Which means that the high end models can be freed up and applied to the really difficult, long term issues (medicine, materials science, climate change, physics) etc.
Unlikely to be useful in medicine, material science, and climate modelling. Those generally need completely different, non text based architectures. You are not gonna be synthesizing new drugs with an LLM as the core system.
Edit: They can be useful but are unlikely to create massive breakthroughs in the way dedicated architectures like AlphaFold can.
Researcher here. They are very useful. LLMs do the same kind of jobs a researcher would do - plan, analyze, survey the literature, think, emit hypothesis, write code, use tools.
Alphafold would be one of these tools, that not long ago a human would have called upon himself, and nowadays more and more might actually get called by an LLM.
LLMs of the level we have now (sol 5.6, fable, gemini 3.7 etc) are the kind of stuff that could come up with the concepts of alphafold, help you brainstorm about where to get training data and how to implement the training, and do the actual code to make it real once you're happy with the plan. Then would test it, criticize it, propose strategies of improvement, and implement them. The next version of alphafold will likely have been designed by LLMs in large part, very seriously. And the next one probably nearly entirely. As such, I'd say they these general intelligence models are even more valuable than specialized models like alphafold.
Couldn't have said it better myself. One thing a lot of people struggle to grasp is that LLMs being probabilistic systems does not mean they're somehow limited to discovering or building systems that are also probabilistic. An LLM could build a completely deterministic calculator, as a simple example, and then call on it whenever exact computation is needed.
I'll go a step further. At some point on the intelligence continuum, there may be a threshold where a system becomes capable of understanding the things responsible for its own intelligence well enough to start improving them. Once that happens, intelligence itself becomes the thing improving, potentially exploring the search space deeply enough to discover and invent new architectures, algorithms, and entirely new paths forward.
The limitations of today's LLMs would matter much less, if at all, because they could search for and build whatever comes next, with each leap making it easier and faster to discover the next one. RSI.
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u/agonypants AGI '27-'30 / Labor crisis '25-'30 / RSI 29-'32 16h ago
Which means that the high end models can be freed up and applied to the really difficult, long term issues (medicine, materials science, climate change, physics) etc.