[davebren]

> I think that's just a matter of having them able to work on longer and longer time horizons.

No this will never do the kind of math that humans did when coming up with complex numbers, or hell just regular numbers ex nihilo. No matter how long it's given to combine things in its training data.

[mooreat]

I currently operate under the assumption that humans are at most as powerful as Turing Machines. And from what I understand these models internally are modeling increasingly harder and larger DFAs, so they're at least as powerful as regular languages.

Assuming humans are more powerful than regular languages I could maybe agree that these methods may not eventually yield entirely human like intelligence, but just better and better approximations.

The vibe I get though is that we aren't more powerful than regular languages, cause human beings feel computationally bounded. So I could see given enough "human signal" these things could learn to imitate us precisely.

[davebren]

Well yeah there is likely an equivalence between computability and epistemology, but I'm not sure it matters when comparing LLM intelligence to human intelligence. There is clearly a missing link that prevents the LLM from reaching beyond its training data the way humans do.

[virgildotcodes]

If you look at the life efforts and accomplishments of the ~100 billion humans who have ever lived, how many lifetimes would you discount as having "non-human intelligence" based on the lack of "novel" contributions to frontier of our species' scientific understanding according to the same high bar you apply to LLMs?

Do you pass that bar yourself?

[davebren]

Ordinary humans do novel things all the time. Where do you think LLMs got all the training data that their responses come from?

[virgildotcodes]

You're not quite addressing the question. More and more of the training data is now synthetic.

To be very specific - what novel things did the majority of the ~8 bil humans on Earth do say, yesterday, that you wouldn't otherwise dismiss as non-intelligent rehashing of the same tired patterns they always inhabit were those same actions attributed to LLMs?

What I'm getting at is that I think you're falling into the trap of thinking of the rare geniuses of human history, and furthermore their rare moments of accomplishment (relative to the long span of their lifetimes filled mostly without these accomplishments) when you think of "human intelligence", which is of course far overstating what actual human intelligence is.

[davebren]

Synthetic training data is carefully crafted by humans. The rare geniuses of human history use a different magnitude and configuration of the same kind of human intelligence that posted a dad joke on a site that got scraped into the training set and repeated, convincing people that it is intelligent like humans.

> that you wouldn't otherwise dismiss as non-intelligent rehashing of the same tired patterns they always inhabit were those same actions attributed to LLMs?

Regardless of whether something's been done before people still come up with them on their own without directly copying or amalgamating several copies. Pretty much every skilled profession includes figuring things out on the fly through the use of general reasoning that doesn't involve pattern matching against millions of examples.

[necovek]

You seem to be missing their point (which I agree with). The type of intelligence we are equipped with allows us not to have the level of memory an LLM does and still complete tasks that are novel to us every single day. Like navigating a shopping cart through tricky coridors in a store, coming up with a dad joke as in sibling example, combining a set of tools to achieve something we have never seen before, etc.

LLMs approximate a lot of that very well by simply having seen it before.

Also watch kids develop language: they learn patterns with much less training data than LLMs.

[killerstorm]

The act of discovery is usually associated with "abductive reasoning", i.e. finding a novel pattern in data.

Usually people point out that humans are more sample efficient: they might notice a novel pattern in a handful of samples, whereas training NN might require take millions.

However a claim that LLMs fundamentally cannot do abductive reasoning at all is not warranted - we don't see a clear cut, it just looks like the way LLMs do it is less efficient.

[ex-aws-dude]

You're just stating the opposite of the commenter with no additional discussion

Its like just commenting "I disagree" its totally pointless for discussion.

That's why you're getting downvoted if you're wondering.

[davebren]

What did you say that added to the discussion? I wasn't wondering at all. More compute time won't create new mathematics. To believe otherwise is to misunderstand the technology and there is no amount of hackernews votes that will change that.