Obstacles on the Path to AI

#1

Post by evc123 » Thu, Oct 22, 2015, 9:06 PM UTC

Obstacles on the Path to AI
drive.google.com

Re: Obstacles on the Path to AI

#2

Post by tomp » Thu, Oct 22, 2015, 10:45 PM UTC

Everytime I read something like this, I get sad that I don't understand most of it.

But then I get happy because at least I understand a little bit :)

Re: Obstacles on the Path to AI

#3

Post by grondilu » Thu, Oct 22, 2015, 10:59 PM UTC

That's only the slides. Is there a video of the talk?(assuming there is a talk, that is)

Re: Obstacles on the Path to AI

#4

Post by vkhuc » Thu, Oct 22, 2015, 11:01 PM UTC

Although I'm working on deep neural nets, this material is too advanced to me. Looks like deep nets + bayesian reasoning is the next big thing.

Re: Obstacles on the Path to AI

#5

Post by vonnik » Thu, Oct 22, 2015, 11:02 PM UTC

"There is no way in hell that you can learn billions of parameters with RL." I really love LeCun's provocative stances, but I get suspicious when people talk about impossibilities. RL is making huge strides. People adopted the same tone with neural nets years ago, and LeCun proved them wrong...

Re: Obstacles on the Path to AI

#6

Post by paulsutter » Thu, Oct 22, 2015, 11:13 PM UTC

vonnik wrote: post #5

"There is no way in hell that you can learn billions of parameters with RL." I really love LeCun's provocative stances, but I get suspicious when people talk about impossibilities. RL is making huge strides. People adopted the same tone with neural nets years ago, and LeCun proved them wrong...

He's saying you won't learn billions is parameters with RL /alone/ because "one scalar reward per trial isn't going to cut it".

I find that convincing and a key insight. RL is going to be fundamental to AGI, but he's saying curiosity / unsupervised learning will be necessary. And I say this as a big believer in the need for more work on RL.

Re: Obstacles on the Path to AI

#7

Post by haddr » Thu, Oct 22, 2015, 11:15 PM UTC

grondilu wrote: post #3

That's only the slides. Is there a video of the talk?(assuming there is a talk, that is)

I'd love to see the video too... LeCun is probably most interesting person to hear in this topic...

Re: Obstacles on the Path to AI

#8

Post by [deleted] » Thu, Oct 22, 2015, 11:26 PM UTC

[deleted]

Re: Obstacles on the Path to AI

#9

Post by imh » Thu, Oct 22, 2015, 11:37 PM UTC

vkhuc wrote: post #4

Although I'm working on deep neural nets, this material is too advanced to me. Looks like deep nets + bayesian reasoning is the next big thing.

Pick up this book. It's fantastic.

https://mitpress.mit.edu/books/probabilistic-graphical-model...

Re: Obstacles on the Path to AI

#10

Post by vonnik » Thu, Oct 22, 2015, 11:39 PM UTC

paulsutter wrote: post #6

vonnik wrote: post #5

"There is no way in hell that you can learn billions of parameters with RL." I really love LeCun's provocative stances, but I get suspicious when people talk about impossibilities. RL is making huge strides. People adopted the same tone with neural nets years ago, and LeCun proved them wrong...

He's saying you won't learn billions is parameters with RL /alone/ because "one scalar reward per trial isn't going to cut it". I find that convincing and a key insight. RL is going to be fundamental to AGI, but he's saying curiosity / unsupervised learning will be necessary. And I say this as a big believer in the need for more work on RL.

Yes, I should have added the context about the scalar reward, but I'm not sure why that changes anything.

This may seem like a naive question, but it's sincere: What makes a scalar reward less effective at modifying a Q function than a scalar error that's used in backprop and assigned to a neural network's coefficients?