec 1988 a b Brooks 1990 Developmental robotics Weng et al 2001 Lungarella et al 2003 Asada et al 2009 Oudeyer 2010 Russell amp Norvig 2021 p 25 Crevier 1993 pp 214 215 Russell amp Norvig 2021 pp 24 26 Russell amp Norvig 2021 p 26 Formal and narrow methods adopted in the 1990s Russell amp Norvig 2021 pp 24 26 McCorduck 2004 pp 486 487 AI widely used in the late 1990s Kurzweil 2005 p 265 NRC 1999 pp 216 222 Newquist 1994 pp 189 201 Wong 2023 Moore s Law and AI Russell amp Norvig 2021 pp 14 27 a b c Clark 2015b Big data Russell amp Norvig 2021 p 26 Intellectual Property and Frontier Technologies WIPO Archived from the original on 2 April 2022 Retrieved 30 March 2022 DiFeliciantonio 2023 Goswami 2023 a b Turing 1950 p 1 Turing s original publication of the Turing test in Computing machinery and intelligence Turing 1950 Historical influence and philosophical implications Haugeland 1985 pp 6 9 Crevier 1993 p 24 McCorduck 2004 pp 70 71 Russell amp Norvig 2021 pp 2 and 984 Turing 1950 Under The Argument from Consciousness Russell amp Norvig 2021 chpt 2 Russell amp Norvig 2021 p 3 Maker 2006 McCarthy 1999 Minsky 1986 What Is Artificial Intelligence AI Google Cloud Platform Archived from the original on 31 July 2023 Retrieved 16 October 2023 Nilsson 1983 p 10 Haugeland 1985 pp 112 117 Physical symbol system hypothesis Newell amp Simon 1976 p 116 Historical significance McCorduck 2004 p 153 Russell amp Norvig 2021 p 19 Moravec s paradox Moravec 1988 pp 15 16 Minsky 1986 p 29 Pinker 2007 pp 190 91 Dreyfus critique of AI Dreyfus 1972 Dreyfus amp Dreyfus 1986 Historical significance and philosophical implications Crevier 1993 pp 120 132 McCorduck 2004 pp 211 239 Russell amp Norvig 2021 pp 981 982 Fearn 2007 Chpt 3 Crevier 1993 p 125 Langley 2011 Katz 2012 Neats vs scruffies the historic debate McCorduck 2004 pp 421 424 486 489 Crevier 1993 p 168 Nilsson 1983 pp 10 11 Russell amp Norvig 2021 p 24 A classic example of the scruffy approach to intelligence Minsky 1986 A modern example of neat AI and its aspirations in the 21st century Domingos 2015 Pennachin amp Goertzel 2007 a b Roberts 2016 Russell amp Norvig 2021 p 986 Chalmers 1995 Dennett 1991 Horst 2005 Searle 1999 Searle 1980 p 1 Russell amp Norvig 2021 p 9817 Searle s Chinese room argument Searle 1980 Searle s original presentation of the thought experiment Searle 1999 Discussion Russell amp Norvig 2021 pp 985 McCorduck 2004 pp 443 445 Crevier 1993 pp 269 271 Robot rights Russell amp Norvig 2021 pp 1000 1001 BBC 2006 Maschafilm 2010 the film Plug amp Pray Evans 2015 McCorduck 2004 pp 19 25 Henderson 2007 The Intelligence explosion and technological singularity Russell amp Norvig 2021 pp 1004 1005 Omohundro 2008 Kurzweil 2005 I J Good s intelligence explosion Good 1965 Vernor Vinge s singularity Vinge 1993 Russell amp Norvig 2021 p 1005 Transhumanism Moravec 1988 Kurzweil 2005 Russell amp Norvig 2021 p 1005 AI as evolution Edward Fredkin is quoted in McCorduck 2004 p 401 Butler 1863 Dyson 1998 AI in myth McCorduck 2004 pp 4 5 McCorduck 2004 pp 340 400 Buttazzo 2001 Anderson 2008 McCauley 2007 Galvan 1997 AI textbooks The two most widely used textbooks in 2023 See the Open Syllabus Russell Stuart J Norvig Peter 2021 Artificial Intelligence A Modern Approach 4th ed Hoboken Pearson ISBN 978 0134610993 LCCN 20190474 Rich Elaine Knight Kevin Nair Shivashankar B 2010 Artificial Intelligence 3rd ed New Delhi Tata McGraw Hill India ISBN 978 0070087705 These were the four of the most widely used AI textbooks in 2008 Luger George Stubblefield William 2004 Artificial Intelligence Structures and Strategies for Complex Problem Solving 5th ed Benjamin Cummings ISBN 978 0 8053 4780 7 Archived from the original on 26 July 2020 Retrieved 17 December 2019 Nilsson Nils 1998 Artificial Intelligence A New Synthesis Morgan Kaufmann ISBN 978 1 55860 467 4 Archived from the original on 26 July 2020 Retrieved 18 November 2019 Russell Stuart J Norvig Peter 2003 Artificial Intelligence A