Jokes aside, I think a brain and a computer solve two different problem sets. A computer needs to be exact, deterministic. A brain needs to be practical, very much at the expense of precision.

I wouldn’t want a brain to do my math calculations for me… brains suck at those, that’s why I use a computer.

If it didnt introduce a spectrum of new issues, I’d wager we could start encoding data into more complex structures than binary. Like using continuous values instead of just on/off. But, from what I understand, that makes the problem of computing damn near impossible and unaffordable.

Maybe we can find ways to encode more data into the same volume of transistors. Like, if current state could imply additional information. But that would probably impede on performance, as I’m sure the data structures used in a CPU prioritize performance not “adding more implied data.”

Hmm… tough one.

[–] Axolotl_cpp@feddit.it 1 points 15 hours ago

Using trinary wouldn't really be a problem, they also already did it over 50 years ago, we just never used it because their tech wasn't precise enough

But i am just a junior in the sector so i know little, if someone can explaim to me why it wouldn't work well, i'd appreciate

[–] 1rre@discuss.tchncs.de 3 points 23 hours ago (1 children)

They're talking about AI datacenters though... The whole point is they're not exact and deterministic, but usually about right.

[–] partofthevoice@lemmy.zip 2 points 23 hours ago* (last edited 16 hours ago) (2 children)

Oh, I guess I read transistor. Not transformer. Thanks!

Yeah, a brain does exactly what it’s supposed to do. I can’t imagine how a simulation could be more efficient, especially when that CPU wasn’t designed to do the same thing. If the brain had any inefficiencies, I would imagine that it would be a trade off in reality… we’re going with hundreds of millions of years of evolution. It turns cooked meat into power for cognition machines—gonna be hard to beat that efficiency.

That’s still talking about transistors, though. Even more directly, transformers are just translating text into high dimensional arrays where the semantic structure is captured (relative to all other possible embedding values). It’s an interesting approach to navigating semantic information, but there’s not any guarantee that our brain does the same thing. Either way, I’d bet our brain is doing its job without much accidental complexity, whereas modern transformers will always have the added complexity of encoding / decoding semantic information using hardware not designed for it.

Computer should maybe try staying in its lane. Leave the cognitive dissonance and delusional confidence to the experts.

[–] WoodScientist@lemmy.world 2 points 17 hours ago

Yeah, a brain does exactly what it’s supposed to do. I can’t imagine how simulating something with alternative hardware could be any more efficient than the original, especially when that new hardware wasn’t designed to do the same thing.

That's what Neuralink is for! I can see it now:

The jobs of the future will involve 12 hour shifts serving as a biological GPU. A large helmet is strapped to your head. It remotely, non-invasively takes over your neurons and forces them to perform calculations commanded by a remote server, rather than your own thoughts. It doesn't override the vital parts of the brain involved in say, keeping your heart beating. But it does override most everything else. And it's not like a dream. You're not experiencing a virtual reality in there. For 12 hours, all of your senses are just full static at maximum volume. The experience would quickly drive you mad, if your own conscious awareness was not also dissolved into so much computational grist. You're essentially a vegetable for 12 hours. Your shift is performed wearing an adult diaper. This is nearly the only form of wage labor available. Not even jobs like janitorial are available. One human mind, suitably harnessed, can remotely pilot a dozen humanoid androids. Half of your coworkers are there involuntarily, unpaid, caught up in one of anti-vagrancy law or another.

[–] xthexder@l.sw0.com 1 points 19 hours ago* (last edited 19 hours ago)

Yeah, I said transformer because that seems to be the state of the art in AI architectures, but purpose built neural network hardware might not actually benefit from the same architecture.

A neural network made from analog hardware could theoretically replace a significant portion of an LLM's processing and not be limited by things like floating point precision or clock speeds. Who needs floating point when you can literally just multiply voltages together with a couple transistor junctions at the speed of light and read the output?