A big problem with the conclusions of this article is the assumptions around possible extrapolations.
We don't know if a meaningfully superintelligent entity can exist. We don't understand the ingredients of intelligence that well, and it's hard to say how far the quality of these ingredients can be improved, to improve intelligence. For example, an entity with perfect pattern recognition ability, might be superintelligent, or just a little smarter than Terrance Tao. We don't know how useful it is to be better at pattern recognition to an arbitrary degree.
A common theory is that the ability modeling processes, like the behavior of the external world is indicative of intelligence. I think it's true. We also don't know the limitations of this modeling. We can simulate the world in our minds to a degree. The abstractions we use make the simulation more efficient, but less accurate. By this theory, to be superintelligent, an entity would have to simulate the world faster with similar accuracy, and/or use more accurate abstractions.
We don't know how much more accurate they can be per unit of computation. Maybe you have to quadruple the complexity of the abstraction, to double the accuracy of the computation, and human minds use a decent compromise that is infeasible to improve by a large margin. Maybe generating human level ideas faster isn't going to help because we are limited by experimental data, not by the ideas we can generate from it. We can't safely assume that any of this can be improved to an arbitrary degree.
We also don't know if AI research would benefit much from smarter AI researchers. Compute has seemed to be the limiting factor at almost all points up to now. So the superintelligence would have to help us improve compute faster than we can. It might, but it also might not.
This article reminds me of the ideas around the singularity, by placing too much weight on the belief that any trendline can be extended forever.
It is otherwise pretty interesting, and I'm excitedly watching the 'LLM + search' space.
We don't know if a meaningfully superintelligent entity can exist. We don't understand the ingredients of intelligence that well, and it's hard to say how far the quality of these ingredients can be improved, to improve intelligence. For example, an entity with perfect pattern recognition ability, might be superintelligent, or just a little smarter than Terrance Tao. We don't know how useful it is to be better at pattern recognition to an arbitrary degree.
A common theory is that the ability modeling processes, like the behavior of the external world is indicative of intelligence. I think it's true. We also don't know the limitations of this modeling. We can simulate the world in our minds to a degree. The abstractions we use make the simulation more efficient, but less accurate. By this theory, to be superintelligent, an entity would have to simulate the world faster with similar accuracy, and/or use more accurate abstractions.
We don't know how much more accurate they can be per unit of computation. Maybe you have to quadruple the complexity of the abstraction, to double the accuracy of the computation, and human minds use a decent compromise that is infeasible to improve by a large margin. Maybe generating human level ideas faster isn't going to help because we are limited by experimental data, not by the ideas we can generate from it. We can't safely assume that any of this can be improved to an arbitrary degree.
We also don't know if AI research would benefit much from smarter AI researchers. Compute has seemed to be the limiting factor at almost all points up to now. So the superintelligence would have to help us improve compute faster than we can. It might, but it also might not.
This article reminds me of the ideas around the singularity, by placing too much weight on the belief that any trendline can be extended forever.
It is otherwise pretty interesting, and I'm excitedly watching the 'LLM + search' space.
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