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Comment by asdfasgasdgasdg | Hacker News Reader

The game is difficult to watch, but does anyone honestly believe that an AI is going to have a difficult time parsing the scene if it is trained to do so? That to me just seems like a question of resources. We're pretty good at image recognition and segmentation now, and that's without the unlimited amounts of training data one could generate when using a controlled game environment with a limited range of possible animations and effects. This is why I find the prospect of the AI agent having to parse the screen entirely uninteresting.

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For real life applications, parsing the ”scene” would have impact as it could only convey imperfect information retained. In the game of starcraft the information is perfect when fog of war have been removed this together with unlimited attention (camera viewport) helps action potential and macro planning. No player is ever going to be able to consider precise strategy on the whole map perfectly in their mind. If deepmind wanted to mimic human limitations perfectly they would have to provide imperfect information for AlphaStar, e.g when providing information of locations of objects sample a random variable from a probability distribution which represent the location imperfectly and making that distribution bigger the longer the attention of the A.I wanders from the object both spatialy and temporal. Of course the usefulness of having these limitations is purely to model maximum theoretical human mental capacity and it’s use case could be to help explore strategies that work for actual humans.

There is another potential use: given these limitations, an AI might be able to learn to be better strategically, which could translate to an even greater advantage once the limitations were removed later on.

windowing the focus perhaps, yes, but I'd assume it's the opposite and the focus is applied more freely.

You talk about a static image, but navigating the camera requires strategy, attention, and adds to the focus. If you take that away, it's just a turbo charged pen-and-paper RPG with a time limit on rounds.

They could train against the API, reinforcing the AI trying to predict the state from vision. But with limited APM it would be pretty difficult for the AI to keep track of everything. And, potentially, it would still not be the same as a human looking at it. I'm not sure whether human attention is a particularly bad example of efficient resource allocation. I'm very biased to think it is still the gold standard. But the fact that deepmind didn't focus on this implies they were not finding it interesting enough, and/or too difficult.

Anyhow, (visual) exploration is a step up from mere image recognition

But on the other hand, an AI that beats humans using brute force in a game where it makes a ton of difference isn't much fair too.

The funny thing is that once we're talking about the real world, which will come, that incentive actually reverses.

At that point the name of the game will be maximizing the advantage the body/infrastructure provides the AI, not minimizing it.

Weird.

It's the same principle as a baseball player putting extra weights on the bat in practice.

Yes, since DeepMind chose SC2 for having the right characteristics for mapping to the real world, ie imperfect information and real time response, they should have had at least one run without any speed governors. And maybe another with the CPU limited to some level we might find in an embedded system of near future.

> using brute force

"Brute force" in AI context is usually reserved for traversal of the entire search space. I think "superhuman micromanagement" is a better term. And before AlphaStar superhuman micro wasn't insurmountable obstacle for human players.

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