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The article is less awful than the title. In short, the thesis is that ML seems only able to learn associations, rather than stronger, causal models. It should

The article is less awful than the title. In short, the thesis is that ML seems only able to learn associations, rather than stronger, causal models. It should be fairly obvious that ‘curve fitting’ is a misleading category—these models are clearly learning highly meaningful latent spaces that no prior approaches ever did. But I would agree that the actual high-level ability to make causal inferences seems to be lacking. Where I disagree with Pearl is simply with the idea that these stronger models won't emerge through future research. It's too early to say this, after barely a decade of large-scale AI research that has been undergoing continual rapid progress. Greater generality and more powerful models are some of the most well-established goals of the field.

manifestsilence 7y ago
This is interesting, as this appears to be similar to the difference between Daniel Kahneman's System1 and System2 modes of human thought. ML is perhaps beginning to approach our subconscious, associative intelligence. This shows up in its excellence for things like image processing, which we do instantly and automatically. Perhaps growing the tech equivalent of a prefrontal cortex is what's going to be hard, or require a different approach.
- _0ffh 7y ago
  We're getting there. I have the feeling more an more people subscribe to the hunch that combining symbolic (GOFAI) with subsymbolic (PDP/NN) techniques is the way forward. Research is going on, an example is the Neuro-Symbolic Concept Learner described in this [1] paper. [1] https://deepai.org/publication/the-neuro-symbolic-concept-learner-interpreting-scenes-words-and-sentences-from-natural-supervision https://deepai.org/publication/the-neuro-symbolic-concept-le...
- Symmetry 7y ago
  Yeah, that seems to be part of the reason that AlphaZero, which has its System2-like alpha-beta search seems so much smarter than AlphaStar which plays like a brilliant somnambulist. Maybe if we had some way of abstracting out the things a machine learning system implicitly learns so we could deal with them in a more classical AI-like way?
  - davidhowlett 7y ago
    Instead of an alpha-beta search, AlphaZero uses a general-purpose Monte Carlo tree search (MCTS) algorithm. Source: https://science.sciencemag.org/content/362/6419/1140.full https://science.sciencemag.org/content/362/6419/1140.full I agree with your comment about System 2 like reasoning not being common right now. I am not an expert in the field but the closest thing I have seen to learned planning is: https://arxiv.org/pdf/1911.08265.pdf https://arxiv.org/pdf/1911.08265.pdf
darksaints 7y ago
> But I would agree that the actual high-level ability to make causal inferences seems to be lacking. That should be expected. Humans also lack the ability to make causal inference. The vast majority of us have extremely primitive causal reasoning abilities and get even simple causality wrong. Reasoning about causality in complex systems still isn't a solved problem for humans, and we have entire fields within philosophy trying to make sense of the hundreds of paradoxes within causal reasoning. It's not a solved problem, and it's not clear that it ever will be.
- lowracle 7y ago
  I am glad to read this, because it really sounds weird to me that so much people blame AI for not being able to do causal reasoning, while us human don't even do in my sense "strict" causal reasoning. I feel like we just learned a small subset of causal rules just like AI algorithms, by having experienced a lot of events that followed those rules.
- Akababa 7y ago
  I don't quite understand why our ability to teach machines causal reasoning should hinge on the lowest common denominator of human ability. If I interpreted your point correctly, then calculators are an obvious counterexample, and a table of common human arithmetic mistakes doesn't have any bearing on our ability to program calculators. In terms of causal reasoning for computers, it's more of a "common sense" problem than a reasoning one. In nice, closed systems we can do symbolic computation and automated theorem proving without mistakes. The only reason this doesn't work in the real world is the lack of axioms and consistency.

manifestsilence 7y ago

This is interesting, as this appears to be similar to the difference between Daniel Kahneman's System1 and System2 modes of human thought. ML is perhaps beginning to approach our subconscious, associative intelligence. This shows up in its excellence for things like image processing, which we do instantly and automatically. Perhaps growing the tech equivalent of a prefrontal cortex is what's going to be hard, or require a different approach.

_0ffh 7y ago
We're getting there. I have the feeling more an more people subscribe to the hunch that combining symbolic (GOFAI) with subsymbolic (PDP/NN) techniques is the way forward. Research is going on, an example is the Neuro-Symbolic Concept Learner described in this [1] paper. [1] https://deepai.org/publication/the-neuro-symbolic-concept-learner-interpreting-scenes-words-and-sentences-from-natural-supervision https://deepai.org/publication/the-neuro-symbolic-concept-le...
Symmetry 7y ago
Yeah, that seems to be part of the reason that AlphaZero, which has its System2-like alpha-beta search seems so much smarter than AlphaStar which plays like a brilliant somnambulist. Maybe if we had some way of abstracting out the things a machine learning system implicitly learns so we could deal with them in a more classical AI-like way?
- davidhowlett 7y ago
  Instead of an alpha-beta search, AlphaZero uses a general-purpose Monte Carlo tree search (MCTS) algorithm. Source: https://science.sciencemag.org/content/362/6419/1140.full https://science.sciencemag.org/content/362/6419/1140.full I agree with your comment about System 2 like reasoning not being common right now. I am not an expert in the field but the closest thing I have seen to learned planning is: https://arxiv.org/pdf/1911.08265.pdf https://arxiv.org/pdf/1911.08265.pdf

darksaints 7y ago

> But I would agree that the actual high-level ability to make causal inferences seems to be lacking. That should be expected. Humans also lack the ability to make causal inference. The vast majority of us have extremely primitive causal reasoning abilities and get even simple causality wrong. Reasoning about causality in complex systems still isn't a solved problem for humans, and we have entire fields within philosophy trying to make sense of the hundreds of paradoxes within causal reasoning. It's not a solved problem, and it's not clear that it ever will be.

lowracle 7y ago
I am glad to read this, because it really sounds weird to me that so much people blame AI for not being able to do causal reasoning, while us human don't even do in my sense "strict" causal reasoning. I feel like we just learned a small subset of causal rules just like AI algorithms, by having experienced a lot of events that followed those rules.
Akababa 7y ago
I don't quite understand why our ability to teach machines causal reasoning should hinge on the lowest common denominator of human ability. If I interpreted your point correctly, then calculators are an obvious counterexample, and a table of common human arithmetic mistakes doesn't have any bearing on our ability to program calculators. In terms of causal reasoning for computers, it's more of a "common sense" problem than a reasoning one. In nice, closed systems we can do symbolic computation and automated theorem proving without mistakes. The only reason this doesn't work in the real world is the lack of axioms and consistency.