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.
Yes and it also reminds me of one of my favorite books, Andreas Wagner's "Arrival Of the Fittest" and, connected, the Miller-Urey experiment of course.
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?
> Do you have any idea why the authors chose Z80 as the program language?
In that previous paper you cite (by the same group) they tested several substrates for spontaneous replication (BFF, Forth, SUBLEQ, and emulated real CPUs. Z80 and 8080 CPU exps confirmed the rise of self-replicators, with the Z80 notably exhibiting multiple waves of increasingly capable self-replicators.
And the instruction set seems quite appropriate for the experiment: Z80 has native block-copying instructions, while it doesn't have MUL, so the task of evaluating polynomials is somewhat more challenging
This paper is by the same team. My guess is that the BF code that evaluates polynomials is much much longer than the equivalent Z80 code. Thus it may not be expected to evolve in the space and time constraints of this experiment.
A small update here: another replication using a different CPU, the 6502. In this case, replicators emerge 100 times less frequently than in Z80, due to a lacking LDIR-like instruction
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
> We show that when random, non self-replicating programs are placed in an environment lacking any explicit fitness landscape, self-replicators tend to arise. We demonstrate how this occurs due to random interactions and self-modification, and can happen with and without background random mutations. We also show how increasingly complex dynamics continue to emerge following the rise of self-replicators.
Thanks for posting this, indeed that group at google has produced some interesting research in this space.
That paper was also discussed here back in the day: https://news.ycombinator.com/item?id=40820022
If evolution had access to TSMC's process technology, do you think it wouldn't leverage it? The signals in my AMD CPU propagate ~1,000,000x faster than the ones in my brain.
I did something quite similar https://github.com/daedalus/syntropy but with riscv
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.
[0] https://tomray.me/tierra/whatis.html
[1] https://en.wikipedia.org/wiki/Thomas_S._Ray
[2] https://www.stevenlevy.com/artificial-life
[3] https://en.wikipedia.org/wiki/Core_War
Yes and it also reminds me of one of my favorite books, Andreas Wagner's "Arrival Of the Fittest" and, connected, the Miller-Urey experiment of course.
https://www.goodreads.com/en/book/show/20821275-arrival-of-t...
https://en.wikipedia.org/wiki/Miller%E2%80%93Urey_experiment
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?
> Do you have any idea why the authors chose Z80 as the program language?
In that previous paper you cite (by the same group) they tested several substrates for spontaneous replication (BFF, Forth, SUBLEQ, and emulated real CPUs. Z80 and 8080 CPU exps confirmed the rise of self-replicators, with the Z80 notably exhibiting multiple waves of increasingly capable self-replicators.
And the instruction set seems quite appropriate for the experiment: Z80 has native block-copying instructions, while it doesn't have MUL, so the task of evaluating polynomials is somewhat more challenging
This paper is by the same team. My guess is that the BF code that evaluates polynomials is much much longer than the equivalent Z80 code. Thus it may not be expected to evolve in the space and time constraints of this experiment.
A small update here: another replication using a different CPU, the 6502. In this case, replicators emerge 100 times less frequently than in Z80, due to a lacking LDIR-like instruction
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
See also:
https://arxiv.org/abs/2406.19108
> We show that when random, non self-replicating programs are placed in an environment lacking any explicit fitness landscape, self-replicators tend to arise. We demonstrate how this occurs due to random interactions and self-modification, and can happen with and without background random mutations. We also show how increasingly complex dynamics continue to emerge following the rise of self-replicators.
Thanks for posting this, indeed that group at google has produced some interesting research in this space. That paper was also discussed here back in the day: https://news.ycombinator.com/item?id=40820022
Interesting. But, evolution is way too unconstrained to provide us a path to "agi". It would require too much compute.
Evolution also eventually gets frustrated and creates the brain, capable of in context learning.
Maybe we should take some notes from these massively parallel, shallow, and highly recurrent constructions.
If evolution had access to TSMC's process technology, do you think it wouldn't leverage it? The signals in my AMD CPU propagate ~1,000,000x faster than the ones in my brain.
Yeah. But we need throw money at studying the brain, instead of another billion to "agents which understand you truly"
^ yes. this is what we hope.
Super big fan of your work on cellular automata. Do you have anything interesting papers coming up?
Real evolution produced human intelligence.
Constrained evolutionary algorithms may have some promise.
authors here - happy to answer any questions! we’re excited for this line of work and see this as the first step on a longer journey.
Super cool work! What's next? Do you think with longer memory limits more interesting programs might emerge? Or is it substrate dependent?
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.
Time for Sunday-vibe-coding a distributed computing project..
Why Z-80 assembly?
See replies at this other comment https://news.ycombinator.com/item?id=48963513
Thanks, what a cool reason to keep Z-80 alive!