Formal Theory of Fun & Creativity

author: Jurgen Schmidhuber, IDSIA
published: Dec. 13, 2010,   recorded: September 2010,   views: 2126
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Slides

Slides
0:00 Formal Theory of Fun & Creativity Explains Art, Science, Music, Humor
1:13 The Swiss AI Lab IDSIA
1:19 TU München COGBOTLAB Focus on Robot Learning
1:31 Fun = External Reward + Intrinsic Reward
3:33 Science, Art, Humor Are Driven by Rint for Finding / Creating Novel Patterns - 1
3:42 Pattern: Data That One Can Compress
6:10 Science, Art, Humor Are Driven by Rint for Finding / Creating Novel Patterns - 2
8:35 Science, Art, Humor Are Driven by Rint for Finding/ Creating Novel Patterns - 3
9:15 Creative Systems Need 2 Learning Modules
10:42 Novelty and Surprise Relative to Subjective Observer O at Time t
13:35 Continuous Time Formulation
15:13 This Image is Output of a Very Short Program
15:49 Self-similar Femme Fractale - 1
16:25 Self-similar Femme Fractale - 2
16:36 Encode Image with Fractal Patterns - 1
16:42 Encode Image with Fractal Patterns - 2
16:55 Encode Image with Fractal Patterns - 3
16:59 Encode Image with Fractal Patterns - 4
17:02 Encode Image with Fractal Patterns - 5
17:18 Encode Image with Fractal Patterns - 4
17:22 Encode Image with Fractal Patterns - 3
17:23 Encode Image with Fractal Patterns - 2
17:25 Encode Image with Fractal Patterns - 1
17:58 Self-similar Femme Fractale - 2
18:05 Self-similar Femme Fractale - 1
18:09 This Image is Output of a Very Short Program
18:17 Self-similar Femme Fractale - 1
18:17 Self-similar Femme Fractale - 2
18:18 Encode Image with Fractal Patterns - 1
18:19 Encode Image with Fractal Patterns - 2
18:21 Encode Image with Fractal Patterns - 3
18:24 Encode Image with Fractal Patterns - 4
18:25 Encode Image with Fractal Patterns - 5
18:27 Most of my Slides also Share a Pattern ...
19:21 Jokes
21:20 For Artificial Scientists and Artists
22:56 Creative Systems Need 2 Learning Modules - 1
25:13 Neural Nets are Underestimated!
27:19 MNIST
29:10 Robocup World Champion 2004
30:32 Expectation / Surprise for Robots
32:15 More General Predictors or Compressors
35:19 Supervised LSTM-RNN
39:19 Symbols and Self-symbols
40:23 Creative Systems Need 2 Learning Modules - 2
42:19 Creative Systems Need 2 Learning Modules - 3
47:02 Creative Systems Need 2 Learning Modules - 4
49:02 Learning of Motor Skills
51:32 Thank You
53:13 - Questions

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Description

To build a creative agent that never stops generating non-trivial & novel & surprising data, we need two learning modules: (1) an adaptive predictor or compressor or model of the growing data history as the agent is interacting with its environment, and (2) a general reinforcement learner. The LEARNING PROGRESS of (1) is the FUN or intrinsic reward of (2). That is, (2) is motivated to invent interesting things that (1) does not yet know but can easily learn. To maximize expected reward, in the absence of external reward (2) will create more and more complex behaviors that yield temporarily surprising (but eventually boring) patterns that make (1) quickly improve. We discuss how this principle explains science & art & music & humor, and how to scale up previous toy implementations of the theory since 1991, using recent powerful methods for (1) prediction and (2) reinforcement learning.

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Reviews and comments:

Comment1 js8765, January 1, 2011 at 10:58 p.m.:

What's with the hat? All it does it focus people's attention on the fact that you must be bald...


Comment2 Mike, January 3, 2011 at 4:37 p.m.:

This is fantastic stuff. The first general algorithmic theory of scientific and artistic creativity, based on maximizing a reward function that measures "fun" obtained by making new non-trivial patterns with previously unknown regularities. Almost frightening to imagine what might happen if this stuff gets implemented on humanoid robots. They will want to have fun, too, and pursue their own "creative" goals all the time, not necessarily the goals of their teachers ...


Comment3 Ed Warner, January 6, 2011 at 1:16 p.m.:

Dr. Schmidhuber's theory of humor views jokes as subjectively unexpected patterns in the sense of algorithmic information theory. After learning or understanding a new joke one needs fewer bits to encode it than before. To evaluate the compression progress or fun, just count the saved bits. I feel this is a simple yet very deep insight. And he's applying the theory of humor well throughout the talk :-)


Comment4 Erick, February 27, 2011 at 4:31 p.m.:

too primitive, disputable and useless theory. author has to study more abstract mathematics, he seems to be bad in it :). cap is funny, it seems to be a part of image or as it was told substitution of hair :-D


Comment5 Office Neighbor, March 4, 2011 at 9:33 a.m.:

Erick S.K. writing letters again ... if you found a flaw in the math, which I greatly doubt, why don't you publish your findings in a peer-reviewed journal? You could submit your analysis to the IEEE Transactions, which published a survey of the theory.

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