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Comment by firebirdn99 | Hacker News Reader

you just need to look at Mythos to see the jump in performance from a 10T(?) model. As they scale, they get more capable. We might have an yearly release, but I believe the releases will continue, as long as scaling laws are in tact, and there's huge problems still need solving. (think cancer)

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And how are we meant to look at Mythos? Do you have access?

Through the lenses of anthropic's marketing department of course

Through association with a large company:

https://www.anthropic.com/glasswing

Ive seen the tickets generated by the model that have trickled to my team. They are legitimate, but i can’t speak to model improvement because its a pilot program.

no but they tell me it's TERRIFYING and DANGEROUS and we should INVEST MORE MONEY

>you just need to look at Mythos to see the jump in performance from a 10T(?) model

Mythos is a bunch of likely overhyped claims at this point. A few experts who looked into the claimed results weren't that impressed.

And there seems to be a ton of experts on the opposite side.

As they say, the truth tends to be somewhere in the middle.

They all looked like real CVEs to me.

Nothing that special about finding a real CVE. They're not that different than what non-Mythos could spot.

You forget that these models are still only interpolating between human-generated datapoints fed to them. They cannot reason beyond the data they've been given, so unless everything you want to create with AI is a synthesis of prior art, you're back to relying on the stone-age human brain that created AI in the first place.

Not all training data is human generated, and it's also not clear that being ridiculously good at interpolating between data points (whatever that means) will not lead to superhuman capabilities.

I could make a robotic picture coloring machine with truly superhuman capabilities - picking only the most beautiful color combinations and staying 100% in the lines while finishing entire murals in < 1 second. However, if you need a completely new and original image rendered, the machine is of only partial utility for you. It is very well possible that your cure for cancer (if that's even feasible) or whatever else you desire is a completely new picture.

We have these breathless conversations about the new AI frontier at the peril of losing sight of reality and our own human potential.

Do you know if anyone has trained, say, a pre-2017 model and tried to get it to come up with Attention Is All You Need? If it did, would you say that was only because it's a synthesis of prior art? If so, what isn't?

Allow me to restate my point: human beings and AI both create via synthesis, but we are the only ones capable of what we could categorize as true original thought or creativity. It could be argued that nothing we do as humans is truly original or creative either, but I would counter that with the claim that an LLM could not have created any element of the society and culture that gave birth to LLMs. Maybe in six more months.

>human beings and AI both create via synthesis, but we are the only ones capable of what we could categorize as true original thought or creativity.

And how is that anything other than synthesis? Do we pull concepts out of thin air?

>these models are still only interpolating between human-generated datapoints fed to them. They cannot reason beyond the data they've been given

Are you sure that humans can?

Didn't a SOTA recently solved a mathematical theorem, one escaping mathematicians for 80 years?

Maybe a human "novel" invention is just a good interpolating from the datapoints (knowledge) fed to the human.

Your phrasing ("you forget") implies this is a fact and common knowledge, while in fact there's little reason to think that's true.

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