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> (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

> (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.

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.