AI BriefingStraight Lines on Graphs: The Regularity of AI Progress
Key point
Despite jagged capability differences across domains, AI progress shows a consistent regularity in its rate of advancement.
Details
The intuition that AI progress is highly regular and predictable — 'straight lines on graphs' — is easy to be skeptical of at first, but it actually shows high accuracy when it comes to predicting the future.
AI capabilities show a 'jagged frontier' with large differences across domains. However, even though the current level differs by domain, the rate of progress (the slope) tends to be remarkably similar.
There is a view that recent advances in RL (Reinforcement Learning) have accelerated the pace of AI progress, but this may be an illusion caused by the following:
- A phenomenon arising as benchmark tasks become increasingly close to AI training data
- Certain tasks having scoring methods that favor AI
- Statistical noise due to insufficient data
AI progress can be divided into progress driven by Pre-training, which broadly lifts performance across all areas, and progress driven by Post-training, which focuses on solving specific problems.
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