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I think this still holds some water. The go bot was excellent at beating really good go players because it had a ton of data on high level go games. When it enc
by __loam 3y ago
I think this still holds some water. The go bot was excellent at beating really good go players because it had a ton of data on high level go games. When it encountered someone intentionally doing a moronic strategy, it was trounced. It just didn't have enough data on bad players, so it lost to an obviously flawed strategy.
I think that's the huge flaw in all of these ml systems. They don't build fundamental understanding. We're brute forcing it in a way, but perhaps we're losing something in the long tail.
E: https://arstechnica.com/information-technology/2022/11/new-go-playing-trick-defeats-world-class-go-ai-but-loses-to-human-amateurs/ https://arstechnica.com/information-technology/2022/11/new-g...
- joshfee 3y agoThis was a flaw in the original AlphaGo, but the subsequent AlphaZero (https://en.wikipedia.org/wiki/AlphaZero https://en.wikipedia.org/wiki/AlphaZero) trained entirely from self play with no prior information. So essentially it _does_ build fundamental understanding. I think the ability to learn by self play (essentially in a closed room without external training data) is where the line between "fundamental understanding" and "regurgitating information" from these AIs lie.
- famouswaffles 3y agoThere isn't really any difference between self play and no self play in terms of "fundamental understanding" and "regurgitation". It's the same training scheme just with different data.
- white_beach 3y agomore recently https://www.science.org/doi/10.1126/sciadv.adg3256 https://www.science.org/doi/10.1126/sciadv.adg3256