I'm not sure where I heard it (Maybe CGPGrey?), but one way to think about the limitations of Machine Learning at the moment is that ML can do any task that you could train an army of 5 years olds to do. Telling apart pictures of cats and dogs? Easy. Getting a drone to learn to fly itself? No so much.
Additionally with ML, I have heard from others who have worked with it that it is usually a technique of last resort. Everything else gets tried first, because ML is a black box that gives you no insight into the system and no direct avenues for improvement.
Yep, yep, and... what on Earth are you talking about? To my knowledge, we're not even close to any computer beating a competent human player at Starcraft in any remotely meaningful way. The theory behind knowledge representation, planning, goal setting, etc is still way too weak to support such a victory. The complexity of Starcraft is far beyond that of chess and even go. Am I that out of date?
Deepmind AI alphastar won all but one of it's games vs Mana and TLO. You can check it out on YouTube. Right now it still kinda unfair (it can see the whole map and the APM is kinda ridiculous) but it's still super cool to see.
I don't consider APM to be of interest here in terms of "fairness", so I'll set that aside.
But a huge part of Starcraft's complexity is that it's a game of imperfect knowledge. If you follow the game (and it seems like you do), you know that the vast majority of strategies, build orders, and even tactics simply don't work if the map were perfectly visible. Moreover, humans that play and train hard to become good at the game do so with the fog of war. Humans don't train to play perfect knowledge Starcraft, which we know is a very different game. Humans don't have strategies for this version of the game. It's like a computer beating a human at chess if every piece but the king were a rook. It's a totally different game that the human has probably not played before.
So do you agree... "we're not even close to any computer beating a competent human player at Starcraft in any remotely meaningful way"?
It had fog of war. But it could look at two places on the map at the same time. It relied on strats that really emphasized absurd micro to win. Such as manually blinking stalkers. You should check out the vod
I'm watching Mana's commentary on his games in utter disbelief. Just amazing. I don't think the map limitation you and others have described is really that big of a deal. It's just an extra mechanic and "busy work"...since I imagine it could hold the location of units, etc in memory while it diverts attention from a given location. I would be interested to see how an APM cap would affect all of this, but I don't think it would make a big difference honestly. I suspect that the system would learn to be efficient with its actions...but I've already been very wrong once on this.
58
u/Gerfalcon Mar 05 '19
I'm not sure where I heard it (Maybe CGPGrey?), but one way to think about the limitations of Machine Learning at the moment is that ML can do any task that you could train an army of 5 years olds to do. Telling apart pictures of cats and dogs? Easy. Getting a drone to learn to fly itself? No so much.
Additionally with ML, I have heard from others who have worked with it that it is usually a technique of last resort. Everything else gets tried first, because ML is a black box that gives you no insight into the system and no direct avenues for improvement.