According to Thomas S. Kuhn in his classic work, The Structure of Scientific Revolutions:

...'normal science' presupposes a conceptual and instrumental framework or paradigm accepted by an entire scientific community ... [T]he resulting mode of scientific practice inevitably invokes 'crises' which cannot be resolved within this framework...

...the analytical thought experimentation that bulks so large in the writings of Galileo, Einstein, Bohr and others is perfectly calculated to expose the old paradigm to existing knowledge in ways that isolate the root of crisis with a clarity unobtainable in the laboratory.

For the 70 years since inception, AI has made no significant progress towards its original goal of human-like general intelligence in a machine (electronic digital computer). Forty years ago John Searle first published his Chinese room thought experiment that (along with associated arguments) concludes that the computer for a fundamental reason is incapable of human-like intelligence. The argument still stands - plenty of attempted rebuttals but none widely accepted as successful.

Does the Chinese room thought experiment "expose the old paradigm [computationalism] to existing knowledge in ways that isolate the root of crisis with a clarity unobtainable in the laboratory"? Is AI in a Crisis of Science? Will it only make progress towards AGI when it adopts a different and better paradigm for understanding the device it calls the computer?

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    "For the 70 years since inception, AI has made no significant progress towards its original goal of human-like general intelligence in a machine" False. In the past 10 years there have been huge strides forward in machine learning. AlphaStar allows computers to play a complex strategy game that for a long time had been the holy grail of reinforcement learning. GPT3 allows computers to generate realistic text based on a writing prompt. – causative Apr 29 at 7:13
  • But there are the serious problems of edge cases, adversarial attack, noisy datasets and catastrophic forgetting. And the gazillion iterations of back propagation. I know ML has seen great progress in limited domains and achieved the much-awaited commercial success. But this is still in limited domains. The issues edge cases and adversarial attack seem fundamental (small sticker added to STOP sign prevents "recognition" as a stop sign, etc.). – Roddus Apr 29 at 7:22
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    There remain flaws, but it's still very significant progress. – causative Apr 29 at 7:28
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    Systems and virtual minds replies to the Chinese Room are pretty broadly accepted among AI researchers. In that field, at least, CR is not taken seriously for a while now. It is similar to (and correlates with) attitudes towards the hard problem of consciousness, those of the more scientific persuasion do not see it as cogent. It is not that the argument still stands, but rather that it became clear that it depends on certain articles of faith that are perennially unresolvable, and is orthogonal to progress or lack thereof in the AI field. – Conifold Apr 29 at 7:57
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    "The computer for a fundamental reason is incapable of human-like intelligence" is not something that Searle believes or was trying to argue, unless by "the computer" you only mean the particular computer in the Chinese room argument. He was only arguing that you can't take a purely functionalist approach to deciding whether a machine understands what it's doing. Even if correct, the argument doesn't imply that we can never make a machine that genuinely understands Chinese. – benrg Apr 29 at 17:15

Your question appears to be ill informed.

Neural networks. Image processing, through layered convolutional neural networks. Natural language processing by Watson (able to beat humans at Jeopardy). Deep Blue, Alpha Go, and Alpha Zero, able to beat humans at some of our most complex games. Tegmark-&-Wu's AI Physicist.

These are all substantial steps, proven in practice.

What we have discovered, is that what our brains do is a lot more complex than we thought. Image processing in particular, turned out to be a lot harder than expected initially.

It's important to distinguish between Artificial Intelligence, which is already ubiquitous, and Artificial General Intelligence or synthetic sentience, which we just don't know when will be possible - it has seemed 'a few decades away' for probably at least a century.

Hofstadter's strange-loops model alone can potentially account for minds being different to Chinese-rooms. Discussed here What is intelligence?

Personally, I am with Penrose-Hammeroff & OrchOR. That interpretation does not necessarily require quantum effects, but it does involve emergent dynamics ('orchestration').

On the paradigms, from my post in that linked discussion:

There is a powerful tendency for people in science and computing to think there is nothing very interesting or special about human minds. And unfortunately, a powerful strand in philosophy (& theology) which says there is something so special about them, scientists aren't on track to figuring them out - the 'qualia' idea and the so called Hard Problem Of Consciousness. I strongly recommend not joining either camp. The story of physics has been from thinking we were a few results away from explaining everything in 1900, and now we don't know what 95% of the universe is made of - our greatest progress has been to begin understanding the scope of our ignorance. I feel strongly we are on a similar trajectory about intelligence.

