Below is an email exchange I had with Stephen Wolfram about Strange Binary in September '20.

Hi Stephen -

You changed and enriched my worldview when I read NKS over ten years ago in college, and I'm excited for your latest project. I imagine you don't have time to answer emails like this, but wanted to share a side-project, and ask three questions.

Strange Binary is a 'game' that allows you to explore the computational universe in an aesthetically pleasing way. It's not clear what the goal of the game is for the player - but it has the side-effect of building some small amount of computational intuition in the player.

Essentially you navigate through 2D toroidal cellular automata, by toggling the system rule up or down randomly in 'dynamism' (a parameter that reflects how many system rules mandate a change).

As you navigate, the system state (ie 'on' cells) is ported into the new system. This is aesthetically fun, but probably un-useful. It's sort of like having objects that couldn't have formed in universe A show up in universe B.

If you have a chance to read this, or look at Strange Binary, I'd love to know if you have any heuristics for thinking about how to make discovered systems useful beyond fun and aesthetics. From the point of view of building intuition, what other system parameters would you expose to players? Have you ever trained something like a neural net with many steps of system data - with a view to something like pockets of reducibility across similar systems?

Thank you if you were able to read!

Myles

Myles ---

Thanks for being in touch, and apologies for a slow response…

About your game. I think it’d be useful to be able to see the effect of a “simple seed” for each rule. It’d also be nice to see the “spacetime” structure, rather than just the 2D+time version movie. I’m not sure that your universe A -> B thing is bad; it’s kind of reminiscent of successive layers in neural nets.

Plucking systems from the computational universe and having them be useful is, I think, a core challenge of future technology. In a sense, my lifelong effort on computational language is about making a bridge between human goals and what’s computationally implementable. But how best to go from “computation in the wild” [e.g. in your game] to things we humans find “useful" is a big (and important) challenge.

A few perhaps relevant writings:
https://writings.stephenwolfram.com/2011/06/music-mathematic...
https://writings.stephenwolfram.com/2017/05/a-new-kind-of-sc...
https://writings.stephenwolfram.com/2019/05/what-weve-built-...
https://writings.stephenwolfram.com/2020/10/our-mission-and-...

We’ve done quite a few experiments on finding pockets of reducibility using neural nets. The results haven’t been too impressive so far. Though as I think about it, I don’t believe we’ve really done cross-system studies of the kind you’re suggesting. In other words, are there “generically useful” abstractions/reductions that work across lots of systems? Or, conceivably, if you have an “oracle” for one neural net, that can compute with one system, can “just the neural net” be useful for another system? But viewed this way, one’s approaching the question of whether inside a neural net one can have something like a CA. And usually the more sophisticated the computational behavior, the more difficult it is to train. You should try to figure this out!

Looks like you’re quite busy with tech projects, but I’d encourage you to consider our Summer School if you can spare the time. By the way, rather to my surprise, our physics project may provide a new, very practical approach to blockchain-like systems … so there may yet be a way to merge your tech and science interests….

Best wishes and happy holidays,

— Stephen