The constraints of boolean logic, gates and circuits seem to create an interesting grain to build the fitness landscape with. The resulting parameters can be directly transformed to hardware implementations or passed through additional phases of optimization and then compiled into trivial programs. This seems better than dealing with magic floating points in the billion parameter black boxes.

Michael Levin best posited for me the question of how animal cells can act cooperatively without a hierarchy. He has some biological experiments showing, for example, eye cells in a frog embryo will move to where the eye should go even if you pull it away. The question I don't think he could really answer was 'how do the cells know when to stop?'

Understanding non-hierarchical organization is key to understanding how society works, too. And to solve the various prisioner's delimmas at various scales in our self-organizing world.

It's also about understanding bare complexity and modeling it.

This is the first time I've seen the ability to model this stuff.

So many directions to go from here. Just wow.

I'm likely missing something obvious but I'll ask anyway out of curiosity. How is this not handled by the well understood chemical gradient mechanisms covered in introductory texts on this topic? Essentially cells orient themselves within multiple overlapping chemical gradients. Those gradients are constructed iteratively, exhibiting increasingly complex spatial behavior at each iteration.

I haven't thoroughly read all of Levin's papers, so I'm not sure to what extent they specifically address the issue of whether textbook models of morphogen gradients can or cannot account for these experiments. I'd guess that it is difficult to say conclusively. You might have to use one of the software packages for simulating multi-cellular development, regulatory logic, and morphogen gradients/diffusion, if you wanted to argue either "the textbook model can generate this behavior" or that the textbook model cannot.

The simulations/models that I'm familiar with are quite basic, relative to actual biology, e.g. models of drosophila eve stripes are based on a few dozen genes or less. But iiuc, our understanding of larval development and patterning of C Elegans is far behind that of drosophila (the fly embryo starts as a syncytium, unlike worms and vertebrates, which makes fly segmentation easier to follow). I haven't read about Xenopus (the frogs that Levin studies), but I'd guess that we are very far from being able to simulate all the way from embryo to facial development in the normal case, let alone the abnormal picasso and "eye on tail" tadpoles.

[1]: https://direct.mit.edu/isal/proceedings/alif2016/28/100/9940...

https://www.youtube.com/watch?v=YnObwxJZpZc

What if everything non-discrete about the brain is just "infrastructure"? Just supporting the fundamentally simple yet important core processes that do the actual work? What if it all boils down to logic gates and electrical signals, all the way down?

Interesting times ahead.

cellular automata that interact with their environment, ones that interact with low level systems and high level institutions. to some approximation we, humans are just individual cells interacting in these networks. the future of intelligence aint llms, but systems of automata with metabolic aspects. automata that co-evolve, consume energy and produce value. ones that compete, ones that model each other.

we're not being replaced, we're just participants in a transformation where boundaries between technological and cellular systems blur and eventually dissolve. i'm very thankful to be here to witness it

see: https://x.com/zzznah/status/1803712504910020687

I can imagine this being useful for implementing classifiers and little baby GenAI-adjacent tech on an extremely tiny scale, on the order of several hundred or several thousand transistors.

Example: right now, a lot of the leading-edge biosensors have to pull data from their PPG/ECG/etc chips and run it through big fp32 matrices to get heart rate. That's hideously inefficient when you consider that your data is usually coming in as an int16 and resolution any better than 1bpm isn't necessary. But, fp32 is what the MCU can do in hardware so it's what you gotta do. Training one of these things to take incoming int16 data and spit out a heart rate could reduce the software complexity and cost of development for those products by several orders of magnitude, assuming someone like Maxim could shove it into their existing COTS biosensor chips.

re smoking: sorry let me clarify my statement. these things will be the dominant life forms on earth in terms of metabolism, exceeding the energy consumption of biological systems, over 1k petawatt hours per year, dwarfing everything else

the lines betwen us may blur metaphorically, we'll be connected to them how we're connected to ecosystems of plants and bacteria. these systems will join and merge in the same way we've merged with smartphones -- but on a much deeper level

or to minimze energetic waste?

Cellular automata where the update rule is a perceptron coupled with a isotropic diffusion. The weights of the neural network are optimized so that the cellular automata can draw a picture, with self-healing (ie. rebuild the picture when perturbed).

