The Nonlinear Library EA - LLMs won't lead to AGI - Francois Chollet by tobycrisford
Jun 12, 2024
02:00
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLMs won't lead to AGI - Francois Chollet, published by tobycrisford on June 12, 2024 on The Effective Altruism Forum.
I found this interview with Francois Chollet fascinating, and would be curious to hear what other people make of it.
I think it is impressive that he's managed to devise a benchmark of tasks which are mostly pretty easy for most humans, but which LLMs have so far not been able to make much progress with.
If you don't have time to watch the video, then I think these tweets of his sum up his views quite well:
The point of general intelligence is to make it possible to deal with novelty and uncertainty, which is what our lives are made of. Intelligence is the ability to improvise and adapt in the face of situations you weren't prepared for (either by your evolutionary history or by your past experience) -- to efficiently acquire skills at novel tasks, on the fly.
Meanwhile what the AI of today does is to combine extremely weak generalization power (i.e. ability to deal with novelty and uncertainty) with a dense sampling of everything it might ever be faced with -- essentially, use brute-force scale to *by-pass* the problem of intelligence entirely.
If intelligence is the ability to deal with what you weren't prepared for, then the modern AI strategy is to prepare for everything, so you never need intelligence. This is of course a terrible strategy, because it is impossible to prepare for everything. The problem isn't just scale, the problem is the fact that the real world isn't sampled from a static distribution -- it is ever changing and ever novel.
If his take on things is correct, I am not sure exactly what this implies for AGI timelines. Maybe it would mean that AGI is much further off than we think, because the impressive feats of LLMs that have led us to think it might be close have been overinterpreted. But it seems like it could also mean that AGI will arrive much sooner? Maybe we already have more than enough compute and training data for superhuman AGI, and we are just waiting on that one clever idea.
Maybe that could happen tomorrow?
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org
I found this interview with Francois Chollet fascinating, and would be curious to hear what other people make of it.
I think it is impressive that he's managed to devise a benchmark of tasks which are mostly pretty easy for most humans, but which LLMs have so far not been able to make much progress with.
If you don't have time to watch the video, then I think these tweets of his sum up his views quite well:
The point of general intelligence is to make it possible to deal with novelty and uncertainty, which is what our lives are made of. Intelligence is the ability to improvise and adapt in the face of situations you weren't prepared for (either by your evolutionary history or by your past experience) -- to efficiently acquire skills at novel tasks, on the fly.
Meanwhile what the AI of today does is to combine extremely weak generalization power (i.e. ability to deal with novelty and uncertainty) with a dense sampling of everything it might ever be faced with -- essentially, use brute-force scale to *by-pass* the problem of intelligence entirely.
If intelligence is the ability to deal with what you weren't prepared for, then the modern AI strategy is to prepare for everything, so you never need intelligence. This is of course a terrible strategy, because it is impossible to prepare for everything. The problem isn't just scale, the problem is the fact that the real world isn't sampled from a static distribution -- it is ever changing and ever novel.
If his take on things is correct, I am not sure exactly what this implies for AGI timelines. Maybe it would mean that AGI is much further off than we think, because the impressive feats of LLMs that have led us to think it might be close have been overinterpreted. But it seems like it could also mean that AGI will arrive much sooner? Maybe we already have more than enough compute and training data for superhuman AGI, and we are just waiting on that one clever idea.
Maybe that could happen tomorrow?
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org