11 ms·

Growing Neural Cellular Automata: A Differentiable Model of Morphogenesis

210 points by eyvindn 7y ago

RosanaAnaDana 7y ago
Game changing!
- openfuture 7y ago
  Why?
  - m_que 7y ago
    This is the foundation for 'programming' morphogenesis of multi-cellular synthetic life in the far future. Of course you first have to get the programming of monocelluar life to work. Also, the reverse might be possible, i.e., decompiling genetic code from phenotype and so on.
    - dropit_sphere 7y ago
      >Also, the reverse might be possible, i.e., decompiling genetic code from phenotype and so on. I don't know anything about decompiling or ML, but doesn't the black box nature of NN rules this out?
      - m_que 7y ago
        NNs are not a black box in the sense that you cannot see what is going on in them, but in the sense that we currently just very badly understand. You have a function approximator that you have to translate into some other representation. As long as you know how basic building blocks map or can be approximated, it might be possible.
    - imvetri 7y ago
      Foundation for programming the morphogenesis. Yes. morphogenesis - concept is fundamentally flawed. It's not thoroughly put in theories. Computing has touring test. Does concepts like morphogenesis has any tests? Nope. So building something on top of half baked concepts will not scale/sustain/be a foundation. If we really had understood morphogenesis in the past and the it is 100% put in theories, we would be having a virtual world mimicking biological world by now.
      - m_que 7y ago
        Yes, morphological models can be tested. You can hypothesize to reactions to alterations of an organism structurally and chemically (morphogenes). Diffusion-reaction models are available for certain kind of morphogenetic behavior. See also https://www.brandeis.edu/now/2014/march/turingpnas.html https://www.brandeis.edu/now/2014/march/turingpnas.html
eyvindn 7y ago
One of the authors here. Feel free to ask any questions! We encourage you to play with the attached Colab with which you can train models from scratch in <30min.
- plafl 7y ago
  Congratulations, it's really cool. It's late here now, I will play with it tomorrow. I actually have a lot of questions! - How well the CA rules "compress" the image against a typical image compression algorithm? - Have you tried 3D? Animations? - What happens if you insert small noise to the learned parameters? It gets destroyed or mutates the image to something similar? - Is it practical with more complex images, for example, a human face?
  - heyitsguay 7y ago
    I'm wondering these things too. You say in the paper that your net is quite small - I wonder if you could first extend it to regenerative sprite animations for some fun procedural NPCs in a roguelike? If you can efficiently add or change a pixel or two per frame in a sprite+collision mask, imagine battle sequences where things get hacked off, maybe regrow if you wait too long, etc.
  - eyvindn 7y ago
    1) This is often the first question many people familiar with NNs ask and rightly so. Compression was not one of our goals with this article, and it would look like a terrible compression algorithm if that were its purpose. In fact, the model displayed here is about 8.3k parameters, although the WebGL model is quantized (more info on this in the last section), and each model learns to encode an image consisting of 44x44x3 = 5808 integers. We made no attempt to minimize this number. The key thing to bear in mind is that all the cells have the exact same rule and the image generation starts from a single one of them - meaning they have to learn to communicate locally with their neighbours to self-organize in the correct pattern. This is a very non-trivial task and the majority of the model’s parameters are likely going towards this communication protocol and growth behavior. 2) We have not tried 3D. As for animations, some of our earliest experiments suggested one could achieve “animations” by applying the loss at key-points to have the model learn to iterate through these points across several time steps. 3) One could argue the WebGL implementation does this to some extent by quantizing the learned weights we take from the Tensorflow training code. The model remains very resilient and worked out of the box in almost all cases. Moreover, if one tried to inject explicit noise to the CA in a given location, some models would have no problems adapting to it, while others would fail miserably. Some early experiments yielded some remarkably resistant models, able to resist while being subject to continuous globally occurring noise. We suspect explicitly training them while introducing noise would allow us to drive the model towards more consistently resistant behaviors. 4) One of the main obstacles to larger patterns at the moment is memory usage during a forward/backward pass. There are optimization and tricks we plan to employ to generate larger and more complex patterns, which may be discussed in a follow up thread.
tehsauce 7y ago