Modern Approach 2nd ed Upper Saddle River New Jersey Prentice Hall ISBN 0 13 790395 2 Poole David Mackworth Alan Goebel Randy 1998 Computational Intelligence A Logical Approach New York Oxford University Press ISBN 978 0 19 510270 3 Archived from the original on 26 July 2020 Retrieved 22 August 2020 Later editions Poole David Mackworth Alan 2017 Artificial Intelligence Foundations of Computational Agents 2nd ed Cambridge University Press ISBN 978 1 107 19539 4 Archived from the original on 7 December 2017 Retrieved 6 December 2017 History of AI Crevier Daniel 1993 AI The Tumultuous Search for Artificial Intelligence New York NY BasicBooks ISBN 0 465 02997 3 McCorduck Pamela 2004 Machines Who Think 2nd ed Natick MA A K Peters Ltd ISBN 1 56881 205 1 Newquist H P 1994 The Brain Makers Genius Ego And Greed In The Quest For Machines That Think New York Macmillan SAMS ISBN 978 0 672 30412 5 Other sources AI amp ML in Fusion AI amp ML in Fusion video lecture Archived 2 July 2023 at the Wayback Machine AlphaGo Google DeepMind Archived from the original on 10 March 2016 Alter Alexandra Harris Elizabeth A 20 September 2023 Franzen Grisham and Other Prominent Authors Sue OpenAI The New York Times Altman Sam Brockman Greg Sutskever Ilya 22 May 2023 Governance of Superintelligence openai com Archived from the original on 27 May 2023 Retrieved 27 May 2023 Anderson Susan Leigh 2008 Asimov s three laws of robotics and machine metaethics AI amp Society 22 4 477 493 doi 10 1007 s00146 007 0094 5 S2CID 1809459 Anderson Michael Anderson Susan Leigh 2011 Machine Ethics Cambridge University Press Arntz Melanie Gregory Terry Zierahn Ulrich 2016 The risk of automation for jobs in OECD countries A comparative analysis OECD Social Employment and Migration Working Papers 189 Asada M Hosoda K Kuniyoshi Y Ishiguro H Inui T Yoshikawa Y Ogino M Yoshida C 2009 Cognitive developmental robotics a survey IEEE Transactions on Autonomous Mental Development 1 1 12 34 doi 10 1109 tamd 2009 2021702 S2CID 10168773 Ask the AI experts What s driving today s progress in AI McKinsey amp Company Archived from the original on 13 April 2018 Retrieved 13 April 2018 Barfield Woodrow Pagallo Ugo 2018 Research handbook on the law of artificial intelligence Cheltenham UK Edward Elgar Publishing ISBN 978 1 78643 904 8 OCLC 1039480085 Beal J Winston Patrick 2009 The New Frontier of Human Level Artificial Intelligence IEEE Intelligent Systems 24 21 24 doi 10 1109 MIS 2009 75 hdl 1721 1 52357 S2CID 32437713 Berdahl Carl Thomas Baker Lawrence Mann Sean Osoba Osonde Girosi Federico 7 February 2023 Strategies to Improve the Impact of Artificial Intelligence on Health Equity Scoping Review JMIR AI 2 e42936 doi 10 2196 42936 ISSN 2817 1705 S2CID 256681439 Archived from the original on 21 February 2023 Retrieved 21 February 2023 Berlinski David 2000 The Advent of the Algorithm Harcourt Books ISBN 978 0 15 601391 8 OCLC 46890682 Archived from the original on 26 July 2020 Retrieved 22 August 2020 Berryhill Jamie Heang Kevin Kok Clogher Rob McBride Keegan 2019 Hello World Artificial Intelligence and its Use in the Public Sector PDF Paris OECD Observatory of Public Sector Innovation Archived PDF from the original on 20 December 2019 Retrieved 9 August 2020 Bertini M Del Bimbo A Torniai C 2006 Automatic annotation and semantic retrieval of video sequences using multimedia ontologies MM 06 Proceedings of the 14th ACM international conference on Multimedia 14th ACM international conference on Multimedia Santa Barbara ACM pp 679 682 Bostrom Nick 2014 Superintelligence Paths Dangers Strategies Oxford University Press Bostrom Nick 2015 What happens when our computers get smarter than we are TED conference Archived from the original on 25 July 2020 Retrieved 30 January 2020 Brooks Rodney 10 November 2014 artificial intelligence is a tool not a threat Archived from the original on 12 November 2014 Brooks Rodney 1990 Elephants Don t Play Chess PDF Robotics and Autonomous