Your question is like saying, 'There hasn't been much progress in physics lately, so probably we won't be able to explain everything'. Ie, both wrong, and misguided, in a way that people familiar with the subject will have very little patience for.

  • "Comically ill informed" is a violation of the rules of this forum. An insult in high-brow language is still an insult. – David Gudeman Apr 29 at 18:10
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    I have edited it down. – Guy Inchbald Apr 29 at 18:53
  • CriglCragl mentions Watson winning at Jeopardy as evidence of general intelligence in current computers. I don't suppose it would help to note that Watson didn't "know" that the wife of a US president was female. – Roddus May 16 at 23:16
  • @Roddus: Arguing natural language processing isn't progress, is like arguing image processing isn't progress. – CriglCragl May 17 at 12:35
  • @CriglCragl “Arguing natural language processing isn't progress, is like arguing image processing isn't progress”. If NLP is statistics on a mega data set then I'd say it's not progress. Work was done on this about 30 years ago by Eugene Charniak. The questions asked of the machine tested generality, or abstraction. I think his conclusions still stand. – Roddus May 19 at 0:46

For CriglCragl and the common view expressed, I think CC is actually demonstrating that AI is indeed in a crisis of science. CC says the idea that AI is in a crisis of science is:

both wrong, and misguided, in a way that people familiar with the subject will have very little patience for.

(I presume CC's original opening line “"You question appears to be comically ill informed" indicates that they fully believe they are indeed among those familiar with the subject.)

But as everyone knows, a crisis of science comes about precisely because a research programme has failed to make fundamental progress, and a thought experiment exposes that failure in ways that isolate the root of crisis with a clarity unobtainable in the laboratory.

CC agrees there is a fundamental failure:

Artificial General Intelligence [-] we just don't know when [it] will be possible

If CC understands the CRA then they know it exposes this failure by appeal to the fundamental nature of computation. (No one seems to be denying computation is the purely syntactic manipulation of symbols without accessing their meanings.)

And the third ingredient of any crisis of science is that the Illuminati can't conceive of themselves as being wrong; and because of their psychological dependence on unbridled falsehood react emotionally to any suggestion of error.

So I think CC's comment which expresses a common view within AI is quite a clear answer to my question: YES, AI is in a crisis of science.

About the CRA, this is the key thing to me, and it addresses some other of the comments above. The CRA can be boiled down to just one issue: the intrinsic meaninglessness of the symbol. If you say computers manipulate symbols and do nothing else (as Searle does) and given that symbols in themselves say nothing about what they mean, then the computer is forever a prisoner in a universe of meaningless syntax and formality. The system reply, the many mansions reply, the robot reply, all the replies are beside the point. Unless the inherent meaningless of the symbol can be overcome, computers will never be intelligent. This is Searle's core position and I think is clearly a crisis.

Just for the sake of completeness, I've been given a -1 score. Because of this sort of unhelpful and I have to say in a sense arrogant thing I gave up on PSE a while ago and switched to academia.edu. A few days ago I posted exactly the same question there in the form of a short paper. The second respondent was Pat Hayes, AI royalty, who answered with well-thought-out and constructive arguments, as of course one would expect. In all the comments there was no beating of the hairless chest. I'm sure PSE gives good guidance to novices, but I think it's simply wrong to expect balanced debate (Crigl please take note).

According to the voting balloon, a negative vote means "The question does not show any research effort". So (to cut and paste some recent relevant research effort):

...despite seven decades of prodigious funding and effort, nothing approaching AGI has been demonstrated. Instead, serious practical and theoretical difficulties have arisen including those known as the problem of design (1), the problem of machine translation (2), the frame problem (3), the problem of common-sense knowledge (4), the problem of combinatorial explosion (5), the Chinese room argument (6), the infinity of facts (7), the symbol grounding problem (8), and the problem of encodingism (9). And for "deep learning": edge cases (10), noisy data-sets (11) adversarial attack (12) and catastrophic forgetting (13).

