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> “They can find their way across a room,” Mason said. “They can see stuff, and as the light and shadows change they can recognize that it’s still the same stuf

> “They can find their way across a room,” Mason said. “They can see stuff, and as the light and shadows change they can recognize that it’s still the same stuff. They can understand and manipulate objects in space.” Isn't this just adding extra dimensions to the input space? We have 2D now (plus time?), we're missing Z, sound, sensation, maybe emotions. Each added dimension gives the algorithm exponentially more bits to crunch but if computer speed is doubling every 2 years or so, why is this so obviously a dead-end to Mason?

simonbyrne 12y ago
I think the distinction is that the algorithm gets to observe the 2D space directly, whereas the whole notion of 3D space has to be learned from 2D projections. Also, note that the games they do best on have a nice clear objective function: Montezuma's Revenge on the other hand does not have such a numeric objective to optimise. To be fair, Hassabis does freely concede these limitations in his talks (at least in the ones targeted to academic audiences), and it will be certainly interesting to see where they go from here.
- woodchuck64 12y ago
  > the whole notion of 3D space has to be learned from 2D projections. That might be extremely difficult, but nature does it with binocular vision so I would think dual vision inputs are the best way to do 3D space learning.
- deleted 12y ago
  [deleted]
- nemo44x 12y ago
  That's a good point about Montezuma's Revenge, and similar games. Very rarely does a greedy algorithm help you in that game unlike in a breakout style game where you simply want to acquire as many points as quickly as possible.
clickok 12y ago
The cynic in me would guess it's because he's not a big fan of neural nets, and prefers a more symbolic/statistical machine learning approach. Personally, I don't think this is a dead end. Reinforcement learning with an effective method of representation learning is pretty much all you need for a general AI. Here, deep neural nets are able to learn how to represent the massive state space for vision quite well, but there's a bit of a mismatch in games where actions have to be taken in a specific order (e.g., where extended pathfinding is required) because for reinforcement learning to work well, your features need to capture that sort of temporally extended state information. That's still a hard problem, but not insurmountable, and there's already strides in that direction. From the RL side, there's things like option models for taking series of actions, and from the deep learning side there's things like long short term memory and DeepMind's own neural Turing machines. Knowing the group at DeepMind, they'll be able to crack it, and I think Mason is being entirely too pessimistic about the timeline in any event. Fifty years to control a drone?

simonbyrne 12y ago

I think the distinction is that the algorithm gets to observe the 2D space directly, whereas the whole notion of 3D space has to be learned from 2D projections. Also, note that the games they do best on have a nice clear objective function: Montezuma's Revenge on the other hand does not have such a numeric objective to optimise. To be fair, Hassabis does freely concede these limitations in his talks (at least in the ones targeted to academic audiences), and it will be certainly interesting to see where they go from here.

woodchuck64 12y ago
> the whole notion of 3D space has to be learned from 2D projections. That might be extremely difficult, but nature does it with binocular vision so I would think dual vision inputs are the best way to do 3D space learning.
deleted 12y ago
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nemo44x 12y ago
That's a good point about Montezuma's Revenge, and similar games. Very rarely does a greedy algorithm help you in that game unlike in a breakout style game where you simply want to acquire as many points as quickly as possible.

clickok 12y ago

The cynic in me would guess it's because he's not a big fan of neural nets, and prefers a more symbolic/statistical machine learning approach. Personally, I don't think this is a dead end. Reinforcement learning with an effective method of representation learning is pretty much all you need for a general AI. Here, deep neural nets are able to learn how to represent the massive state space for vision quite well, but there's a bit of a mismatch in games where actions have to be taken in a specific order (e.g., where extended pathfinding is required) because for reinforcement learning to work well, your features need to capture that sort of temporally extended state information. That's still a hard problem, but not insurmountable, and there's already strides in that direction. From the RL side, there's things like option models for taking series of actions, and from the deep learning side there's things like long short term memory and DeepMind's own neural Turing machines. Knowing the group at DeepMind, they'll be able to crack it, and I think Mason is being entirely too pessimistic about the timeline in any event. Fifty years to control a drone?