AI is 'not smart' so what's next in artificial intelligence?

https://www.bbc.com/news/articles/cj6gr0xkyr3o

by 1vuio0pswjnm7 • 20 days ago

44 75 20 days ago

heohk• 20 days ago

It's inference. It's really good at generating stuff when the example base is extensive. Like for non-esoteric coding.

ramon156• 20 days ago

Is a brain also inference? I know that an LLM works very different from the brain, but I wonder what makes a brain more capable of thinking. Is it the long term context? Is it a different type of neuron activation?

feverzsj• 20 days ago

AI winter.

bmacho• 20 days ago

Human winter.

karahime3• 20 days ago

Not likely. Take with whatever grain of salt you'd like, but that was largely a property of development being academicized and subject to things like grant cycles, research topic fashionability trends, and institutional structure. It would be wrong to assume it's some baked in thing that's guaranteed to happen independent of how development looks.

JsonDemWitOster7• 20 days ago

But _AI today_ is heavily subsidized by investor capital in the same way investors subsidized social/mobile/big data/VR/blockchain in the past. It's not unlikely "AI" would get a soft taboo in the same way as if you just presented a mobile-first, big-data driven, VR social media app today.

Which, judging by the terrible PR optics AI has nowadays, could unfortunately seep into academia too. Fund grants wouldn't want their names associated with anything with "AI" in its name even if it's a return to expert systems.

SwellJoe• 20 days ago

We're past the point where there's a feasible argument that there is an AI winter coming.

The models work remarkably well for several classes of problem that seemed impossible a few years ago. They're not going away. There will still absolutely be a lot of ups and down and crazy stuff that happens in AI, but it won't be that AI almost completely stops being developed/funded for a decade or more. The biggest risk, I think, is regulatory capture; it's what Anthropic and OpenAI seem to be aiming for with their scaremongering about how capable and dangerous their models are. That'll put a damper on the industry for everyone except the companies that bribe the right people.

card_zero4• 19 days ago

They're not going away, in the same sense that Henry Winkler is still alive and working.

dosisking• 20 days ago

AI climate change.

arisAlexis• 20 days ago

Ha before reading the article I thought "this must be an interview of Lecun". A bitter scientist that hates he was left behind the revolution.

dgellow2• 20 days ago

In what way was he left behind? If he wanted to actually work on LLMs all the AI labs would fight to get him

imtringued• 20 days ago

Left behind how? It's been transformers since 2016 and not much actual progress in basic architectures has happened 10 years later. I'm honestly struggling to see how you can be left behind in this field.

nok22kon3• 20 days ago

and CPUs have the same basic architecture since 2000. no progress happened, right?

menaerus• 20 days ago

Obviously, transformers architecture is just one of the ingredients. Otherwise we wouldn't be seeing competing labs in this race. I also read all his interviews as a marketing material.

MrScruff• 20 days ago

Considering all of the great research that has come from his labs (eg. DINO, Segment Anything) I don’t think that’s fair (no pun intended).

dagss5• 20 days ago

The article seems to define "smart" as being good at spatial awareness and navigating a body through 3D space and such. Thus, a mice is smarter than an LLM.

That's the first time in my life I hear this definition. Until now, the word "smart" has meant doing exactly the things LLMs do, and mice don't.

I guess it is a sign we are re-evaluating what makes humans special.

JsonDemWitOster7• 20 days ago

I guess it is a sign we are re-evaluating what makes humans special.

Always has been: https://en.wikipedia.org/wiki/AI_effect

Tangentially: https://en.wikipedia.org/wiki/Moravec%27s_paradox

cauch5• 20 days ago

While we should be careful of a bias, it is also a good practice in the scientific method to review definitions that may have been not precise enough.

For example, initially, a "planet" was just a big body in space. Then when people started to see more and more nuances, the definition just refined, and some objects stopped being called "planet".

I would not be surprised if there is a bias that pushes some people to redefine "intelligence" away from machine, but I would not be surprised if there is a bias that pushes some people to ignore newly discovered nuance and put into the same "intelligence" bag things that are in fact very different. I personally can see how LLM are not really "intelligent", and I don't think it is a good idea to say: well, yesterday we said the minimum criteria is X, now that we noticed that X can be reached without really doing the real thing, let's just ignore that and pretend it is the same thing.

(: the biggest clue for me is to use an early model, and see that it sometimes looks very intelligent, and then sometimes you can see that it gets it wrong in a way that shows that it never "understood" it at all. Newer models are better, but because it is an iteration on the same bases, the increase of performances cannot really due to replacing the things that "looked smart by aren't" by "real smart", but more replacing the things that "don't look smart" by "look smart by aren't")

yread• 19 days ago

I still remember when "smart" meant knowing the number of Rs in strawberry

krackers2• 19 days ago

Was that ever solved? It seems that entire retort faded overnight, yet to my knowledge there was never any systematic analysis on cause or tokenizer change that fixed it. Maybe we just decided that this failure mode doesn't have any practical bearing given the existence of tool-use?

agenticup• 20 days ago

i guess inference engineering, like dpsark or dflash specific speculative decoding technqiues

nok22kon3• 20 days ago

Yann LeCun was saying 3 years ago that because token generation is auto-regressive, its mathematically impossible to generate a long stream of coherent tokens, because errors amplify exponentially.

