[dfdz]

> This is a criticism that the late philosopher Hubert Dreyfus made for half a century to no avail.

Whenever someone says a particular machine learning application is not going to work, and their justification is something a philosopher said, I always laugh.

I’m sure this philosopher was a great thinker with many interesting ideas about human nature, but likely they had little familiarity with convolution neural networks.

It’s unclear why the criticism in the paper does not apply to many existing “AI” technologies.

For example, clearly, video filters (like those developed by Apple, Meta, Snapchat, etc) that create a cartoon figure that mimics the movement of a users face are impossible since there is a temporal component and we cannot possibly perfectly represent every single face perfectly? Tesla self driving cars already work. Sure they might crash sometimes, but humans also crash sometimes. At this point is only a matter of time until the crash rate is deemed acceptable

[jfengel]

Especially a dead philosopher. "Here's a guy who could not conceivably have known anything about the topic, and therefore must be an expert."

[SirIsaac]

Dreyfus was a very intelligent man. He knew the difference between representational and non-representational systems. As Yann LeCun has said many times, deep learning is the learning of representations. That's all anyone needs to know in order to understand Dreyfus's thesis. The brain does not need a prior representation of a bicycle to perceive a bicycle. A DNN would be blind to a bicycle without a prior representation. Did you read the article?

[Firadeoclus]

What exactly does it mean to "perceive a bicycle"? Noticing shapes and colours and recognising them as a distinct object? Recognising an obstacle? Noticing qualities like smoothness and straightness and associating the concept "man-made"? Being able to explain its purpose? Predicting how it might move, if ridden by a person?

[SirIsaac]

Yes, pretty much. In my opinion, perception is generalization. To perceive a bicycle is to perceive many types of qualities or properties about it that can also be applied to a potentially infinite number of other objects. The brain can perform this generalization instantly without having stored previous representations (bicycle patterns) in memory.

A great example of non-representational intelligence is the honeybee's brain. It has less than 1 million neurons but it can handle zillions of patterns/objects in its 3D environment. It would be impossible for it to store all those zillions of patterns in its tiny brain. It uses the world itself as its own model.

For these reasons, deep learning is irrelevant to AGI.

[tim333]

I agree the criticism on the basis of philosophy is kind of silly. Elon Musk's recent description of how the thing is going in real life on Lex Fridman Podcast 28 dec 21 was interesting https://youtu.be/DxREm3s1scA?t=3972