Strong upvote. I think the methods used in this post are very promising for accurately forecasting TAI for the reasons explained below.
While writing GPT-4 Predictions I spent a lot of time playing around with the parametric scaling law L(N, D) from Hoffmann et al. 2022 (the Chinchilla paper). In the post, I showed that scaling laws can be used to calculate model losses and that these losses seem to correlate well with performance on the MMLU benchmark. My plan was to write a post extrapolating the progress further to TAI until I read this post which has already done that!
Scaling laws for language models seem to me like possibly the most effective option we have for forecasting TAI accurately for several reasons:
It seems as though the closest ML models to TAI that currently exist are language models and therefore predictive uncertainty should be lower for forecasting TAI from language models than from other types of less capable models.
A lot of economically valuable work such as writing and programming involves text and therefore language models tend to excel at these kinds of tasks.
The simple training objective of language models makes it easier to reason about their properties and capabilities. Also, despite their simple training objective, large language models demonstrate impressive levels of generalization and even reasoning (e.g. chain-of-thought prompting).
Language model scaling laws are well-studied and highly accurate for predicting language model losses.
There are many existing examples of language models and their capabilities. Previous capabilities can be used as a baseline for predicting future capabilities.
Overall my intuition is that language model scaling laws require much fewer assumptions and guesswork for forecasting TAI and therefore should allow narrower and more confident predictions which your post seems to show (<10 OOM vs 20 OOM for the bio anchors method).
As I mentioned in this post there are limitations to using scaling laws such as the possibility of sudden emergent capabilities and the difficulty of predicting algorithmic advances.
Strong upvote. I think the methods used in this post are very promising for accurately forecasting TAI for the reasons explained below.
While writing GPT-4 Predictions I spent a lot of time playing around with the parametric scaling law L(N, D) from Hoffmann et al. 2022 (the Chinchilla paper). In the post, I showed that scaling laws can be used to calculate model losses and that these losses seem to correlate well with performance on the MMLU benchmark. My plan was to write a post extrapolating the progress further to TAI until I read this post which has already done that!
Scaling laws for language models seem to me like possibly the most effective option we have for forecasting TAI accurately for several reasons:
It seems as though the closest ML models to TAI that currently exist are language models and therefore predictive uncertainty should be lower for forecasting TAI from language models than from other types of less capable models.
A lot of economically valuable work such as writing and programming involves text and therefore language models tend to excel at these kinds of tasks.
The simple training objective of language models makes it easier to reason about their properties and capabilities. Also, despite their simple training objective, large language models demonstrate impressive levels of generalization and even reasoning (e.g. chain-of-thought prompting).
Language model scaling laws are well-studied and highly accurate for predicting language model losses.
There are many existing examples of language models and their capabilities. Previous capabilities can be used as a baseline for predicting future capabilities.
Overall my intuition is that language model scaling laws require much fewer assumptions and guesswork for forecasting TAI and therefore should allow narrower and more confident predictions which your post seems to show (<10 OOM vs 20 OOM for the bio anchors method).
As I mentioned in this post there are limitations to using scaling laws such as the possibility of sudden emergent capabilities and the difficulty of predicting algorithmic advances.
Exceptions include deep RL work by DeepMind such as AlphaTensor.