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World-class chess, Go, and Jeopardy-playing programs are impressive, but they prove nothing about whether computers can be made to achieve AGI.
Back in 1998, I moderated a discussion at which Ray Kurzweil gave listeners a preview of his then forthcoming book The Age of Spiritual Machines, in which he described how machines were poised to match and then exceed human cognition, a theme he doubled down on in subsequent books (such as The Singularity Is Near and How to Create a Mind). For Kurzweil, it is inevitable that machines will match and then exceed us: Moore’s Law guarantees that machines will attain the needed computational power to simulate our brains, after which the challenge will be for us to keep pace with machines..
Kurzweil’s respondents at the discussion were John Searle, Thomas Ray, and Michael Denton, and they were all to varying degrees critical of his strong AI view. Searle recycled his Chinese Room thought experiment to argue that computers don’t/can’t actually understand anything. Denton made an interesting argument about the complexity and richness of individual neurons and how inadequate is our understanding of them and how even more inadequate our ability is to realistically model them computationally. At the end of the discussion, however, Kurzweil’s overweening confidence in the glowing prospects for strong AI’s future were undiminished. And indeed, they remain undiminished to this day (I last saw Kurzweil at a Seattle tech conference in 2019 — age seemed to have mellowed his person but not his views).
Erik Larson’s The Myth of Artificial Intelligence (published by Harvard/Belknap) is far and away the best refutation of Kurzweil’s overpromises, but also of the hype pressed by those who have fallen in love with AI’s latest incarnation, which is the combination of big data with machine learning. Just to be clear, Larson is not a contrarian. He does not have a death wish for AI. He is not trying to sabotage research in the area (if anything, he is trying to extricate AI research from the fantasy land it currently inhabits). In fact, he has been a solid contributor to the field, coming to the problem of strong AI, or artificial general intelligence (AGI) as he prefers to call it, with an open mind about its possibilities.
Keys Under a Light Post
The problem, as he sees it with the field, is captured in the parable of the drunk looking for keys under a light post even though he dropped them far from it because that’s where the light is. In the spirit of this parable, Larson makes a compelling case that actual research on AI is happening in those areas where the keys to artificial general intelligence simply cannot exist. But he goes the parable even one better: because no theory exists of what it means for a machine to have a cognitive life, he suggests it’s not clear that artificial general intelligence even has a solution — human intelligence may not in the end be reducible to machine intelligence. In consequence, if there are keys to unlocking AGI, we’re looking for them in the wrong places; and it may even be that there are no such keys.
Larson does not argue that artificial general intelligence is impossible but rather that we have no grounds to think it must be so. He is therefore directly challenging the inevitability narrative promoted by people like Ray Kurzweil, Nick Bostrom, and Elon Musk. At the same time, Larson leaves AGI as a live possibility throughout the book, and he seems genuinely curious to hear from anybody who might have some good ideas about how to proceed. His central point, however, is that such good ideas are for now wholly lacking — that research on AI is producing results only when it works on narrow problems and that this research isn’t even scratching the surface of the sorts of problems that need to be resolved in order to create an artificial general intelligence. Larson’s case is devastating, and I use this adjective without exaggeration.
I’ve followed the field of AI for four decades. In fact, I received an NSF graduate fellowship in the early 1980s to make a start at constructing an expert system for doing statistics (my advisor was Leland Wilkinson, founder of SYSTAT, and I even worked for his company in the summer of 1987 — unfortunately, the integration of LISP, the main AI language back then, with the Fortran code that underlay his SYSTAT statistical package proved an intractable problem at the time). I witnessed in real time the shift from rule-based AI (common with expert systems) to the computational intelligence approach to AI (evolutionary computing, fuzzy sets, and neural nets) to what has now become big data and deep/machine learning. I saw the rule-based approach to AI peter out. I saw computational intelligence research, such as conducted by my colleague Robert J. Marks II, produce interesting solutions to well-defined problems, but without pretensions for creating artificial minds that would compete with human minds. And then I saw the machine learning approach take off, with its vast profits for big tech and the resulting hubris to think that technologies created to make money could also recreate the inventors of those technologies.
A Philosopher and a Programmer
Larson comes to this project with training as a philosopher and as a programmer, a combination I find refreshing in that his philosophy background makes him reflective and measured as he considers the inflated claims made for artificial general intelligence (such as the shameless promise, continually made, that it is just right around the corner — is there any difference with the Watchtower Society and its repeated failed prophecies about the Second Coming?). I also find it refreshing that Larson has a humanistic and literary bent, which means he’s not going to set the bar artificially low for what can constitute an artificial general intelligence.
The mathematician George Polya used to quip that if you can’t solve a given problem, find an easier problem that you can solve. This can be sound advice if the easier problem that you can solve meaningfully illuminates the more difficult problem (ideally, by actually helping you solve the more difficult problem). But Larson finds that this advice is increasingly used by the AI community to substitute simple problems for the really hard problems facing artificial general intelligence, thereby evading the hard work that needs to be done to make genuine progress. So, for Larson, world-class chess, Go, and Jeopardy-playing programs are impressive as far as they go, but they prove nothing about whether computers can be made to achieve AGI.
Larson presents two main arguments for why we should not think that we’re anywhere close to solving the problem of AGI. His first argument centers on the nature of inference, his second on the nature of human language. With regard to inference, he shows that a form of reasoning known as abductive inference, or inference to the best explanation, is for now without any adequate computational representation or implementation. To be sure, computer scientists are aware of their need to corral abductive inference if they are to succeed in producing an artificial general intelligence. True, they’ve made some stabs at it, but those stabs come from forming a hybrid of deductive and inductive inference. Yet as Larson shows, the problem is that neither deduction, nor induction, nor their combination are adequate to reconstruct abduction. Abductive inference requires identifying hypotheses that explain certain facts of states of affairs in need of explanation. The problem with such hypothetical or conjectural reasoning is that that range of hypotheses is virtually infinite. Human intelligence can, somehow, sift through these hypotheses and identify those that are relevant. Larson’s point, and one he convincingly establishes, is that we don’t have a clue how to do this computationally.
