Hacker News

points by peterlk 3 days ago | 0 comments

I think this is an outcome of playing with systems that we don’t actually understand very well on a deep level. We have found recipes that work, and have used graduate descent (so many PhD theses) to understand and optimize these systems, but we’ve built a model of language that also encodes reasoning, and those things are extraordinarily complex. Providing proper mathematical rigor for their explanation requires, as you say, the level of commitment equivalent to a math degree. Also, because language is so complicated and subtle, you need highly precise statistical models of it, and that just takes a shitload of data; hence the scale. You can train toy models at home, but they will suck because you actually need massive amounts of data to capture the nuance that language requires. And that’s to say nothing of multimodal models.

[–] billiam 3 days ago

I think the level of interaction on forums like HN is a great example of the still-yawning gulf between LLMs and humans when it comes to encoding reasoning into language. Even with the self-selection that comes with posting here, think of how many times it is almost impossible to tell if posters are shit-posting, serious, angry, sock puppets, or just weird.

[–] giardini 3 days ago

You say "...because you actually need massive amounts of data to capture the nuance that language requires..." but humans don't require that! So why do LLMs?

How many books did you read in your education? Far fewer that what LLMs read, no doubt. Of course we "have a life" and so we interact with hundreds of people, which is also a form of learning.

But in any case there's are fundamental differences here between these two systems (LLMs and humans) that we have not yet discerned clearly.

[–] jodrellblank 3 days ago

Far fewer than an LLM but then I “know” far less than an LLM. Sam Altman talked about a PhD level expert on any topic; if we expect a SOTA model to answer any question on any topic in a library, presumably that requires a library worth of books go into it?

Say I ask it who are the most beloved Indian cricketers and why, it might not be correct but it will have the right vocabulary - cricket terms and gameplay I’ve not heard of, player names and styles I don’t recognise, things critics and commentators like and dislike about game strategies. And it is like that for almost any topic.

If they hallucinate a Python function, it will look like a plausible function e.g. “hn.create_user_account()”. They don’t hallucinate like

    # create HN user account
    # requires eye of newt and frog leg
    “Oh great spirit of the internet I request and command you make a new account…”

It’s a terrific amount of facts to be able to write even plausibly about tons of topics.

[–] giardini 3 days ago

We don't need LLMs that can "answer any question on any topic in a library"!

We don't even need machines that can answer any question in a specific topic area. But we could use machines that can answer "I don't know." when they cannot compute the answer.

Consider the possibility of much-smaller LLMs that answer questions only in limited domains (physics, chemistry, math, medicine, etc.)?

Could not such LLMs exist? If not, why not? Would they not likely be more useful and far, far easier and cheaper to create and run (possibly even with far less hardware) than the current "know-all" LLMs? Also might they possibly avoid certain problems ( e.g., hallucinations, deceit, apparently undesirable behavior, etc.) associated with the "know-all" LLMs we now have?

[–] jodrellblank 1 day ago

I do wonder if a medical LLM needs to know anything about popstars or films or classic literature. But your particular examples make me wonder how you could train an LLM to talk medically about "blood pressure" without knowing any physics about what pressure is, or any of the other ways the STEM subjects overlap and blend into each other without clear boundaries - chemistry of drugs, math of dose calculations, language of drug and disease naming etymology, etc.

If a patient with high blood pressure asks "will my face look like Dizzy Gillespie?" isn't it better to answer "No, that was air pressure and cheek damage" than to answer "I don't know who that is, call your doctor"?

[–] whatshisface 2 days ago

I feel the intuition leads us astray here because the combined text of every science book ever written is much less than the size of a complete description of the human brain. To the hamburger of reason the library is a sesame seed.

[–] reactordev 3 days ago

We need to up the ante, we all know it's the Mumbai Indians. ;)

One can train a decent model at home, for a specific task but you are absolutely correct that in order to have a model that's an expert in any topic it requires training and data on all topics.

[–] splatter9859 3 days ago

I nearly spit out coffee reading this. Amazing use of vocabulary; especially "... oh great spirit of the internet".

I sometimes jokingly explain to certain people not immersed in tech that sometimes saying the "magic words" (commands) from the "codex" (script) in the right sequence with a lot of hand waving allow "magic" (applications/web apps/etc.) to happen.

It generally brightens a patients day and makes things relatable to those non-technical, children, the elderly, or the like.

TLDR: thanks for the chuckle.

[–] tclancy 2 days ago

Humans don’t need that? How many words have you taken in from all possible sources by the time you’re 18? And many of those words came with additional data due to tone of voice, inflection and context. And then some of those (in-person, filmed art) came with incredibly subtle data from body language and facial expressions. I think we take in a massive corpus of data every year. Think just of any book you re-read: why did you do it? Did you glean additional insight?

[–] nextaccountic 2 days ago

We experience tons of data, but it's still much less than the pretraining step of LLMs

I think it's clear that LLMs are massively less intelligent than they could be, based on the amount of training data and compute they consume. Or rather, humans are orders of magnitude more efficient on training, and we don't know why

The answer is probably in the DNA. We are born highly intelligent and our brain is still growing. The DNA itself was optimized for billions of years

[–] tclancy 2 days ago

How have you quantified this that you are asserting it to me?

[–] nextaccountic 2 days ago

We have DNA though. It was subject to "pretraining" for billions of years. Alongside some epigenetic stuff

Maybe in the future we will have fewer robots interacting in the environment with continuous learning, to mimic the human experience, rather than a massive fleet of stateless, inference-only models

[–] ordersofmag 3 days ago

I think it's more accurate to say that the fundamental differences (between LLM's and humans) are abundantly clear and what we haven't really got a handle on are the similarities. Why is it that an LLM's can so convincingly mimic some things that humans do? Of course the fact that we don't really understand how humans do it in the first place is a real barrier. Perhaps looking at the intersection will shed some light on how humans work.