Graph entropy 7: slides

After the series of six posts about Tree-Graph Entropy (starting here), I have prepared a short presentation about Graph Entropy, mainly to clarify the concepts to my own (and to anyone interested) and present some real-world use cases.



One of the most interesting ideas introduced in this presentation is a method for, once you had defined the entropy of all the nodes in a static disrected and acyclic and directed graph (a tree), to easily update all those entropy values as the graph evolves over time, both altering the conditional probability of some connections, as also by adding or taking connections, by considering nodes and connection as cellular automaton that can adjust its internal entropies asynchronously.

You can also jump to the original google slides version if you want to comment on a particular slide.

If this was not enought for you and what to read more weird things about those entropies, you can dive into the unknown realm of negative probabilities entropy here!

Update (24 Oct 2018): this post was referenced in the article "A Brief Review of Generalized Entropies"where the (c, d) exponents of these generalized entropies are calculated.

Fractal AI goes public!

Today we are glad to announce that we are finally releasing the "Fractal AI" artificial intelligence framework to the public!

This first release includes:

• Fractal AI: A fragile theory of intelligence A nice to read small book in PDF with theory, algorithm, pseudo-code, experiments, etc.
• github.com/FragileTheory/FractalAI with working python code to beat almost all of the actual Atari games.
• Tons of videos showing Fractal AI at work, along with some talks, etc.

We have modified Fractal AI a little so now it is more powerful than ever, in fact we have been able to beat a lot of the actual records (from state-of-the-art neural network like DQN or A3C) with about 1000 samples per strep (and remember, no learning, no adaptation to any game, same code for all of them).

We are specially proud of beating even some absolute human world records, but hey, it was going to happen anyhow!

Fractal AI playing Ms Packman. It reached an unknow score limit of 999,999 present in a total of 15 games. Fractal AI beated the best human records in 49 out of 50 games.

Fractal optimising, a first paper

The fractal "family" of algorithms actually started as a very naïve optimising algorithm: after all, intelligence is just about maximising a certain "utility function", so they are quite related.

Once the fractal AI was done, the optimisation facet was again re-visited with a much more promissing results to later abandon it again.

And finally, with the help of our friend José María Amigó from the Miguel Hernandez University, we wrote an article about this fractal algorithm we named "GAS" (namely for "General Algorithmic Search" but it was actually for the capitals on our names, Guillem, Amigó and Sergio) and compared it against other similar ones out there (Basin hopping, Descent evolution and Cuckoo search).