Growing Neural Cellular Automata

The Game of Life on steroids! This model learns to grow complex patterns in an entirely local way. Each cell is trained to listen to its neighbors and update itself in a way such that, collectively, an overall goal is reached. Fascinating and interactive!
distill.pub/2020/growing-ca/
en.wikipedia.org/wiki/Conway%...
Abstract:
Most multicellular organisms begin their life as a single egg cell - a single cell whose progeny reliably self-assemble into highly complex anatomies with many organs and tissues in precisely the same arrangement each time. The ability to build their own bodies is probably the most fundamental skill every living creature possesses. Morphogenesis (the process of an organism’s shape development) is one of the most striking examples of a phenomenon called self-organisation. Cells, the tiny building blocks of bodies, communicate with their neighbors to decide the shape of organs and body plans, where to grow each organ, how to interconnect them, and when to eventually stop. Understanding the interplay of the emergence of complex outcomes from simple rules and homeostatic 1 feedback loops is an active area of research. What is clear is that evolution has learned to exploit the laws of physics and computation to implement the highly robust morphogenetic software that runs on genome-encoded cellular hardware.
This process is extremely robust to perturbations. Even when the organism is fully developed, some species still have the capability to repair damage - a process known as regeneration. Some creatures, such as salamanders, can fully regenerate vital organs, limbs, eyes, or even parts of the brain! Morphogenesis is a surprisingly adaptive process. Sometimes even a very atypical development process can result in a viable organism - for example, when an early mammalian embryo is cut in two, each half will form a complete individual - monozygotic twins!
The biggest puzzle in this field is the question of how the cell collective knows what to build and when to stop. The sciences of genomics and stem cell biology are only part of the puzzle, as they explain the distribution of specific components in each cell, and the establishment of different types of cells. While we know of many genes that are required for the process of regeneration, we still do not know the algorithm that is sufficient for cells to know how to build or remodel complex organs to a very specific anatomical end-goal. Thus, one major lynch-pin of future work in biomedicine is the discovery of the process by which large-scale anatomy is specified within cell collectives, and how we can rewrite this information to have rational control of growth and form. It is also becoming clear that the software of life possesses numerous modules or subroutines, such as “build an eye here”, which can be activated with simple signal triggers. Discovery of such subroutines and a mapping out of the developmental logic is a new field at the intersection of developmental biology and computer science. An important next step is to try to formulate computational models of this process, both to enrich the conceptual toolkit of biologists and to help translate the discoveries of biology into better robotics and computational technology.
Imagine if we could design systems of the same plasticity and robustness as biological life: structures and machines that could grow and repair themselves. Such technology would transform the current efforts in regenerative medicine, where scientists and clinicians seek to discover the inputs or stimuli that could cause cells in the body to build structures on demand as needed. To help crack the puzzle of the morphogenetic code, and also exploit the insights of biology to create self-repairing systems in real life, we try to replicate some of the desired properties in an in silico experiment.
Authors: Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson, Michael Levin
Links:
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Twitter: / ykilcher
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Minds: www.minds.com/ykilcher

[@oneman7094]

This channel is gold.

[@nikolayandcards]

Agree. Found this hidden gem just today.

[@-into-the-void-]

The smileys look like they are in excruciating pain.

[@SuperCobraUltra]

Excellent video... much clearer explanation than 2-minute papers.

[@luna010]

fucking robots

[@SuperCobraUltra]

Really inspired that they backpropagate through time instead of using evolutionary methods like most "artificial embryology", that was a suitable update rule could be discovered much more quickly and perhaps more accurate rules could be discovered.

[@PunmasterSTP]

This was fascinating, and you broke things down really well! I'm very glad I came across your video today.

[@SuperCobraUltra]

Crucial insight buried in the video here about aging (not a new idea, but nice to see it implied here)... how the update rules' result seems to go awry after the number of steps it has been trained for.... maybe this is why our bodies start breaking down and going wonky after we live past the typical reproductive period of our ancestors! Maybe part of aging is that our morphogenic+morphostatic system has not been trained/evolved to maintain our structure past a certain point. The methods they used to overcome this might inspire distant future attempts at combating or preventing aging. Really really excellent video...made it simple for dumb people like me to understand and nerd out on, thanks!

