Zurada was one of our AI textbook that makes it visual that right from a simple classifier to a large language model, we are mathematically creating a shape(, that the signal interacts with). More parameters would mean shape can be curved in more ways and more data means the curve is getting hi-definition.
They reach something with data, treating neural network as blackbox, which could be derived mathematically using the information we know.
David looks into the LLM finds the thinking layers and cut duplicates then and put them back to back.
This increases the LLM scores with basically no over head.
Very interesting read.
But what's in the context window is sharp, the exact text or video frame right in front of them.
The goal is to bring more of the world into that context.
Compression gives it intuition. Context gives it precision.
Imagine if we could extract the model's reasoning core and plug it anywhere we want.
Zurada was one of our AI textbook that makes it visual that right from a simple classifier to a large language model, we are mathematically creating a shape(, that the signal interacts with). More parameters would mean shape can be curved in more ways and more data means the curve is getting hi-definition.
They reach something with data, treating neural network as blackbox, which could be derived mathematically using the information we know.