Just to be clear, your position is that 25 years from now when LLMs are trained using trillions of times as much compute and routinely doing task that take humans months to years that they will still be unable to run a business worth $1B?
I think your comment is poorly worded, in that you’re stating certain trend extrapolations as facts rather than hypotheses. But anyway, yes my position is that LLMs (including groups of LLMs) will be unable to autonomously write a business plan and then found a company and grow it to $1B/year revenue, all with zero human intervention, 25 years from now.
The thing I actually expect is “LLMs with lots of RL training of diverse gamelike environments and problem sets, and some algorithmic tweaks”. Do you not expect that to work, or just by the time it does work, it will have evolved sufficiently beyond the current LLM paradigm, the resulting model will be better thought of as a new kind of thing?
I would include “constructivist learning” in your list, but I agree that LLMs seem capable of this. By “constructivist learning” I mean a scientific process where the learning conceives of an experiment on the world, tests the idea by acting on the world, and then learns from the result. A VLA model with incremental learning seems close to this. RL could be used for the model update, but I think for ASI we need learnings from real-world experiments.
Just to be clear, your position is that 25 years from now when LLMs are trained using trillions of times as much compute and routinely doing task that take humans months to years that they will still be unable to run a business worth $1B?
I think your comment is poorly worded, in that you’re stating certain trend extrapolations as facts rather than hypotheses. But anyway, yes my position is that LLMs (including groups of LLMs) will be unable to autonomously write a business plan and then found a company and grow it to $1B/year revenue, all with zero human intervention, 25 years from now.
The thing I actually expect is “LLMs with lots of RL training of diverse gamelike environments and problem sets, and some algorithmic tweaks”. Do you not expect that to work, or just by the time it does work, it will have evolved sufficiently beyond the current LLM paradigm, the resulting model will be better thought of as a new kind of thing?
I would include “constructivist learning” in your list, but I agree that LLMs seem capable of this. By “constructivist learning” I mean a scientific process where the learning conceives of an experiment on the world, tests the idea by acting on the world, and then learns from the result. A VLA model with incremental learning seems close to this. RL could be used for the model update, but I think for ASI we need learnings from real-world experiments.