Systems 6 1 2 3 15 CiteSeerX 10 1 1 588 7539 doi 10 1016 S0921 8890 05 80025 9 Archived PDF from the original on 9 August 2007 Buiten Miriam C 2019 Towards Intelligent Regulation of Artificial Intelligence European Journal of Risk Regulation 10 1 41 59 doi 10 1017 err 2019 8 ISSN 1867 299X Bushwick Sophie 16 March 2023 What the New GPT 4 AI Can Do Scientific American Butler Samuel 13 June 1863 Darwin among the Machines Letters to the Editor The Press Christchurch New Zealand Archived from the original on 19 September 2008 Retrieved 16 October 2014 via Victoria University of Wellington Buttazzo G July 2001 Artificial consciousness Utopia or real possibility Computer 34 7 24 30 doi 10 1109 2 933500 Cambria Erik White Bebo May 2014 Jumping NLP Curves A Review of Natural Language Processing Research Review Article IEEE Computational Intelligence Magazine 9 2 48 57 doi 10 1109 MCI 2014 2307227 S2CID 206451986 Cellan Jones Rory 2 December 2014 Stephen Hawking warns artificial intelligence could end mankind BBC News Archived from the original on 30 October 2015 Retrieved 30 October 2015 Chalmers David 1995 Facing up to the problem of consciousness Journal of Consciousness Studies 2 3 200 219 Archived from the original on 8 March 2005 Retrieved 11 October 2018 Christian Brian 2020 The Alignment Problem Machine learning and human values W W Norton amp Company ISBN 978 0 393 86833 3 OCLC 1233266753 Ciresan D Meier U Schmidhuber J 2012 Multi column deep neural networks for image classification 2012 IEEE Conference on Computer Vision and Pattern Recognition pp 3642 3649 arXiv 1202 2745 doi 10 1109 cvpr 2012 6248110 ISBN 978 1 4673 1228 8 S2CID 2161592 Clark Jack 2015b Why 2015 Was a Breakthrough Year in Artificial Intelligence Bloomberg com Archived from the original on 23 November 2016 Retrieved 23 November 2016 CNA 12 January 2019 Commentary Bad news Artificial intelligence is biased CNA Archived from the original on 12 January 2019 Retrieved 19 June 2020 Cybenko G 1988 Continuous valued neural networks with two hidden layers are sufficient Report Department of Computer Science Tufts University Deng L Yu D 2014 Deep Learning Methods and Applications PDF Foundations and Trends in Signal Processing 7 3 4 1 199 doi 10 1561 2000000039 Archived PDF from the original on 14 March 2016 Retrieved 18 October 2014 Dennett Daniel 1991 Consciousness Explained The Penguin Press ISBN 978 0 7139 9037 9 DiFeliciantonio Chase 3 April 2023 AI has already changed the world This report shows how San Francisco Chronicle Archived from the original on 19 June 2023 Retrieved 19 June 2023 Dickson Ben 2 May 2022 Machine learning What is the transformer architecture TechTalks Retrieved 22 November 2023 Dockrill Peter 27 June 2022 Robots With Flawed AI Make Sexist And Racist Decisions Experiment Shows Science Alert archived from the original on 27 June 2022 Domingos Pedro 2015 The Master Algorithm How the Quest for the Ultimate Learning Machine Will Remake Our World Basic Books ISBN 978 0465065707 Dreyfus Hubert 1972 What Computers Can t Do New York MIT Press ISBN 978 0 06 011082 6 Dreyfus Hubert Dreyfus Stuart 1986 Mind over Machine The Power of Human Intuition and Expertise in the Era of the Computer Oxford Blackwell ISBN 978 0 02 908060 3 Archived from the original on 26 July 2020 Retrieved 22 August 2020 Dyson George 1998 Darwin among the Machines Allan Lane Science ISBN 978 0 7382 0030 9 Archived from the original on 26 July 2020 Retrieved 22 August 2020 Edelson Edward 1991 The Nervous System New York Chelsea House ISBN 978 0 7910 0464 7 Archived from the original on 26 July 2020 Retrieved 18 November 2019 Edwards Benj 17 May 2023 Poll AI poses risk to humanity according to majority of Americans Ars Technica Archived from the original on 19 June 2023 Retrieved 19 June 2023 Evans Woody 2015 Posthuman Rights Dimensions of Transhuman Worlds Teknokultura 12 2 doi 10 5209 rev TK 2015 v12 n2 49072 Fearn Nicholas 2007 The Latest Answers to the