5 ms·

Precisely so. You can, given enough time, brute force your way to victory in any game of perfect information. This is not the case with reality, as far as we un

Precisely so. You can, given enough time, brute force your way to victory in any game of perfect information. This is not the case with reality, as far as we understand it so far. Thus, from a theoretical perspective, the class of problems that contains all games is strictly easier than the class of problems that exist in a less artificially constrained environment.

Agebor 9y ago
Perfect information is not the case when the AI is reading pixel colors off the screen and pressing keys on the keyboard. This is very like the real-world situation. As for goals, they are indeed better defined in games. But we can create artificial goals attached to the real life: - goal: GPS sensor reports specific position - goal: the camera reports a recognised object in a specific position - goal: a button was pressed (signal reaching the AI e.g. through the cloud)
- dmreedy 9y ago
  Even then, it still reduces to a brute-forceable game with finite, explorable states; 'just' with an extra layer of (granted, quite interesting and technically impressive) parsing. We don't know what the case is for reality. And yes, we can do our best to turn real life in to a game, but all such models leak pretty badly, and the leaks tend to cause much more fundamental instability.
  - Agebor 9y ago
    I think the main problem is with simulating enough games/tries. In the game, you can easily let the AI play against itself countless times. Much, much slower in real life. Until we have good enough simulators/approximators of reality, AI can't learn fast. However, they can already learn driving in GTA, so who knows?
    - _emacsomancer_ 9y ago
      > However, they can already learn driving in GTA, so who knows? Yeah, that sounds like it'll end well.
- deong 9y ago
  You'll find pretty quickly that the exact techniques that worked so well in learning to play Atari games very often fail spectacularly when you have to introduce goal-steering. Reinforcement learning turns out to be fantastically clever at finding really stupid solutions if you give it the tiniest opening to do so. You put a camera in a room to provide feedback for an agent to learn to move an object closer to the camera, and it will happily learn to knock the camera over.
  - AnIdiotOnTheNet 9y ago
    To be fair to our AI brethren, this is true of Humans too. I watch people game metrics every single day, and many of them fail spectacularly when presented with real world problems outside of their experience.
    - jacobush 9y ago
      Gaming metrics is the very soul of corporate life
      - _emacsomancer_ 9y ago
        Then which is real and which illusion?
        jacobush 9y ago
        Good question. Sometimes, the metrics must be gamed to make real progress. (As opposed to the more usual gaming them for personal gain.) Between these two, some self serving bias and delusion, mission statements etc.. what is real?!
  - taneq 9y ago
    If they meet the spec, they're not stupid solutions, it's a stupid spec.
    - candiodari 9y ago
- taeric 9y ago
  I think you are misusing the term "perfect information." In this case, chess is a real world game where both players have perfect information. That is, neither player can keep a secret of where any of the pieces have moved. So, the complexities of how the AI is taught to interact ultimately don't matter. It may have a lot of effort to parse the visual of the board to get the perfect information, but the game is defined as one of perfect information. Wikipedia calls out that there does not seem to be consensus on what this term means for games with chance or concurrent play. https://en.wikipedia.org/wiki/Perfect_information https://en.wikipedia.org/wiki/Perfect_information
  - Agebor 9y ago
    True. I think my point was more about games with non-trivial rules (or many degrees of freedom). For example going from chess/go to turn-based video games like civ, to starcraft. Usually it involves vastly more possibly positions in time and space.

Agebor 9y ago

Perfect information is not the case when the AI is reading pixel colors off the screen and pressing keys on the keyboard. This is very like the real-world situation. As for goals, they are indeed better defined in games. But we can create artificial goals attached to the real life: - goal: GPS sensor reports specific position - goal: the camera reports a recognised object in a specific position - goal: a button was pressed (signal reaching the AI e.g. through the cloud)

dmreedy 9y ago
Even then, it still reduces to a brute-forceable game with finite, explorable states; 'just' with an extra layer of (granted, quite interesting and technically impressive) parsing. We don't know what the case is for reality. And yes, we can do our best to turn real life in to a game, but all such models leak pretty badly, and the leaks tend to cause much more fundamental instability.
- Agebor 9y ago
  I think the main problem is with simulating enough games/tries. In the game, you can easily let the AI play against itself countless times. Much, much slower in real life. Until we have good enough simulators/approximators of reality, AI can't learn fast. However, they can already learn driving in GTA, so who knows?
  - _emacsomancer_ 9y ago
    > However, they can already learn driving in GTA, so who knows? Yeah, that sounds like it'll end well.
deong 9y ago
You'll find pretty quickly that the exact techniques that worked so well in learning to play Atari games very often fail spectacularly when you have to introduce goal-steering. Reinforcement learning turns out to be fantastically clever at finding really stupid solutions if you give it the tiniest opening to do so. You put a camera in a room to provide feedback for an agent to learn to move an object closer to the camera, and it will happily learn to knock the camera over.
- AnIdiotOnTheNet 9y ago
  To be fair to our AI brethren, this is true of Humans too. I watch people game metrics every single day, and many of them fail spectacularly when presented with real world problems outside of their experience.
  - jacobush 9y ago
    Gaming metrics is the very soul of corporate life
    - _emacsomancer_ 9y ago
      Then which is real and which illusion?
      - jacobush 9y ago
        Good question. Sometimes, the metrics must be gamed to make real progress. (As opposed to the more usual gaming them for personal gain.) Between these two, some self serving bias and delusion, mission statements etc.. what is real?!
- taneq 9y ago
  If they meet the spec, they're not stupid solutions, it's a stupid spec.
  - candiodari 9y ago
taeric 9y ago
I think you are misusing the term "perfect information." In this case, chess is a real world game where both players have perfect information. That is, neither player can keep a secret of where any of the pieces have moved. So, the complexities of how the AI is taught to interact ultimately don't matter. It may have a lot of effort to parse the visual of the board to get the perfect information, but the game is defined as one of perfect information. Wikipedia calls out that there does not seem to be consensus on what this term means for games with chance or concurrent play. https://en.wikipedia.org/wiki/Perfect_information https://en.wikipedia.org/wiki/Perfect_information
- Agebor 9y ago
  True. I think my point was more about games with non-trivial rules (or many degrees of freedom). For example going from chess/go to turn-based video games like civ, to starcraft. Usually it involves vastly more possibly positions in time and space.