Probably not, because transistors produce some heat, and you can't really sandwich surface mount transistors between PCB layers either. You could vertically stack PCBs with some spacing, however, but then normal PCBs will do the job.
It is possible to embed components in the middle of a pcb, but it's a pretty specialist process (read: a pain in the neck because it interleaves the pcb fabrication and assembly stages, making both steps more complicated).
The ARM1 was a 32-bit CPU and it only had about 25K transistors so that one might be fair game if you can bring the components a little bit closer together, but you don't need a fancy PCB for that, and I guess the clock speed would need to be very slow if it's several square feet in size.
Probably AVX-512. AFAIK Apple CPUs are still using ARM NEON instructions with 128-wide SIMD registers. I guess they are banking on you using the GPU if you want to parallelize those kinds of workload.
It might be conceptually similar to a single-output-token LLM (sort of). LLMs output next-token probabilities. You can ask LLMs to output yes/no, or to output only a color, or only a digit or something like that.
In this case I would imagine that they probably embed your input data into a vector space, and they embed your questions/outputs into another space, and manage to predict probabilities/classes/scores for your outputs very quickly. Embedding the output classes/questions into a vector spaces gives you something you can reuse across runs cheaply, as opposed to an LLM where you can prefill the KV cache but this is an expensive operation in terms of memory.
Agreeing with you. Trump's attitude on the global stage has been to disrespect, insult and threaten every ally. Come at everyone with a "we're better than you" attitude and show that he can't be trusted. The only rational response is to take steps to be more independent and find ways to work around the US. What's the alternative? Give in to a narcissistic bully who won't keep his word and will inevitably come asking for more later?
In the medium to long term, Trump is very old, his approval ratings are in the toilet. I hope that the democrats do well in the midterms and put up a viable candidate to beat him next election (if he's still alive). I also hope that the republicans manage to put up someone smarter and less morally reprehensible next time. Because I mean, this guy doesn't actually represent any kind of conservative ideal in any way.
The only people he appeals to are those one or two standard deviations on the left of the bell curve.
If the next US president is halfway sane, Canada and the US will likely begin to repair trade relationships, but I think that Canada and the rest of the world will be a bit more careful next time. The US may never regain the international standing they enjoyed before. Trump has made China look like the more stable and rational trading partner, and accelerated the decline of the US.
Most people (and most companies) can't tell bad ideas from good ideas. This is also true of LLMs to some degree. They need post-training in specific domains (e.g. coding) to become competent in that specific area.
You can use it for things like classifying documents, spam detection and detecting certain events, etc. You're right that it's not strong enough for coding but it can still be quite good for question answering and such. Or even simple things like converting documents from one format to another.
And yeah, for NPCs, this model and their other A1B model can fit inside of a game and run at a decent speed on CPU-only. It would probably be workable for game characters.
I would like to see an examples directory with a diverse set of example programs. What would be most interesting to me is programs with non-trivial memory usage and data structures, because that's where differences in the memory model would matter. I want to know what's possible and what's not allowed in this language. What makes this different and better than Rust's borrow checker.
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