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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

by lebek 12y ago

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 perception and learning to walk involves much more than that.

jbattle 12y ago
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?
- shmageggy 12y ago
  Their algorithm uses a sequence of four slightly spaced out frames as its state. So it has a memory, but a very short one. 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.
wutbrodo 12y ago
> (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?
- tomp 12y ago
  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".
  - wutbrodo 12y ago
    > "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).
- sinwave 12y ago
  cf. Searle's Chinese Room argument!
- sytelus 12y ago
  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 communicate and apply very efficiently. 1. http://en.wikipedia.org/wiki/Correlation_does_not_imply_causation http://en.wikipedia.org/wiki/Correlation_does_not_imply_caus...
  - Houshalter 12y ago
    That's not really relevant. These models can easily infer causation by seeing what happens as a result of their actions.
  - wutbrodo 12y ago
    > 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).
    - Padding 12y ago
      > Let's exclude things like rigorous scientific studies for the purpose of the discussion and focus on day-to-day human reasoning I think you'll need to look at the other end of the spectrum to see an abundance of (wrong?) models of causality: Religion and Law. There are no "confirmed" cases of anyone actually going to heaven or hell or purgatory (or whatever else), and yet many of us still conform to some arbitary ruleset in the hopes of eventually ending (or not ending) up in one of thoses places, because we have constructed some model of how doing this gets you into hell and doing that gets you into heaven. Similarly, we have plenty of evidence on how companies spend huge effort on finding loopholes in tax laws in order to avoid taxes, and yet instead of simplyfing the ruleset (so that there are obviously no holes in it) we still opt for piling on more laws (so that there are no obvious holes in it) because we construct (faulty?) models of how those new rules will prevent further exploits.
- zep15 12y ago
  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.
  - deleted 12y ago
    [deleted]
  - wutbrodo 12y ago
    Right, inference is essential to any attempt to build an intelligence and DeepMind in particular doesn't do inference (AFAIK). I just wanted to clarify what the parent commenter was saying; I work in the field, and in conversations around AI, I very often hear "reasoning" used as an ill-defined, unattainable (for machines) _je ne sais quoi_ that's used to prognosticate about the potential for AI in general. Being specific about what one means by "reasoning" (in this context, inference) is useful for removing those kinds of useless, unmoored-from-logic[1] perspectives from a conversation. [1] To be clear, I'm not dismissing a viewpoint that I think is wrong as "unmoored from logic", I'm specifically talking about the very common situation where people confidently assert this with no attempt (and no ability) to back it up in any way other than confidence that intelligence is simply natural and non-biological entities can never get arbitrarily close.
throwawaymsft 12y ago
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 12y ago
  [deleted]
  - throwawaymsft 12y ago
    I think the insight with DeepMind is that it's built on layers upon layers. There might be a "manager" algorithm that is taking input from your memories, another from your mood, and a "manager of managers", and so on. 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.
    - musername 12y ago
      The impression of a photo is taken from context, a photo isn't a closed system. That implies a larger set of probability functions, sure, some of it from genetic evolution, some from the evolution of thoughts. Reason draws from its intelligence from emotion, ie. emotional intelligence, though. There's convolution of primary senses, finely tuned, highly developed mechanisms and chemical reactions that motivate the strategic management you mentioned. In that sense, what we percieve as intelligent often enough is the abillity to do more with less, ie. compression of information. Therefore, one goal is to keep the probability functions few. What happens with ML is just that, it uses much less layers and neurons for context then the brain, but makes up for it in raw speed. To add to my wild musings from before, the tell in intel might be related to to tell which used to mean to count. Compare that with german erzaehlen and zaehlen. Same for to reckon, cognate with german rechnen (calculate).
- musername 12y ago

by lebek 12y ago

jbattle 12y ago

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?

shmageggy 12y ago
Their algorithm uses a sequence of four slightly spaced out frames as its state. So it has a memory, but a very short one. 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.

wutbrodo 12y ago

> (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?

tomp 12y ago
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".
- wutbrodo 12y ago
  > "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).
sinwave 12y ago
cf. Searle's Chinese Room argument!
sytelus 12y ago
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 communicate and apply very efficiently. 1. http://en.wikipedia.org/wiki/Correlation_does_not_imply_causation http://en.wikipedia.org/wiki/Correlation_does_not_imply_caus...
- Houshalter 12y ago
  That's not really relevant. These models can easily infer causation by seeing what happens as a result of their actions.
- wutbrodo 12y ago
  > 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).
  - Padding 12y ago
    > Let's exclude things like rigorous scientific studies for the purpose of the discussion and focus on day-to-day human reasoning I think you'll need to look at the other end of the spectrum to see an abundance of (wrong?) models of causality: Religion and Law. There are no "confirmed" cases of anyone actually going to heaven or hell or purgatory (or whatever else), and yet many of us still conform to some arbitary ruleset in the hopes of eventually ending (or not ending) up in one of thoses places, because we have constructed some model of how doing this gets you into hell and doing that gets you into heaven. Similarly, we have plenty of evidence on how companies spend huge effort on finding loopholes in tax laws in order to avoid taxes, and yet instead of simplyfing the ruleset (so that there are obviously no holes in it) we still opt for piling on more laws (so that there are no obvious holes in it) because we construct (faulty?) models of how those new rules will prevent further exploits.
zep15 12y ago
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.
- deleted 12y ago
  [deleted]
- wutbrodo 12y ago
  Right, inference is essential to any attempt to build an intelligence and DeepMind in particular doesn't do inference (AFAIK). I just wanted to clarify what the parent commenter was saying; I work in the field, and in conversations around AI, I very often hear "reasoning" used as an ill-defined, unattainable (for machines) _je ne sais quoi_ that's used to prognosticate about the potential for AI in general. Being specific about what one means by "reasoning" (in this context, inference) is useful for removing those kinds of useless, unmoored-from-logic[1] perspectives from a conversation. [1] To be clear, I'm not dismissing a viewpoint that I think is wrong as "unmoored from logic", I'm specifically talking about the very common situation where people confidently assert this with no attempt (and no ability) to back it up in any way other than confidence that intelligence is simply natural and non-biological entities can never get arbitrarily close.

throwawaymsft 12y ago

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 12y ago
[deleted]
- throwawaymsft 12y ago
  I think the insight with DeepMind is that it's built on layers upon layers. There might be a "manager" algorithm that is taking input from your memories, another from your mood, and a "manager of managers", and so on. 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.
  - musername 12y ago
    The impression of a photo is taken from context, a photo isn't a closed system. That implies a larger set of probability functions, sure, some of it from genetic evolution, some from the evolution of thoughts. Reason draws from its intelligence from emotion, ie. emotional intelligence, though. There's convolution of primary senses, finely tuned, highly developed mechanisms and chemical reactions that motivate the strategic management you mentioned. In that sense, what we percieve as intelligent often enough is the abillity to do more with less, ie. compression of information. Therefore, one goal is to keep the probability functions few. What happens with ML is just that, it uses much less layers and neurons for context then the brain, but makes up for it in raw speed. To add to my wild musings from before, the tell in intel might be related to to tell which used to mean to count. Compare that with german erzaehlen and zaehlen. Same for to reckon, cognate with german rechnen (calculate).
musername 12y ago