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"In the longer term, after DeepMind has worked its way through Warcraft, StarCraft, and the rest of the Blizzard Entertainment catalogue, the team’s goal is to build an A.I. system with the capability of a toddler. " Wait ... what? You're going to teach this thing using violent video games? This seems like a bad plan...
Earlier quoted context omitted.
There are two sides to this, world simulation and AI. As the other replies already said, current AI isn't close to toddler-level (there's no reasoning going on in the DeepMind work, just statistical correlation). We're also way off on the world simulation side - show me a realistic world simulator that can run close to realtime. Physically-based rendering is indeed impressive but this only accounts for visual percept…
Isn't human reasoning mostly a bunch of statistical correlation? We see a ball drop, think "things fall when dropped", and that's our model, reinforced by thousands of everyday experiences. It's purely based on outcomes. We don't naturally reason through potential causes like "Mass exerts a gravitational force which attracts other mass."
> “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 bit…
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…
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.
> “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 bit…
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…
Earlier quoted context omitted.
There are two sides to this, world simulation and AI. As the other replies already said, current AI isn't close to toddler-level (there's no reasoning going on in the DeepMind work, just statistical correlation). We're also way off on the world simulation side - show me a realistic world simulator that can run close to realtime. Physically-based rendering is indeed impressive but this only accounts for visual percept…
> (there's no reasoning going on in the DeepMind work, just statistical correlation) I've seen 100 people make this statement and mean 100 different things, so I just wanted to clarify: How are you defining "reasoning" here as distinct from statistical correlation?
Earlier quoted context omitted.
There are two sides to this, world simulation and AI. As the other replies already said, current AI isn't close to toddler-level (there's no reasoning going on in the DeepMind work, just statistical correlation). We're also way off on the world simulation side - show me a realistic world simulator that can run close to realtime. Physically-based rendering is indeed impressive but this only accounts for visual percept…
Do you know how much 'state' DeepMind is tracking? Is it just choosing the best action given perceptions at the moment, or does it have some level of memory to work from? I wonder how DeepMind would do at path-finding in a maze for example - would it get stuck in an oscillating state?
It could probably solve a maze quite easily if the entire maze fit on screen. That problem requires no memory. If it had to make decisions based on information not present on screen, it would fail.
Earlier quoted context omitted.
Isn't human reasoning mostly a bunch of statistical correlation? We see a ball drop, think "things fall when dropped", and that's our model, reinforced by thousands of everyday experiences. It's purely based on outcomes. We don't naturally reason through potential causes like "Mass exerts a gravitational force which attracts other mass."
[deleted]
A photo evokes a deeper meaning than is contained within the individual pixels. Similarly, I think the right algorithm may be able to extract more intelligent behavior from a larger set of myopic probability functions.
I'm looking forward to the upcoming entry in next week's New Yorker "Corrections" section.
Earlier quoted context omitted.
There are two sides to this, world simulation and AI. As the other replies already said, current AI isn't close to toddler-level (there's no reasoning going on in the DeepMind work, just statistical correlation). We're also way off on the world simulation side - show me a realistic world simulator that can run close to realtime. Physically-based rendering is indeed impressive but this only accounts for visual percept…
> (there's no reasoning going on in the DeepMind work, just statistical correlation) I've seen 100 people make this statement and mean 100 different things, so I just wanted to clarify: How are you defining "reasoning" here as distinct from statistical correlation?