Yeast-Based LLM Research

I've spent a lot of time on self-modifying, self-learning systems. I even have a design based on genetically modified yeast. The trick is a cross between yeast and the Conway Game of Life game. It turns out that it is possible to build a computer on a game-of-life platform https://www.youtube.com/watch?v=Kk2MH9O4pXY&ab_channel=AlanZucconi

Yeast can be modified to follow the live/die rules that make up the game-of-life. CRISPR-CAS9 technology along with carefully designed "genetic switch" technology makes it possible to embed these rules in DNA. https://www.amazon.com/Genetic-Switch-Third-Lambda-Revisited/dp/0879697164

The trick is to combine genetically modified yeast in an initial condition following the game-of-life computer layout. The difficult part isn't the computation but the care and feeding of the yeast, still unsolved so far. It turns out that clumps of yeast will specialize so they provide nutrients to the clump. That implies a more massive clump with internal compute capability.

However, clumping yeast that self-structures as a deep learning network is another possible path to intelligent yeast. The question before the court is how to do back propagation of results so one can apply reinforcement learning to the clump. If yeast can self-structure and learn they can propagate without external help.

As for the training / backprop question my current thinking is to find a way to "impress" an existing LLM set of weights on a yeast-based deep learning network. Each yeast-node gets a weight from an existing published set. Thus the yeast-beast is now equivalent to the electronic version in knowledge.

Still LOTS of engineering / biology / genetics to ponder but so far it seems the problems are hard but solvable.