>may not already become a superhuman predictor of e. g. time-series data, with the right model architecture, amount of data, and compute.
We have that (to the degree possible) with non-LLM analysis - all kinds of regression, machine learning techniques, etc. LLM might not even be applicable here, or have any edge over something specilialized.
And usually this breaks down because the world (e.g. weather or stock market prices) are not predictable enough to the granularity we're interested at, but full of chaotic behavior and non-linearities.
We have that (to the degree possible) with non-LLM analysis - all kinds of regression, machine learning techniques, etc. LLM might not even be applicable here, or have any edge over something specilialized.
And usually this breaks down because the world (e.g. weather or stock market prices) are not predictable enough to the granularity we're interested at, but full of chaotic behavior and non-linearities.