Oldest Questions A Philosophical Adventure with the World s Greatest Thinkers New York Grove Press ISBN 978 0 8021 1839 4 Ford Martin Colvin Geoff 6 September 2015 Will robots create more jobs than they destroy The Guardian Archived from the original on 16 June 2018 Retrieved 13 January 2018 Fox News 2023 Fox News Poll PDF Fox News Archived PDF from the original on 12 May 2023 Retrieved 19 June 2023 Frank Michael 22 September 2023 US Leadership in Artificial Intelligence Can Shape the 21st Century Global Order The Diplomat Retrieved 8 December 2023 Instead the United States has developed a new area of dominance that the rest of the world views with a mixture of awe envy and resentment artificial intelligence From AI models and research to cloud computing and venture capital U S companies universities and research labs and their affiliates in allied countries appear to have an enormous lead in both developing cutting edge AI and commercializing it The value of U S venture capital investments in AI start ups exceeds that of the rest of the world combined Frey Carl Benedikt Osborne Michael A 1 January 2017 The future of employment How susceptible are jobs to computerisation Technological Forecasting and Social Change 114 254 280 CiteSeerX 10 1 1 395 416 doi 10 1016 j techfore 2016 08 019 ISSN 0040 1625 From not working to neural networking The Economist 2016 Archived from the original on 31 December 2016 Retrieved 26 April 2018 Galvan Jill 1 January 1997 Entering the Posthuman Collective in Philip K Dick s Do Androids Dream of Electric Sheep Science Fiction Studies 24 3 413 429 JSTOR 4240644 Geist Edward Moore 9 August 2015 Is artificial intelligence really an existential threat to humanity Bulletin of the Atomic Scientists Archived from the original on 30 October 2015 Retrieved 30 October 2015 Gertner Jon 18 July 2023 Wikipedia s Moment of Truth Can the online encyclopedia help teach A I chatbots to get their facts right without destroying itself in the process comment The New York Times Archived from the original on 18 July 2023 Retrieved 19 July 2023 Gibbs Samuel 27 October 2014 Elon Musk artificial intelligence is our biggest existential threat The Guardian Archived from the original on 30 October 2015 Retrieved 30 October 2015 Goffrey Andrew 2008 Algorithm In Fuller Matthew ed Software studies a lexicon Cambridge Mass MIT Press pp 15 20 ISBN 978 1 4356 4787 9 Goldman Sharon 14 September 2022 10 years later deep learning revolution rages on say AI pioneers Hinton LeCun and Li VentureBeat Retrieved 8 December 2023 Good I J 1965 Speculations Concerning the First Ultraintelligent Machine Goodfellow Ian Bengio Yoshua Courville Aaron 2016 Deep Learning MIT Press archived from the original on 16 April 2016 retrieved 12 November 2017 Goodman Bryce Flaxman Seth 2017 EU regulations on algorithmic decision making and a right to explanation AI Magazine 38 3 50 arXiv 1606 08813 doi 10 1609 aimag v38i3 2741 S2CID 7373959 Government Accountability Office 13 September 2022 Consumer Data Increasing Use Poses Risks to Privacy gao gov Report Grant Nico Hill Kashmir 22 May 2023 Google s Photo App Still Can t Find Gorillas And Neither Can Apple s The New York Times Goswami Rohan 5 April 2023 Here s where the A I jobs are CNBC Archived from the original on 19 June 2023 Retrieved 19 June 2023 Harari Yuval Noah October 2018 Why Technology Favors Tyranny The Atlantic Archived from the original on 25 September 2021 Retrieved 23 September 2021 Harari Yuval Noah 2023 AI and the future of humanity YouTube Haugeland John 1985 Artificial Intelligence The Very Idea Cambridge Mass MIT Press ISBN 978 0 262 08153 5 Henderson Mark 24 April 2007 Human rights for robots We re getting carried away The Times Online London Archived from the original on 31 May 2014 Retrieved 31 May 2014 Hinton G Deng L Yu D Dahl G Mohamed A Jaitly N Senior A Vanhoucke V Nguyen P Sainath T Kingsbury B 2012 Deep Neural Networks for Acoustic Modeling in Speech