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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 wh

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.

dr_dshiv 4y ago
Thank you for engaging! Are you saying it isn’t enough to have a model? That it must be accurate in all ways to be an adequate explanatory model? Describing the spin of an atom as spin is explanatory—but only incompletely. It still has value. In this case, the article described how astrocytes and neurons interact in some way that can produce properties of transformers. Correct? Why, in your opinion, is this a fruitless end? We won’t find a memory system or CPU either, but we may find accumulators and registers, and we are damn likely to find convolutions. Why not attempt to explicate a mechanism for transformer-based computation in the brain? Maybe they are wrong. But to call this pseudoscience…
- mjburgess 4y ago
  Well, again, I didnt call this paper pseudoscience. The "culture of papers" around this is pseudoscience. It's aim is to misapply computational descriptions to things in order to build PR credibility for their own projects -- everyone from Stanford, MIT, etc. does this, and it's awful. Attributing spin to particles is explanatory. You can, for example, perform the stern-gerlash experiment. The problem is something like this, take the formula f(a, b, c; A, B, C) = a^A.b^B.c^C That formulae describes gravity (a=m,b=M,c=r; units of GN), electrostatics (a=q,...), pendulums, oceans, thermodynamics... etc. The formulae is a pure syntax on to which we can impose a wide variety of radically different semantic. Knowing that formulae can be used with a system is meaningless. It can be used to describe an aspect of almost any system. The explanation part comes from the semantics of that formulae. What computational descriptions do is simply show that some formulae can be used. A priori, we basically already know that even, eg., that formulae above "describes the brain". That doesnt mean the brain is anything like gravity, thermo, etc. The false equivocation here is this: since the syntax of transformer models applies to the brain, their semantics as used on digital computers shows the brain is like a digital computer. This is a false equivocation, and extremely unlikely. If people want to make the above claim they need to perform an experiment which shows that the brain is operating like a digital computer, that a formulae applies to both is unsurprising. We should expect some aspect of the airflow in a room to be describale with the syntax of a transformer.

dr_dshiv 4y ago

Thank you for engaging! Are you saying it isn’t enough to have a model? That it must be accurate in all ways to be an adequate explanatory model? Describing the spin of an atom as spin is explanatory—but only incompletely. It still has value. In this case, the article described how astrocytes and neurons interact in some way that can produce properties of transformers. Correct? Why, in your opinion, is this a fruitless end? We won’t find a memory system or CPU either, but we may find accumulators and registers, and we are damn likely to find convolutions. Why not attempt to explicate a mechanism for transformer-based computation in the brain? Maybe they are wrong. But to call this pseudoscience…

mjburgess 4y ago
Well, again, I didnt call this paper pseudoscience. The "culture of papers" around this is pseudoscience. It's aim is to misapply computational descriptions to things in order to build PR credibility for their own projects -- everyone from Stanford, MIT, etc. does this, and it's awful. Attributing spin to particles is explanatory. You can, for example, perform the stern-gerlash experiment. The problem is something like this, take the formula f(a, b, c; A, B, C) = a^A.b^B.c^C That formulae describes gravity (a=m,b=M,c=r; units of GN), electrostatics (a=q,...), pendulums, oceans, thermodynamics... etc. The formulae is a pure syntax on to which we can impose a wide variety of radically different semantic. Knowing that formulae can be used with a system is meaningless. It can be used to describe an aspect of almost any system. The explanation part comes from the semantics of that formulae. What computational descriptions do is simply show that some formulae can be used. A priori, we basically already know that even, eg., that formulae above "describes the brain". That doesnt mean the brain is anything like gravity, thermo, etc. The false equivocation here is this: since the syntax of transformer models applies to the brain, their semantics as used on digital computers shows the brain is like a digital computer. This is a false equivocation, and extremely unlikely. If people want to make the above claim they need to perform an experiment which shows that the brain is operating like a digital computer, that a formulae applies to both is unsurprising. We should expect some aspect of the airflow in a room to be describale with the syntax of a transformer.