Comment by mjburgess | Hacker News Reader

Comment by mjburgess | Hacker News Reader

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Suppose you touch a fireplace once, do you touch it again? No.

OK, here's something much stranger. Suppose you see your friend touch the fireplace, he recoils in pain. Do you touch it? No.

Hmm... whence statistics? There is no frequency association here, in either case. And in the second, even no experience of the fireplace.

The entire history of science is supposed to be about the failure of statistics to produce explanations. It is a great sin that we have allowed pseudosciences to flourish in which this lesson isnt even understood; and worse, to allow statistical showmen with their magic lanterns to preach on the scientific method. To a point where it seems, almost, science as an ideal has been completely lost.

The entire point was to throw away entirely our reliance on frequency and association -- this is ancient superstition. And instead, to explain the world by necessary mechanisms born of causal properties which interact in complex ways that can never uniquely reveal themselves by direct measurement.

> The entire point was to throw away entirely our reliance on frequency and association -- this is ancient superstition. And instead, to explain the world by necessary mechanisms born of causal properties which interact in complex ways that can never uniquely reveal themselves by direct measurement.

Who said that? You make it sound like this was some important trend in the past, that got derailed by the evil statisticians (spoiler: there never was such a trend that was big enough to have momentum).

Your rant against statistics is all nice and dandy, but when you have to translate that website from a foreign language into English automatically, when you ask ChatGPT to generate you some code for a project you work on, or when you are glad that Google Maps predicted your arrival time at the office correctly, you rely on the statistics you vilify in essential ways. You basically are a customer of statistics every day (unless you live under a rock, which I don't think you do).

Statistics is good because it works, and it works well in 90% cases which is enough. What you advocate for so zealously (whatever such a causally validated theory would be) currently doesn't.

Well if you want something like the actual history... we have francis bacon getting us close to an abductive (ie., explanatory) method, decartes helped a bit -- then a great catastrophe befell science called Hume.

Since Hume it become popular to somehow rig measurement to make it necessarily informative (Kant), or to claim that measurement has no necessarily informative relation to reality at all (in the end, Russell, Ayer et al.).

It took a while to dig out of that nightmarish hole that philosophers largely created, back into the cold light of experimental reality.

It wasnt statisticians who made the mess; it was first philosophers, and today, people learning statistical methods without learning statistics at all.

Thankfully philosophers started actually reading science, and then they realised they'd go it all wrong. So today, professional research philosophy is allied against the forces of nonsense.

As far as the success of causal explanations, you owe to that everything, including the very machine which runs ChatGPT. That we can make little trinkets on association alone pales in comparison to what we have done by knowing how the world works.

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.

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.

(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.

If we define biomimetic so broadly that merely some vague inspiration from nature is enough, than I would say the Turing machine is also a biomimetic model. After all, Turing very explicitly modeled it after the activity of a mathematician working with a notebook. The read head represents the eyes of the mathematician scanning the notebook for symbols, the write head is their pencil, and the tape is the notebook itself.

Now, whether CPUs are an instance of a Turing machine or not is again quite debatable, but it's ultimately moot.

I think what matters more for deciding whether it makes sense to call a model biomimetic or not is whether it draws more than a passing inspiration from biology. Do practitioners keep referring back to biology to advance their design (not exclusively, but at least occasionally) or is it studied using other tools? Computers are obviously not biomimetic by this definition, as, beyond the original inspiration, no one has really looked at how mathematicians do their computations on paper to help build a better computer - the field evolved entirely detached from the model that inspired it.

With ANNs, admittedly, the situation is slightly murkier. The majority of advancements happen on mathematical grounds (e.g. choosing nonlinear activation functions to be able to approximate non-linear functions; various enhancements for faster or more stable floating point computations) or from broader computer science/engineering (faster GPUs, the single biggest factor in the advancement of the field).

However, there have been occasional forays back into biology, like the inspiration behind CNNs, and perhaps attention in Transformers. So, perhaps even by my own token, there is some (small) amount of biomimetic feedback in the design of ANNs.

I love that you said this.

>After all, Turing very explicitly modeled it after the activity of a mathematician working with a notebook. The read head represents the eyes of the mathematician scanning the notebook for symbols, the write head is their pencil, and the tape is the notebook itself.

My feeling on this is complete opposite to yours. To me, this is completely valid mode of discovery, and possibly even what led to the thought of the Turing machine. We are after all, interested in mimicking/reproducing the we way think. So it's perfectly sensible that one would "think about how we think" to try and come up with a model of computation.

I dont care at all about this argument of whether to call something biomemic or not. Thats just semantics. What you associate with meaning "biomemic" is subject to interpretation and one can only establish an objective criteria for it by asserting ones own mental model is the only correct one.

> My feeling on this is complete opposite to yours. To me, this is completely valid mode of discovery, and possibly even what led to the thought of the Turing machine. We are after all, interested in mimicking/reproducing the we way think. So it's perfectly sensible that one would "think about how we think" to try and come up with a model of computation.

I'm not sure if you thought I was being sarcastic, but what I was describing there is literally how Turing came up with the idea, he describes this in the paper where he introduces the concept of computable numbers [0]. I just summarized the non-math bits of his paper.

If you haven't read it, I highly recommend it, it's really easily digestible if you ignore the more mathematical parts. This particular argument appears in section 9.

[0] https://www.cs.virginia.edu/~robins/Turing_Paper_1936.pdf

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