One day Von Neumann architecture will be superseded by something more similar to cellular automata. Von Neumann architecture makes sense from a logical perspective which is not concerned with physical space or scaling efficiency. Chips designed as distributed meshes similar to neurons in the brain will eventually take us much further.
- UncleOxidant 7y ago
  In as much as CAs are a specific type of systolic array, yes, I think we'll move more in that direction towards systolic arrays.
- ColanR 7y ago
  From what I understand, what we call Von Neumann architecture was actually his 'first draft' prototype, and what you describe is what he would have built next - if he hadn't died of brain cancer.
  - DonHopkins 7y ago
    I posted about this and typed in some stuff from his book earlier: https://news.ycombinator.com/item?id=21858465 https://news.ycombinator.com/item?id=21858465 John von Neuman's 29 state cellular automata machine is (ironically) a classical decidedly "non von Neumann architecture". https://en.wikipedia.org/wiki/Von_Neumann_cellular_automaton https://en.wikipedia.org/wiki/Von_Neumann_cellular_automaton He wrote the book on "Theory of Self-Reproducing Automata": https://archive.org/details/theoryofselfrepr00vonn_0 https://archive.org/details/theoryofselfrepr00vonn_0 He designed a 29 state cellular automata architecture to implement a universal constructor that could reproduce itself (which he worked out on paper, amazingly): https://en.wikipedia.org/wiki/Von_Neumann_universal_constructor https://en.wikipedia.org/wiki/Von_Neumann_universal_construc... He actually philosophized about three different kinds of universal constructors at different levels of reality: First, the purely deterministic and relatively harmless mathematical kind referenced above, an idealized abstract 29 state cellular automata, which could reproduce itself with a Universal Constructor, but was quite brittle, synchronous, and intolerant of errors. These have been digitally implemented in the real world on modern computing machinery, and they make great virtual pets, kind of like digital tribbles, but not as cute and fuzzy. https://github.com/SimHacker/CAM6/blob/master/javascript/CAM6.js#L4569 https://github.com/SimHacker/CAM6/blob/master/javascript/CAM... Second, the physical mechanical and potentially dangerous kind, which is robust and error tolerant enough to work in the real world (given enough resources), and is now a popular theme in sci-fi: the self reproducing robot swarms called "Von Neumann Probes" on the astronomical scale, or "Gray Goo" on the nanotech scale. https://en.wikipedia.org/wiki/Self-replicating_spacecraft#Von_Neumann_probes https://en.wikipedia.org/wiki/Self-replicating_spacecraft#Vo... https://grey-goo.fandom.com/wiki/Von_Neumann_probe https://grey-goo.fandom.com/wiki/Von_Neumann_probe >The von Neumann probe, nicknamed the Goo, was a self-replicating nanomass capable of traversing through keyholes, which are wormholes in space. The probe was named after Hungarian-American scientist John von Neumann, who popularized the idea of self-replicating machines. Third, the probabilistic quantum mechanical kind, which could mutate and model evolutionary processes, and rip holes in the space-time continuum, which he unfortunately (or fortunately, the the sake of humanity) didn't have time to fully explore before his tragic death. p. 99 of "Theory of Self-Reproducing Automata": >Von Neumann had been interested in the applications of probability theory throughout his career; his work on the foundations of quantum mechanics and his theory of games are examples. When he became interested in automata, it was natural for him to apply probability theory here also. The Third Lecture of Part I of the present work is devoted to this subject. His "Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components" is the first work on probabilistic automata, that is, automata in which the transitions between states are probabilistic rather than deterministic. Whenever he discussed self-reproduction, he mentioned mutations, which are random changes of elements (cf. p. 86 above and Sec. 1.7.4.2 below). In Section 1.1.2.1 above and Section 1.8 below he posed the problems of modeling evolutionary processes in the framework of automata theory, of quantizing natural selection, and of explaining how highly efficient, complex, powerful automata can evolve from inefficient, simple, weak automata. A complete solution to these problems would give us a probabilistic model of self-reproduction and evolution. [9] [9] For some related work, see J. H. Holland, "Outline for a Logical Theory of Adaptive Systems", and "Concerning Efficient Adaptive Systems". https://www.deepdyve.com/lp/association-for-computing-machinery/outline-for-a-logical-theory-of-adaptive-systems-efsWyqMa1l https://www.deepdyve.com/lp/association-for-computing-machin... https://deepblue.lib.umich.edu/bitstream/handle/2027.42/5578/bac4296.0001.001.pdf?sequence=5 https://deepblue.lib.umich.edu/bitstream/handle/2027.42/5578... https://www.worldscientific.com/worldscibooks/10.1142/10841 https://www.worldscientific.com/worldscibooks/10.1142/10841
gwenzek 7y ago
