eyvindn

the unity/minecraft level of abstraction is a common one in AFIFE, and too often ends up being Spore - looking and behaving in very interesting ways, but remaining just that. those approaches are on the wrong side of the performance vs evolvability curve - scale is a necessary ingredient and running things at a physics sim level is just too taxing.

it is of course entirely possible we are on the flip side and too low-level - this is something we're trying to find out.

yes! very soon!

this is my personal take;

I agree, for the same reasons you mentioned, resource constraints will need to be baked in (they are already, to some extent, if you consider the constrained resource to be z80-CPU-seconds the program has access to). something more akin to energy in our real world, which can be manipulated, aggregated, shared, pooled, stolen, etc feels more natural, however.

imo meaningful intelligence could conceivably developed in a soup (even in-silico), unclear on what timeline, but grounding it with human and/or real-world data is necessary to make it useful to us (bio-compatible, if you will?)

there are many bottlenecks in the substrate - memory limits being one of them. other things like the A-B concatenation inherited from the bff paper also severely limit the possible dynamics. understanding and avoiding these bottlenecks are some of our next steps!

as other folks have replied, Z80 is more expressive and yields more interesting replicators faster. however the question of how complex to make the "base layer", if you will, remains open. we expect it to also be an ease of auto-evolution vs. performance tradeoff.

second question is a good question :)

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.

^ yes. this is what we hope.

:)

colab with all code will be available next week, will add link from the article.

thanks for catching this, the figure for the target was inverted when exporting for publication, corrected now.

could you link the drone swarm command article?

Authors here - will do our best to answer any questions.

There's some recent work that involves NCAs in a 3D setting by Horibe et. al [1] and tweet [2]. Other work by Risi and collaborators is definitely worth checking out as well.

[1] https://arxiv.org/abs/2102.02579 [2] https://twitter.com/risi1979/status/1358018266824912897

That repo contains the code, figures and text for the article. It's set to public now.

The texture template images come from [1], a collection of textures categorised by "description".

[1] https://www.robots.ox.ac.uk/~vgg/data/dtd/

The second cell looks a section title ("Imports and Notebook Utilities"), but contains the definitions of these functions and the imports. Run this cell and I suspect things should work.

Apologies, not my intention. I was also under the impression seminal could be used to mean ”first” in the succession of our works and this is what I had intended to communicate.

Thanks for noticing. This is a typo stemming from early experiments. We started out with 16 channels, but switched to 12 channels of state when this worked just as well. I've submitted a correction.

EDIT: Alex replied below. For more details on quantisation see footnotes in our seminal work [1]

[1] https://distill.pub/2020/growing-ca/

Authors here. If you have any questions we'll do our best to answer them! Glad to see people find our work interesting thus far.

We also encourage anyone interested to play with the linked Google Colabs [1][2] and read the other articles in the Distill thread. In the Colabs you'll find a bunch more pre-trained textures as well as a workflow to train on your own images, plus some of the scaffolding to recreate figures.

[1] https://colab.sandbox.google.com/github/google-research/self... [2] https://colab.sandbox.google.com/github/google-research/self...