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Predictive Learning [pdf]

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Re: Predictive Learning [pdf]

#21

Post by donmatito »

quonn wrote: post #16
sytelus wrote: post #12

Earlier quoted context omitted.

this is an excellent point. This model has to be recursive and may be depth of recursion has something to do with self-perception?

I don't understand this argument. (It keeps coming up.) The two issues I have with this are: a) Self-perception does not seem like consciousness to me at all. In meditation, if done properly, there is very little self left. It feels more like pure awareness. It is almost the opposite of the model of the self that the brain constructs. b) I fail to see how the fact that a mechanism refers to itself should somehow give…

Answers to "can we give consciousness to AI" is heavily dependent on how you define consciousness in the first place.

Many definitions can coexist, some more actionable than others. "Being aware of the existence of oneself in the world, and being able to reflect on oneself's decision" seems relatively practical. So, Self-perception + self-reflection = consciouness (as a definition)

From this starting point, it seems reasonable to derive that consciousness can arise from 1) mental representation of the world that include oneself 2) empathy for others (I can guess why this other worker has taken this decision) that, once applied to the actions of the self as if it were an external agent, gives self-reflection.

Re: Predictive Learning [pdf]

#22

Post by splike »

chessweb01 wrote: post #9

If page 33 depicts the working of the brain on a very high level, the world model (or simulator) residing inside the agent must contain a model/simulator of the agent itself. Could this give rise to self perception or consiousness?

I happened to be reading The Selfish Gene by Richard Dawkins and Gödel, Escher, Bach by Douglas Hofstadter at the same time, and both of them point at exactly this being the reason for consciousness. I was stunning at how both reached the same conclusion, that consciousness arrises from recursion of self perception, from very different points.

Also, if anyone is watching Westworld (spoilers), it seems to come to the same conclusion funnily enough. What finally gives the androids consciousness is some kind of recursive idea of listening to themselves.

Re: Predictive Learning [pdf]

#24

Post by mac01021 »

lowglow wrote: post #2

For anyone that doesn't know Yann LeCun, he's the head of AI over at Facebook, but surprisingly he's positively and consistently straightforward concerning the current hype driving AI and its technologies. He deserves respect because of this alone.

"Straightforward" meaning what? Just bluntly dismissive of undue hype? What statements has he made to this effect?

Re: Predictive Learning [pdf]

#25

Post by padobson »

splike wrote: post #22
chessweb01 wrote: post #9

If page 33 depicts the working of the brain on a very high level, the world model (or simulator) residing inside the agent must contain a model/simulator of the agent itself. Could this give rise to self perception or consiousness?

I happened to be reading The Selfish Gene by Richard Dawkins and Gödel, Escher, Bach by Douglas Hofstadter at the same time, and both of them point at exactly this being the reason for consciousness. I was stunning at how both reached the same conclusion, that consciousness arrises from recursion of self perception, from very different points. Also, if anyone is watching Westworld ( spoilers ), it seems to come to th…

Michael Crichton, who wrote and directed the original Westworld (also of Jurassic Park fame), describes the same idea in his novel Prey[0], which is about emergent AI from swarms of self-replicating nano-bots.

0. https://www.amazon.com/Prey-Michael-Crichton/dp/0061703087/r...

Re: Predictive Learning [pdf]

#27

Post by pakl »

Lecun has identified a real problem for AI -- the need to understand the real world, the link between intelligence and prediction over time. But the tools he is using are not the right ones.

Deep conv nets were not designed with prediction over time in mind. Here's one reason: Deep convolutional nets do not handle dynamical information from their lowest layers. By design, conv layers and pooling layers immediately begin discarding spatial information that could be used for building up predictions.

In contrast it is possible to start with recurrent/feedback networks at the very first layers of the network. These initial layers can begin building up predictions at the pixel, color, and lighting level (example: our recent preprint[1]).

My colleague, who as some of you know enjoys blogging, wrote a more thorough post in response to LeCun's recent CMU lecture on the same topic as these slides[2].

[1] https://arxiv.org/abs/1607.06854

[2] Blog: "A few comments on the Yann LeCun lecture at CMU, 11.2016" http://blog.piekniewski.info/2016/11/21/yann-lecun-cmu-11-20...

Re: Predictive Learning [pdf]

#29

Post by felippee »

pakl wrote: post #27

Lecun has identified a real problem for AI -- the need to understand the real world, the link between intelligence and prediction over time. But the tools he is using are not the right ones. Deep conv nets were not designed with prediction over time in mind. Here's one reason: Deep convolutional nets do not handle dynamical information from their lowest layers. By design, conv layers and pooling layers immediately be…

Hi, I'm the blogger.

I just want to add a very simple statement:

In order to create a model of the world, the machine learning substrate has to have the capacity to cover the observed dynamics.

Hard not to agree with this, almost sounds like a tautology. Now let's try to derive conclusions:

- World dynamics is full of multi-scale interactions (e.g. in vision illumination of a single pixel depends on the whole scene and the whole scene depends on many tiny details). To capture that, the machine learning substrate has to allow low level representations access high level stuff. Hence feedback all over the place. Which is exactly what is seen in the biological cortex. This is not recurrent layer made of LSTMs, this is a FULLY RECURRENT system.

This will not be achieved with any franken-neocognitron deep network neither with MSE, nor adversarial nor even triple adversarial loss function. This requires a new approach and together with several colleagues after a few years of continuous and intense thinking and modelling we have proposed a solution:

http://blog.piekniewski.info/2016/11/04/predictive-vision-in...

As well as a full paper https://arxiv.org/abs/1607.06854

Now, I'm not saying this is all done. This is just a beginning of a really exciting research adventure and many things look very promising. It will require however for the AI field to get out of a pretty "deep" local minimum it is in right now.

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