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The goal of physics isn't to produce video games -- that a video game can generate a frame using unphysical formulae and unphysical processes (eg., rasterisatio

The goal of physics isn't to produce video games -- that a video game can generate a frame using unphysical formulae and unphysical processes (eg., rasterisation) does not invalidate physics. There's linguistics which produces descriptive theories of the practice of language. There's "computational linguistics" which aims to build discrete algorithmic models of language. Both of these are interested in explanations of actual physical phenomena. Machine Learning is largely an engineering discipline which, like video games, can generate output using any method which will convince a human user. Indeed, often more radically, ML works by replaying variations of past convincing output rather than have any explicit simulation involved at all. Thus there's no sense in which LLMs supercede linguistics, nor somehow computational linguistics superseding this. This would be like claiming rasterisation means we should retire optics and the physics of light.

HPsquared 2y ago
That's a nice dividing line, science and engineering are different for sure. But science makes use of engineering knowledge and engineering makes use of scientific knowledge.
light_hue_1 2y ago
That's like saying that it's not astronomy if I'm using a radio telescope, only optical telescopes are real astronomy. It makes zero sense. The tools are irrelevant. We don't have physics with and without ml journals. It's like saying AlphaFold isn't biology because it uses ml. It's absurd One goal of linguistics is to produce an account of how language works. If you understand how something works you can reproduce it. This was always a core part of linguistics. Just look at Chomsky's work. Or at what counts as evidence in a linguistics paper. It is a model that "explains" some feature of language. NLP does exactly the same thing as linguistics. It produces explanations and makes predictions about language. But it does so with ML. I think you're confused about optics and rasterization. There are countless ways to relate the two. Rasterization is not unphysical, no more so than say, finite element methods are unphysical. Heck physics is in the name of PBR. It's an approximation. And a fine one that people actually use in both science and games.
- mjburgess 2y ago
  My master's project was applying non-gradient-based ML methods to parameter optimisation in quantum metrology -- I'm aware that the reskin on curve-fitting algorithms called "Machine Learning" may be used as part of science. We've had taylor series approximations to functions for 300 years. But insofar as we're talking about LLMs, and most products you can use, we're talking about an engineering use. Engineers build systems that "perform" as you say explanatory accuracy is not a kind of engineering performance. It would equally well be said that, for a video game studio, "the more physicists I fired, the more the games perform better" -- for, of course, physicists have never studied 'making a video-game image convincing', and their techniques are best run on supercomputers, since they'd build explanatory simulations with explanatory accuracy. Video game developers do not do this. I have developed video games also, so I know. Convincingly placing pixels on a screen as-if governed by the laws of physics does not require actually simulating them. It requires generating frames from a "point of view" that never expose the absense of real physics, or approximations thereof. The world isnt made of triangles, and you cannot clip thru solid objects. The goal of a person making an LLM is similar to the goal of a video game developer, these are engineering goals: to give a user an experience which they will pay for. Now, a 2d 80s video-game has more actual physics in it than an LLM has models of language use, but still, this is irrelevant to the goals of these creators. A linguist could analyse an LLM as the conditional probability structure of the english language, as captured largely recent electronic texts -- but this structure is partly what precisely linguists and others are trying to explain. This is why all curve-fitting to historical data isn't in itself science: it is a restatement of the very target of explanation, the data. The job of scientific fields is to explain that curve. Its explotation by engineers is explanation-free. It isnt a science, and replaces no science.
  - light_hue_1 2y ago
    And my PhD and dayjob is doing research on this. If a student told me they had this view of what ML is, I would tell them that we've failed to educate them. The thought that physics doesn't care about performance or approximation is silly. Just look at AlphaFold. Heck, I talk to climatologists and material scientists that want the equivalent all the time. Prediction is the heart of all science. Whether we're talking neroscience, linguistics, physics, etc. You think people who run things on supercomputers want to do so for some idealistic notion of what science is? No. They have to do so because they don't have good approximations. Just like with protein folding. Places like DESRES used to build supercomputers for that. This is over now. ML models learn representations of data which can then be reused for many tasks. There's a whole field where we try to understand those representations. Those are explanations of what's going on given that they're such good predictions. The embeddings you get from an LLM are better models of language than anything linguistics ever accomplished. Their goal should be to explain them and probe their limits instead of complaining. If linguists has come up with gpt they would have been celebrating, the method by which you do science is irrelevant as long as it works. I'll close by quoting Dawkins. "Science. It works, bitches". That's the value of science. Can we predict which molecule will cure cancer? Everything else is ideology and silly thoughts from before the paradigm shift.

