Co-evolution of self-replication and function in a digital primordial soup

EvanAnderson 7 hours ago | parent | next [-]

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

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bob1029 an hour ago | parent | prev | next [-]

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.

	▲	vicgalle_ an hour ago \| parent [-]
		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

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vatsachak 3 hours ago | parent | prev | next [-]

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.

	▲	bob1029 an hour ago \| parent [-]
		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.

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vicgalle_ 12 hours ago | parent | prev | next [-]

An independent reproduction of the main result: https://github.com/vicgalle/coevolution-soup

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markisus 9 hours ago | parent [-]

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?

	▲	vicgalle_ an hour ago \| parent \| next [-]
		> 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
	▲	BadAstronomer 4 hours ago \| parent \| prev [-]
		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.

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ericbarrett 9 hours ago | parent | prev | next [-]

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

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dofdial an hour ago | parent | prev | next [-]

Time for Sunday-vibe-coding a distributed computing project..

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gtsnexp an hour ago | parent | prev | next [-]

Why Z-80 assembly?

	▲	vicgalle_ an hour ago \| parent [-]
		See replies at this other comment https://news.ycombinator.com/item?id=48963513

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HarHarVeryFunny 10 hours ago | parent | prev [-]

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.