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?
HarHarVeryFunny•3h ago
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.
ericbarrett•2h ago
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
EvanAnderson•35m ago
Tierra[0], written by Tom Ray[1], immediately comes to mind. I was captivated when I read about it, as a teenager, in Steven Levy's "Artificial Life"[2]. Having played Core War[3], the description of Tierra in Levy's book inspired me to play around with making a virtual machine in Turbo Pascal and trying my hand at making a pale and naive clone. It was a lot of fun, and arguably has influenced a lot of my thinking about the origin of biological life.
vicgalle_•5h ago
markisus•3h ago
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?