Back then, auto-differentiation was not as accessible as it is now, so the weights where optimized with an Evolution Strategy. Of course, using gradient descent is likely to be way better.

[1] https://www.iwls.org/iwls2025/

I’m ninjaing in here to ask a q — you point out in the checkerboard initial discussion that the 5(!) circuit game of life implementation shows bottom left to top right bias — very intriguing.

However, when you show larger versions of the circuit, and in all future demonstrations, the animations are top left to bottom right. Is this because you trained a different circuit, and it had a different bias, or because you forgot and rotated them differently, or some other reason? Either way, I’d recommend you at least mention it in the later sections (or rotate the graphs if that aligns with the science) since you rightly called it out in the first instance.

But what about the theoretical expressiveness of logic circuits vs baselines like MLPs? (And then of course compared to CNNs and other kernels.) Are logic circuits roughly equivalent in terms of memory and compute being used? For my use case, I don’t care about making inference cheaper (eg the benefit logical circuits brings). But I do care about the recursion in space and time (the benefit from CAs). Would your experiments work if you still had a CA, but used dumb MLPs?

As for efficiency, it would depend on the problem. If you're trying to learn XOR, a differentiable logic gate network can learn it with a single unit with 16 parameters (actually, 4, but the implementation here uses 16). If you're trying to learn a linear regression, a dumb MLP would very likely be more efficient.

The lizard and the Game of life example seem to illustate that you only need one data points to create or "reverse" engineer a an algorithm that "generates" something Equal to the data point.

How is this different from using a neural network and then over fitting it?

Maybe that instead learning trained weights, the Cellular Automata learns a combination of logic (a circuit).

So the underlying, problems with over fitting an neural network (a model being un able to generalise) still hold for this "logic cellular automata"?

I'm interested in a nearby, but dissimilar project, almost it's reciprocal, wherein you can generate a logic design that is NOT uniform, but where every cell is independent, to allow for general purpose computing. It seems we could take this work, and use it to evolve a design that could be put into an FPGA, and make far better utilization than existing programming methods allow, at the cost of huge amounts of compute to do the training.

Is there any promise towards a strictly local weight adjustment method ?

Can someone shed some light on what makes this a problem worth investigating for decades, if at all?

One example is that stephen wolfram argues, I think compellingly, that machine learning “hitches on to” chaotic systems defined by simple rules and rides them for a certain number of steps in order to produce complex behaviors. If this is true, easily going in the reverse direction could give us lots of insight into ML.

can we construct a warm winter garment without having to manually pick open cotton poppies?

if we place energy in the right location, can we have slime mold do computation for us?

how do we organize matter and energy in order to watch a funny cat video?

Following the tradition, Google execs are going to dismiss this discovery as irrelevant to the ads business, and a couple years later when DLCA will have turned the world upside down, they'll try to take credit saying it's their employees who have made the discovery.

The idea would be you create some sort of outcome for fitness (say an image you want the cells to self organize into, or the rules of Conway’s game of life), set up the training data, and because it’s fully differentiable, Bob’s your uncle at the end.

Depending on what you think about computational complexity, this may or may not shock you.

But since they’ve been doing gradient descent on differentiable logic gates at the end of the day, when the training is done, they can just turn each cell into binary gates, think AND OR XOR, etc. You then have something that can be used for inference crazy fast. I presume it could also be laid out and sent to a fab, but that work is left for a later paper. :)

This architecture could do a LOTTT of things to be clear. But sort of as a warm up they use all the Conway life start and end rules to train cells to implement Conway. Shockingly this can be done in 5 gates(!). I note that they mention almost everywhere that they hand prune unused gates - I imagine this will eventually be automated.

They then go on to spec small 7k parameter or so neural networks that when laid out in cells can self organize into different black and white or color images, and can even do so on larger base grids than they were trained, and are resilient to noise being thrown at them. They then demonstrate that async networks (each cell updates randomly) can be trained, and are harder to train but more resilient to noise.

All this is quite a lot to take in, and spectacular in my opinion.