I think I broke the Matrix: https://i.ibb.co/8zGRcNd/Screenshot-20200211-230621-Firefox-Beta.jpg https://i.ibb.co/8zGRcNd/Screenshot-20200211-230621-Firefox-... I erased the lezard but the tail and it grew back very weirdly. How robust is this method?
fishmaster 7y ago
This kind of reminds me of this comment (moveable feast engine): https://news.ycombinator.com/item?id=17877155 https://news.ycombinator.com/item?id=17877155
- DonHopkins 7y ago
  David Ackley's talks are amazing and mind expanding. Public Lecture: Artificial Life for Bigger & Safer Computing Talk presented October 8, 2014 as part of the John von Neumann Public Lecture series at the Center for Complexity and Collective Computation (C4) in Madison, Wisconsin. Video recorded by Alan Ruby of the Wisconsin Insitute for Discovery. https://www.youtube.com/watch?v=NqSnoJ-VGH4 https://www.youtube.com/watch?v=NqSnoJ-VGH4 He stakes out a bold iconoclastic position that the ridiculously successful deterministic approach to computing is a dead end that will not scale, but he's not just like Clifford Stoll predicting that nobody's going to ever shop over the internet (bless his heart for owning up to that ;), because he's taking a much longer term view, and his beliefs don't contradict von Neumann, but actually reflect what von Neumann predicted. John von Neumann actually predicted that his own approach was going to fall down. The future "...logic of automata will differ from the present system of formal logic in two relevant respects: 1. The actual length ... of the chains of operations will have to be considered. 2. The operations of logic ... will all have to be treated by procedures which allow exceptions (malfunctions) with low but non-zero probabilities." "...natural organisms are constructed to make errors as ... harmless as possible. Artificial automata are designed to make errors as ... disastrous as possible ... We are ... much more 'scared' by the occurrence of an isolated error and by the malfunction ... behind it. Our behavior is clearly that of overcaution, generated by ignorance." -John von Neumann, 1948
DonHopkins 7y ago
Also check out the "Moveable Feast Machine", Robust-first Computing, and this Distributed City Generation example: https://news.ycombinator.com/item?id=21858577 https://news.ycombinator.com/item?id=21858577 DonHopkins on Oct 26, 2017 | parent | favorite | on: Cryptography with Cellular Automata (1985) [pdf] A "Moveable Feast Machine" is a "Robust First" asynchronous distributed fault tolerant cellular-automata-like computer architecture. It's similar to a Cellular Automata, but it different in several important ways, for the sake of "Robust First Computing". These differences give some insight into what CA really are, and what their limitations are. Cellular Automata are synchronous and deterministic, and can only modify the current cell: all cells are evaluated at once (so the evaluation order doesn't matter), so it's necessary to double buffer the "before" and "after" cells, and the rule can only change the value of the current (center) cell. Moveable Feast Machines are like asynchronous non-deterministic cellular automata with large windows that can modify adjacent cells. Here's a great example with an amazing demo and explanation, and some stuff I posted about it earlier: https://news.ycombinator.com/item?id=14236973 https://news.ycombinator.com/item?id=14236973 Robust-first Computing: Distributed City Generation: https://www.youtube.com/watch?v=XkSXERxucPc https://www.youtube.com/watch?v=XkSXERxucPc
- otoburb 7y ago
  David Ackley's[1] real-life hardware implementation of Robust-First Computing is at a new YouTube channel called the T2 Tile Project[2]. He jumpstarted the new channel after retiring from University of New Mexico. He releases new updates every Tuesday like clockwork. This is all he thinks, talks, dreams about and builds. It's amazing. [1] https://www.cs.unm.edu/~ackley/ https://www.cs.unm.edu/~ackley/ [2] https://www.youtube.com/channel/UC1M91QuLZfCzHjBMEKvIc-A https://www.youtube.com/channel/UC1M91QuLZfCzHjBMEKvIc-A
  - voldacar 7y ago
    Ackley is literally thinking hundreds of years ahead of the hardware we have. It's a shame that his ideas will probably never make much of a dent in the current computing ecosystem
deleted 7y ago
[deleted]
caetris1 7y ago
Spectacular. I think most people might be lost on the significance of what has been done here. I suspect that it's because the framing the solution focuses on is based on morphogenesis. That is, in fact, not what is going on here. A more suitable metaphor would be the functional basis of a visual hallucination. The very same mechanics of the demonstrated result are described in Oliver Sack's book on the subject. Learned patterns are procedurally generated through probabilistic inference of adjacent feature vectors. Dreams work in a similar way. To prove that this is what is going on, I suggest training the CA on multiple frames of an animation of the lizard walking. The research is really promising but the conclusion that the authors come to is that this system can be used to generate self-organizing agents that evolve and replicate. There is no possibility of this result based on the approach. However, they've actually created something much better than the intended result... a real-time auto-complete for visual scenes.