  1. Ada Lovelace, 1843, "Note G", quoted in "The Turing Test," Stanford Encyclopedia of Philosophy, section 2.6, https://plato.stanford.edu/entries/turing-test. Also see John von Neumann, "First Draft of a Report on the EDVAC," (Moore School of Electrical Engineering, University of Pennsylvania, 30 June 1945), 1
  2. Yehoshua Bar-Hillel, "The Present Status of Automatic Translation of Languages," in Advances in Computers, ed. Franz L. Alt (Academic Press, 1960), 1: 91-163.
  3. J. McCarthy and P. J. Hayes, "Some Philosophical Problems from the Standpoint to Artificial Intelligence," in Bernard Meltzer and Donald Michie (eds.) Machine Intelligence 4 (American Elsevier, 1969), 463-502. Also see Dreyfus, "Alchemy and Artificial Intelligence," 29, 39, 68.
  4. Hubert L. Dreyfus, "Alchemy and Artificial Intelligence," (The RAND Corporation, P-3244, December 1965), 39.
  5. James Lighthill, "Artificial Intelligence: A General Survey," section 3 Conclusion, in Artificial Intelligence: A Paper Symposium (Science Research Council of Great Britain, July 1972). Also Dreyfus, "Alchemy and Artificial Intelligence," 39.
  6. John R. Searle, "Minds, Brains, and Programs," Behavioral and Brain Sciences 3, no. 3 (1980): 417-457.
  7. Dreyfus, "Alchemy and Artificial Intelligence," 39. Also Daniel C. Dennett, "Cognitive Wheels: The Frame Problem of AI," in The Robot's Dilemma: The Frame Problem in Artificial Intelligence, ed. Zenon W. Pylyshyn (1984; Ablex, 1987), 49.
  8. Stevan Harnad, “The Symbol Grounding Problem,” Physica D 42 (June 1990): 335-346.
  9. Mark Bickhard and Lauren Terveen, Foundational Issues in Artificial Intelligence: Impasse and Solution (Elsevier, 1995).
  10. Overview of issues: Philip Koopman, "Edge Cases and Autonomous Vehicle Safety," (paper presented at the Safety-Critical Systems Symposium, Bristol UK, 7 February 2019).
  11. Overview of literature: Gupta, Shivani and Gupta, Atul, "Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review," Procedia Computer Science 161 (Elsevier, 2019): 466-474.
  12. Survey: Anirban Chakraborty et al., "Adversarial Attacks and Defences: A Survey," (arXiv:1810.00069v1, 28 September 2018).
  13. James Kirkpatrick et al., "Overcoming Catastrophic Forgetting in Neural Networks," Proceedings of the National Academy of sciences 114, no. 13, (2017): 3521-3526.
  • We don't know when we'll understand dark matter. That doesn't mean it can't be explained. Are we in a 'dark matter crisis'? No. It's just and open question. Disproving the luminiferous ether, or the ultraviolet catastrophe, they were crisees - & not due to thought experiments. Penrose exactly developed OrchOr to address issues you mention. You get minuses for asserting daft things like there's an emotional denial of the issue by 'illuminati'. It seems you have philosophers hubris. Wider discussion here philosophy.stackexchange.com/questions/33555/… – CriglCragl May 11 at 22:07
  • @CriglCragl Well, we don't understand intelligence either, but that doesn't mean it can't be explained. But what it does mean is that computation is not an adequate explanation. If it was then instead of 70 years of abject failure to demonstrate human-like general intelligence, there would have been more or less steady progress. David Deutsch reiterates this in his 3 October 2012 Guardian article. Asserting there is an emotional denial by the luminaries isn't a daft thing, and has been made by many. How else do you explain 70 years of abject failure, yet continued embrace of computationalism? – Roddus May 15 at 1:24
  • I find your choice of words childish and irritating, it is far from Socratic dialogue, it's just chippy egotism. What I'm really interested in at the moment is VonNeumann's generalisation of Turing machines to universal constructors, with implications for the topological transformations they are capable of. I lost faith in Deutsch a long time ago. But Chiara is a boss quantamagazine.org/… – CriglCragl May 15 at 1:30
  • @CriglCragl Putting aside ad hominem issues, what about the key question: if computationalism is right, why is there still, after 70 years, no idiomatic conversation, ability to generalize, or common-sense knowledge? This is really a fundamental issue. The second key question being, if symbols in themselves are meaningless, how could a purely syntactic device such as a computer understand what it manipulates? – Roddus May 16 at 22:56
  • Because those things aren't fundamental, they are high level and emergent. Your stance is like saying, making insects from scratch wouldn't be progress on how to make humans. There isn't a discontinuity between us and insects, only gradual improvements. Meaning and concepts emerged from biological soup, and will do from electron soup. – CriglCragl May 17 at 12:40

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