Recognition The shared views of four research groups IEEE Signal Processing Magazine 29 6 82 97 Bibcode 2012ISPM 29 82H doi 10 1109 msp 2012 2205597 S2CID 206485943 Holley Peter 28 January 2015 Bill Gates on dangers of artificial intelligence I don t understand why some people are not concerned The Washington Post ISSN 0190 8286 Archived from the original on 30 October 2015 Retrieved 30 October 2015 Hornik Kurt Stinchcombe Maxwell White Halbert 1989 Multilayer Feedforward Networks are Universal Approximators PDF Neural Networks Vol 2 Pergamon Press pp 359 366 Horst Steven 2005 The Computational Theory of Mind The Stanford Encyclopedia of Philosophy Archived from the original on 6 March 2016 Retrieved 7 March 2016 Howe J November 1994 Artificial Intelligence at Edinburgh University a Perspective Archived from the original on 15 May 2007 Retrieved 30 August 2007 IGM Chicago 30 June 2017 Robots and Artificial Intelligence www igmchicago org Archived from the original on 1 May 2019 Retrieved 3 July 2019 Iphofen Ron Kritikos Mihalis 3 January 2019 Regulating artificial intelligence and robotics ethics by design in a digital society Contemporary Social Science 16 2 170 184 doi 10 1080 21582041 2018 1563803 ISSN 2158 2041 S2CID 59298502 Jordan M I Mitchell T M 16 July 2015 Machine learning Trends perspectives and prospects Science 349 6245 255 260 Bibcode 2015Sci 349 255J doi 10 1126 science aaa8415 PMID 26185243 S2CID 677218 Kahneman Daniel 2011 Thinking Fast and Slow Macmillan ISBN 978 1 4299 6935 2 Archived from the original on 15 March 2023 Retrieved 8 April 2012 Kahneman Daniel Slovic D Tversky Amos 1982 Judgment under uncertainty Heuristics and biases Science New York Cambridge University Press 185 4157 1124 1131 Bibcode 1974Sci 185 1124T doi 10 1126 science 185 4157 1124 ISBN 978 0 521 28414 1 PMID 17835457 S2CID 143452957 Kasperowicz Peter 1 May 2023 Regulate AI GOP much more skeptical than Dems that government can do it right poll Fox News Archived from the original on 19 June 2023 Retrieved 19 June 2023 Katz Yarden 1 November 2012 Noam Chomsky on Where Artificial Intelligence Went Wrong The Atlantic Archived from the original on 28 February 2019 Retrieved 26 October 2014 Kismet MIT Artificial Intelligence Laboratory Humanoid Robotics Group Archived from the original on 17 October 2014 Retrieved 25 October 2014 Kissinger Henry 1 November 2021 The Challenge of Being Human in the Age of AI The Wall Street Journal Archived from the original on 4 November 2021 Retrieved 4 November 2021 Kobielus James 27 November 2019 GPUs Continue to Dominate the AI Accelerator Market for Now InformationWeek Archived from the original on 19 October 2021 Retrieved 11 June 2020 Kuperman G J Reichley R M Bailey T C 1 July 2006 Using Commercial Knowledge Bases for Clinical Decision Support Opportunities Hurdles and Recommendations Journal of the American Medical Informatics Association 13 4 369 371 doi 10 1197 jamia M2055 PMC 1513681 PMID 16622160 Kurzweil Ray 2005 The Singularity is Near Penguin Books ISBN 978 0 670 03384 3 Langley Pat 2011 The changing science of machine learning Machine Learning 82 3 275 279 doi 10 1007 s10994 011 5242 y Larson Jeff Angwin Julia 23 May 2016 How We Analyzed the COMPAS Recidivism Algorithm ProPublica Archived from the original on 29 April 2019 Retrieved 19 June 2020 Laskowski Nicole November 2023 What is Artificial Intelligence and How Does AI Work TechTarget Enterprise AI Retrieved 30 October 2023 Law Library of Congress U S Global Legal Research Directorate issuing body 2019 Regulation of artificial intelligence in selected jurisdictions LCCN 2019668143 OCLC 1110727808 Lee Timothy B 22 August 2014 Will artificial intelligence destroy humanity Here are 5 reasons not to worry Vox Archived from the original on 30 October 2015 Retrieved 30 October 2015 Lenat Douglas Guha R V 1989 Building Large Knowledge Based Systems Addison Wesley ISBN 978 0 201 51752 1 Lighthill James 1973 Artificial