This article is a bit misleading. I believe NNs are a lot like the human brain. But just the lowest level of our brain. What psychologists might call "procedural knowledge".
Example: learning to ride a bike. You have no idea how you do it. You can't explain it in words. It requires tons of trial and error. You can give a bike to a physicist that has a perfect deep understanding of the laws of physics. And they won't be any better at riding than a kid.
And after you learn to ride, change the bike. Take one where the handle is inversed. And turning it right turns the wheel left. No matter how good you are at riding a normal bike, no matter how easy it seems it should be, it's very hard. Requires relearning how to ride basically from scratch. And when you are done, you will even have trouble going back to a normal bike. This sounds familiar to the problems of deep reinforcement learning, right?
If you use only the parts of the brain you use to ride a bike, would you be able to do any of the tasks described in the article? E.g. learn to guide spacecraft trajectories with little training, through purely analog controls and muscle memory? Can you even sort a list in your head without the use of pencil and paper?
Similarly recognizing a toothbrush as a baseball bat isn't as bizarre as you think. Most NNs get one pass over an image. Imagine you were flashed that image for just a millisecond. And given no time to process it. No time to even scan it with your eyes! You certain you wouldn't make any mistakes?
But we can augment NNs with attention, with feedback to lower layers from higher layers, and other tricks that might make them more like human vision. It's just very expensive.
And that's another limitation. Our largest networks are incredibly tiny compared to the human brain. It's amazing they can do anything at all. It's unrealistic to expect them to be flawless.
It's a good article in a lot of ways, and provides some warnings that many neural net evangelists should take to heart, but I agree it has some problems.
It's a bit unclear whether Fchollet is asserting that (A) Deep Learning has fundamental theoretical limitations on what it can achieve, or rather (B) that we have yet to discover ways of extracting human-like performance from it.
Certainly I agree with (B) that the current generation of models are little more than 'pattern matching', and the SOTA CNNs are, at best, something like small pieces of visual cortex or insect brains. But rather than deriding this limitation I'm more impressed at the range of tasks "mere" pattern matching is able to do so well - that's my takeaway.
But I also disagree with the distinction he makes between "local" and "extreme" generalization, or at least would contend that it's not a hard, or particularly meaningful, epistemic distinction. It is totally unsurprising that high-level planning and abstract reasoning capabilities are lacking in neural nets because the tasks we set them are so narrowly focused in scope. A neural net doesn't have a childhood, a desire/need to sustain itself, it doesn't grapple with its identity and mortality, set life goals for itself, forge relationships with others, or ponder the cosmos. And these types of quintessentially human activities are what I believe our capacities for high-level planning, reasoning with formal logic etc. arose to service. For this reason it's not obvious to me that a deep-learning-like system (with sufficient conception of causality, scarcity of resources, sanctity of life and so forth) would ALWAYS have to expend 1000s of fruitless trials crashing the rocket into the moon. It's conceivable that a system could know to develop an internal model of celestial mechanics and use it as a kind of staging area to plan trajectories.
I think there's a danger of questionable philosophy of mind assertions creeping into the discussion here (I've already read several poor or irrelevant expositions of Searle's Chinese Room in the comments). The high-level planning, and "true understanding" stuff sounds very much like what was debated for the last 25 years in philosophy of mind circles, under the rubric of "systematicity" in connectionist computational theories of mind. While I don't want to attempt a single-sentence exposition of this complicated debate, I will say that the requirement for "real understanding" (read systematicity) in AI systems, beyond mechanistic manipulation of tokens, is one that has been often criticised as ill-posed and potentially lacking even in human thought; leading to many movements of the goalposts vis-à-vis what "real understanding" actually is.
It's not clear to me that "real understanding" is not, or at least cannot be legitimately conceptualized as, some kind of geometric transformation from inputs to outputs - not least because vector spaces and their morphisms are pretty general mathematical objects.
I similarly find myself frustrated with philosophy of mind "contributions" to conversations on deep learning/consciousness/AI. There seems to be a lot of equivocation between the things you label as (a) and (b) above, and a lot of apathy toward distinguishing between them. But (a) and (b) are completely different things, and too often it seems like critics of computers doing smart things treat arguments for one like they are arguments for the other.
Probably the most famous AI critic, Hubert Dreyfus, said "current claims and hopes for progress in models for making computers intelligent are like the belief that someone climbing a tree is making progress toward reaching the moon." But it is progress. Because by climbing a tree I've gained much more than height. I actually did move toward the moon. I've gained the insight that I'm using the right principle.
Example: learning to ride a bike. You have no idea how you do it. You can't explain it in words. It requires tons of trial and error. You can give a bike to a physicist that has a perfect deep understanding of the laws of physics. And they won't be any better at riding than a kid.
And after you learn to ride, change the bike. Take one where the handle is inversed. And turning it right turns the wheel left. No matter how good you are at riding a normal bike, no matter how easy it seems it should be, it's very hard. Requires relearning how to ride basically from scratch. And when you are done, you will even have trouble going back to a normal bike. This sounds familiar to the problems of deep reinforcement learning, right?
If you use only the parts of the brain you use to ride a bike, would you be able to do any of the tasks described in the article? E.g. learn to guide spacecraft trajectories with little training, through purely analog controls and muscle memory? Can you even sort a list in your head without the use of pencil and paper?
Similarly recognizing a toothbrush as a baseball bat isn't as bizarre as you think. Most NNs get one pass over an image. Imagine you were flashed that image for just a millisecond. And given no time to process it. No time to even scan it with your eyes! You certain you wouldn't make any mistakes?
But we can augment NNs with attention, with feedback to lower layers from higher layers, and other tricks that might make them more like human vision. It's just very expensive.
And that's another limitation. Our largest networks are incredibly tiny compared to the human brain. It's amazing they can do anything at all. It's unrealistic to expect them to be flawless.