I agree that it's a bit of a over hype tactic in a toy situation which might be marking to maybe make people think the technique and bot are more capable than i

I agree that it's a bit of a over hype tactic in a toy situation which might be marking to maybe make people think the technique and bot are more capable than it is. But I think people are overly down on it too. It's a demonstration of a technique in a way the general public (and gamers) will understand. OpenAI isn't going to make money off of building game bots... people wouldn't watch. The human drama is a major ingredient in e-sports. But we shouldn't be down on this while we were going gaga over a lego sorter done the same way a month or so back. It looks like for some things we can almost have a plug and play ai solution. EG, like we are seeing with image classifiers, this doesn't take years of phd doctoral research and game theory to build up a world class bot. Which is what everyone used to do. This is moving some of these techniques into the "get data set, get hardware, download library, train" plug and play type solution which we're seeing more and more with in other areas like machine classification. Eg stuff anyone with a few years of experience can do, maybe not amazingly, but better than they could hand coding the solution. The problem becomes one of gathering good training sets or building an accurate simulation to train in. This means, I think, that you'll see way more of these types of ai solutions where people would have balked at a hand coded solution before. This in turn looks a lot like mobile's change to computing where things that were annoying to do on your home pc became different just because you had a camera + gps + computer + radio in your pocket. I know my company has started using classifiers a lot more for things that are kinda sliding bad user actions instead of coding up huge rules engines. We may not be as effective as a several area deep engineers writing rules and doing data analysis, but instead we have 1 engineer per problem space being about 70% as effective which is still a huge win over not solving the problems at all. The funny thing is that this bot actually pulled off the stereotypical hollywood training montage with just a few weeks of hard work it beat the best in the world. Just get some sweet rock in there and you've got it all.