Future Computers Will Be Radically Different (Analog Computing)

[link] This is a rather intuitive mathematical proof that neural nets can emulate all functions in many dimensions. Knowing integrals (Calc 102) would help. With this information it’s either that self-driving is eventually possible or human brains are not mathematically representable (i.e. something akin to a soul). Meanwhile, the data collection/storage is getting better (all Teslas are continually testing FSD and reporting failure cases, synthetic generation of “good and unique” perfectly labeled data[0] is only a year or two “old”). Sensors/cameras are improving (and have a lot more room to improve). The compute hardware is getting better and cheaper (analog computers[1], TSMC 2nm and beyond, Apple/Google/Teslas neural cores, etc). Additionally, given Waymo is already in production without humans in PHX it’s arguable that we already have “autonomous vehicles”. We now just need them to improve slightly and for Waymo or similar to test in the top 25 cities in the US. Which is mostly a matter of capital and time not technology limits. Maybe weather will be an issue for a few more years/decades - but even if Waymo only operated in Summer, I’d be happy to reduce my Lyft costs - and with enough data and maybe better sensors the neural nets will be able to handle weather too. Especially if the car just goes slow. [0] [link] [1] [this video] [2] [link]

Warning: Bit of a 'Hitch hikers guide to the galaxy"/conway game of life yak shave for a reply. Nice start to providing a simple way to visually interact/experiment/manipulate different multiple Neural network concepts by loading/transforming any traditional code (working or not) & reshuffling/tesselating the structual representation of the code to generate a turing tape path via color execution paths. Switching to pi calculus could provide the theory to support coroutine/coprossing/ threading" multiple interacting tape paths/color combinations/path diffs/scaled recusion to perform assembly language instructions. Raster graphics without the yak ( [link] ) Feed back between tape & "traditional code" as neural network layer(s)! Flexiable code (via tessilations) without changing the actual imported source code! So, complete svg (tape interactions) web page as code source on how to interact with web page, ( [link] ) what's displayed/played, documentation & usage manual included within code structure! Unicode permits implied piping rich kern font without all the tech fluff. Unicode fails because allows infinate multics and is there for k&r incomplete. But, this approach is way more dimensional/colorful than unicode/tech stuff. ?? can one's code unintentially provide a wordle problem & solution in one package ?? ( [link] ) ?? turing complete fourier transforms [link] toolkit concepts: Neural networks / analog stuff : [this video] perceptrons: [this video] spreadsheets (: [link] (turing sprea

Considering evolution optimized for biological fitness and not intelligence, it would be incredibly surprising if it wasn't possible to do better, especially using a vastly different architecture. You could also build an AGI to run on a digital CPU but interact with a simulation that used analog coprocessors (which is a thing these days [this video] ). You can also include quantum coprocessors (which already exist in their infancy) for various things (probably only useful for quantum simulations at this point though). Also considering recent ML work, an AGI is more likely going to run on something similar to a GPU than a CPU.

This paradigm for computing was already covered three years ago by Veratasium in [this video] . Maybe not the specific photonic system that they are describing. Which I'm sure has some significant improvements over what existed then. But the idea of using analog approximations of existing neural net AI models, to allow us to run AI models far more cheaply, with far less energy. Whether or not this system is the one that wins out, I'm very sure that AI run on an analog system will have a very important role to play in the future. It will allow technologies like guiding autonomous robots with AI models running on hardware inside of the robot.