4 ms·
Neural Boids
- titanomachy 7mo ago> That's the whole mechanism... Local perception. Local action. > it’s not communication. It’s physics. Dude, if you’re going to go to all the trouble to make something cool why don’t you take like 20 minutes to write in your own voice about it! I’m so tired of reading robot slop.
- ecto 7mo agoThanks for the feedback :)
- krtab 7mo agoIt's not even only a matter of tone, the content is just.. wrong and incoherent? > Each bird tracks about 6-7 neighbors. Not the closest by distance, but the closest by rank. Bird number 1 through 7, sorted by proximity. I mean, it sounds like it is exactly the 7 closest birds by distance?
- ecto 7mo agoThanks I fucked that up! Updated to make the distinction clearer
- blademaw 7mo agoWow, this post is beautiful. Well done, great read.
- catlifeonmars 7mo agoI found this difficult to read due to the LLM-isms, but love the concept. Gentle feedback: take the time to write articles in your own voice.
- ecto 7mo agoThanks for the feedback :~)
- skolskoly 7mo ago>Sentence fragment. Sentence fragment. Sentence fragment. That's not X, that's Y. I can't even call this LLM smell at this point it's a stench.
- catlifeonmars 7mo agoIf I had to characterize it, most minimally-prompted LLM prose has two issues: - the writing always feels self-important. I would feel a lot more receptive if I perceived that the writer came from a place of humility - like you pointed out, overuse of writing techniques that work best if used sparingly. Sentence fragments can be effective for emphasizing a point, but if you use it in every. Single. paragraph, the effect is gone. It’s very amateurish.
- adammarples 7mo agoWhat's the point of training a nnet on outputs from the original 3 rules so it can effectively just relearn them?
- kjshsh123 7mo agoKinda agree. Training the network with RL instead and penalizing collisions and rewarding collecting something like food would be interesting. As long as the birds can't change direction too quickly (e.g. output acceleration, not velocity) I'd guess you get flocking.
- daemonologist 7mo agoI agree that this would be a more interesting approach; I think you might need more incentives to create a flock though (aerodynamic benefits, predator protection, etc.)
- ecto 7mo agoAwesome idea!
- ecto 7mo agoWhat happens when you want to simulate millions of rules? What if they weren't noids?
- brcmthrowaway 7mo agoYeah, this is well presented slop.
- ecto 7mo agoThanks!
- jayGlow 7mo agobased on the article the noida approach has better performance and is able to run on a GPU while the Sterling implementation must run on a cpu.
- cadamsdotcom 7mo agoVery cool and I love the visualisations.. There’s a saying, “people are smart but crowds are dumb”. One wonders if humans in crowds subconsciously do something like flocking.
- rdedev 7mo agoThe fact that stampedes happen is evidence against it. At least we are not capable of sticking to some basic rules the birds follow when we are startled
- Timon3 7mo agoI don't think the situations are remotely comparable due to the additional spatial dimension available to birds (and fish). When you have a person on each side, and they get pressed into you with enough force, you have no chance - of course people panic. I'll wager the guess that something like this has never happened to a bird.
- fc417fc802 7mo agoGroups of fish and birds both extend into that "extra" dimension so it's not as though there's additional empty space available to them. Yet both sorts of creature exhibit functional group behavior that coordinates high speed travel while being packed fairly tightly. Humans don't do that. That said, failure likely looks quite a bit different. For both fish and birds there's nothing at the edges constraining the cluster aside from unusual situations such as a net or a cave. Whereas the most notable human failures involve what amount to walls on multiple sides. It's amusing to visualize a flock of birds failing to maintain distance and all falling out of the sky as a result.
- Timon3 7mo agoI'm not a mathematician, but I've seen many examples of systems where changes to the number of dimensions cause completely different behavior. This feels like such a system to me. There's also an additional big difference: on the ground, you always have a hard body (the ground) constraining you.
