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I get the Chomskyan objection re. statistical machine learning, I am partial to it. But consider these LLMs and such as extremely crude simulations of biologic

by calf 3y ago

I get the Chomskyan objection re. statistical machine learning, I am partial to it. But consider these LLMs and such as extremely crude simulations of biological neural networks. They aren't just any statistics; these are biomimetic computations. Then we can in principle "do science" here. We can study skyscrapers and bridges; we can study LLMs and say some scientific things about them. That is quite different than maybe what is going on in industry, but AFAIK there are lots of academic computer scientists who are trying to do just that, bring the science back to the study of these artifacts so that we can have some theorems and proofs, etc. That is - hopefully - more sophisticated a science than trying to poke and prod at a black box and call that empirical science.

tsimionescu 3y ago
The only relationship between artificial neural networks and biology is the vague principle of an activation threshold. In all other ways, the way biological neural networks are organized are ignored. The majority of ANN characteristics are instead influenced by statistics and mathematical arguments, and simple tinkering. For some ways in which these differ: - real neurons are vastly complex cells which seem to perform significant non-trivial computations of their own, including memory, and can't be abstracted as a single activation function - real neural networks include "broadcast neurons" that affect other neurons based on their geometric organization, not on direct synapse connections - there are various neurotransmitters in the brain with different effects, not just a single type of "signal" - several glands affect thought and computation in the brain that are not made of neurons - real neural networks are not organized in a simple set of layers, they form much more complex graphs And these are just talking about the structure. The way we actually operate these networks has nothing in common with how real neural networks work. Real neural networks have no separate training and inference phases: at all times, they both learn and produce actionable results. Also, the way in which we train ANNs, backpropagation with stochastic gradient descent, is entirely unrelated from how real neural networks learn (which we don't really understand in any significant amount). So no, I don't think it makes any sense to say that ANNs are a form of biomimetic computation. They are at best as similar to brains as a nylon coat is to an animal's coat.
- calf 3y ago
  (P.S. Just to head off a possible diction issue - biomimetics just means taking something from nature as inspiration for doing something, it doesn't mean the reverse which is to "try to understand / emulate nature completely well". E.g. solar panels arranged on a stalk to maximize light is acceptably biomimetic and there is no issue about whether solar panels are faithful enough to model chloroplasts.) I'm coming from the context of theoretical models of computation, of which there are only so many general ones - Turing machines, lambda calculus, finite state machines, neural networks, Petri nets, a bunch of other historical ones, ... etc. etc. Consider just two, the Turing machine model, versus the most abstract possible neural network. We know that the two are formally computationally equivalent. Abstractly, the distinguishing feature of theoretical neural networks is that they do computations through graphs. Our brains are graphs (and graphs with spatial constraints as well as many other constraints and things). The actually-existing LLMs are graphs. Consider, C++ code is not only better modeled by the not-graph Turing machine model, it is also easily an instance of a Turing machine. These man-made computers are instances as well as modeled by von-Neumann architectures, which can be thought of as a real implementation of the Turing machine model of computation proper. I think this conceptual relationship could be the same for biological brains. They are doing some kind of computable computation. They are not only best modeled by some kind of extremely sophisticated - but computable - neural network model of computation that nobody knows how to define yet (well, Yann LeCun has some powerpoint slides about that apparently). They are also an instance of that highly abstract, theoretical model. It's a consequence of the Church-Turing thesis which I generally buy (because of aforementioned equivalence, etc.): if one thinks the lambda calculus is a better model than neural network for the human brain, I'd like to see it! (It turns out there are cellular models of computation as well, called membrane models.) But that's the granularity I'm interested in. In different words, the fact that many neural network models (rather, metamodels like "the category of all LLMs") can be bad models or rudimentary models is not a dealbreaker in my opinion, since that is analogous to focusing on implementation details. The goal of scientific research (along the neural network paradigm) would be to try sort that out further (in the form of theory and proofs, in opposition to further "statistical tinkering"). Hope that argument wasn't too handwavy.
  - 3y ago

by calf 3y ago

tsimionescu 3y ago

The only relationship between artificial neural networks and biology is the vague principle of an activation threshold. In all other ways, the way biological neural networks are organized are ignored. The majority of ANN characteristics are instead influenced by statistics and mathematical arguments, and simple tinkering. For some ways in which these differ: - real neurons are vastly complex cells which seem to perform significant non-trivial computations of their own, including memory, and can't be abstracted as a single activation function - real neural networks include "broadcast neurons" that affect other neurons based on their geometric organization, not on direct synapse connections - there are various neurotransmitters in the brain with different effects, not just a single type of "signal" - several glands affect thought and computation in the brain that are not made of neurons - real neural networks are not organized in a simple set of layers, they form much more complex graphs And these are just talking about the structure. The way we actually operate these networks has nothing in common with how real neural networks work. Real neural networks have no separate training and inference phases: at all times, they both learn and produce actionable results. Also, the way in which we train ANNs, backpropagation with stochastic gradient descent, is entirely unrelated from how real neural networks learn (which we don't really understand in any significant amount). So no, I don't think it makes any sense to say that ANNs are a form of biomimetic computation. They are at best as similar to brains as a nylon coat is to an animal's coat.

calf 3y ago
(P.S. Just to head off a possible diction issue - biomimetics just means taking something from nature as inspiration for doing something, it doesn't mean the reverse which is to "try to understand / emulate nature completely well". E.g. solar panels arranged on a stalk to maximize light is acceptably biomimetic and there is no issue about whether solar panels are faithful enough to model chloroplasts.) I'm coming from the context of theoretical models of computation, of which there are only so many general ones - Turing machines, lambda calculus, finite state machines, neural networks, Petri nets, a bunch of other historical ones, ... etc. etc. Consider just two, the Turing machine model, versus the most abstract possible neural network. We know that the two are formally computationally equivalent. Abstractly, the distinguishing feature of theoretical neural networks is that they do computations through graphs. Our brains are graphs (and graphs with spatial constraints as well as many other constraints and things). The actually-existing LLMs are graphs. Consider, C++ code is not only better modeled by the not-graph Turing machine model, it is also easily an instance of a Turing machine. These man-made computers are instances as well as modeled by von-Neumann architectures, which can be thought of as a real implementation of the Turing machine model of computation proper. I think this conceptual relationship could be the same for biological brains. They are doing some kind of computable computation. They are not only best modeled by some kind of extremely sophisticated - but computable - neural network model of computation that nobody knows how to define yet (well, Yann LeCun has some powerpoint slides about that apparently). They are also an instance of that highly abstract, theoretical model. It's a consequence of the Church-Turing thesis which I generally buy (because of aforementioned equivalence, etc.): if one thinks the lambda calculus is a better model than neural network for the human brain, I'd like to see it! (It turns out there are cellular models of computation as well, called membrane models.) But that's the granularity I'm interested in. In different words, the fact that many neural network models (rather, metamodels like "the category of all LLMs") can be bad models or rudimentary models is not a dealbreaker in my opinion, since that is analogous to focusing on implementation details. The goal of scientific research (along the neural network paradigm) would be to try sort that out further (in the form of theory and proofs, in opposition to further "statistical tinkering"). Hope that argument wasn't too handwavy.
- 3y ago