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Comment by ablatt89 | Hacker News Reader

How exactly is the T2 architecture "indefinitely" scalable? You're adding various computers next to each other, fine. You might also claim that you have some generic cellular automata that in the future would somehow correlate with a dynamically scales according to some computation. This doesn't provide generic compute for arbitrary problems and your limits are still based on the cellular automata running on classical computers. You can claim all you want about dynamically scaling computation that's implemented fundamentally via some cellular automata controller, it doesn't make it true.

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It's indefinitely scalable exactly in the sense that a machine on this architecture can be made bigger as long as you can keep providing power, cooling, real estate, and tiles. There is no head node, no unique tile addresses, no global communications, no global clocking, and no pretense of deterministic execution. In that sense it's quite different than traditional cellular automata. Figuring out how to do useful work in this architecture is the research challenge.

What aspect(s) in the premise of this interesting/exotic system suggests that useful work may be done by/with it?

Shifting contexts this much tends to shift existing intractable problems into new classes of tractable (and still intractable) problems. Answering this will require a lot of people with differing domain expertise discovering it and trying it out. This is a potential (I’m inclined to say inevitable) revolution, once all the pieces are in place.

Yay thanks this!

The most basic aspect is: Natural life does useful work, suggesting that artificial life might also.

I'm like if you want a spreadsheet, fine, use a von Neumann machine. But if you want do inherently robust system control, that has a chance of doing something sensible even in situations that were neither programmed in nor trained upon, what you want is an overprovisioned system that is intrinsically aware of its deployment in space, and is constantly repairing and rebuilding itself.. and this video is another baby step on that road.

Would it be fair to say that the next challenge would be to develop a new kind of programming language architecture/concept that would take advantage of a t2-tile system? Otherwise, what is the strictest bottleneck?

We've got the ulam programming language custom tailored for the MFM, and the SPLAT spatial programming language built on top of ulam. I expect we'll want more languages or language features as we scale up, but we need to earn our way to them with design experience. I'd love to build a T3 tile, probably FPGA-based, using lessons learned, that could provide perhaps 10x or 100x the average event rate of the T2s. And get them out into researchers and hackers in quantity.

Any chance of re-purposing PiZeros for this? That might be a softer approach that can be tried relatively cheaply.

Maybe! One main challenge is the (at least) six-way local intertile communications. The T2s use BeagleBone Greens, which have two PRUs that I slice three ways each to do packet transfers. The RP2040 anyway has two 'PIO' instances that seem similar.

But I'm unsure that any redo at that scale would improve delivered performance that much.

If I had it to do over I think I'd've tried to put an ethernet router chip on each tile and basically do backplane ethernet between tiles.

But I dream of LVDS serdes between tiles with low-level packet stuff handled in FPGA fabric..

Are you aware of Jecel de Assumpção? If not you really should hit him up it sounds like the two of you would have a lot to talk about.

I wasn't, thanks. "SiliconSqueak", nice!

You're welcome, he's here on HN as well ('jecel').

Can you quantitatively say that some cellular automata compute framework would give a broad or more accurate output than a typical linear compute framework? I fail to see how embedding some complex computation into a cellular automata framework with over-provisioned resources gives unique compute insight and it almost seems synonymous with some ML auto-scaler or some ML controller that dynamically scales compute when needed.

What specific problems are not tractable via traditional autoscaling methods that cellular automata can compute more efficiently or accurately? I understand you think stochastic type/life type computations are better suited for this, but that would be more of a hunch than verifiable proof.

I accept that lots of folks don't and won't get this, but one specific problem I think is intractable via traditional means is: Actual computer security.

I think if you can write some pseudocode that summarizes the computation, it would help. I think I and others have seen the Github repo, but generally don't know where to start to analyze what it's doing.

For example, for computer security, if you write a stochastic algorithm pseudocode such that the cellular automata are essentially doing a "search" for something, an that the cellular automata replicate and scale for the purpose of this stochastic search, I think that would help people understand your computational model better. At least for me!

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