So, to me, based purely on the news coverage, it's not clear that it has learned anything like "superhuman" levels of strategy. We already know that computers have superhuman reaction times and precision calculation abilities, so it seems to me the interesting question is whether an advantage would remain after factoring those out.
On top of that they also reduced the complexity of the game quite significantly by limiting items etc., which further reduced what humans could do against the bot. Even then, the bot utterly failed once humans were allowed to use a tiny bit of creativity.
And that's not even taking into account that this was not even close to the complexity of a real dota match. The big challenge in dota is in the decision making with incomplete information and in coordinating 5 people with only voice and the ability to ping the mini map, in a giant "search space" created by hundreds of different heroes, items and game mechanic interactions.
Several years ago, League of Legends released an upgraded suite of bot characters. One of them, Cassiopeia, had to be turned down enormously from her best play to make her viable. She could beat many of the game's devs (mid-tier hobbyists) and was a non-zero threat to professional players. This was, to my knowledge, achieved with little or no machine learning at all.
The defining traits were similar to what we see here. She had area of effect spells with casting delays, meaning that the ability to precisely evaluate how other players could move was crucial. And she had a spell which refreshed based on the effect of those AOEs, meaning that millisecond precision was a major source of her ability to deal damage. And her ultimate was an exceedingly touchy and unpredictable disable based on the angle opponents were facing (in a game with instantaneous turning). Even top-tier pro players regularly lost its effect because of latency or judgement issues.
The results, by all accounts, were terrifying. She was barely competent strategically, but as long as she could afford items (and admittedly, the LoL bots don't need to farm) she could win all of her tactical fights simply by inhuman precision.
The OpenAI project is more admirable than that. It uses real farm, makes item purchasing decisions, and apparently has a rate-limited API. (That last seems especially important.) But I still wonder how much of the bot capabilities are derived simply from inhuman accuracy.
In terms of AI, I don't /think/ there's anything groundbreaking here. Correct me if I'm wrong, as I don't follow AI research, but this technology is nothing we haven't already seen. I believe the development of AI for Dota is a publicity move to get people excited about what AI could be for humanity. This might be the way to introduce AI to non-technologists and get people excited about it.
Yeah, I'm almost positive that this exhibition is intended mostly to raise awareness and create this hype. Go and Chess, for most people, are simple games compared to Dota2, so if Elon is worried about AI and want people to be more aware of the threat he perceives it certainly helps to make this big show and get all those impressions with a game that is considered by the majority of people (especially younger) to be more complex/harder than what has been done before.
The bot would be more impressive if it used vision or buffer to analyze everything. The bot has all of the information which a human does not have. If the human had the same information I think it would be a better competition.
To me it seems that this bot is good only by the fact that is has information and input advantage.
I also want to clarify that this is not an unflavourful feat and that I think it is cool. But I noticed that a lot of members in the Dota community did not know of the bot API which is likely the case here.
Read the whole thing.
I think there a little bit of strategy already in there since it learned about positioning, and not exposing itself too much (which is of course easier without fog of war). The only thing I was excited to see was the creep blocking, but the chance that this would turn out to be hand-trained was pretty high.
This is partly it. From the interviews with the pros it mostly close to perfect micro, calculating results very well (it knows when it will win the 3 raze spam + 2 autoattacks with 2hp left and thus wins the match etc. for a human that would be hard to calculate) and due to those two it punishes every small mistake the human player makes very hard.
From the actual thinking parts it had to learn creep control (pushing too far just gives free exp/last hits to the other player etc.) and itemization (what counters what, when to get ward etc.)
But still, it's learned "human" levels of strategy, and that actually amazing. And let's not forget, that this is only beginning, more stuff to come. IIRC - some third-party StarCraft AI actually invented a trick that was later used by human players on pro-level tournaments.
This certainly is an advantage for the bot. But it's also clear the bot understands some sophisticated parts of dota. It understands how to control the creep equilibrium via the aggro mechanics. Skill at this is one of the things that separates pros from casual dota players. In the video footage you can see sumail and rtz are surprised by this aspect of the bot. It also understands how to cancel salves as well as bait with them. That's all pretty impressive for a bot trained up from self play.
I don't know why you think the visual/aural problem is trivial. The biggest achievement of AIs so far in this field is classification of static images
Not only do challenges come in the form of input mechanism but potentially many rewards as well in terms of how humans process and off-load information.
While I don't think DotA would be the most important example, I think having a more realistic interface is a good step.
[1] https://en.m.wikipedia.org/wiki/Embodied_cognition artificial intelligence section probably most relevant
hardcoded by a pro dota 2 player hired as a consultant
The key problem is teaching the AI strategy and tactics. What heroes to pick? Where to lane them? When to rotate? What items to buy? What spells to level up? What enemies to target with which spells and in which order? These are the hard problems and they are very hard indeed. A 5v5 AI will have to become expert at risk calculation, Pareto optimization, basic military principles, and many more things. Compared to these problems, the choice of input mechanism is trivial.