AI-complete

In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems, assuming intelligence is computational, is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI.^[1] To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.

AI-complete problems are hypothesised to include computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem.^[2]

Currently, AI-complete problems cannot be solved with modern computer technology alone, but would also require human computation. This property could be useful, for example, to test for the presence of humans as CAPTCHAs aim to do, and for computer security to circumvent brute-force attacks.^[3][4]

History

The term was coined by Fanya Montalvo by analogy with NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems.^[5] Early uses of the term are in Erik Mueller's 1987 PhD dissertation^[6] and in Eric Raymond's 1991 Jargon File.^[7]

AI-complete problems

AI-complete problems are hypothesized to include:

• AI peer review (composite natural language understanding, automated reasoning, automated theorem proving, formalized logic expert system)
• Bongard problems^{[citation needed]}
• Computer vision (and subproblems such as object recognition)^[8]
• Natural language understanding (and subproblems such as text mining,^[9] machine translation,^[10] and word-sense disambiguation^[11])
• Autonomous driving^[12]
• Dealing with unexpected circumstances while solving any real world problem, whether it's navigation or planning or even the kind of reasoning done by expert systems.^{[citation needed]}

Software brittleness

Main article: Software brittleness

Current AI systems can solve very simple and/or restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempt to "scale up" their systems to handle more complicated, real-world situations, the programs tend to become excessively brittle without commonsense knowledge or a rudimentary understanding of the situation: they fail as unexpected circumstances outside of its original problem context begin to appear. When human beings are dealing with new situations in the world, they are helped immensely by the fact that they know what to expect: they know what all things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. A machine without strong AI has no other skills to fall back on.^[13]

DeepMind published a work in May 2022 in which they trained a single model to do several things at the same time. The model, named Gato, can "play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens."^[14]

Formalization

Computational complexity theory deals with the relative computational difficulty of computable functions. By definition, it does not cover problems whose solution is unknown or has not been characterised formally. Since many AI problems have no formalisation yet, conventional complexity theory does not allow the definition of AI-completeness.

To address this problem, a complexity theory for AI has been proposed.^[15] It is based on a model of computation that splits the computational burden between a computer and a human: one part is solved by computer and the other part solved by human. This is formalised by a human-assisted Turing machine. The formalisation defines algorithm complexity, problem complexity and reducibility which in turn allows equivalence classes to be defined.

The complexity of executing an algorithm with a human-assisted Turing machine is given by a pair ${\displaystyle \langle \Phi _{H},\Phi _{M}\rangle }$ , where the first element represents the complexity of the human's part and the second element is the complexity of the machine's part.

Results

The complexity of solving the following problems with a human-assisted Turing machine is:^[15]

• Optical character recognition for printed text: ${\displaystyle \langle O(1),poly(n)\rangle }$ 
• Turing test:
  • for an ${\displaystyle n}$ -sentence conversation where the oracle remembers the conversation history (persistent oracle): ${\displaystyle \langle O(n),O(n)\rangle }$ 
  • for an ${\displaystyle n}$ -sentence conversation where the conversation history must be retransmitted: ${\displaystyle \langle O(n),O(n^{2})\rangle }$ 
  • for an ${\displaystyle n}$ -sentence conversation where the conversation history must be retransmitted and the person takes linear time to read the query: ${\displaystyle \langle O(n^{2}),O(n^{2})\rangle }$ 
• ESP game: ${\displaystyle \langle O(n),O(n)\rangle }$ 
• Image labelling (based on the Arthur–Merlin protocol): ${\displaystyle \langle O(n),O(n)\rangle }$ 
• Image classification: human only: ${\displaystyle \langle O(n),O(n)\rangle }$ , and with less reliance on the human: ${\displaystyle \langle O(\log n),O(n\log n)\rangle }$ .