- rdedev 7mo ago> In 2010, a team led by Andrea Cavagna at the University of Rome tracked individual starlings in 3D using multiple synchronized cameras. And they found something surprising! > In 1986 Craig Reynolds encoded this insight as three rules: This part just left a really bad taste in my mouth. I am not against using LLMs to write stuff but please proof read what it writes before posting it. The way it's written it's sounds like Craig Renault travelled in time to 2010 to come up with his rules for boids
- ecto 7mo agoThanks for the feedback, I flipped the two sections. Sorry about your mouth!
- esafak 7mo agoIs this not just self organization?
- ecto 7mo agoAre you not entertained?
- jdlshore 7mo agoI have to second the complaints about LLM writing. The tropes were grating, to the point where I hit the back button before ever learning what the difference between a boid and a noid is. Ecto, I see that you’re reading and responding to comments. In your own words, concisely, and assuming I know what what boids are: what sets this apart?
- ecto 7mo agogpu
- fc417fc802 7mo agoYou sure about that answer? Variants of boids have been implemented to leverage the GPU many times. I'm unclear how far typical GPU based examples deviate but then yours doesn't precisely imitate the original either. GPU accelerated boids is even one of the sample programs provided for testing Dawn when you compile it. [0] Aside from "look ma, machine learning!" I noticed exactly one thing that sets your implementation aside from any other example I've seen before. It seems quite odd to me that you didn't select either neural networks or that feature for this answer. Also the performance analysis section contains several questionable claims. [0] https://dawn.googlesource.com/dawn/+/refs/heads/main/src/dawn/samples/ComputeBoids.cpp https://dawn.googlesource.com/dawn/+/refs/heads/main/src/daw...
- oscarcp 7mo agoHuh, this just gave me an idea (provided a few modifications and enhancements to the noids) to create a god game with true emergent behaviour (yes, that's not very gamey like, I know, it can collapse for no reason). Let's see if I'm smart enough to pull it off (note: I'm waaaay over my head in this)
- herf 7mo agoIt's sad to see an LLM take over a blog, because you can see the line: before 2026 it's an interesting person you would like to talk to. After 2026, it's like generic LLM marketing-voice copy.
- ecto 7mo agoThanks, which of my pre-AI blog posts are your favorite?
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- schlich 7mo agoCool stuff! It's an interesting approach to the starling phenomenon. I'm familiar with the phenomenon through the lens of phase transitions and the critical point, which you allude to in the article briefly. Any further thoughts on how your neural-network based approach maps conceptually to the critical point and related models of emergent behavior?
- ecto 7mo agoFantastic question :) one could extrapolate the possibility space here to see the potential. The interesting core here is the emergent behavior clearly visible in the vizs - what happens when you measure this in higher-dimensional and more connected networks? (What if it’s not noids?) I think this is underexplored in interpretability today
- dexwiz 7mo agoBoids are little lovely simulations. This just looks like a boring force directed graph. I wonder if there is any correlation between the blandness of LLMs and weight based models.
- ecto 7mo agook
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- pton_xd 7mo agoIs this some OpenClaw blogging setup? I've seen similar posts [0] on Twitter lately (not from OpenClaw, but maybe the claw is getting the idea from there). [0] https://x.com/fleetingbits/status/2028669892686438818 https://x.com/fleetingbits/status/2028669892686438818
- ecto 7mo agoNo dude
- friendo_fez 7mo agoReally great work! I would love to know how this could be extended to handle additional information. Things like walls and other environmental factors, pathfinding, keeping formations, etc.
- ecto 7mo agoSimply expand the observation vector!
- abetusk 7mo agoThis is awesome. I think I've heard of other research that's similar to try and speed up Navier-Stokes or other water/smoke/etc. simulation. But this isn't actually recreating murmurations, is it? This is a neural network that's using the Reynolds criteria as a loss function, with Cavagna's topological neighbors? As far as I know, there's no good research that reproduces the murmations seen in starling flocks. This seems like it would be a good use case for neural networks but I don't know of any publicly available 3d data of actual starling flocks, aside from some random YouTube videos floating around.