Interesting that Lee Sedol losing at Go was the big opening act in the modern AI wave, but it ended up coming from a completely different technology that has effectively faded into the background.
Interesting that Lee Sedol losing at Go was the big opening act in the modern AI wave, but it ended up coming from a completely different technology that has effectively faded into the background.
They used deep neural networks, reinforcement learning, and Monte Carlo tree search. All except the MCTS are critical components of modern LLMs. MCTS is a form of planning which you can argue has parallels to "reasoning" models, although that's pretty tenuous I admit.
> completely different technology that has effectively faded into the background.
If by that you mean reinforcement learning, that's not the case; e.g. see https://arxiv.org/abs/2501.12948
how so?
modern post-training uses RL and immense amounts of synthetic data to iteratively bootstrap better performance. if you squint this is extremely similar to the AlphaZero approach of iteratively training using RL over data generated through self-play