Intelligence A General Survey Artificial Intelligence a paper symposium Science Research Council Lipartito Kenneth 6 January 2011 The Narrative and the Algorithm Genres of Credit Reporting from the Nineteenth Century to Today PDF Unpublished manuscript doi 10 2139 ssrn 1736283 S2CID 166742927 archived PDF from the original on 9 October 2022 Lohr Steve 2017 Robots Will Take Jobs but Not as Fast as Some Fear New Report Says The New York Times Archived from the original on 14 January 2018 Retrieved 13 January 2018 Lungarella M Metta G Pfeifer R Sandini G 2003 Developmental robotics a survey Connection Science 15 4 151 190 CiteSeerX 10 1 1 83 7615 doi 10 1080 09540090310001655110 S2CID 1452734 Machine Ethics aaai org Archived from the original on 29 November 2014 Madrigal Alexis C 27 February 2015 The case against killer robots from a guy actually working on artificial intelligence Fusion net Archived from the original on 4 February 2016 Retrieved 31 January 2016 Mahdawi Arwa 26 June 2017 What jobs will still be around in 20 years Read this to prepare your future The Guardian Archived from the original on 14 January 2018 Retrieved 13 January 2018 Maker Meg Houston 2006 AI 50 AI Past Present Future Dartmouth College Archived from the original on 3 January 2007 Retrieved 16 October 2008 Marmouyet Francoise 15 December 2023 Google s Gemini is the new AI model really better than ChatGPT The Conversation Retrieved 25 December 2023 Minsky Marvin 1986 The Society of Mind Simon and Schuster Maschafilm 2010 Content Plug amp Pray Film Artificial Intelligence Robots plugandpray film de Archived from the original on 12 February 2016 McCarthy John Minsky Marvin Rochester Nathan Shannon Claude 1955 A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence Archived from the original on 26 August 2007 Retrieved 30 August 2007 McCarthy John 2007 From Here to Human Level AI Artificial Intelligence 171 McCarthy John 1999 What is AI archived from the original on 4 December 2022 retrieved 4 December 2022 McCauley Lee 2007 AI armageddon and the three laws of robotics Ethics and Information Technology 9 2 153 164 CiteSeerX 10 1 1 85 8904 doi 10 1007 s10676 007 9138 2 S2CID 37272949 McGarry Ken 1 December 2005 A survey of interestingness measures for knowledge discovery The Knowledge Engineering Review 20 1 39 61 doi 10 1017 S0269888905000408 S2CID 14987656 McGaughey E 2022 Will Robots Automate Your Job Away Full Employment Basic Income and Economic Democracy p 51 3 Industrial Law Journal 511 559 doi 10 2139 ssrn 3044448 S2CID 219336439 SSRN 3044448 archived from the original on 31 January 2021 retrieved 27 May 2023 Merkle Daniel Middendorf Martin 2013 Swarm Intelligence In Burke Edmund K Kendall Graham eds Search Methodologies Introductory Tutorials in Optimization and Decision Support Techniques Springer Science amp Business Media ISBN 978 1 4614 6940 7 Moravec Hans 1988 Mind Children Harvard University Press ISBN 978 0 674 57616 2 Archived from the original on 26 July 2020 Retrieved 18 November 2019 Morgenstern Michael 9 May 2015 Automation and anxiety The Economist Archived from the original on 12 January 2018 Retrieved 13 January 2018 Muller Vincent C Bostrom Nick 2014 Future Progress in Artificial Intelligence A Poll Among Experts PDF AI Matters 1 1 9 11 doi 10 1145 2639475 2639478 S2CID 8510016 Archived PDF from the original on 15 January 2016 Neumann Bernd Moller Ralf January 2008 On scene interpretation with description logics Image and Vision Computing 26 1 82 101 doi 10 1016 j imavis 2007 08 013 S2CID 10767011 Nilsson Nils 1995 Eyes on the Prize AI Magazine vol 16 pp 9 17 Newell Allen Simon H A 1976 Computer Science as Empirical Inquiry Symbols and Search Communications of the ACM 19 3 113 126 doi 10 1145 360018 360022 Nicas Jack 7 February 2018 How YouTube Drives People to the Internet s Darkest Corners The Wall Street Journal ISSN 0099 9660 Retrieved 16 June 2018 Nilsson Nils 1983 Artificial Intelligence Prepares for 