Yeah that was my original interpretation of the title too! Perhaps something like:
How did early humans understand their situation and what did they think the 'world' was like, and what did they think they should do with their lives?! I find it fascinating to think how that longing to know what it's all about has changed so much for humans over time.
Mirrors are still heaps interesting though, as is reflection/refraction/light-transport in general I'd say! But it wasn't about what I expected when I read it.
Indeed! I rather like the idea that solitons are something like the simplest self-propagating 'things' in any medium/computation-reigeme, so gliders in Game-of-Life might qualify and in more complex/subtle systems they can have more complex behaviours as well (like bacteria, or flies? Hehe) Here's a fun example I made in gollygang/Ready (and Houdini) of PDE solitons that spin around with rippling wakes:
Only if they retain their original shape. The point is not that any wave is a soliton, but a soliton never changes shape as it moves (through time, a medium, or whatever). The soliton can decrease in amplitude, and expand in width, but otherwise remains the same.
A pure, single Gaussian hump is the soliton for homogenous linear media. If you create an audible with the spectral shape of a Gaussian (and therefore also the time shape), it might get quieter as it moves across the room, and longer, but will still "sound" the same.
I believe so, although the way I usually think about Solitons is like a single packet.. so just one cycle of a wave. Continuous sound could probably be thought of as a continuous stream of solitons (I think ppl call them phonons when it's sound though). I haven't studied PDEs nor solitons in a formal way I just love playing with them. Gray Scott with History and Wave (a formula I contributed to Gollygang/Ready) supports many fascinating soliton behaviours. Here's 25mins of one of the strangest parameter settings I've found:
I'm not an expert and have not yet worked with splats, however I understood that unlike super-sharp-edged triangles they can represent complicatedly-transparent 'soft' phenomena like fur or clouds or similar that would ordinarily need to be rendered using possibly semi-transparent curves/sheathes (for fur/grass) or voxels for cloudy things like steam/mist. I gather splats can also represent and reproduce a limited amount of view-dependent specularity, as other commenters have said this is not dynamic and cannot easily deal with changing scene geometry or light sources.. still sounds like a fun research-project I make it do more in terms of illumination though!
I've been interested in continuous automata for a good while! I usually call it "Reaction Diffusion" (good old Turing!) but a lot of people hearing that might think I might just mean "Gray Scott", but I actually mean the whole realm of solving various types of equations (in feedback of course) on continuous-valued fields. My software of choice for that is gollygang/Ready (where some of my experiments are available to play with in the release) and also Houdini, to use the resulting simulation data in whatever way one creatively desires!
I want to know more about an intuitive take on how the Zeta function does what it does! I know it must relate somehow to finding (or perhaps excluding) all the composite numbers but I'd really love to get more of a feeling about what each 'octave' of the function is adding-in. Seems like it must be something that 'flattens' the composites but increases sharply (in the infinite sum) at each prime.. but it's still a mystery to me how one could intuitively realise or discover that it's this specific function!? How did he do it?!
One suprising thing that seems to work with video/footage (a shot) once you've watched it a few hundred times and can't really 'see' it naturally any more, is to 'flip' it (horizontal mirror (or negation)) the result is usually surprising at least and can reveal things you couldn't see before! Or, if flipping has run out of juice too, even flopping (vertical mirror or negation, yes the shot is upside-down now, so not as helpful as flipping) but either/or and their combinations can definitely provide some extremely useful new perspective on the visual content one is trying to produce (VFX day job as Pipeline TD now, FX artist previously and this idea helped me numerous times!)
What a thoughtful comment! I concur that the chance of mirror-life 'evolving naturally' (or "in the wild" let's say) is basically zero, but then I imagined a future cell-printing machine that 'wild' humans might make that prints (maybe at near the atomic level), and that made me wonder whether given such a printer you could just swap to opposite-chirality (mirror) ingredients, and maybe mirror the 3d plan that you're printing? Doesn't seem that far fetched if we ever get printing at a very small scale level? But then I remembered a cell is more like a wave than a static object, so all of the above are probably meaningless ramblings.. but all in the spirit of curiosity!
I am not alarmed by the possibility of mirror life because I think it would be at a disadvantage to all other life on earth at present, so it probably wouldn't get very far. (famous last words!?)
That would definitely be possible but if humans had the kind of molecular nanotechnology that could do that I'd be much more confident in our ability to whip up some phages to counter the threat.
I can see why Mordvintsev et al are up to what they are doing, but to be honest I'm struggling with understanding the point of using a neural-net to 'emulate' CAs like OP seems to be doing (and as far as I can gather, only totalistic ones too?).
It sounds a bit like swatting a fly using an H-bomb tbh, but maybe someone who knows more about the project can share some of the underlying rationale?
I'm not involved in this project, but I partially replicated the results from Mordvintsev et al. a few years ago because I found the idea interesting. The key idea for me was learning the possibly unknown rules of a CA from training examples. This sounded to me like something that could be useful in science, to learn about spatially distributed processes. Or in ML as a new idea for image classification or segmentation. The hope was always that a CA could be learned which would have a simple discrete representation which could then be used in inference with much lower computational needs than a full neural net. But unfortunately we never managed to succeed here, and I have the impression that this area is not as active anymore as it was some years ago.
I suppose the idea of this project is the same: show the correspondence between both in order to understand them better.
I recently swapped from adding strings together (just what I've always done) to f-strings and I'm not looking back!
I even get to keep the idea of being able to read the vars in-place in the string which is certainly the last thing that I needed to be happy to use them! Full convert now!
How did early humans understand their situation and what did they think the 'world' was like, and what did they think they should do with their lives?! I find it fascinating to think how that longing to know what it's all about has changed so much for humans over time.
Mirrors are still heaps interesting though, as is reflection/refraction/light-transport in general I'd say! But it wasn't about what I expected when I read it.