- m_que 7y ago
  Other works of the authors on deep dreaming reflect better what you describe here. The astonishing results with regard to morphogeneses cannot be emphesized enough: A complex structure is robustly encoded in a single function and can be reproduced from a single grid cell.
HelloNurse 7y ago
I played with "regenerating" models by filling the field with multiple overlapping copies and repeatedly changing the angle, with rather weak results. As the shapes are disintegrated and rebuilt, patterns can form: the eye forms brown and white thick straight "sausages", the yellow striped fish forms similar but smaller strips, the butterfly and the pretzel exhibit crawling shapes with partial features and empty space, the christmas tree can develop stripes on a green background instead of balls, the lizard reforms very effectively recovering empty space, the ladybug reforms partially (heads in a sea of wing material), the explosion produces an uniform patch of the interior section as growth extinguishes the border.
zackmorris 7y ago
The models described in this article run on the powerful GPU of a modern computer or a smartphone. Yet, let’s speculate about what a “more physical” implementation of such a system could look like. We can imagine it as a grid of tiny independent computers, simulating individual cells. Each of those computers would require approximately 10Kb of ROM to store the “cell genome”: neural network weights and the control code, and about 256 bytes of RAM for the cell state and intermediate activations. The cells must be able to communicate their 16-value state vectors to neighbors. Each cell would also require an RGB-diode to display the color of the pixel it represents. A single cell update would require about 10k multiply-add operations and does not have to be synchronised across the grid. We propose that cells might wait for random time intervals between updates. The system described above is uniform and decentralised. Yet, our method provides a way to program it to reach the predefined global state, and recover this state in case of multi-element failures and restarts. We therefore conjecture this kind of modeling may be used for designing reliable, self-organising agents. On the more theoretical machine learning front, we show an instance of a decentralized model able to accomplish remarkably complex tasks. We believe this direction to be opposite to the more traditional global modeling used in the majority of contemporary work in the deep learning field, and we hope this work to be an inspiration to explore more decentralized learning modeling. I've been hoping that someone would build the above computer for over 20 years. I used to just get laughed at, or people would mock me for not knowing what I'm talking about (even though I have a computer engineering degree). That transitioned to at least speculating on what I was saying, and will soon transition to being old hat. Oh well. But I look at computers today with a billion transistors, running at the same 3 GHz as they did 20 years ago, and it breaks my heart. I imagine what might have been, if we had arrays of hundreds/thousands of z80 or x86 or 68000 or DEC Alpha or MIPS chips to do whatever we wanted with. Imagine the emergent behavior that might have happened. Instead we're locked into this proprietary/narrow way of working with shaders and specialized neural computers doing SIMD and I just find it all so uninspiring. But I'm hopeful that some of the recent work with getting these older processors running on FPGAs will bear fruit. https://news.ycombinator.com/item?id=17833829 https://news.ycombinator.com/item?id=17833829 https://news.ycombinator.com/item?id=19329083 https://news.ycombinator.com/item?id=19329083 https://news.ycombinator.com/item?id=10089807 https://news.ycombinator.com/item?id=10089807 https://news.ycombinator.com/item?id=3267428 https://news.ycombinator.com/item?id=3267428 https://news.ycombinator.com/item?id=19885901 https://news.ycombinator.com/item?id=19885901 https://www.google.com/search?q=processor+on+FPGA+site:news.ycombinator.com https://www.google.com/search?q=processor+on+FPGA+site:news....
sasaciric 7y ago
Great work, very much related to our previous work on CA morphogenesis and replication: https://ieeexplore.ieee.org/abstract/document/8004527 https://ieeexplore.ieee.org/abstract/document/8004527 https://link.springer.com/chapter/10.1007/978-3-319-45823-6_2 https://link.springer.com/chapter/10.1007/978-3-319-45823-6_... https://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_00191 https://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_0019... https://www.mitpressjournals.org/doi/abs/10.1162/978-0-262-33027-5-ch010 https://www.mitpressjournals.org/doi/abs/10.1162/978-0-262-3... https://ieeexplore.ieee.org/abstract/document/7008733 https://ieeexplore.ieee.org/abstract/document/7008733

