> I don't even know what vector addition should look like.
I think you're trying to imply you're inventing something new and racket enables you to explore... But what I read (as someone with a PhD in deep learning that has worked on sparsity) is you actually don't know the prior art and you're using racket as an excuse to reinvent a whole bunch of stuff that already exists in plenty of mature libraries in more mundane languages (including python/pytorch). Which is of course fine for personal growth but please don't oversell racket as a "superpower" - to wit I can manipulate any part of my stack too because it's all written in cpp.
>Unlike their binary counterparts, posits and takums, tekums simultaneously accommodate both ∞ and NaR, while retaining the simplicity of negation by flipping the underlying trit string. Perhaps most strikingly, tekums enable rounding by truncation, a property that eradicates at a stroke some notorious problems of rounding in binary arithmetic: double rounding errors, cascading carries in hardware, and the attendant inefficiencies.
You do realise that you need to store arbitrary binary blobs which don't nicely align to memory words?
And that once you can store them you need to write custom functions that do bitwise manipulation on those arbitrary blocks of memory?
The stuff that's done in hardware for you on all binary fp?
Meanwhile in racket I got arbitrary balanced ternary manista and exponent precision in less time it took to write this post. Something that not available in C/Cpp even for binary fp?
This is a complete tangent, but since you mentioned MNIST: I accidentally discovered Tsetlin machines this week when someone on r/Julia asked if anyone with an AMD GPU could run the benchmark in their package called Tsetlin.jl. I've got an AMD GPU so I was happy to oblige. Then I looked at what the benchmark was doing: it was training an MNIST classifier to 98% accuracy in 9 seconds - that seemed like a couple of orders of magnitude too fast. I was flabbergasted and wondered what the heck this thing was and that's when I learned about Tsetlin machines. I went on (with the help of Claude) to implement one in an FPGA and again was flabbergasted when it only took 2k LUTs to implement a Tsetlin machine for MNIST classification in hardware.
Well yes, you have to use one of the newer mnist variants these days if you want to get anything meaningful. A linear classifier gets something like 87% on the original one.
I think you're trying to imply you're inventing something new and racket enables you to explore... But what I read (as someone with a PhD in deep learning that has worked on sparsity) is you actually don't know the prior art and you're using racket as an excuse to reinvent a whole bunch of stuff that already exists in plenty of mature libraries in more mundane languages (including python/pytorch). Which is of course fine for personal growth but please don't oversell racket as a "superpower" - to wit I can manipulate any part of my stack too because it's all written in cpp.