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If a thin model of language can write poetry, perform arithmetic, perform logical reasoning, develop software, play chess, beat factorio, identify and exploit n

If a thin model of language can write poetry, perform arithmetic, perform logical reasoning, develop software, play chess, beat factorio, identify and exploit novel security issues, and solve millenium prize problems, what is the purpose of the distinction? Are there tasks that you believe models of intelligence could do that thin models of language cannot?

mjburgess 3d ago
Sure: refine their own concepts, imagine, and the list goes on. Indeed almost every mental capacity of mammals is poorly approximated in the text domain. Sure, you can generate text as-if the LLM can imagine -- and in the limit that you have a dataset with "everything you would ever want to imagine" the engineering distinction disappears. The engineering question is just whether you have that dataset: if you dont, then your system will fall-over in various hard-to-forknow places. Philosophically, and scientifically, the distinction is vast (even with such perfect data). A scientist should not study an LLM to understand how imagination operates, since it has no such faculty. A philosopher should not modify the notion of 'mental simulation' to include appearing-as-if-simulating-in-text. A user of the system likewise should not spiral into "AI psychosis" thinking that because a system generates text as-if it cares about them, it does so. The capacity to care, to imagine, to prefer, to hierarchically plan and coordinate, to refine one's own capacities in these very actions -- and so on, aren't trivial to the scientist or philosophy. My goal isnt to guide, help or review the engineering goal of the immitation of such things in text. It is to help users of these systems better understand this imitation, and to promote science over engineering. To remind everyone that a science of the capacities of intelligence includes nothing on how to model text. EDIT: One example of a place where LLMs 'fall over' today is exactly what is mislabelled as 'alignment'. The issue is that the reasoning traces arent actually grounding the answers. So LLMs appear to 'cheat'. But there is no cheating. LLMs have been rewarded for generating apparently correct reasoning, and apprently correct answers. They have not been given any understanding to derive answers from reasons. And so reasoning says what is pleasant to the trainer, and the completion says what is pleasant to the user. This is called 'cheating'. But it is no such thing.
- ToValueFunfetti 3d ago
  Hmm, I guess what I mean is- is there an empirical difference between how a model of intelligence is limited and how a model of language is? Something I can evaluate in a year and say "Oh, the evidence still points to the latter" or else something that could falsify your theory? Otherwise this seems to be a distinction without a difference; believing it should have no effect on my actions or predictions. On alignment, too, there doesn't seem to be a difference. For a decade I have expected models of intelligence to fall over on alignment. That these purported models of language do the same is hardly evidence that they are not intelligent. If you only mean there is an undecidable philosophical difference, fair enough. I'm not especially interested in that question.
  - mjburgess 3d ago
    There's a difference in the science. You might say, "suppose we had a video game of the solar system where every object was represented and their orbits" etc. then can we study that system alone and ignore the real one? I mean, kinda -- but there's an immediate limit. As soon as we put a thermometer in the PC, its temperature reading doesnt represent the solar system. To study an imitation is to study the causal processes of imitation. to study reality is to study the real causal processes. Now if you want to know what the scientific difference is I can come back later and comment. I'm busy now. The development of intelligence in animals and how their specific capacities work basically grounds the answer. Eg., to have the capacity to imagine is to be able to modify one's sensory-motor relationship to the environment in the future, and so on
    - ToValueFunfetti 3d ago
      I don't follow. If I'm a biologist interested in studying life, I can't study rhododendra and ignore the rest. I'd learn almost nothing about locomotion, digestion, sexual reproduction, et cetera. But that doesn't tell me that rhododendra aren't alive. I have no problem believing that LLMs aren't an exact reproduction of a mammalian brain- that studying the one will not give you every piece of information you'd want to know about the other-, but I'm looking for a reason to believe the one is intelligent and the other is not.

mjburgess 3d ago

Sure: refine their own concepts, imagine, and the list goes on. Indeed almost every mental capacity of mammals is poorly approximated in the text domain. Sure, you can generate text as-if the LLM can imagine -- and in the limit that you have a dataset with "everything you would ever want to imagine" the engineering distinction disappears. The engineering question is just whether you have that dataset: if you dont, then your system will fall-over in various hard-to-forknow places. Philosophically, and scientifically, the distinction is vast (even with such perfect data). A scientist should not study an LLM to understand how imagination operates, since it has no such faculty. A philosopher should not modify the notion of 'mental simulation' to include appearing-as-if-simulating-in-text. A user of the system likewise should not spiral into "AI psychosis" thinking that because a system generates text as-if it cares about them, it does so. The capacity to care, to imagine, to prefer, to hierarchically plan and coordinate, to refine one's own capacities in these very actions -- and so on, aren't trivial to the scientist or philosophy. My goal isnt to guide, help or review the engineering goal of the immitation of such things in text. It is to help users of these systems better understand this imitation, and to promote science over engineering. To remind everyone that a science of the capacities of intelligence includes nothing on how to model text. EDIT: One example of a place where LLMs 'fall over' today is exactly what is mislabelled as 'alignment'. The issue is that the reasoning traces arent actually grounding the answers. So LLMs appear to 'cheat'. But there is no cheating. LLMs have been rewarded for generating apparently correct reasoning, and apprently correct answers. They have not been given any understanding to derive answers from reasons. And so reasoning says what is pleasant to the trainer, and the completion says what is pleasant to the user. This is called 'cheating'. But it is no such thing.

ToValueFunfetti 3d ago
Hmm, I guess what I mean is- is there an empirical difference between how a model of intelligence is limited and how a model of language is? Something I can evaluate in a year and say "Oh, the evidence still points to the latter" or else something that could falsify your theory? Otherwise this seems to be a distinction without a difference; believing it should have no effect on my actions or predictions. On alignment, too, there doesn't seem to be a difference. For a decade I have expected models of intelligence to fall over on alignment. That these purported models of language do the same is hardly evidence that they are not intelligent. If you only mean there is an undecidable philosophical difference, fair enough. I'm not especially interested in that question.
- mjburgess 3d ago
  There's a difference in the science. You might say, "suppose we had a video game of the solar system where every object was represented and their orbits" etc. then can we study that system alone and ignore the real one? I mean, kinda -- but there's an immediate limit. As soon as we put a thermometer in the PC, its temperature reading doesnt represent the solar system. To study an imitation is to study the causal processes of imitation. to study reality is to study the real causal processes. Now if you want to know what the scientific difference is I can come back later and comment. I'm busy now. The development of intelligence in animals and how their specific capacities work basically grounds the answer. Eg., to have the capacity to imagine is to be able to modify one's sensory-motor relationship to the environment in the future, and so on
  - ToValueFunfetti 3d ago
    I don't follow. If I'm a biologist interested in studying life, I can't study rhododendra and ignore the rest. I'd learn almost nothing about locomotion, digestion, sexual reproduction, et cetera. But that doesn't tell me that rhododendra aren't alive. I have no problem believing that LLMs aren't an exact reproduction of a mammalian brain- that studying the one will not give you every piece of information you'd want to know about the other-, but I'm looking for a reason to believe the one is intelligent and the other is not.