3 ms·

This reads like something a retired professor of physics used to write: "how differential calculus solves the need for talking therapy". Ie., a slightly cooky m

This reads like something a retired professor of physics used to write: "how differential calculus solves the need for talking therapy". Ie., a slightly cooky misapplication of the tools of physics to some problem more complex than the author realises. I think we're in an age of pseudoscience where rather than wear lab coats, pseudoscientists "wear statistics" in a self-deluding attempt to ape science. However, I think this paper falls closer on the "cooky speculation" side. It does, nevertheless, participate in the culture of pseudoscience around these issues. It would be great if the paper owned up to its radical speculative nature, and didnt brush aside it's lack of parameterisation on time (which is pretty fatal to taking this seriously). Then it would be a pretty good model for what "radical considered speculation" looks like which we could contrast with the genuine pseudoscience of many papers in this area.

dr_dshiv 4y ago
Labeling a scientific paper as pseudoscience is an intense insult. Why not explain the point that you disagree with and explain why you disagree? That would be valuable for the conversation.
- mjburgess 4y ago
  Well my comment is that this paper isnt pseudoscience, but falls into the category of "cooky speculative science", which I'm generally reasonably OK with. But I would prefer it makes that point clearer. It is certainly part of the "pseudoscience culture" wherein many papers are just "not even wrong". The reason it's, at best, speculative is that we have no idea at what layer the brain-body-env performs computations. If we're interested in a dynamical model of the brain-body-env, then we want a series of partial differential equations which are parameterised by space and time. Just as we would describe the electrical field of a digital machine. However since we know that +/- voltage states of the machines we create are the relevant discrete states to give a computational description, we can also give a non-dynamical account of a digital computer (ie., we can say it realises the sequence: 0001, 1001, 0101, etc. What this paper does it take arbitrary aspects of arbitrary parts of the brain, and tries to marry them to some existing computational framework. If an alien were to analyse a CPU that way, for example, it could easily conclude that it's the heat of air particles warmed by the CPU which "did computational work". Since they do computational work, but none relevant to the digital computer we are using. This paper treads the line, some others cross it, in breathlessly claiming their series of wild, indefensible, and pretty clearly wrong assumptions which motivate their fashionable computational framework (ie., NNs, etc.) show that their press-released new projects (eg., GPT, etc.) are like brains somehow.
  - dr_dshiv 4y ago
    > What this paper does it take arbitrary aspects of arbitrary parts of the brain, and tries to marry them to some existing computational framework. Can you try describing what they specifically claim that you find arbitrary? Like, what are the “clearly wrong assumptions?”
    - mjburgess 4y ago
      You can find bits of a plant that you can apply this analysis to; and likewise, bits of a river. Even if we buy that parts of the brain have some properties which discretely encoded are "plausibly modelled" by a transformer network, this claim isnt surprising. It isnt surprising applied to a plant. In other words a descriptive "computationalist" project is almost always going to succeed. I can describe the orbit of the earth around the sun as 010101... But the transition from the earth in front of the sun (0) to it being behind (1) simply describes what it is we're trying to explain. Likewise pretty much every possible system has some aspect that can be described by the sequence 0101010... `F = GMm/r^2` however is not a descriptive project. It provides properties, causes, measures, and so on... it says what exists, what it is like, how it functions, and how the target of analysis (gravity) is controllable... ie., we can produce more of it with more mass, or less distance. For computational explanations to count as explanations they must elucidate some explanatory aspect of the system's function. In general, it's very hard for computational descriptions to do this; and this issue is made worse because the people who use it are (discrete) mathematicians and not scientists and really think that saying "orbits are ellipses", "leaves are ellipses", so "orbits are like leaves" is an actual explanation. The purpose of a transformer network, as used by data scientists, is not captured by brain function, nor by plant function. And finding a pattern in the brain which could be modelled this way, carries over no "explanatory power" of transformers as ordinarily used. This mistake is at the heart of much "computationalist pseudoscience". > System S has abstract discrete properties P, System T so too has them, so our explanations of T provide explanations of S. This is magical thinking, and a product of the people in this game never having really been familiarised with experimentation and the scientific method.
      - 4y ago

dr_dshiv 4y ago

Labeling a scientific paper as pseudoscience is an intense insult. Why not explain the point that you disagree with and explain why you disagree? That would be valuable for the conversation.

mjburgess 4y ago
Well my comment is that this paper isnt pseudoscience, but falls into the category of "cooky speculative science", which I'm generally reasonably OK with. But I would prefer it makes that point clearer. It is certainly part of the "pseudoscience culture" wherein many papers are just "not even wrong". The reason it's, at best, speculative is that we have no idea at what layer the brain-body-env performs computations. If we're interested in a dynamical model of the brain-body-env, then we want a series of partial differential equations which are parameterised by space and time. Just as we would describe the electrical field of a digital machine. However since we know that +/- voltage states of the machines we create are the relevant discrete states to give a computational description, we can also give a non-dynamical account of a digital computer (ie., we can say it realises the sequence: 0001, 1001, 0101, etc. What this paper does it take arbitrary aspects of arbitrary parts of the brain, and tries to marry them to some existing computational framework. If an alien were to analyse a CPU that way, for example, it could easily conclude that it's the heat of air particles warmed by the CPU which "did computational work". Since they do computational work, but none relevant to the digital computer we are using. This paper treads the line, some others cross it, in breathlessly claiming their series of wild, indefensible, and pretty clearly wrong assumptions which motivate their fashionable computational framework (ie., NNs, etc.) show that their press-released new projects (eg., GPT, etc.) are like brains somehow.
- dr_dshiv 4y ago
  > What this paper does it take arbitrary aspects of arbitrary parts of the brain, and tries to marry them to some existing computational framework. Can you try describing what they specifically claim that you find arbitrary? Like, what are the “clearly wrong assumptions?”
  - mjburgess 4y ago
    You can find bits of a plant that you can apply this analysis to; and likewise, bits of a river. Even if we buy that parts of the brain have some properties which discretely encoded are "plausibly modelled" by a transformer network, this claim isnt surprising. It isnt surprising applied to a plant. In other words a descriptive "computationalist" project is almost always going to succeed. I can describe the orbit of the earth around the sun as 010101... But the transition from the earth in front of the sun (0) to it being behind (1) simply describes what it is we're trying to explain. Likewise pretty much every possible system has some aspect that can be described by the sequence 0101010... `F = GMm/r^2` however is not a descriptive project. It provides properties, causes, measures, and so on... it says what exists, what it is like, how it functions, and how the target of analysis (gravity) is controllable... ie., we can produce more of it with more mass, or less distance. For computational explanations to count as explanations they must elucidate some explanatory aspect of the system's function. In general, it's very hard for computational descriptions to do this; and this issue is made worse because the people who use it are (discrete) mathematicians and not scientists and really think that saying "orbits are ellipses", "leaves are ellipses", so "orbits are like leaves" is an actual explanation. The purpose of a transformer network, as used by data scientists, is not captured by brain function, nor by plant function. And finding a pattern in the brain which could be modelled this way, carries over no "explanatory power" of transformers as ordinarily used. This mistake is at the heart of much "computationalist pseudoscience". > System S has abstract discrete properties P, System T so too has them, so our explanations of T provide explanations of S. This is magical thinking, and a product of the people in this game never having really been familiarised with experimentation and the scientific method.
    - 4y ago