I read Judea Pearl's The Book of Why, in which he mentions that deep learning is just a glorified curve fitting technology, and will not be able to produce human-like intelligence.

From his book there is this diagram that illustrates the three levels of cognitive abilities:

Three levels of cognitive abilities

The idea is that the "intelligence" produced by current deep learning technology is only at the level of association. Thus the AI is nowhere near the level of asking questions like "how can I make Y happen" (intervention) and "What if I have acted differently, will X still occur?" (counterfactuals), and it's highly unlikely that curve fitting techniques can ever bring us closer to a higher level of cognitive ability.

I found his argument persuasive on an intuitive level, but I'm unable to find any physical or mathematical laws that can either bolster or cast doubt on this argument.

So, is there any scientific/physical/chemical/biological/mathematical argument that prevents deep learning from ever producing strong AI (human-like intelligence)?

  • Few hours back,we had some sort of Quetion which talks about neuroscience and artificial intelligence,so I think this question is right fit in data applications,that is besides machine learning. – quintumnia Jun 15 at 12:25
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    One of the problems faced in answering this is, is that "Deep Learning" is a kind of brand name, where things done with neural networks that go beyond statistical curve fitting - e.g. RNNs for learning sequences, and "deep reinforcement learning" - may also be considered part of it by adherents. If you allow for the term to evolve like this, it is very hard to nail down any argument about its capabilities. – Neil Slater Jun 15 at 12:29
  • godel's incompleteness theorem – thecomplexitytheorist Jun 15 at 16:35
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    @thecomplexitytheorist: Godel's incompleteness, entscheidungsproblem and similars , all they aply also to human mind. As conclusion, if they was a reason to do not reach AI, then neither humans are intelligent. Modus tollens, Godels is not a problem in the path to AGI – pasaba por aqui Jun 15 at 23:03
  • Who is to say that 'Doing' and 'Imagining' are also not simply 'Curve-Fitting' going on in the brain? – Dunk Jun 18 at 23:26

It is a paradox, but a deep learning machine (defined as a NeuralNet variant) is unable to learn anything. It is a flexible and configurable hardware/software architecture that can be parametrized to solve a lot of problems. But the optimal parameters to solve a problem are obtained by an external system, i.e. back-propagation algorithm.

Back-propagation subsystem uses conventional programming paradigms, it is not a Neural Net. This fact is in absolute opposition to human mind, where learning and use of knowledge is done by the same system (the mind).

If all the real interesting things are done outside the NN, it is difficult to claim that a NN (in any variant) can develop in an AGI.

It is also possible to find some more differences. Neural nets are strongly numerical in its interface and internals. From this point of view, they are an evolution of support vector machines.

Too much differences and restrictions to expect an AGI.

Note: I strongly disagree in the draw included in the original question. "Seeing", "doing", "imaging" are levels absolutely wrong. It ignores from basic and common software concepts as "abstraction" or "program state" (of mind, in Turing words); applied AI ones as "foresee"; and AGI ones as "free will", "objectives and feelings", ...

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    I think removing back-propagation (or any part of the training framework) from consideration, and claiming that the remaining part is the "Deep Learning" part is artificial, and kind of dodging the question. I think it is reasonable to assume that OP means Deep Learning as it is practiced, including the training processes available. – Neil Slater Jun 15 at 15:50
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    @NeilSlater: If we say a DL is a kind of NeuralNet ( in order to remark the similarity with the human mind and, as consequence, its possible power to reach an AGI) , we are excluding the learning part, that is not a NN. If we include the learning subsystem in the definition of a DL, then it is not a NN, it is only conventional programming, with the power of any conventional program, and has the same posibilities to reach AGI than any other program system or paradigm. – pasaba por aqui Jun 15 at 15:58
  • Human brains learn by receiving and processing input from external 'systems' exclusively. The optimal parameters to solve problems are obtained via trial and error, applying rules and processing input from external systems. Training starts while the baby is still in the uterus and continues 24/7 thereafter. The current state of AI is almost certainly not comparable to emulating the human brain; but claiming that AI can't learn (or isn't already learning in a similar way to the human brain) assumes knowledge of how the human brain 'learns' and functions that science doesn't know yet. – Dunk Jun 18 at 23:40

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