210 points by eyvindn 7y ago

RosanaAnaDana 7y ago

Game changing!

openfuture 7y ago
Why?
- m_que 7y ago
  This is the foundation for 'programming' morphogenesis of multi-cellular synthetic life in the far future. Of course you first have to get the programming of monocelluar life to work. Also, the reverse might be possible, i.e., decompiling genetic code from phenotype and so on.
  - dropit_sphere 7y ago
    >Also, the reverse might be possible, i.e., decompiling genetic code from phenotype and so on. I don't know anything about decompiling or ML, but doesn't the black box nature of NN rules this out?
    - m_que 7y ago
      NNs are not a black box in the sense that you cannot see what is going on in them, but in the sense that we currently just very badly understand. You have a function approximator that you have to translate into some other representation. As long as you know how basic building blocks map or can be approximated, it might be possible.
  - imvetri 7y ago
    Foundation for programming the morphogenesis. Yes. morphogenesis - concept is fundamentally flawed. It's not thoroughly put in theories. Computing has touring test. Does concepts like morphogenesis has any tests? Nope. So building something on top of half baked concepts will not scale/sustain/be a foundation. If we really had understood morphogenesis in the past and the it is 100% put in theories, we would be having a virtual world mimicking biological world by now.
    - m_que 7y ago
      Yes, morphological models can be tested. You can hypothesize to reactions to alterations of an organism structurally and chemically (morphogenes). Diffusion-reaction models are available for certain kind of morphogenetic behavior. See also https://www.brandeis.edu/now/2014/march/turingpnas.html https://www.brandeis.edu/now/2014/march/turingpnas.html

eyvindn 7y ago

One of the authors here. Feel free to ask any questions! We encourage you to play with the attached Colab with which you can train models from scratch in <30min.

plafl 7y ago
Congratulations, it's really cool. It's late here now, I will play with it tomorrow. I actually have a lot of questions! - How well the CA rules "compress" the image against a typical image compression algorithm? - Have you tried 3D? Animations? - What happens if you insert small noise to the learned parameters? It gets destroyed or mutates the image to something similar? - Is it practical with more complex images, for example, a human face?
- heyitsguay 7y ago
  I'm wondering these things too. You say in the paper that your net is quite small - I wonder if you could first extend it to regenerative sprite animations for some fun procedural NPCs in a roguelike? If you can efficiently add or change a pixel or two per frame in a sprite+collision mask, imagine battle sequences where things get hacked off, maybe regrow if you wait too long, etc.
- eyvindn 7y ago
  1) This is often the first question many people familiar with NNs ask and rightly so. Compression was not one of our goals with this article, and it would look like a terrible compression algorithm if that were its purpose. In fact, the model displayed here is about 8.3k parameters, although the WebGL model is quantized (more info on this in the last section), and each model learns to encode an image consisting of 44x44x3 = 5808 integers. We made no attempt to minimize this number. The key thing to bear in mind is that all the cells have the exact same rule and the image generation starts from a single one of them - meaning they have to learn to communicate locally with their neighbours to self-organize in the correct pattern. This is a very non-trivial task and the majority of the model’s parameters are likely going towards this communication protocol and growth behavior. 2) We have not tried 3D. As for animations, some of our earliest experiments suggested one could achieve “animations” by applying the loss at key-points to have the model learn to iterate through these points across several time steps. 3) One could argue the WebGL implementation does this to some extent by quantizing the learned weights we take from the Tensorflow training code. The model remains very resilient and worked out of the box in almost all cases. Moreover, if one tried to inject explicit noise to the CA in a given location, some models would have no problems adapting to it, while others would fail miserably. Some early experiments yielded some remarkably resistant models, able to resist while being subject to continuous globally occurring noise. We suspect explicitly training them while introducing noise would allow us to drive the model towards more consistently resistant behaviors. 4) One of the main obstacles to larger patterns at the moment is memory usage during a forward/backward pass. There are optimization and tricks we plan to employ to generate larger and more complex patterns, which may be discussed in a follow up thread.

tehsauce 7y ago

One day Von Neumann architecture will be superseded by something more similar to cellular automata. Von Neumann architecture makes sense from a logical perspective which is not concerned with physical space or scaling efficiency. Chips designed as distributed meshes similar to neurons in the brain will eventually take us much further.

