François Chollet is bullish on achieving AGI by the early 2030s but is critical of the current deep learning and LLM paradigm. He argues that while this approach may succeed, it is fundamentally inefficient and suboptimal, and will likely be replaced by more elegant solutions in the long run.
Chollet's lab, Endia, is pioneering an alternative to deep learning based on program synthesis. This approach, using a method called "symbolic descent," seeks to find the simplest possible symbolic model to explain data, which should require less training data, be more efficient, and generalize better.
The recent success of AI coding agents is not just due to better models, but because code provides a clear, verifiable reward signal (e.g., unit tests pass or fail). Chollet posits that any domain with such a signal, like mathematics, can be fully automated with current technology.
The ARC-AGI benchmark is continuously evolving to create a more accurate measure of general intelligence. While V1 and V2 were eventually saturated, the new V3 benchmark introduces interactive, game-like environments to test "agentic intelligence," focusing on exploration, goal-setting, and planning in unfamiliar contexts.
Keep pulling the thread on François Chollet.