What if we could start automating AI research today? What if we didn’t have to wait for a 2030 supercluster to cure cancer? What if ASI was in the room with us already?
Granting foundation models ‘search’ (the ability to think for longer) might upend Scaling Laws and change AI’s trajectory.
In 2019, a team of researchers built a cracked chess computer. She was called Leela Chess Zero — ’zero’ because she started knowing only the rules. She learned by playing against herself billions of times. She made moves that overturned centuries of human chess canon. She was inventive and made long-term sacrifices. Leela played with her food and exhibited weird human tendencies. She won the world championship. And then she was brutally usurped.
I loved Leela. I had sunk years into knowing, benchmarking, and researching her. As a kid, I always wondered what it would be like to meet superintelligent aliens and have them tell us how they play chess. There was a moment, watching Leela play, when I realized I just found out.
Leela’s magic, of course, was in deep learning. By teaching herself, she gained deeper chess knowledge than humans could ever hard-code. Years later, I still think Leela is the best example of The Bitter Lesson. Leela won by putting aside human arrogance; she figured things out on her own.
Leela also proved scaling laws before they were cool. In 2018, others on the team and I noticed that larger networks consistently outperformed smaller ones, position-for-position. We even observed remarkable emergent properties—larger networks seemed to ‘look ahead’ several moves without explicit instruction or search.
In 2020, armed with deep learning and scaling laws, the Leela team raced to train larger networks. She sourced compute from corporate donors and friends’ GTX 1070s. We feverishly tracked self-play metrics like many track Wandb loss curves today. Just before the world championship, Leela’s largest model came out of the oven. And then we lost.
Stockfish was the dominant chess-playing program in the 2010s and a relic of old-world AI. In 2019, Stockfish’s code was hand-crafted by humans who distilled their game knowledge into clever math. Leela stunningly overthrew Stockfish in 2019 using deep learning and tabula rasa heuristics. So how did Stockfish regain the crown just as Leela used bigger networks?
Stockfish had better search.