Breaking the Patterns

Ideology = Calibrating the "Sentiment Neuron"

Gauging/ Scaling the Limbic Response thru Language

Striding towards AGI?

1.3 Kaleidoscope Hypothesis and Abstract Building Blocks

...Because when you when you say program, I think a lot of people think of a program as being something with conditional logic and with an LLM. 

That's not what they are there.

Yeah, it's almost like in an input sensitive way. You see this kind of traversal through the model and it's like a mapping. So it feels more input to output mapping.

And that mapping is continuous and it is implemented via a curve.

But but we can describe that as a program.

Yes of course there are functions.

Yes. And and you said they were compositional.

Yes. Because these functions are vector functions. You can sum them. For instance you can interpolate between them to produce new functions.

 
I love this kaleidoscope hypothesis. So can you, you know, dramatically introduce the kaleidoscope hypothesis.

Sure. So everyone knows where the kaleidoscope is, right. It's like this cardboard tube with a few bits of colored glass in it. And and. this is just like a few bits of original information gets mirrored and repeated and transformed, and they create this tremendous richness of complex patterns. It's beautiful. And the kaleidoscope hypothesis is this idea that the world in general, and any domain in particular, follows the same structure that it appears on the surface to be extremely rich and complex and infinitely novel with every passing moment. But in reality it is made from the repetition and composition of just a few atoms of meaning. And a big part of intelligence is the process of mining your experience of the world to identify bits that are repeated and to extract them. Extract these unique atoms of meaning and when we extract them, we call them abstractions. And then as we build a sort of like inner banks of such abstractions, then we can reuse them to make sense of novel situations, of situations that appear to be extremely unique and novel on the surface, but actually they can be interpreted by composing together these reusable abstractions. That's the fundamental idea behind intelligence. Intelligence is a cognitive mechanism that you use to adapt to novelty, to make sense of situations you've never seen before. And it works by creating models on the fly of the new situation, by combining together existing building blocks. Abstract building blocks which were mined from your past experience. And there are two key tricks here. One trick is the synthesis trick, whereby you take these building blocks and quickly assemble them toform a program model that matches the current task or the current situation that you're facing in synthesis. And there's abstraction generation, which is the reverse process in which you're looking at the information you've you've got available about the world, like your your experience, your perception, also the models that you've created to respond to it. And you're going to turn that, distill it into reusable abstractions, which you then store in your memory so that you can use it the next time around. So synthesis and abstraction generation, and together they form intelligence in my model, at least in my architecture of AGI.