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.

Imperfect information

In the actual incarnation of the fractal AI, we need to supply it with its exact state, all the interactions with environment (the simulation) and the potential function, hand crafted, to be followed. This is know as having "perfect information".

Having perfect information of any system is just not feasible, so my models are not usable in real environments, with real drones, moving real motors, as all of them are unknow for us and we will have just some sensor's outputs as our information.

This week I have visited the Cognitive Sciencies research team at Zaragoza University as a guest for a short but intense seminary about my fractal inteligence algoritms in an effort to team with them here and there, but they really emphasized on the sensorial approach -imperfect information case- in order to make our works compatible.

Generating consciousness

One week ago I wrote this post about an insight on what could "consciousness" be like, and I imagined it as something not-so hard to gasp as we always thought. Today I come back with a "pseudo-code" version of it on my mind.

Those new ideas have come along with an effort in our company to port the fractal algorithm into a distributed, highly scalable architecture. A work in progress that is already producing a great speed-up in our tests.

This new architecture allows me to play with big groups of fractals diseminated over a network of PCs, all doing the same decision work in paralell, to later join all their findings and take a "collegiated" decision.

More interestingly, I can now "pack" some fractals to work as one big fractal and replicate it endlessly to build a tree of cooperating fractals as a nice way to distribute work over the PCs on the network.

But how to arrange them to distribute the work even more efficiently? By building it as a "fractal of fractals", a tree of fractals whose structure evolves dynamically as you use it, to finally form a nice tree-like fractal that adapts its form to live in the environment you gave to it.

What is Consciousness?

Some days ago I wrote a little about how this fractal AI works, it was not too detailed and the really important details were intentionally left unsolved. I promise to fill the gaps in short for you to be able to try the fractals by your self, but not now.

Today I want to give an overview of how the "complete fractal AI algorithm" could look like in some months, the ideas I am actually working on, and specially some random thoughts about consciousness.

The part I am now working at is about how to add memory to the fractal AI. This far, fractal AI was totally memory-less, meaning it does not learn from experience at all. I now call this an pure instinct-drive or intuitive mind. When you ask something to this AI, it thinks on the problem from scratch, and gives you an answer that is good enough for evolving in this medium with intelligence, a "real time " decision making algorithm good enough for many task.

But while driving a rocket on a difficult environment is hard but can be done with just an intuitive fractal AI, most NP-hard problems -problems where the time needed to solve it grows exponentially with the size of the problem to be solved- are usually not so easy to solve just with pure intelligence, usually you need something more to guide the intuition.

Paretto frontiers

Last Thursday professor El-Ghazali Talbi from Lille University hold a talk about metaheuristic classification and his unified framework ParadisEO to deal with it on the Elx University Miguel Hernandez CIO, and after the talk (I enjoyed it a lot and learn things a was supposed to already know and some more!) I had a little time to explain my latest fractal methods and show some of the videos I had been producing about them.

There was some debate after the talks about the classification itself and the Pareto frontier way of working on multi objective optimisation problems.

Basically, actual approaches try to maximise all the functions at the same time, if possible, and if not, then obtain the optimum solution for any weighted combination of those functions, so for any possible combination you get a point, and all those points form the "Pareto Frontier" of the problem.

Are maths the real languaje of nature?

I have just had a vision random thought I wanted to share before going to bed: What if mathematics were not the real basement of real world physics? What if the laws of nature are not really physicals laws written in pure maths as we all have naively assumed? Can physics be rethink in a absolutely different way?

I think there exists a way to describe all known physics without using any mathematical formula, a way to describe what a "wave equation" really is without using mathematically defined fields of any kind, just using algorithmics, in fact using just a single, small and compact algorithm.

Fractal minds

Yesterday I found a video that really caught my attention: Starting from the fractal shape of our brain, the speaker Wai H. Tsang derives the fractal working of the intelligence (hey! like my fractal intelligence!) and propose a fractal way to model intelligence... a 90% match with my actual work!

The second part of the video go through consciousness, the great missing part in my actual schema (ouch!) and from this point, it jumps to the universe, religions, god... I have walked down this road too, with almost exactly the same results, except I used entropic principles to make the walk, while he used fractals. My trip was months before I jumped into fractals, that is why there is a difference. Any how, I agree with Wai 100%.

If you liked my fractal intelligence videos and want to know what makes this idea work so great (and great by it self) please take this journey, you wont regreat.

Is it the Terminator AI?

Most of the people I talk about this magic algortihm use to bring into the conversation the terminator that using a powerfull artificial intelligence decided to destroy the humans. Scaring.

It has been something that has really preocupated in the process, really, I don't want to help create a perfect weapon anymore than you would do!.

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.

Negative goals

We have seen how "common sense" works and how to bend it to our likings by adding positive and reductive goals, the video clearly showed the benefit of the mix of goals used, but are they enough avoid danger, or do we need something more... powerful?

Negative goals are quite natural for us: if the kart lower its health by a 10%, you can think of it as a mere "reduction" applied to the possitive goals -distance raced squared in this case- or something purely negative: a -10 in the final score.

If we try to get the same results as in previous video but using some short of negative goals, we will end up with something odd: the fear is ok in some really dangerous situations, they help you avoiding them efectively, but too much fear, a big negative scoring arising in some moment, will make the "common sense" to freeze. You have added a "phobia" to something.