2001 PDF AI Magazine 1 1 Archived PDF from the original on 17 August 2020 Retrieved 22 August 2020 Presidential Address to the Association for the Advancement of Artificial Intelligence NRC United States National Research Council 1999 Developments in Artificial Intelligence Funding a Revolution Government Support for Computing Research National Academy Press Omohundro Steve 2008 The Nature of Self Improving Artificial Intelligence presented and distributed at the 2007 Singularity Summit San Francisco CA Oudeyer P Y 2010 On the impact of robotics in behavioral and cognitive sciences from insect navigation to human cognitive development PDF IEEE Transactions on Autonomous Mental Development 2 1 2 16 doi 10 1109 tamd 2009 2039057 S2CID 6362217 Archived PDF from the original on 3 October 2018 Retrieved 4 June 2013 Pennachin C Goertzel B 2007 Contemporary Approaches to Artificial General Intelligence Artificial General Intelligence Cognitive Technologies Berlin Heidelberg Springer pp 1 30 doi 10 1007 978 3 540 68677 4 1 ISBN 978 3 540 23733 4 Pinker Steven 2007 1994 The Language Instinct Perennial Modern Classics Harper ISBN 978 0 06 133646 1 Poria Soujanya Cambria Erik Bajpai Rajiv Hussain Amir September 2017 A review of affective computing From unimodal analysis to multimodal fusion Information Fusion 37 98 125 doi 10 1016 j inffus 2017 02 003 hdl 1893 25490 S2CID 205433041 Archived from the original on 23 March 2023 Retrieved 27 April 2021 Rawlinson Kevin 29 January 2015 Microsoft s Bill Gates insists AI is a threat BBC News Archived from the original on 29 January 2015 Retrieved 30 January 2015 Reisner Alex 19 August 2023 Revealed The Authors Whose Pirated Books are Powering Generative AI The Atlantic Roberts Jacob 2016 Thinking Machines The Search for Artificial Intelligence Distillations Vol 2 no 2 pp 14 23 Archived from the original on 19 August 2018 Retrieved 20 March 2018 Robitzski Dan 5 September 2018 Five experts share what scares them the most about AI Archived from the original on 8 December 2019 Retrieved 8 December 2019 Robots could demand legal rights BBC News 21 December 2006 Archived from the original on 15 October 2019 Retrieved 3 February 2011 Rose Steve 11 July 2023 AI Utopia or dystopia The Guardian Weekly pp 42 43 Russell Stuart 2019 Human Compatible Artificial Intelligence and the Problem of Control United States Viking ISBN 978 0 525 55861 3 OCLC 1083694322 Sainato Michael 19 August 2015 Stephen Hawking Elon Musk and Bill Gates Warn About Artificial Intelligence Observer Archived from the original on 30 October 2015 Retrieved 30 October 2015 Sample Ian 5 November 2017 Computer says no why making AIs fair accountable and transparent is crucial The Guardian Retrieved 30 January 2018 Rothman Denis 7 October 2020 Exploring LIME Explanations and the Mathematics Behind It Codemotion Retrieved 25 November 2023 Scassellati Brian 2002 Theory of mind for a humanoid robot Autonomous Robots 12 1 13 24 doi 10 1023 A 1013298507114 S2CID 1979315 Schmidhuber J 2015 Deep Learning in Neural Networks An Overview Neural Networks 61 85 117 arXiv 1404 7828 doi 10 1016 j neunet 2014 09 003 PMID 25462637 S2CID 11715509 Schmidhuber Jurgen 2022 Annotated History of Modern AI and Deep Learning Schulz Hannes Behnke Sven 1 November 2012 Deep Learning KI Kunstliche Intelligenz 26 4 357 363 doi 10 1007 s13218 012 0198 z ISSN 1610 1987 S2CID 220523562 Searle John 1980 Minds Brains and Programs PDF Behavioral and Brain Sciences 3 3 417 457 doi 10 1017 S0140525X00005756 S2CID 55303721 Archived PDF from the original on 17 March 2019 Retrieved 22 August 2020 Searle John 1999 Mind language and society New York Basic Books ISBN 978 0 465 04521 1 OCLC 231867665 Archived from the original on 26 July 2020 Retrieved 22 August 2020 Simonite Tom 31 March 2016 How Google Plans to Solve Artificial Intelligence MIT Technology Review Smith Craig S 15 March 2023 ChatGPT 4 Creator Ilya Sutskever on AI Hallucinations and AI Democracy