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What Google DeepMind Means for A.I.

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Re: What Google DeepMind Means for A.I.

#41

Post by wutbrodo »

tomp wrote: post #25
wutbrodo wrote: post #15

Earlier quoted context omitted.

> (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?

A possible definition would be "use past experience to figure out a new strategy without trying " - i.e. not learning from mistakes, but learning from logic - "maybe it would be good to send the ball above the blocks, so that it would bounce between the blocks and the wall and clear many blocks for free".

> "maybe it would be good to send the ball above the blocks, so that it would bounce between the blocks and the wall and clear many blocks for free"

That's a superficial definition in the sense that it doesn't account for how that line of thought is generated: in humans, this is often a combination of statistical correlation and transfer learning (e.g. I have observed round things hitting perpendicular surfaces and assume that that transfers here).

Re: What Google DeepMind Means for A.I.

#42

Post by wutbrodo »

sytelus wrote: post #33
wutbrodo wrote: post #15

Earlier quoted context omitted.

> (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?

Reasoning involves inferring and applying causation which is different from correlation [1]. One can possibly define process of "understanding" as building a "model" of the system where previously unseen events can be predicated or justified using the model. The big difference in "human understanding" seems to be that we can extract fairly minimal set of laws that govern the system from our observations that we can c…

> Reasoning involves inferring and applying causation which is different from correlation

A couple points here:

* The way humans model causation is just non-naive statistical correlation (controlling for variables). That technique is still accurately described as "statistical correlation"

* I'm not even convinced that human reasoning _does_ imply generating a model of causation. Let's exclude things like rigorous scientific studies for the purpose of the discussion and focus on day-to-day human reasoning: I think the thought processes of most of the people I know could most accurately be explained by correlating things across time. Modeling causation is often incidental (X often happens after Y is a reasonable enough heuristic for general use).

Re: What Google DeepMind Means for A.I.

#43

Post by zep15 »

wutbrodo wrote: post #15
lebek wrote: post #9

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?

Reasoning means things like this: suppose you are holding a ball and want to make it drop. A rule of inference tells you that if you release it, it will drop. You can then reason out that you should release it.

Sure, the rule of inference may have ultimately been derived from experience, by a process which in some sense involved statistical correlation. But you have to distinguish that ultimate basis for the inference rule from _the process of logical inference itself_. It's the latter that is generally called reasoning.

Reasoning in the above sense is essential to intelligence, even at the toddler level, and the DeepMind work doesn't address reasoning. I think that may be the point the parent was getting at.

Re: What Google DeepMind Means for A.I.

#44

Post by Retric »

Animats wrote: post #31

This is impressive. The current approach will work only for games where the whole state is on-screen and planning isn't required. A pure reactive system will work for that. I used to say that a key component of AI that was missing was the ability to get through the next few seconds of life without falling down or bumping into anything. I went through Stanford CS when the top-down logicians were in charge of AI. That…

These low level approaches can do planning, there simply ridiculously inefficient at it.

A lot of AI research is focused on the idea that 1 Trillion floating point operations per second on 1,000,000,000 bytes of data is now cheap. Efficiency is simply less important.

Re: What Google DeepMind Means for A.I.

#45

Post by nsxwolf »

"the A.I. has not only become better than any human player but has also discovered a way to win that its creator never imagined."

That's a pretty standard Breakout/Arkanoid technique - getting the ball behind the board and letting it do the work for you.

Not knocking the AI, just nitpicking this writer.

Re: What Google DeepMind Means for A.I.

#46

Post by [deleted] »

zep15 wrote: post #43
wutbrodo wrote: post #15

Earlier quoted context omitted.

> (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?

Reasoning means things like this: suppose you are holding a ball and want to make it drop. A rule of inference tells you that if you release it, it will drop. You can then reason out that you should release it. Sure, the rule of inference may have ultimately been derived from experience, by a process which in some sense involved statistical correlation. But you have to distinguish that ultimate basis for the inferenc…

[deleted]

Re: What Google DeepMind Means for A.I.

#48

Post by Mobiu5 »

This is actually pretty scary. It is basically giving AI a human-like form of will. It "desires" what you program it to desire and goes about achieving it, learning from its' own mistakes and becoming increasingly proficient at manipulating its' environment to achieve its' goal(s) along the way. It makes me excited, but also quite frightened to think what goals people might give AIs like this in the future...

Re: What Google DeepMind Means for A.I.

#49

Post by solarmist »

Retric wrote: post #44
Animats wrote: post #31

This is impressive. The current approach will work only for games where the whole state is on-screen and planning isn't required. A pure reactive system will work for that. I used to say that a key component of AI that was missing was the ability to get through the next few seconds of life without falling down or bumping into anything. I went through Stanford CS when the top-down logicians were in charge of AI. That…

These low level approaches can do planning, there simply ridiculously inefficient at it. A lot of AI research is focused on the idea that 1 Trillion floating point operations per second on 1,000,000,000 bytes of data is now cheap. Efficiency is simply less important.

When the alternative is achieving nothing then sure 1 Trillion fops IS cheap.

Plus how do you know that's actually inefficient? It seems like a large number to us, but that may be completely reasonable for a biological system to accomplish the same we don't have a good sense of scale for these kinds of problem.

Re: What Google DeepMind Means for A.I.

#50

Post by _abattoir »

chockablock wrote: post #37
iandanforth wrote: post #4

"video games" (read "world simulator"). The important thing about their work is that it is deliberately marching down the path of more and more complex world simulations. We experience the world at one second per second. To learn to walk we must first fall, and we fall at 32 feet/second^2. There's a hard limit on how fast we can make mistakes (like tripping) and so there is a hard limit on how fast we can learn. Comp…

>We experience the world at one second per second There is evidence from animal studies that the hippocampus (a brain structure critical for memory) can 'replay' remembered events at 10-20x speedup. See, for example: http://www.ncbi.nlm.nih.gov/m/pubmed/19709631/ Video at: http://youtu.be/Bv7zN2Or6Mg (Full-disclosure: I am the first author.) And in fact the OP uses biologically-inspired off-line replay as part of the…

Fascinating. Does this relate to the speed of dreams? i.e. a "dream" might seem to have taken hours when in fact the REM sequence was on the order of seconds?
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