I should say first that I completely agree with you about the extreme data inefficiency of many systems that get enthusiastically labeled “AI” these days—it is a big problem which calls into question many claims about these systems and their displays of “intelligence.”
Especially a few years ago (the field has been getting better about this over time), there was a tendency to define performance with reference to some set collection of tasks similar to the training task without acknowledging that broader generalization capacity, and generalization speed in terms of “number of data points needed to learn the general rule,” are key components of any intuitive/familiar notion of intelligence. I’ve written about this in a few places, like the last few sections of this post, where I talk about the “strange simpletons.”
However, it’s not clear to me that this limitation is inherent to neural nets or to “AI” in the way you seem to be saying. You write:
Comparing AI to human neurology is off the mark in my estimation, because AIs don’t really learn rules. They can predict outcomes (within a narrow context), but the AI has no awareness of the actual “rules” that are leading to that outcome—all it knows is weights and likelihoods.
If I understand you correctly, you’re taking a position that Marcus argued against in The Algebraic Mind. I’m taking Marcus’ arguments there largely as a given in this post, because I agree with them and because I was interested specifically in the way Marcus’ Algebraic Mind arguments cut against Marcus’ own views about deep learning today.
If you want to question the Algebraic Mind stuff itself, that’s fine, but if so you’re disagreeing with both me and Marcus more fundamentally than (I think) Marcus and I disagree with one another, and you’ll need a more fleshed-out argument if you want to bridge a gulf of this size.
I should say first that I completely agree with you about the extreme data inefficiency of many systems that get enthusiastically labeled “AI” these days—it is a big problem which calls into question many claims about these systems and their displays of “intelligence.”
Especially a few years ago (the field has been getting better about this over time), there was a tendency to define performance with reference to some set collection of tasks similar to the training task without acknowledging that broader generalization capacity, and generalization speed in terms of “number of data points needed to learn the general rule,” are key components of any intuitive/familiar notion of intelligence. I’ve written about this in a few places, like the last few sections of this post, where I talk about the “strange simpletons.”
However, it’s not clear to me that this limitation is inherent to neural nets or to “AI” in the way you seem to be saying. You write:
If I understand you correctly, you’re taking a position that Marcus argued against in The Algebraic Mind. I’m taking Marcus’ arguments there largely as a given in this post, because I agree with them and because I was interested specifically in the way Marcus’ Algebraic Mind arguments cut against Marcus’ own views about deep learning today.
If you want to question the Algebraic Mind stuff itself, that’s fine, but if so you’re disagreeing with both me and Marcus more fundamentally than (I think) Marcus and I disagree with one another, and you’ll need a more fleshed-out argument if you want to bridge a gulf of this size.