[@rodrigoff7456]

That is a very interesting thought! A few months ago Veritasium made a video about some of the new insights on body aging: kzbin.info/www/bejne/h4PXaH-gpq9na80

[@revimfadli4666]

Some of them really looked like cancerous growth

[@paulfrischknecht3999]

Very interesting research. This kind of model definitely gets close to what must be going on in a body growing out of a lump of cells. Clearly, there cannot be much reliable communication (with chamicals or otherwise) at great distances, especially not before the blood and nervous systems are in place, so the body-shaping decisions must be made locally. However, each cell has the same complex program so "The network parametrizing this update rule consists of approximately 8,000 parameters. " does not sound excessive. "Typical cellular automata update all cells simultaneously. This implies the existence of a global clock, synchronizing all cells. Relying on global synchronisation is not something one expects from a self-organising system. " is also another important consideration.

[@dhiahassen9414]

maybe the womb does the synchronization at some stages

[@ianweckhorst3200]

I remember experimenting with this thing, all I ever did was make walls of chaos or accidentally delete everything

[@ClosiusBeg]

Amazing! This is regeneration process. Its interesting how to apply it to something

[@jrkirby93]

Reminds me a lot of the wavefunction collapse procedural generation technique. Of course that algorithm isn't entirely local like this, and it doesn't require training a model either. This begs the question, how big and how complex can you make the images before the model starts failing to reproduce them with low error? If you increase the hidden state size and model size, how does that change things? Could you use this as a way to measure image complexity in a more nuance way than resolution?

[@YannicKilcher]

True. It would be interesting to analyze how much of the overall structure is encoded in each cell's hidden state. Maybe this whole thing is just trivial and all global information is hidden in there.

[@simonstrandgaard5503]

Interesting explanation. And awesome to interact with.

[@mersidems7437]

it reminds me of the principle of "free energy". Where the system fights the external environment and maintains itself in a stable state.

[@anyabataeva729]

loved it

[@von_nobody]

Only thing missing is encoding this neural network per pixel and have multiple different network competing with each other :D

[@dionbridger5944]

Decent explanation of the article. One point of confusion for me though - when you mentioned the 'residual connection' being the key to making the whole thing work ; did you mean that the fact that they output a delta pixel and add that to the input pixel to get value of that pixel at the next time step?

[@revimfadli4666]

Sort of, since it computes deltas rather than the new value itself, there's an "identity" connection from a timestep to its previous one, which could let gradients pass nearly undisturbed/without being shrunk/exploded due to matrix multiplications & activation functions

[@taku8751]

It is amazing, god said "To build an eye", then the eye comes out. The only problem is when organs combine together, the cell on the border can not make clear which organ should it belongs to.

[@masonhunter2748]

God said: “here’s atoms, do stuff”

[@ianweckhorst3200]

Although the website is non functional now as the original patterns were replaced with black dots and it is NOT configured to grow from that, it’s only configured to have a preexisting pattern, I know this is an old video, but can someone fix that?

[@yabdelm]

Do you know how I could sort of re-do this but for sounds?

[@revimfadli4666]

Perhaps make it a 1D cellular automata? With amplitude in place of the RGB channels. Do you wanna use it to recover sounds? Because it seems that this algorithm will only output a specific pattern it's been trained on. I wonder if a GAN could generalize better

[@eladwarshawsky7587]

Would this be improved if each pixel was also embedded with it's global positional embedding normalized to between 0 and 1?

[@k.k.9378]

Giving cells any global knowledge defeats the point and makes it trivial.

[@revimfadli4666]

10:00 is that the CA equivalent of cancer? Lol 13:41 really reminds me of two-headed flatworms

[@Kerrosene]

Like the T-1000 this is

[@surferbois]

Unfortunately, I still don't quite get how this thing actually works with only so few information at hand.

[@CheapDeath96]

lol one guy did not like this video

[@lenasearcy9511]

Is cnn the reason why people are so hooked on watching the news, which loops? I think it creates anxiety and a false sense of reality in people. Just a "dumb" question.

[@paddy9609]

what?