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I believe we are witnessing the Cambrian explosion of intelligences. The techniques behind Libratus (abstraction algorithm and game theory [1]) appear to be qua

I believe we are witnessing the Cambrian explosion of intelligences. The techniques behind Libratus (abstraction algorithm and game theory [1]) appear to be qualitatively distinct from those behind AlphaGo (deep reinforcement learning and MCMC) and DeepBlue (search and heuristics). An ecology of Artificial Intelligences, unbounded by our evolutionary history and neural architecture, could evolve to suit each particular task more effectively than our brains can. Promises and perils abound. [1] http://www.cs.cmu.edu/~sandholm/ http://www.cs.cmu.edu/~sandholm/ section "Algorithms and complexity of solving games"

chii 10y ago
> Promises and perils abound. what's the peril? I can only see promise ahead, if AI's progress continue unimpeded.
- FeepingCreature 10y ago
  Can I recommend Bostrom's Superintelligence: Paths, Dangers, Strategies?
- Certhas 10y ago
  Collapse of the current economic and political system as increased consumption can no longer compete with increased efficiencies. For once. But back when we were thinking about the perils of the internet we didn't correctly anticipate homogenization of media consumption and filter bubbles. People rather thought that it would be the other way around, everyone would have access to high quality information and enjoy diverse (long tail) media content. So we're likely to be wrong about the precise perils here, too.
echelon 10y ago
Chess, Go, Poker... All feel like variations on the same theme. While it's obvious there is innovation being done, I want to see something more challenging. Something with more dimensionality and integration of several types of input. How about a machine that can beat someone at Smash Bros, a game with varied characters, complex comboing mechanics, and a nontrivial computer vision task? Or--more difficult by a few orders of magnitude--what about a robot that can beat someone at tennis? Or a team of robots that can best a professional basketball team? When do you suppose we'll begin to see these sorts of things? Within our lifetime, I hope?
- TulliusCicero 10y ago
  Smash bros shouldn't be too hard. Reflexes alone would give the CPU an enormous advantage.
  - merlincorey 10y ago
    Indeed, from 2015: http://kotaku.com/overpowered-bot-puts-top-smash-bros-player-in-his-plac-1686670459 http://kotaku.com/overpowered-bot-puts-top-smash-bros-player...
    - pfisch 10y ago
      This is not really a good example. That bot is massively cheating and if you played against him a few times you would find very simple patterns that always beat him. (I don't even think the base AI can actually see your location in the air very well, and it has no air DI itself so it really could not win for long.)
      - levthedev 10y ago
        This one is a better example - https://github.com/vladfi1/phillip https://github.com/vladfi1/phillip. It does not have such perfect reactions and can be trained on a tensorflow model that you can build in a few hours/days by playing against it. Although it plays weird, it plays a lot more like a human than the other bot. And yes, it is very very good - it can pretty easily beat top 50 players.
- baq 10y ago
  google brain^W deep mind is working on starcraft. on a related note, hopefully a benevolent gai emerges before human society implodes.
  - cybertronic 10y ago
    Google Deep Mind is working on Starcraft, not Google Brain (a different project)
cgearhart 10y ago
I tend to disagree that this represents an explosion of approaches. For example, AlphaGo and DeepBlue are more similar than they may at first seem. AlphaGo used MCTS (a search algorithm), and value and policy networks as heuristics to evaluate board positions and search order. AlphaGo scored a couple of big wins by showing that you don't need exhaustive search (in fact, AlphaGo evaluates orders of magnitude fewer nodes in the game tree than previous state-of-the art MCTS Go agents, and Go is notorious for such a high branching factor that minimax, et. al., are straight out), and that the heuristic can be learned through reinforcement learning.
nopinsight 10y ago
Correction: One of AlphaGo's techniques is MCTS (Monte Carlo Tree Search), not MCMC (Markov Chain Monte Carlo).

chii 10y ago

> Promises and perils abound. what's the peril? I can only see promise ahead, if AI's progress continue unimpeded.

FeepingCreature 10y ago
Can I recommend Bostrom's Superintelligence: Paths, Dangers, Strategies?
Certhas 10y ago
Collapse of the current economic and political system as increased consumption can no longer compete with increased efficiencies. For once. But back when we were thinking about the perils of the internet we didn't correctly anticipate homogenization of media consumption and filter bubbles. People rather thought that it would be the other way around, everyone would have access to high quality information and enjoy diverse (long tail) media content. So we're likely to be wrong about the precise perils here, too.

echelon 10y ago

Chess, Go, Poker... All feel like variations on the same theme. While it's obvious there is innovation being done, I want to see something more challenging. Something with more dimensionality and integration of several types of input. How about a machine that can beat someone at Smash Bros, a game with varied characters, complex comboing mechanics, and a nontrivial computer vision task? Or--more difficult by a few orders of magnitude--what about a robot that can beat someone at tennis? Or a team of robots that can best a professional basketball team? When do you suppose we'll begin to see these sorts of things? Within our lifetime, I hope?

TulliusCicero 10y ago
Smash bros shouldn't be too hard. Reflexes alone would give the CPU an enormous advantage.
- merlincorey 10y ago
  Indeed, from 2015: http://kotaku.com/overpowered-bot-puts-top-smash-bros-player-in-his-plac-1686670459 http://kotaku.com/overpowered-bot-puts-top-smash-bros-player...
  - pfisch 10y ago
    This is not really a good example. That bot is massively cheating and if you played against him a few times you would find very simple patterns that always beat him. (I don't even think the base AI can actually see your location in the air very well, and it has no air DI itself so it really could not win for long.)
    - levthedev 10y ago
      This one is a better example - https://github.com/vladfi1/phillip https://github.com/vladfi1/phillip. It does not have such perfect reactions and can be trained on a tensorflow model that you can build in a few hours/days by playing against it. Although it plays weird, it plays a lot more like a human than the other bot. And yes, it is very very good - it can pretty easily beat top 50 players.
baq 10y ago
google brain^W deep mind is working on starcraft. on a related note, hopefully a benevolent gai emerges before human society implodes.
- cybertronic 10y ago
  Google Deep Mind is working on Starcraft, not Google Brain (a different project)

cgearhart 10y ago

I tend to disagree that this represents an explosion of approaches. For example, AlphaGo and DeepBlue are more similar than they may at first seem. AlphaGo used MCTS (a search algorithm), and value and policy networks as heuristics to evaluate board positions and search order. AlphaGo scored a couple of big wins by showing that you don't need exhaustive search (in fact, AlphaGo evaluates orders of magnitude fewer nodes in the game tree than previous state-of-the art MCTS Go agents, and Go is notorious for such a high branching factor that minimax, et. al., are straight out), and that the heuristic can be learned through reinforcement learning.

nopinsight 10y ago

Correction: One of AlphaGo's techniques is MCTS (Monte Carlo Tree Search), not MCMC (Markov Chain Monte Carlo).