UncleOxidant 7y ago
In as much as CAs are a specific type of systolic array, yes, I think we'll move more in that direction towards systolic arrays.
ColanR 7y ago
From what I understand, what we call Von Neumann architecture was actually his 'first draft' prototype, and what you describe is what he would have built next - if he hadn't died of brain cancer.
- DonHopkins 7y ago
  I posted about this and typed in some stuff from his book earlier: https://news.ycombinator.com/item?id=21858465 https://news.ycombinator.com/item?id=21858465 John von Neuman's 29 state cellular automata machine is (ironically) a classical decidedly "non von Neumann architecture". https://en.wikipedia.org/wiki/Von_Neumann_cellular_automaton https://en.wikipedia.org/wiki/Von_Neumann_cellular_automaton He wrote the book on "Theory of Self-Reproducing Automata": https://archive.org/details/theoryofselfrepr00vonn_0 https://archive.org/details/theoryofselfrepr00vonn_0 He designed a 29 state cellular automata architecture to implement a universal constructor that could reproduce itself (which he worked out on paper, amazingly): https://en.wikipedia.org/wiki/Von_Neumann_universal_constructor https://en.wikipedia.org/wiki/Von_Neumann_universal_construc... He actually philosophized about three different kinds of universal constructors at different levels of reality: First, the purely deterministic and relatively harmless mathematical kind referenced above, an idealized abstract 29 state cellular automata, which could reproduce itself with a Universal Constructor, but was quite brittle, synchronous, and intolerant of errors. These have been digitally implemented in the real world on modern computing machinery, and they make great virtual pets, kind of like digital tribbles, but not as cute and fuzzy. https://github.com/SimHacker/CAM6/blob/master/javascript/CAM6.js#L4569 https://github.com/SimHacker/CAM6/blob/master/javascript/CAM... Second, the physical mechanical and potentially dangerous kind, which is robust and error tolerant enough to work in the real world (given enough resources), and is now a popular theme in sci-fi: the self reproducing robot swarms called "Von Neumann Probes" on the astronomical scale, or "Gray Goo" on the nanotech scale. https://en.wikipedia.org/wiki/Self-replicating_spacecraft#Von_Neumann_probes https://en.wikipedia.org/wiki/Self-replicating_spacecraft#Vo... https://grey-goo.fandom.com/wiki/Von_Neumann_probe https://grey-goo.fandom.com/wiki/Von_Neumann_probe >The von Neumann probe, nicknamed the Goo, was a self-replicating nanomass capable of traversing through keyholes, which are wormholes in space. The probe was named after Hungarian-American scientist John von Neumann, who popularized the idea of self-replicating machines. Third, the probabilistic quantum mechanical kind, which could mutate and model evolutionary processes, and rip holes in the space-time continuum, which he unfortunately (or fortunately, the the sake of humanity) didn't have time to fully explore before his tragic death. p. 99 of "Theory of Self-Reproducing Automata": >Von Neumann had been interested in the applications of probability theory throughout his career; his work on the foundations of quantum mechanics and his theory of games are examples. When he became interested in automata, it was natural for him to apply probability theory here also. The Third Lecture of Part I of the present work is devoted to this subject. His "Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components" is the first work on probabilistic automata, that is, automata in which the transitions between states are probabilistic rather than deterministic. Whenever he discussed self-reproduction, he mentioned mutations, which are random changes of elements (cf. p. 86 above and Sec. 1.7.4.2 below). In Section 1.1.2.1 above and Section 1.8 below he posed the problems of modeling evolutionary processes in the framework of automata theory, of quantizing natural selection, and of explaining how highly efficient, complex, powerful automata can evolve from inefficient, simple, weak automata. A complete solution to these problems would give us a probabilistic model of self-reproduction and evolution. [9] [9] For some related work, see J. H. Holland, "Outline for a Logical Theory of Adaptive Systems", and "Concerning Efficient Adaptive Systems". https://www.deepdyve.com/lp/association-for-computing-machinery/outline-for-a-logical-theory-of-adaptive-systems-efsWyqMa1l https://www.deepdyve.com/lp/association-for-computing-machin... https://deepblue.lib.umich.edu/bitstream/handle/2027.42/5578/bac4296.0001.001.pdf?sequence=5 https://deepblue.lib.umich.edu/bitstream/handle/2027.42/5578... https://www.worldscientific.com/worldscibooks/10.1142/10841 https://www.worldscientific.com/worldscibooks/10.1142/10841

gwenzek 7y ago

I think I broke the Matrix: https://i.ibb.co/8zGRcNd/Screenshot-20200211-230621-Firefox-Beta.jpg https://i.ibb.co/8zGRcNd/Screenshot-20200211-230621-Firefox-... I erased the lezard but the tail and it grew back very weirdly. How robust is this method?

