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I think that on typical desktop hardware, there's little doubt that its cycles are much better spent on alpha beta search (with all its modern enhancements like
by tromp 9y ago
I think that on typical desktop hardware, there's little doubt that its cycles are much better spent on alpha beta search (with all its modern enhancements like late move reduction) and hand built evaluation function than on evaluating neural nets.
If, however, your hardware were to include a few tensor processing units, then the answer is less clear (but I'd still bet on alpha beta).
- Recursing 9y agoI think it's really hard to parallelize alpha beta search, there might be a point where other approaches become more useful
- ZirconiumX 9y agoParallelising A-B has been done, but even the state of the art approaches like Lazy SMP (where there is no synchronisation between threads, and only at the root is effort made to disperse threads), YBWC (where only the first node is synchronised, and if it doesn't produce a cut is then parallelised) and DTS (where the threads choose their own work points) produce sub-linear scaling. I think research needs to be done on algorithms that are designed from the start to be parallel, but I'm not smart enough to do that.
- bluetwo 9y agoAlphaGo Zero is really about the MCTS not neural nets.
- Jach 9y agoMore specifically it's about driving MCTS with neural nets rather than pure random sampling as prior strong (but quite far from pro level) go bots were. Of course before AlphaGo was released there were papers on a pure deep net version of a go bot that beat Pachi and Fuego (both MCTS), not sure if it beat CrazyStone of the time.