Jane and Joe both achieved the stated goal of moving the coffee cup to the red dot. They did it very differently. While Jane moved the cup the shortest and quickest path, Joe struggled to figure out how to move the cup and ended up moving it all over the place before finally settling on the red dot.
If we had to judge Jane and Joe on this task only, I believe we can all agree that Jane would be considered the more intelligent person. That doesn’t mean she is without a doubt more intelligent than Joe. Joe might be far better at achieving other goals than Jane. But limiting our judgment to this task only, Jane wins!
They both achieved their goal. But, how do we explain it generically? We can say that the coffee cup is at the predefined destination. That explanation cannot be used to describe other goals.
Instead, let’s go deep, deep into nature. If we look at the coffee cups as a collection of particles, we can say that achieving the goal means moving the particles to a new location in space. Once the particles are at the new spatial coordinates, we interpret it as having achieved a goal.
Think about it! Is there a goal that can be achieved without moving a single particle anywhere in space? Anything we want to achieve requires us to rearrange the SpaceTime continuum.
This is often called increasing the entropy, but that is not quite accurate. Entropy is the increase of disorder and randomness in a system. Achieving a goal is about moving the particles to very specific locations.
If a goal is defined as having changed the location of particles, then what is intelligence?
To move the particles, we have to create a series of cause-effect pairs - in other words, we have to create causality chains. Not just on the particles we are moving. To move the coffee cup we have to use our hands, move them to the cup, grab the cup and push it.
If your goal is to get to Hawaii, then you need to use your mobile phone to order an Uber. That Uber takes the particles making up your body to the airport and an airplane moves them to the island.
Everything becomes a resource you can use to create the right causality chains. Understanding how these causality chains interact and the result becomes critical in the process of predicting the best way to move particles to achieve a predefined goal.
That is how the Theory of General Intelligence defines intelligence:
The process of changing the composition of SpaceTime
Creating causality chains is creating a process for moving particles.
The reason why we believe Jane is more intelligent than Joe is that her causality chain is shorter than that of Joe. The shorter chain - or fewer cause-effect pairs - that is needed to achieve a goal, the more intelligent that person is considered.
This is a simple and elegant way of defining intelligence. It is easy to test, and it uses fundamental particles to describe intelligence - meaning, you’ll be hard-pressed to find a more fundamental way of describing intelligence.
There is a lot more to the theory. We are just scraping the surface in this newsletter edition.
The next question is, how can we implement this into a computer algorithm? Before we can answer that question, we need to interpret the theory. The difference between the Kimera algorithm and the new GEA algorithm is the interpretation of this theory.