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>And the response as I've seen it on other threads: It probably doesn't make a big difference, and will outperform humans there too, and it would be a huge wast

>And the response as I've seen it on other threads: It probably doesn't make a big difference, and will outperform humans there too, and it would be a huge waste of computing power to train it that way. Well they may be right about the "outperform" part but they are dead wrong about the waste of time/effort/energy part. I mean if (at least human-like) real-time video/audio recognition and decision making is not an impressive AI feat, I don't know what is. I'm no expert in the field but claiming that plugging into an API and crunching numbers is more important than sensory-based decision making, just doesn't sound right

radarsat1 9y ago
It's their long-term goal: https://blog.openai.com/universe/ https://blog.openai.com/universe/
hobofan 9y ago
> sensory-based decision making You can pretty cleanly split that up into two different problems: "sensory-based data extraction" and "data-based decision making" Though I haven't worked in the field of self-driving cars, I am fairly confident that they employ a similar split: One part that takes in all the (pre-processed) data from LIDAR, cameras, etc. and maps that to a simplified model of the surroundings, and another part that makes the driving decisions based on the simplified model. Sensory->data mapping doesn't raise a lot of eyebrows anymore if you can generate as much sensory information as you want to explore all possible states, as it is possible with Dota.
- lvoudour 9y ago
  To me though as an outside observer it seems that the dota problem is harder than the car problem. There's too much action going on, too many visual/aural cues to keep track of, team cooperation/coordination, fog of war, etc. Car AI is important because there are real life-or-death consequences, but the problem (again to my limited experience) seems more tractable: path choices are limited, action is rare, there's no team element and there's no competition. Even for human drivers driving a car in a city or motorway is a tedious, mostly repetitive task. Now, competitive driving raises the stakes and we haven't seen any self-driving car tackling that problem yet (which will definitely raise a lot of eyebrows)
  - hobofan 9y ago
    > there's no team element and there's no competition I don't think that this is true, considering all the other humans on/near the road, that can influence the system by making actions of their own. > There's too much action going on, too many visual/aural cues to keep track of, team cooperation/coordination, fog of war, etc. I dont see how this is different from a car at all. - too many visual/aural cues to keep track of -> everything you can see, car horns, etc. - team cooperation/coordination -> as I said everyone else on/near the road - fog of war -> blind spots & people hidden behind objects From some experience in the AI world, I can say that I've seen systems that are good enough that they should be able to solve most of those subproblems in the limited system that is Dota. Yes, fog of war might also be interesting given that you have an agressive opponent. The real novel things lie at the strategy level, like picking a hero, buying items, etc., because, like AlphaGo, they demonstrate reasoning in systems with a large action space and delayed payoff.

radarsat1 9y ago

It's their long-term goal: https://blog.openai.com/universe/ https://blog.openai.com/universe/

hobofan 9y ago

> sensory-based decision making You can pretty cleanly split that up into two different problems: "sensory-based data extraction" and "data-based decision making" Though I haven't worked in the field of self-driving cars, I am fairly confident that they employ a similar split: One part that takes in all the (pre-processed) data from LIDAR, cameras, etc. and maps that to a simplified model of the surroundings, and another part that makes the driving decisions based on the simplified model. Sensory->data mapping doesn't raise a lot of eyebrows anymore if you can generate as much sensory information as you want to explore all possible states, as it is possible with Dota.

lvoudour 9y ago
To me though as an outside observer it seems that the dota problem is harder than the car problem. There's too much action going on, too many visual/aural cues to keep track of, team cooperation/coordination, fog of war, etc. Car AI is important because there are real life-or-death consequences, but the problem (again to my limited experience) seems more tractable: path choices are limited, action is rare, there's no team element and there's no competition. Even for human drivers driving a car in a city or motorway is a tedious, mostly repetitive task. Now, competitive driving raises the stakes and we haven't seen any self-driving car tackling that problem yet (which will definitely raise a lot of eyebrows)
- hobofan 9y ago
  > there's no team element and there's no competition I don't think that this is true, considering all the other humans on/near the road, that can influence the system by making actions of their own. > There's too much action going on, too many visual/aural cues to keep track of, team cooperation/coordination, fog of war, etc. I dont see how this is different from a car at all. - too many visual/aural cues to keep track of -> everything you can see, car horns, etc. - team cooperation/coordination -> as I said everyone else on/near the road - fog of war -> blind spots & people hidden behind objects From some experience in the AI world, I can say that I've seen systems that are good enough that they should be able to solve most of those subproblems in the limited system that is Dota. Yes, fog of war might also be interesting given that you have an agressive opponent. The real novel things lie at the strategy level, like picking a hero, buying items, etc., because, like AlphaGo, they demonstrate reasoning in systems with a large action space and delayed payoff.