HPsquared 2y ago

That's a nice dividing line, science and engineering are different for sure. But science makes use of engineering knowledge and engineering makes use of scientific knowledge.

light_hue_1 2y ago

That's like saying that it's not astronomy if I'm using a radio telescope, only optical telescopes are real astronomy. It makes zero sense. The tools are irrelevant. We don't have physics with and without ml journals. It's like saying AlphaFold isn't biology because it uses ml. It's absurd One goal of linguistics is to produce an account of how language works. If you understand how something works you can reproduce it. This was always a core part of linguistics. Just look at Chomsky's work. Or at what counts as evidence in a linguistics paper. It is a model that "explains" some feature of language. NLP does exactly the same thing as linguistics. It produces explanations and makes predictions about language. But it does so with ML. I think you're confused about optics and rasterization. There are countless ways to relate the two. Rasterization is not unphysical, no more so than say, finite element methods are unphysical. Heck physics is in the name of PBR. It's an approximation. And a fine one that people actually use in both science and games.

mjburgess 2y ago
My master's project was applying non-gradient-based ML methods to parameter optimisation in quantum metrology -- I'm aware that the reskin on curve-fitting algorithms called "Machine Learning" may be used as part of science. We've had taylor series approximations to functions for 300 years. But insofar as we're talking about LLMs, and most products you can use, we're talking about an engineering use. Engineers build systems that "perform" as you say explanatory accuracy is not a kind of engineering performance. It would equally well be said that, for a video game studio, "the more physicists I fired, the more the games perform better" -- for, of course, physicists have never studied 'making a video-game image convincing', and their techniques are best run on supercomputers, since they'd build explanatory simulations with explanatory accuracy. Video game developers do not do this. I have developed video games also, so I know. Convincingly placing pixels on a screen as-if governed by the laws of physics does not require actually simulating them. It requires generating frames from a "point of view" that never expose the absense of real physics, or approximations thereof. The world isnt made of triangles, and you cannot clip thru solid objects. The goal of a person making an LLM is similar to the goal of a video game developer, these are engineering goals: to give a user an experience which they will pay for. Now, a 2d 80s video-game has more actual physics in it than an LLM has models of language use, but still, this is irrelevant to the goals of these creators. A linguist could analyse an LLM as the conditional probability structure of the english language, as captured largely recent electronic texts -- but this structure is partly what precisely linguists and others are trying to explain. This is why all curve-fitting to historical data isn't in itself science: it is a restatement of the very target of explanation, the data. The job of scientific fields is to explain that curve. Its explotation by engineers is explanation-free. It isnt a science, and replaces no science.
- light_hue_1 2y ago
  And my PhD and dayjob is doing research on this. If a student told me they had this view of what ML is, I would tell them that we've failed to educate them. The thought that physics doesn't care about performance or approximation is silly. Just look at AlphaFold. Heck, I talk to climatologists and material scientists that want the equivalent all the time. Prediction is the heart of all science. Whether we're talking neroscience, linguistics, physics, etc. You think people who run things on supercomputers want to do so for some idealistic notion of what science is? No. They have to do so because they don't have good approximations. Just like with protein folding. Places like DESRES used to build supercomputers for that. This is over now. ML models learn representations of data which can then be reused for many tasks. There's a whole field where we try to understand those representations. Those are explanations of what's going on given that they're such good predictions. The embeddings you get from an LLM are better models of language than anything linguistics ever accomplished. Their goal should be to explain them and probe their limits instead of complaining. If linguists has come up with gpt they would have been celebrating, the method by which you do science is irrelevant as long as it works. I'll close by quoting Dawkins. "Science. It works, bitches". That's the value of science. Can we predict which molecule will cure cancer? Everything else is ideology and silly thoughts from before the paradigm shift.