fishmaster 7y ago

This kind of reminds me of this comment (moveable feast engine): https://news.ycombinator.com/item?id=17877155 https://news.ycombinator.com/item?id=17877155

DonHopkins 7y ago
David Ackley's talks are amazing and mind expanding. Public Lecture: Artificial Life for Bigger & Safer Computing Talk presented October 8, 2014 as part of the John von Neumann Public Lecture series at the Center for Complexity and Collective Computation (C4) in Madison, Wisconsin. Video recorded by Alan Ruby of the Wisconsin Insitute for Discovery. https://www.youtube.com/watch?v=NqSnoJ-VGH4 https://www.youtube.com/watch?v=NqSnoJ-VGH4 He stakes out a bold iconoclastic position that the ridiculously successful deterministic approach to computing is a dead end that will not scale, but he's not just like Clifford Stoll predicting that nobody's going to ever shop over the internet (bless his heart for owning up to that ;), because he's taking a much longer term view, and his beliefs don't contradict von Neumann, but actually reflect what von Neumann predicted. John von Neumann actually predicted that his own approach was going to fall down. The future "...logic of automata will differ from the present system of formal logic in two relevant respects: 1. The actual length ... of the chains of operations will have to be considered. 2. The operations of logic ... will all have to be treated by procedures which allow exceptions (malfunctions) with low but non-zero probabilities." "...natural organisms are constructed to make errors as ... harmless as possible. Artificial automata are designed to make errors as ... disastrous as possible ... We are ... much more 'scared' by the occurrence of an isolated error and by the malfunction ... behind it. Our behavior is clearly that of overcaution, generated by ignorance." -John von Neumann, 1948

DonHopkins 7y ago

Also check out the "Moveable Feast Machine", Robust-first Computing, and this Distributed City Generation example: https://news.ycombinator.com/item?id=21858577 https://news.ycombinator.com/item?id=21858577 DonHopkins on Oct 26, 2017 | parent | favorite | on: Cryptography with Cellular Automata (1985) [pdf] A "Moveable Feast Machine" is a "Robust First" asynchronous distributed fault tolerant cellular-automata-like computer architecture. It's similar to a Cellular Automata, but it different in several important ways, for the sake of "Robust First Computing". These differences give some insight into what CA really are, and what their limitations are. Cellular Automata are synchronous and deterministic, and can only modify the current cell: all cells are evaluated at once (so the evaluation order doesn't matter), so it's necessary to double buffer the "before" and "after" cells, and the rule can only change the value of the current (center) cell. Moveable Feast Machines are like asynchronous non-deterministic cellular automata with large windows that can modify adjacent cells. Here's a great example with an amazing demo and explanation, and some stuff I posted about it earlier: https://news.ycombinator.com/item?id=14236973 https://news.ycombinator.com/item?id=14236973 Robust-first Computing: Distributed City Generation: https://www.youtube.com/watch?v=XkSXERxucPc https://www.youtube.com/watch?v=XkSXERxucPc

otoburb 7y ago
David Ackley's[1] real-life hardware implementation of Robust-First Computing is at a new YouTube channel called the T2 Tile Project[2]. He jumpstarted the new channel after retiring from University of New Mexico. He releases new updates every Tuesday like clockwork. This is all he thinks, talks, dreams about and builds. It's amazing. [1] https://www.cs.unm.edu/~ackley/ https://www.cs.unm.edu/~ackley/ [2] https://www.youtube.com/channel/UC1M91QuLZfCzHjBMEKvIc-A https://www.youtube.com/channel/UC1M91QuLZfCzHjBMEKvIc-A
- voldacar 7y ago
  Ackley is literally thinking hundreds of years ahead of the hardware we have. It's a shame that his ideas will probably never make much of a dent in the current computing ecosystem

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caetris1 7y ago

Spectacular. I think most people might be lost on the significance of what has been done here. I suspect that it's because the framing the solution focuses on is based on morphogenesis. That is, in fact, not what is going on here. A more suitable metaphor would be the functional basis of a visual hallucination. The very same mechanics of the demonstrated result are described in Oliver Sack's book on the subject. Learned patterns are procedurally generated through probabilistic inference of adjacent feature vectors. Dreams work in a similar way. To prove that this is what is going on, I suggest training the CA on multiple frames of an animation of the lizard walking. The research is really promising but the conclusion that the authors come to is that this system can be used to generate self-organizing agents that evolve and replicate. There is no possibility of this result based on the approach. However, they've actually created something much better than the intended result... a real-time auto-complete for visual scenes.

m_que 7y ago
Other works of the authors on deep dreaming reflect better what you describe here. The astonishing results with regard to morphogeneses cannot be emphesized enough: A complex structure is robustly encoded in a single function and can be reproduced from a single grid cell.

