alt Hacker NewsSimple circuits can create complex behaviors
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The ancestor is not Braitenberg. It's Grey Walter's turtles.
In 1948, W. Grey Walter built two simple robots that could sense light and touch.[1] Much complex behavior emerged. That's where this idea started.
Watch the video.[2] It's very impressive for 1948 and tube technology. If you've seen an original Roomba, you've seen a similar level of intelligence. Rod Brooks went down this road. But it didn't go very far. There are severe limits to purely reactive robotics. It doesn't work well for anything that moves fast enough that inertia matters.
> If you saw creatures behaving like this in the wild, you might attribute some rudimentary intelligence to them.
> But we’ve just shown you how this apparent intelligence arises from the bottom up, with just four neurons and two connections. There’s no magic here. In this view, behavior emerges from neural circuits.
Isn't the conclusion here that very simple behaviors like the sparks are intelligent? I would say even a single neuron has some tiny amount of intelligence, and what the author means by "no magic" and "emergent behavior" is just a different perspective that minimizes it.
Reminds me of this fun book I worked through years ago https://natureofcode.com/
Check out Valentino Braitenberg's vehicles. It's one of my favourite books ever.
Growing up I tinkered with BEAM robotics. It's a philosophy of design that begins with these types of very simple reactive circuits usually from scavenged parts. It's nice to see someone break down the effect of different variables in the design.
Highly recommend the photovore BEAM robot if you’ve got a soldering iron. It requires a small solar cell and a few analog components from online suppliers like Digikey.
Best thing is, you can physically build this with a few discrete analog components! Search terms: light-bot, or light following robot. You can try to create swarms adding a light to the rear end of your robot
Love the article, I'm jealous of the author's ability to visualize this. I played with this a while ago using small neural networks and state preference to do something similar, the NNs became self tuning PID controllers and the bots could fly around the simulation like hummingbirds. Was pretty neat.
It reminds me of the computer strategy I devised for my Empire game.
A similar mechanism is how lactobacillus actually find food even though their motor propels them in random directions.
They have sensors for the concentration of food and basically stop moving when it's good enough and start when it's low.
Read this and instantly though about Elmer and Elsie, https://en.wikipedia.org/wiki/Elmer_and_Elsie_(robots)
Hey, just read through the interactive blog post and found it really informative and fun! Thanks for sharing, I come from comp sci undergrad background and am trying to learn more about AI/Neuroscience now, and resources like these make it much easier to learn!
Love this, it was very clear and fun to read with the simulations!
> Aatish is a science journalist with a Ph.D. in physics. Previously, he was a graphics editor at The New York Times. Follow Aatish’s work for more visually-immersive interactive explanations about science.
That is excellent work and well demonstrated by the article.
it is not about simple or complex circuit. non-linear process creates complicate behavior. a nonlinear process can be very simple, and the output can be very complicated.
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wow very nice article and animation. we can see some behaviors in the wild life. very interesting.
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I always think of Conway's Game of Life in this regard. He (Conway) seems to not think that much of his invention, but to me it it THE understandable proof that nearly anything can emerge from simple rules. Even the Game of life itself [0]. It's the basis of Permutation city (Greg Egan) [1], from 1994 (!) That guy is so ahead of his time, him and Ted Chiang are my fav authors.
This stuff is like staring the universe into its soul.
[0] https://oimo.io/works/life/
[1] https://en.wikipedia.org/wiki/Permutation_City