What about South Korea and in Japan? All three of them together probably get close to the size of the factory in Taiwan

And is LLM's the only thing we're doing?

The spammer can reply.

Can you see the future? Nobody knows for sure how far LLM's will take us. A prediction is not a lie even if it turns out to be false.

You said you don't agree with his opinion. Lies aren't an opinion.

Their fab in Arizona is only a tiny plant compared to the ones in Hsinchu Science Park

Surely you mean quadrillion?

[deleted]

Did you miss the part where I said I didn't agree with him?

Sounds like you have a reading comprehension problem then.

I had a kind of a theory.

There used to be self-modifying code in assembler because computing power was more expensive than programmers' time. So programmers took more time to get more out of the more expensive hardware.

I'm thinking, when transistors can't shrink anymore(quantum effects and all), we're going to need to squeeze out all the computing power we can get to the point where... right back to self-modifying code. Though probably done by AI this time. I don't think a human could debug that though!

I haven't noticed any improvement in memory requirements for 5 months on Stable Diffusion... My RTX2060 still is enough for 1024x640, but not more.

LLaMA does good on tests on small models, but small size could make it not as fit for RLHF.

There is also miniaturisation for inference by reducing precision to int8 or even4. But that does not fit for training, and I believe AGI requires real-time training.

So, in theory, AGI could be achieved even without big "a-ha"-s. Take existing training methods, train on many different domains and data architectures, add tree earch from AlphaGo and real time training - and we probably will be close. But it would require pretty big hardware. And would be "only" superhuman in some specific domains.

That guy doesn't know how much machine learning is used and neither do you.

Aside from war with China, and/or massive cuts in financial investments in AI, I don't think anything would slow it down.

People starting to use it for evil. Frauds, terrorism, etc.

Why do you make up stuff.

[deleted]

How to Tame your LLM.

No actual thought was found in that drivel.

"yea man, I totally agree with this. [downvotes it anyway]"
some kind of neurotoxin for AI training data, probably

Now do the exercise the other way. What if cost of chips approach zero which is very likely looking at trends.

[deleted]

You can't rely on LLMs responses because it just tries to answer even if some things are made up, it's not accurate. Hard to change because that's just how it works.

>Seeing the large variance in the hardware cost/performance of current models, Id think the progression margin for software optimization alone is huge.

>I believe we already have the hardware required for one ASI.

Yep, how many computational "ah-ha" moment tricks are we away from running much better models on the same hardware.

Look at stable diffusion how the memory requirement fell through the floor. We already are seeing similar with LLaMA now getting into public hands (via links from pull requests on Facesbooks github lol) there are already tricks getting implemented in front ends for LLMs that allow for lower VRAM usage.

soon AI will be able to identify people that vote this way and eliminate them, er I mean, shadow vote ban them.