HelloNurse 7y ago

I played with "regenerating" models by filling the field with multiple overlapping copies and repeatedly changing the angle, with rather weak results. As the shapes are disintegrated and rebuilt, patterns can form: the eye forms brown and white thick straight "sausages", the yellow striped fish forms similar but smaller strips, the butterfly and the pretzel exhibit crawling shapes with partial features and empty space, the christmas tree can develop stripes on a green background instead of balls, the lizard reforms very effectively recovering empty space, the ladybug reforms partially (heads in a sea of wing material), the explosion produces an uniform patch of the interior section as growth extinguishes the border.

zackmorris 7y ago

The models described in this article run on the powerful GPU of a modern computer or a smartphone. Yet, let’s speculate about what a “more physical” implementation of such a system could look like. We can imagine it as a grid of tiny independent computers, simulating individual cells. Each of those computers would require approximately 10Kb of ROM to store the “cell genome”: neural network weights and the control code, and about 256 bytes of RAM for the cell state and intermediate activations. The cells must be able to communicate their 16-value state vectors to neighbors. Each cell would also require an RGB-diode to display the color of the pixel it represents. A single cell update would require about 10k multiply-add operations and does not have to be synchronised across the grid. We propose that cells might wait for random time intervals between updates. The system described above is uniform and decentralised. Yet, our method provides a way to program it to reach the predefined global state, and recover this state in case of multi-element failures and restarts. We therefore conjecture this kind of modeling may be used for designing reliable, self-organising agents. On the more theoretical machine learning front, we show an instance of a decentralized model able to accomplish remarkably complex tasks. We believe this direction to be opposite to the more traditional global modeling used in the majority of contemporary work in the deep learning field, and we hope this work to be an inspiration to explore more decentralized learning modeling. I've been hoping that someone would build the above computer for over 20 years. I used to just get laughed at, or people would mock me for not knowing what I'm talking about (even though I have a computer engineering degree). That transitioned to at least speculating on what I was saying, and will soon transition to being old hat. Oh well. But I look at computers today with a billion transistors, running at the same 3 GHz as they did 20 years ago, and it breaks my heart. I imagine what might have been, if we had arrays of hundreds/thousands of z80 or x86 or 68000 or DEC Alpha or MIPS chips to do whatever we wanted with. Imagine the emergent behavior that might have happened. Instead we're locked into this proprietary/narrow way of working with shaders and specialized neural computers doing SIMD and I just find it all so uninspiring. But I'm hopeful that some of the recent work with getting these older processors running on FPGAs will bear fruit. https://news.ycombinator.com/item?id=17833829 https://news.ycombinator.com/item?id=17833829 https://news.ycombinator.com/item?id=19329083 https://news.ycombinator.com/item?id=19329083 https://news.ycombinator.com/item?id=10089807 https://news.ycombinator.com/item?id=10089807 https://news.ycombinator.com/item?id=3267428 https://news.ycombinator.com/item?id=3267428 https://news.ycombinator.com/item?id=19885901 https://news.ycombinator.com/item?id=19885901 https://www.google.com/search?q=processor+on+FPGA+site:news.ycombinator.com https://www.google.com/search?q=processor+on+FPGA+site:news....

sasaciric 7y ago

Great work, very much related to our previous work on CA morphogenesis and replication: https://ieeexplore.ieee.org/abstract/document/8004527 https://ieeexplore.ieee.org/abstract/document/8004527 https://link.springer.com/chapter/10.1007/978-3-319-45823-6_2 https://link.springer.com/chapter/10.1007/978-3-319-45823-6_... https://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_00191 https://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_0019... https://www.mitpressjournals.org/doi/abs/10.1162/978-0-262-33027-5-ch010 https://www.mitpressjournals.org/doi/abs/10.1162/978-0-262-3... https://ieeexplore.ieee.org/abstract/document/7008733 https://ieeexplore.ieee.org/abstract/document/7008733