Retrocausality and AI

Retrocausality is about physical things in the present affecting things in the past. Wow, read it twice. If it sounds to you like breaking the most basic rules of common sense and our most basic intuitions of how things work, you are right, but as weird as it sounds... somehow it makes perfect sense.

Today I found this inspiring article in phys.org about retrocausality. Basically it proves that, if the time symmetry found in all known physic laws is to be accepted as a fundamental law, as it actually is, then causality must go on both directions too, so as unreal it could sound to us, macro-sized humans, it is more than plausibly retrocausality is in the very nature of our world.

Once accepted as a possibility, it solves much of the actual issues with quantum theories: action-at-distance, non-locality, Bell theorem... and it is not more or less plausible than other alternatives, like no-time-symmetry, many-worlds or even the Copenhagen model, so by accepting one "counter-intuitive" possibility, quantum world get less intimidating. I buy it!

Reading it reminded me of one the many variations of the Fractal AI I tried in the past, I wrote about it in the post about the Feynman fractal AI, a model where signal travelled back and forth in time. Here you have a naive drawing of it:

The idea was nice and it was as smart as the "standard" fractal AI, but it could not improve it at all, it was just another way of doing the same stuff, but more complicated, so I finally drooped this idea in the bag of the almost-good ones.

Entropic AI using Excel

Some days ago, Raul Perez (@raulperezrpm) kindly contacted me on twitter to comment about my first talk on entropic intelligence, and just two days later, I received a video from him of the entropic algorithm working inside Excel macros!

Time traveling fractal AI, a first approach

Finding a way for the fractal futures to be able to travel back in time, as in Feynman diagrams and integrals, has proved to be a little tricky, but I feel I am quite near to solve it.

In the mean time, I want to share a "dead end" try to accomplish this by using succesive layers of futures sended to the future in different succesive times, so they act as a serie of concentric wave fronts that travels to the future reinforcing each other.

Let start by showing a nice video of this idea at work:

Using Feynman Integrals

Some posts ago I commented on the strange need of using a "Pauli's Exclusion Principle" on the Fractal AI in order to make it work as spected.

It may sound strange that quantum physics play a role on building a fractal AI, but actually it is the real base for such an algortihm.

Today I want to comment on my very last idea: using real Feynman integrals on the Fractal AI.

First try to draw the fractal

Long Term Proyects

Once the Fractal AI is formally finished (regardless of how many improvements I could still implement on it) it is time to mention some of my wildest proyects for the long term.

Long fractal shots

All of them are long shots, things I feel that can be done using fractals in the exact same way I am applying in the Fractal AI. Some of them are already "half done" but will need a deep rethinking, others are just ideas in an very early stage of maturity.

1) Fractal growing of neuronal networks
This one is quite interesting as it could automate the proccess of building the network itself (adding neurons and connectons fractally as needed, no more need to choose how many layers to use) at the same time it learns from the examples it is exposed to. I have some sketches of it, but it is istill not mature enough to start coding. I call it "Fractal Deep Learning".

Layers and layers of intelligence.

Those days I have been busy ioroning out the ideas about how different "levels" of entropic intelligence could be layered, one over the other, to make up our complex and sophisticated mind.

I have come across with a very simple -once you get the idea- way to arrange it like a bunch of layers of "common sense" placed one on top of the previous one.

There are at least two ways of explaining it: algortihmic (for programmers) or entropy laws (for physicists) so I will focus first in the algortihmic aspect so you can make your own "multilayered common sense intelligence machine" if you are in the need (I already am on the work, but it is still far from done).

So lets go for it the easy way using the old and good "kart simulation" example.

To be clear about the problem we are facing I will just go to the point: usign 100 crazy blind monkeys to randomly drive the kart in the 100 futures I have to imagine, and then take a "common sense" decision based on the things those crazy monkeys did, may be, only may be, was not such a clever idea after all.