Forbes Retrieved 25 December 2023 Smoliar Stephen W Zhang HongJiang 1994 Content based video indexing and retrieval IEEE MultiMedia 1 2 62 72 doi 10 1109 93 311653 S2CID 32710913 Solomonoff Ray 1956 An Inductive Inference Machine PDF Dartmouth Summer Research Conference on Artificial Intelligence Archived PDF from the original on 26 April 2011 Retrieved 22 March 2011 via std com pdf scanned copy of the original Later published asSolomonoff Ray 1957 An Inductive Inference Machine IRE Convention Record Vol Section on Information Theory part 2 pp 56 62 Stanford University 2023 Artificial Intelligence Index Report 2023 Chapter 6 Policy and Governance PDF AI Index Archived PDF from the original on 19 June 2023 Retrieved 19 June 2023 Tao Jianhua Tan Tieniu 2005 Affective Computing and Intelligent Interaction Affective Computing A Review Lecture Notes in Computer Science Vol 3784 Springer pp 981 995 doi 10 1007 11573548 ISBN 978 3 540 29621 8 Taylor Josh Hern Alex 2 May 2023 Godfather of AI Geoffrey Hinton quits Google and warns over dangers of misinformation The Guardian Thompson Derek 23 January 2014 What Jobs Will the Robots Take The Atlantic Archived from the original on 24 April 2018 Retrieved 24 April 2018 Thro Ellen 1993 Robotics The Marriage of Computers and Machines New York Facts on File ISBN 978 0 8160 2628 9 Archived from the original on 26 July 2020 Retrieved 22 August 2020 Toews Rob 3 September 2023 Transformers Revolutionized AI What Will Replace Them Forbes Retrieved 8 December 2023 Turing Alan October 1950 Computing Machinery and Intelligence Mind LIX 236 433 460 doi 10 1093 mind LIX 236 433 ISSN 0026 4423 UNESCO Science Report the Race Against Time for Smarter Development Paris UNESCO 2021 ISBN 978 92 3 100450 6 Archived from the original on 18 June 2022 Retrieved 18 September 2021 Urbina Fabio Lentzos Filippa Invernizzi Cedric Ekins Sean 7 March 2022 Dual use of artificial intelligence powered drug discovery Nature Machine Intelligence 4 3 189 191 doi 10 1038 s42256 022 00465 9 PMC 9544280 PMID 36211133 S2CID 247302391 Valance Christ 30 May 2023 Artificial intelligence could lead to extinction experts warn BBC News Archived from the original on 17 June 2023 Retrieved 18 June 2023 Valinsky Jordan 11 April 2019 Amazon reportedly employs thousands of people to listen to your Alexa conversations CNN com Verma Yugesh 25 December 2021 A Complete Guide to SHAP SHAPley Additive exPlanations for Practitioners Analytics India Magazine Retrieved 25 November 2023 Vincent James 7 November 2019 OpenAI has published the text generating AI it said was too dangerous to share The Verge Archived from the original on 11 June 2020 Retrieved 11 June 2020 Vincent James 15 November 2022 The scary truth about AI copyright is nobody knows what will happen next The Verge Archived from the original on 19 June 2023 Retrieved 19 June 2023 Vincent James 3 April 2023 AI is entering an era of corporate control The Verge Archived from the original on 19 June 2023 Retrieved 19 June 2023 Vinge Vernor 1993 The Coming Technological Singularity How to Survive in the Post Human Era Vision 21 Interdisciplinary Science and Engineering in the Era of Cyberspace 11 Bibcode 1993vise nasa 11V Archived from the original on 1 January 2007 Retrieved 14 November 2011 Waddell Kaveh 2018 Chatbots Have Entered the Uncanny Valley The Atlantic Archived from the original on 24 April 2018 Retrieved 24 April 2018 Wallach Wendell 2010 Moral Machines Oxford University Press Wason P C Shapiro D 1966 Reasoning In Foss B M ed New horizons in psychology Harmondsworth Penguin Archived from the original on 26 July 2020 Retrieved 18 November 2019 Weng J McClelland Pentland A Sporns O Stockman I Sur M Thelen E 2001 Autonomous mental development by robots and animals span clas, wikipedia, wiki, book, books, library,

article

, read, download, free, free download, mp3, video, mp4, 3gp, jpg, jpeg, gif, png, picture, music, song, movie, book, game, games.