Executive summary: Epoch’s Direct Approach uses neural scaling laws and assumptions about distinguishability to forecast when large language models will reach human-level performance at scientific tasks, which they view as a threshold for transformative AI (TAI).
Key points:
The Direct Approach extrapolates the performance of machine learning models using empirical scaling laws, in contrast to indirect approaches that estimate proxies for performance.
It assumes that if an AI’s output is indistinguishable from a human’s (accounting for human judges being suboptimal at distinguishing), the AI is as competent as a human at that task.
Combining the Chinchilla scaling law, a distribution over the resources required for indistinguishable performance, and projections of future algorithmic efficiency, compute investment, and hardware improvements yields a forecast of when TAI will be developed.
The model’s predictions are sensitive to key uncertain parameters like the “human slowdown factor” and the threshold for indistinguishability. Varying these parameters significantly affects median TAI timelines.
Epoch found inconsistencies in the Chinchilla scaling law. Correcting for these shortens TAI timelines in their model. If valid, this suggests many researchers may be expecting longer timelines than is consistent with the data.
While the model provides a valuable framework, it relies on assumptions like scaling laws holding far outside the range they were measured on. It is intended more as a theoretical framework than a precise forecast.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: Epoch’s Direct Approach uses neural scaling laws and assumptions about distinguishability to forecast when large language models will reach human-level performance at scientific tasks, which they view as a threshold for transformative AI (TAI).
Key points:
The Direct Approach extrapolates the performance of machine learning models using empirical scaling laws, in contrast to indirect approaches that estimate proxies for performance.
It assumes that if an AI’s output is indistinguishable from a human’s (accounting for human judges being suboptimal at distinguishing), the AI is as competent as a human at that task.
Combining the Chinchilla scaling law, a distribution over the resources required for indistinguishable performance, and projections of future algorithmic efficiency, compute investment, and hardware improvements yields a forecast of when TAI will be developed.
The model’s predictions are sensitive to key uncertain parameters like the “human slowdown factor” and the threshold for indistinguishability. Varying these parameters significantly affects median TAI timelines.
Epoch found inconsistencies in the Chinchilla scaling law. Correcting for these shortens TAI timelines in their model. If valid, this suggests many researchers may be expecting longer timelines than is consistent with the data.
While the model provides a valuable framework, it relies on assumptions like scaling laws holding far outside the range they were measured on. It is intended more as a theoretical framework than a precise forecast.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.