See also

• ASR-complete
• List of unsolved problems in computer science
• Synthetic intelligence
• Practopoiesis

References

1. Shapiro, Stuart C. (1992). Artificial Intelligence 2016-02-01 at the Wayback Machine In Stuart C. Shapiro (Ed.), Encyclopedia of Artificial Intelligence (Second Edition, pp. 54–57). New York: John Wiley. (Section 4 is on "AI-Complete Tasks".)
2. Roman V. Yampolskiy. Turing Test as a Defining Feature of AI-Completeness. In Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM) --In the footsteps of Alan Turing. Xin-She Yang (Ed.). pp. 3-17. (Chapter 1). Springer, London. 2013. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf 2013-05-22 at the Wayback Machine
3. Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford. CAPTCHA: Using Hard AI Problems for Security 2016-03-04 at the Wayback Machine. In Proceedings of Eurocrypt, Vol. 2656 (2003), pp. 294-311.
4. Bergmair, Richard (January 7, 2006). "Natural Language Steganography and an "AI-complete" Security Primitive". CiteSeerX 10.1.1.105.129. {{cite journal}}: Cite journal requires |journal= (help) (unpublished?)
5. Mallery, John C. (1988), "Thinking About Foreign Policy: Finding an Appropriate Role for Artificially Intelligent Computers", The 1988 Annual Meeting of the International Studies Association., St. Louis, MO, from the original on 2008-02-29, retrieved 2007-04-27{{citation}}: CS1 maint: location missing publisher (link).
6. Mueller, Erik T. (1987, March). Daydreaming and Computation (Technical Report CSD-870017) 2020-10-30 at the Wayback Machine PhD dissertation, University of California, Los Angeles. ("Daydreaming is but one more AI-complete problem: if we could solve anyone artificial intelligence problem, we could solve all the others", p. 302)
7. Raymond, Eric S. (1991, March 22). Jargon File Version 2.8.1 2011-06-04 at the Wayback Machine (Definition of "AI-complete" first added to jargon file.)
8. Strat, Thomas M.; Chellappa, Rama; Patel, Vishal M. (2020). "Vision and robotics". AI Magazine. 42 (2): 49–65. doi:10.1609/aimag.v41i2.5299. S2CID 220687545 – via ABI/INFORM Collection.
9. Krestel, Ralf; Aras, Hidir; Andersson, Linda; Piroi, Florina; Hanbury, Allan; Alderucci, Dean (2022-07-06). "3rd Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2022)". Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid Spain: ACM. pp. 3474–3477. doi:10.1145/3477495.3531702. ISBN 978-1-4503-8732-3. S2CID 250340282. from the original on 2023-04-15. Retrieved 2023-04-15.
10. Orynycz, Petro (2022), Degen, Helmut; Ntoa, Stavroula (eds.), "Say It Right: AI Neural Machine Translation Empowers New Speakers to Revitalize Lemko", Artificial Intelligence in HCI, Lecture Notes in Computer Science, Cham: Springer International Publishing, vol. 13336, pp. 567–580, doi:10.1007/978-3-031-05643-7_37, ISBN 978-3-031-05642-0, retrieved 2023-04-15
11. Ide, N.; Veronis, J. (1998). "Introduction to the special issue on word sense disambiguation: the state of the art" (PDF). Computational Linguistics. 24 (1): 2–40. Archived (PDF) from the original on 2022-10-09.
12. Musk, Elon (April 14, 2022). "Elon Musk talks Twitter, Tesla and how his brain works — live at TED2022". TED (conference) (Interview). Interviewed by Chris_Anderson_(entrepreneur). Vancouver. from the original on December 15, 2022. Retrieved December 15, 2022.
13. Lenat, Douglas; Guha, R. V. (1989), Building Large Knowledge-Based Systems, Addison-Wesley, pp. 1–5
14. "A Generalist Agent". www.deepmind.com. from the original on 2022-08-02. Retrieved 2022-05-26.
15. Dafna Shahaf and Eyal Amir (2007) Towards a theory of AI completeness 2020-11-07 at the Wayback Machine. Commonsense 2007, 8th International Symposium on Logical Formalizations of Commonsense Reasoning 2021-01-19 at the Wayback Machine.