3 ms·

We know plenty about human cognition, and we know everything about how LLMs work. True we don’t know anything about intelligence but that is because “intelligen

We know plenty about human cognition, and we know everything about how LLMs work. True we don’t know anything about intelligence but that is because “intelligence” is it self a fraught and vague term, and we haven’t (and perhaps never will) settled on what it means exactly.

adrianN 2mo ago
I’m certainly no expert in the field but to my knowledge a lot of the LLM science is empirical. I’m not aware of a theory that lets us predict what architecture and what number of parameters is needed to solve a particular set of problems.
pama 2mo ago
I wish we knew everything about how LLMs work! We only know very basic elements related to their construction and traning dynamics, and pretty much every major question we would like to address still has unknown or vague heuristic answers. This is expected for such a young field of study. In physics, we know the Schrodinger equation, but we dont know everything about how the world works or how to create new materials even though we know that these materials are composed by atoms and we can simulate small collections of them. In cell biology, we know the sequences that make up the DNA of a cell and we approach the time we can build minimal synthetic cells with pieces we understand, but we only scratch the surface of our level of understanding of how the cells actually work and new discoveries are added every day. In biology at large we still keep finding new types of tubes inside human brains—not sure what you mean by plenty, but we certainly have an extremely limited understanding of human cognition compared to what we might have in 50 years from now. It is not just anout LLMs and intelligence—I would like us to be able to answer practical questions about how LLMs work in order to improve general or specialized LLMs even faster than today. We “know” about scaling in an empirical sense, and it certainly has a long way to go, but it does not feel close to a complete understanding.
- runarberg 2mo ago
  Both you and your sibling are approaching LLMs like it is some sort of science. If you do that there is no wonder you have a lot of unanswered questions. LLMs are not a science, they are applied statistics. Making predictions to evaluate hypothesis and constructing theories around the hyperparameters of LLMs is no different then making predictions to evaluate hypothesis and constructing theories around the configurations of Nuclear Power Plants, the latter of course being applied physics.
  - pama 2mo ago
    My point is exactly that we cannot even begin to start the process that will lead to “know everything” there is to know unless we make it a science first. Ad hoc statistical models are different than understanding or complete knowledge of a subject.
naasking 2mo ago
> we know everything about how LLMs work No we don't. That we understand the low level mechanics of a system doesn't mean we understand how any high level phenomena emerge from those low level mechanics. This is as true for quantum mechanics as it is for LLMs.
- runarberg 2mo ago
  But we do, and by your logic we can pick any applied statistics, say a Bayesian inference model, and claim we don‘t understand its emergent properties. Heck, we can take any sort of applied mathematics or science, say linguistics, and claim we don‘t understand the emergent properties of language (legal analysis) even though we understand its syntax and phonology.
  - naasking 2mo ago
    Yes. That's why law is a different discipline from linguistics requiring its own rules of analysis, pedagogy, etc.

adrianN 2mo ago

I’m certainly no expert in the field but to my knowledge a lot of the LLM science is empirical. I’m not aware of a theory that lets us predict what architecture and what number of parameters is needed to solve a particular set of problems.

pama 2mo ago

I wish we knew everything about how LLMs work! We only know very basic elements related to their construction and traning dynamics, and pretty much every major question we would like to address still has unknown or vague heuristic answers. This is expected for such a young field of study. In physics, we know the Schrodinger equation, but we dont know everything about how the world works or how to create new materials even though we know that these materials are composed by atoms and we can simulate small collections of them. In cell biology, we know the sequences that make up the DNA of a cell and we approach the time we can build minimal synthetic cells with pieces we understand, but we only scratch the surface of our level of understanding of how the cells actually work and new discoveries are added every day. In biology at large we still keep finding new types of tubes inside human brains—not sure what you mean by plenty, but we certainly have an extremely limited understanding of human cognition compared to what we might have in 50 years from now. It is not just anout LLMs and intelligence—I would like us to be able to answer practical questions about how LLMs work in order to improve general or specialized LLMs even faster than today. We “know” about scaling in an empirical sense, and it certainly has a long way to go, but it does not feel close to a complete understanding.

runarberg 2mo ago
Both you and your sibling are approaching LLMs like it is some sort of science. If you do that there is no wonder you have a lot of unanswered questions. LLMs are not a science, they are applied statistics. Making predictions to evaluate hypothesis and constructing theories around the hyperparameters of LLMs is no different then making predictions to evaluate hypothesis and constructing theories around the configurations of Nuclear Power Plants, the latter of course being applied physics.
- pama 2mo ago
  My point is exactly that we cannot even begin to start the process that will lead to “know everything” there is to know unless we make it a science first. Ad hoc statistical models are different than understanding or complete knowledge of a subject.

naasking 2mo ago

> we know everything about how LLMs work No we don't. That we understand the low level mechanics of a system doesn't mean we understand how any high level phenomena emerge from those low level mechanics. This is as true for quantum mechanics as it is for LLMs.

runarberg 2mo ago
But we do, and by your logic we can pick any applied statistics, say a Bayesian inference model, and claim we don‘t understand its emergent properties. Heck, we can take any sort of applied mathematics or science, say linguistics, and claim we don‘t understand the emergent properties of language (legal analysis) even though we understand its syntax and phonology.
- naasking 2mo ago
  Yes. That's why law is a different discipline from linguistics requiring its own rules of analysis, pedagogy, etc.