But LeCun is right about one thing; there is something that I hate. What I hate is this: the notion that deep learning is without demonstrable limits and might, all by itself, get us to general intelligence, if we just give it a little more time and a little more data, as captured in Andrew Ng’s 2016 suggestion that AI, by which he meant mainly deep learning, would either “now or in the near future“ be able to do “any mental task” a person could do “with less than one second of thought”.

Generally, though certainly not always, criticism of deep learning is sloughed off, either ignored, or dismissed, often in ad hominem way. Whenever anybody points out that there might be a specific limit to deep learning , there is always someone like Jeremy Howard to tell us that the idea that deep learning is overhyped is itself overhyped. Leaders in AI like LeCun acknowledge that there must be some limits, in some vague way, but rarely (and this is why Bengio’s new report was so noteworthy) do they pinpoint that what those limits are, beyond to acknowledge the data-hungry nature of the systems.

Others like to leverage the opacity of the black box of deep learning to suggest that that are no known limits. Last week, for example, Tom Dietterich said (in answer to a question about the scope of deep learning):

Dietterich is of course technically correct; nobody yet has delivered formal proofs about limits on deep learning, so there is no definite answer. And he is also right that deep learning continues to evolve. But the tweet (which expresses an argument I have heard many times, including from Dietterich more than once) neglects the fact we also do have a lot of strong suggestive evidence of at least some limit in scope, such as empirically observed limits reasoning abilities, poor performance in natural language comprehension, vulnerability to adversarial examples, and so forth. (At the end, I will even give an example in the domain of object recognition, putatively deep learning’s strong suit.)

To take another example, consider LeCun, Bengio and Hinton’s widely-read 2015 article in Nature on deep learning, which elaborates the strength of deep learning in considerable detail. There again much of what was said is true, but there was almost nothing acknowledged about limits of deep learning, and it would be easy to walk away from the paper imagining that deep learning is a much broader tool than it really is. The paper’s conclusion furthers that impression by suggesting that deep learning’s historical antithesis — symbol-manipulation/classical AI — should be replaced (“new paradigms are needed to replace the rule-based manipulation of symbolic expressions on large vectors.”). The traditional ending of many scientific papers — limits — is essentially missing, inviting the inference that the horizons for deep learning are limitless; symbol-manipulation soon to be left in the dustbin of history.

The strategy of emphasizing strength without acknowledging limits is even more pronounced in DeepMind’s 2017 Nature article on Go, which appears to imply similarly limitless horizons for deep reinforcement learning, by suggesting that Go is one of the hardest problems in AI. (“Our results comprehensively demonstrate that a pure [deep] reinforcement learning approach is fully feasible, even in the most challenging of domains”) — without acknowledging that other hard problems differ qualitatively in character (e.g., because information in most tasks is less complete than it is Go) and might not be accessible to similar approaches. (I discuss this further elsewhere.)

It worries me, greatly, when a field dwells largely or exclusively on the strengths of the latest discoveries, without publicly acknowledging possible weaknesses that have actually been well-documented.

Here’s my view: deep learning really is great, but it’s the wrong tool for the job of cognition writ large; it’s a tool for perceptual classification, when general intelligence involves so much more. What I was saying in 2012 (and have never deviated from) is that deep learning ought to be part of the workflow for AI, not the whole thing (“just one element in a very complicated ensemble of things”, as I put it then, “not a universal solvent, [just] one tool among many” as I put it in January). Deep learning is, like anything else we might consider, a tool with particular strengths, and particular weaknesses. Nobody should be surprised by this.

When I rail about deep-learning, it’s not because I think it should be “replaced” (cf. Hinton, LeCun and Bengio’s strong language above, where the name of the game is to conquer previous approaches), but because I think that (a) it has been oversold (eg that Andrew Ng quote, or the whole framing of DeepMind’s 2017 Nature paper), often with vastly greater attention to strengths than potential limitations, and (b) exuberance for deep learning is often (though not universal) accompanied by a hostility to symbol-manipulation that I believe is a foundational mistake. In the ultimate solution to AI.

I think it is far more likely that the two — deep learning and symbol-manipulation-will co-exist, with deep learning handling many aspects of perceptual classification, but symbol-manipulation playing a vital role in reasoning about abstract knowledge. Advances in narrow AI with deep learning are often taken to mean that we don’t need symbol-manipulation anymore, and I think that it is a huge mistake.