This is a cool finding; I did not know it was still an active area of study with all the work on ML and LLMs these days. I have done some amateur exploration of the space and the result does not surprise me: https://github.com/ehbar/evol
Do you have any idea why the authors chose Z80 as the program language? I have seen other studies in the same spirit that use simpler toy languages like Brainfuck (https://arxiv.org/abs/2406.19108) and I wonder if you could get higher execution speed if you didn't have to execute so much emulator code.
The programs/genomes are extremely tiny. I would be very interested to see what kind of hardware is needed to scale this approach up. How long until we can feed in giant corpuses of text and evolve these little organisms to predict the next letter?
This is a cool finding; I did not know it was still an active area of study with all the work on ML and LLMs these days. I have done some amateur exploration of the space and the result does not surprise me: https://github.com/ehbar/evol
This reminds me of multi-head neural nets where there is synergy from having to learn two or more tasks at the same time that helps them all.
An independent reproduction of the main result: https://github.com/vicgalle/coevolution-soup
This is every cool research.
Do you have any idea why the authors chose Z80 as the program language? I have seen other studies in the same spirit that use simpler toy languages like Brainfuck (https://arxiv.org/abs/2406.19108) and I wonder if you could get higher execution speed if you didn't have to execute so much emulator code.
The programs/genomes are extremely tiny. I would be very interested to see what kind of hardware is needed to scale this approach up. How long until we can feed in giant corpuses of text and evolve these little organisms to predict the next letter?