Atlas record: https://www.aitimeline.dev/en/events/evt-0053/
Milestone · March 9 – 15, 2016
DeepMind’s AlphaGo won a Go match against Lee Sedol by four games to one.
Go was considered a problem where search alone fails. Combining learning with search showed that position evaluation can be learned from data and self-play.
The source cited is the Nature paper published before the match: it describes the system rather than the match itself.
Nature · Published January 27, 2016
The Korea Times · Published March 15, 2016
British Go Association
The same laboratory and the same line led to AlphaGo three years later.
Both concern public matches in which a system played against the strongest human players.
The closing post itself refers back to the 2016 match with Lee Sedol, whose moves, DeepMind says, brought new knowledge to the Go world.
The last human component is removed: no human games in training.
The preprint compares itself with AlphaGo: a batch 50 to 150 times larger, a model 20 times larger and training 25 times longer.
The paper sets Pluribus's two processors in play against AlphaGo's 1,920 CPUs and 280 GPUs in its 2016 matches.