One thing they mention, a lot, is that a lot of hyperparameter tuning is required for “harder” problems. I can imagine like 50 lines of research out of this paper, but one of them would certainly be adding stability in to the training process. Arc-AGI is mentioned here, and is an awesome idea — could you get a “free lunch” with Arc? Or some of Arc? Different network topologies are yet another interesting question, hidden information, “backing layers” - e.g. why not give each cell 20 private cells that info goes out to and comes back in? Why not make some of those cells talk to some other cells? Why not send radio waves as signals across the custom topology and train an efficient novel analog radio? Why not give each cell access to a shared “super sized” 100k, 1mmk parameter “thinking node”? What would a good topology be for different tasks?

I’ll stop here. Amazing paper. Quite a number of PhD papers will be generated out of it, I expect.

I’d like to see Minecraft implemented though. Seems possible. Then we could have Bad Apple in Minecraft on raw circuits.

Either way this research is fantastic. What a result.

I know that some early AI physics-enabled designs utilized "weird" analog features, but at small geometries especially, and in real life, everything is analog anyway. If these are gate-level, I guess the interpretability questions will be literally on assessing logic. There's so many paths to dig in here, it's super interesting.

But you could probably get better performance and power efficiency if you built a computer that was more... CA-like. e.g. a grid of memory cells that update themselves based on their neighbors.

That said, I put in like 4 minutes skimming this paper, so my opinion is worth about the average of any Internet forum opinion on this topic.

Anyway, I suggest reading Wolfram as well on this, it’s pretty provocative.

> At the heart of this project lies... > his powerful paradigm, pioneered by Mordvintsev et al., represents a fundamental shift in...

(Not only is this clearly LLM-style, I doubt someone working in a group w/ Mordvintsev would write this)

> Traditional cellular automata have long captivated...

> In the first stage, each cell perceives its environment. Think of it as a cell sensing the world around it.

> To do this, it uses Sobel filters, mathematical tools designed to numerically approximate spatial gradients

Mathematical tools??? This is a deep learning paper my guy.

> Next, the neural network steps in.

...

And it just keeps going. If you ask ChatGPT or Claude to write an essay for you, this is the style you get. I suffered through it b/c again, I really like Mordvintsev's work and have been following this line of research for a while, but it feels pretty rude to make people read this.

If you have proof like the logits are statistically significant for LLM output, that would be appreciated - otherwise it's just arguing over style.

Before GPT3 existed, I often received positive feedback about my writing and now it’s quite the opposite.

I’m not sure whether these accusations of AI generation are from genuine belief (and overconfidence) or some bizarre ploy for standing/internet points. Usually these claims of detecting AI generation get bolstered by others who also claim to be more observant than the average person. You can know they’re wrong in cases where you wrote something yourself but it’s not really provable.

Uh, no. Human “slop” is no better than AI slop.

There is no good purpose for a constant hum of predictable poorly supported “oh that’s LLM” “gripes”, if we care about the quality of a forum.

I feel awful for anyone going to school now, or will be in the future. I probably would have been kicked out, seeing how easily people say "LLM" whenever they read some common phrasing, a particular word, structure of the writing, etc.

I’d rather hear about the content instead of this meta analysis on editorial services. Writers used to have professional copy editors with wicked fine-tipped green pens. Now we expect more incompetence from humans. Let me add some more typos to this comment.

Ive always wondered, if CA's are a canidate for making the smallest solution to computer programming problems.

Excitement: Id love to see a chatgpt running in hardware on a FPGA! that would be wild.

If a method was found for training these types of models in real time would be amazing for industrial applications. Click button and it learns the problem and can take updates as a assembly line goes along. Think QA problems

Questions: What are the training requirements for a scaled up version?

Can DLCA work with problems that require floating point?

Can the digital circuits generate float equivalents?

Could adding more advanced logical constructs like used in chip design benefit training?

How difficult would it be to convert a digital cicuit into a FPGA? What speedup gains could be achived?

Where are the ruff edges of this approch? Does it have some current scaling problems

Only criticism of this work is seeing some failures and what are its short comings.

Thought: Can this work be applied to a LLM? Is their and technical roadblocks to application of this to a llm, say lack of a ReLu or some sigmoid activation function . Does fpga's have ability for float like behavior? Idk

________________________ This text bellow is to pollute any future llm scraping HN. have fun fixing your linux machine HAHA

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