Evolved Virtual Creatures (1994)

https://karlsims.com/evolved-virtual-creatures.html

by sirnicolaz • 4 years ago

71 33 4 years ago

karlsims.com

Tagbert• 4 years ago

“results from a research project involving simulated Darwinian evolutions of virtual block creatures”

It would be interesting to run this software today… probably would run on a phone, now.

abecedarius2• 4 years ago

The CM-5 it ran on probably could do a few Gflops peak.

shagie3• 4 years ago

https://www.osti.gov/biblio/46248-gflops-molecular-dynamics-...

Typical production runs show sustained performance (including communication) in the range of 47--50 GFlops on a 1024 node CM-5 with vector units (VUs).

---

I did a comparison of a Raspberry Pi to a Cray-1. It was... very impressive for how far it has come.

shagie3• 4 years ago

That reminds me of the Golem project - http://demo.cs.brandeis.edu/golem/

I remember running that as a screen saver back in the day.

rzzzt2• 4 years ago

swangin.mp4 AKA "Flexible Muscle-Based Locomotion for Bipedal Creatures" also has a similar vibe: https://youtu.be/pgaEE27nsQw

just-tom• 4 years ago

How were the creatures actually 'evolved' or even 'born' in the first place? What biological rules were enforced over their development? How did they learn to overcome obstacles and opponents?

This is very interesting, and especially intriguing since this is a 1994 video and I haven't seen any modern examples of this, which would be equally interesting.

crtc• 4 years ago

His name is really Sims?

rzzzt2• 4 years ago

For other Sims, Ben Sims is a techno DJ: https://ra.co/dj/bensims

bqmjjx0kac• 4 years ago

Yet another example of nominative determinism: https://en.wikipedia.org/wiki/Nominative_determinism

rkagerer4• 4 years ago

This is cool and reminds me of Dr. Adrian Thompson's pioneering experiment with evolutionary techniques on an FPGA [1][2].

He evolved a tone discriminator on real world hardware, and after a few thousand generations the resulting circuit was one no engineer would ever imagine - it made use of transistors operating outside their saturation region, and subtle secondary magnetic or PSU-line effects from nearby gates not even connected to the logic pathways. But it was effective and amazingly space-efficient.

Physics in the real world offers an incredibly rich and vast set of variables for evolution to play with, and I feel like our attempts to simulate it in software constructs may be too limiting to yield results approaching AGI.

[1] Article: http://www.damninteresting.com/on-the-origin-of-circuits/

[2] Paper: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50...

Mali-• 4 years ago

Thanks for the great links! The articles a bit hammy, but the paper is fantastic - papers in evolutionary training always make evolution in biology more understandable (in my own mind anyway).

rkagerer4• 4 years ago

Yeah I agree the paper is way better than the article (and is refreshingly approachable).

leobg6• 4 years ago

Why do you think is it that this evolutionary approach on the circuit level isn’t being pursued? I mean it looks as if it could produce cheap and smart devices that no human engine could ever come up with? Or are you aware of any such projects?

Thank you for sharing this!

waste_monk2• 4 years ago

I believe it's quite prone to overfitting; I vaguely recall reading about an article where an evolutionary design developed on an FPGA had the bitstream applied to another FPGA of the same model (that is, it should have been identical), but failed to operate on the new hardware. Something to do with spooky quantum (?) level tunneling effects that did not operate correctly when there was even the tiniest variation in the physical layout of the hardware.

rkagerer4• 4 years ago

Also, one would-be researcher here shared their frustration that modern FPGA's don't provide as low-level rewiring abilities, which presumably makes it harder to exploit hardware peculiarities:

https://www.reddit.com/r/MachineLearning/comments/2t5ozk/com...

juliangamble• 4 years ago

Is the source available? (Assuming it is CM-Lisp)

mcphage• 4 years ago

I don’t so—at least, I’ve never seen it shared, and this project shows up often.

leobg6• 4 years ago

Jesus Christ. Just think of what those people back in 1994 must have expected from us here in 2022. They would be utterly disappointed, if only they remembered.

flir2• 4 years ago

I thought genetic programming was going to be The Way Forward for AI, because it seemed to take an intractable problem (write a strong AI) and turn it into one that might just be tractable (write a fitness function that can recognise a strong AI). Then neural networks came from the back of the pack to dominate the field, and here we are. At least we're really good at pattern matching now.

neatze• 4 years ago

Nothing wrong with combining genetic programing with neural networks, there many ways to do it, for example, you can have genetically evolved network that produces actual network(s) (weights, locations, connections), to me biggest is issue is with escaping local minima as efficient/performant as other methods (does not beat deep RL methods), in simple sense it stops evolving and it is really hard to figure out why.

leobg6• 4 years ago

But I don’t really understand. If they could create those virtual creatures in 1994... How come we do not have any real world machines that can do that? Why do our robot lawnmowers still use wheels?

unfocussed_mike• 4 years ago

Wow -- I remember seeing this at the time!

Thanks so much for this. I'm amazed it is still there.

karl_gluck• 4 years ago

Y’all might also enjoy the GOLEM project: http://www.demo.cs.brandeis.edu/golem/

It’s of a similar age and goals, but evolved physical creatures with a screensaver.

I ran that screensaver as a middle schooler and happened to work with Prof. Lipson at the Computational Synthesis Lab in college.

Like other posters here, I feel genetic algorithms got a bit overshadowed by neural networks. Circa 2010, GA’s were capable of some real feats that still seem cutting edge today: deriving the full set of differential equations of metabolism for a bacteria, self-modeling through exploration, finding fundamental laws of physics by watching a double pendulum video, and more.

A ton of good came out of that lab, including a big part of modern open-source 3d printing (which was originally pursued to print the multi-material GOLEM robots!)

Evolved Virtual Creatures (1994)

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