Building AGI on the Tokio Runtime. | christo.sh
Note: I am not, nor do I claim to be an expert in machine learning or neuroscience. This will become abundantly obvious as you continue reading.
A few weeks ago I decided to build AGI. Since OpenAI, Deepmind and xAI haven't delivered yet with the smartest researchers and billions in compute I have to take matters into my own hands.
Assume you are racing a Formula 1 car. You are in last place. You are a worse driver in a worse car. If you follow the same strategy as the cars in front of you, pit at the same time and choose the same tires, you will certainly lose. The only chance you have is to pick a different strategy.
The same goes for me. If I go down the transformer / deep learning route I am outgunned. The only hope I have is to try something completely novel (or more precisely think I'm working on something novel only to discover this was done in the 1970s1).
For reasons we'll cover in the following sections, I decided to go down the fully biologically inspired path. I would build a fully asynchronous neural network and run it on a data center.