and then models learned that they can back track and error correct

so much for "mathematically impossible..."

charcircuit• 20 days ago

I think it was largely the introduction of tool calling that allowed models to mitigate the issue of errors amplifying exponentially since it allows the model to understand if what it generated is correct or has issues that it needs to address. This addresses the potential lack of or low quality of world model by being able to reference the current state of the world.

ravenstine• 20 days ago

I've definitely realized this phenomenon after a few occasions of erroneously trying to rely purely on instructions to get an LLM to do a thing or take on a role, especially without persistent cloud-based sessions that have internal checklists and other opaque guidance. They're essentially poor at self-managing, but can do really well when they are limited in scope/context and are worked into a sort of state machine that guarantees they perform certain tasks predictably. They won't always do those tasks the exact way you expect them to, but at least they actually do them, and because of that they are more likely to have the correct prior context to perform the next task better. Because they are so prone to selectively ignoring directions, that can quickly send them down an incorrect path that compounds on itself.

jiggawatts• 20 days ago

Also, almost any argument against LLM intelligence also applies to humans.

I very commonly see someone make some small mistake and end up going in the wrong direction, “accumulating stupid” as they go, sometimes for years.

fragmede• 20 days ago

Also with the stochastic parrot thing. If you say just the right thing to the right human and the right time, they'll very predictibly say their favorite movie/book quote or song lyric, like some sort of parrot.

shevy-java3• 20 days ago

Humans can learn.

AI can not.

For those disagreeing: please explain how a static hardware can learn.

TMWNN• 20 days ago

and then models learned that they can back track and error correct

You mean "Human developers learned ...", yes? Or was there really an all AI-driven, self-improving aspect to this?

rcxdude2• 20 days ago

Well, LLM networks don't have a 'back track and error correct' component in the design, AFAIK.

shevy-java3• 20 days ago

You insinuate here AI "learned".

I doubt that this was AI self-improvement.

rcxdude2• 20 days ago

Was there a particular change to the network or the way that it was trained that introduced the 'backtrack and error correct' mechanism?

infinite_spin• 20 days ago

do you have a problem with this field of research being called "machine learning"?

aswegs8• 20 days ago

Does that take anything away from the argument?

threethirtytwo3• 20 days ago

Stop attacking Yann. I would say like 90% of the HN crowd was parroting Yann too.

linzhangrun• 20 days ago

It depends on how you define "smart".

For me, "smart" means doing things less based on instinct. Things humans can do but mice cannot, things mathematicians can do but normal people cannot, etc.

Considering the unit distance conjecture was disproved by OAI's model last month, I think maybe LLMs should count as "smart".

armchairhacker• 20 days ago

Besides "smart", the headline also conflates AI with LLMs. The real, non-clickbait title is "Yann LeCun, founder of AMI Labs, is developing a new AI system"

randsorex• 20 days ago

It is just so bizarre compared to my everyday experience also.

I never ask Opus or Fable a question and think "what a stupid response".

Quite the opposite. It has actually raised the bar of what I consider an intelligent response to my inquiry. So much so that most responses from humans on most subjects to most forms of inquiry seem stupid and not really thought out.

acpdev• 20 days ago

I’m sure you’re very intelligent and capable so I suspect we work in different problem spaces if you have not seen this, but I definitely think the responses are at times very very junior and I find myself having to explain first principles. Fable less so, but Opus routinely will make very naive assumptions about retry logic, protocols it supposedly has training material on, and it will very often miss the forest for the trees.

This isn’t exactly saying how stupid anyone is but I’d definitely have been concerned about a human’s reasoning ability and understanding of logic if they’d given me similar answers.

dlcarrier• 20 days ago

Everyone nowadays seems to only think of AI as LLMs or maybe also stable diffusion. People want to ban games with AI in them, when by definition every NPC is following some kind of AI algorithm.

jhbadger2• 19 days ago

This chess game has a min-max algorithm in it! AI slop! It should have a short person hidden inside like the original Mechanical Turk!

altmanaltman• 20 days ago

They literally interview another person in it and mention a lot of other labs doing this kind of research including Google. Yes, he's the main starting interview but this is not really clickbait or a marketing piece.

throw2007• 20 days ago

Its definitely not as dumb as MAGA crowd

nananana92• 20 days ago

Was it worth it to log out and create a new account just to post this?

shevy-java3• 20 days ago

What's next is more AI spam-slop. I already noticed this on youtube. Tons of short videos are AI sloppified, making youtube worse in the process.

JsonDemWitOster7• 20 days ago

What's next is more AI spam-slop. I already noticed this on youtube.

I hope not but your observation on YouTube is spot-on. It's really frustrating. I've managed to keep good hygiene for my shorts feed. I practice zero-tolerance for braindead content; one strike is all it takes for the "never recommend" button.

But with the World Cup, the situation is just pandemonium. Football has always been breeding ground for low-effort content on the platform: unofficial highlights, cropped to death to avoid copyright, and always, for some reason, with a blaring background music. But now it's reached peak slop chaos: AI voiceovers, dubious anecdotes ("...and that kid, was Ronaldo"), and STILL the horribly blaring background music. The algorithm makes no distinction between quality and slop content of a topic. It's all just about the topic. So all it takes is for me to view a short related to football (even one of thoughtful commentary) for the slop to come in hordes.

At least I can now share in this forum's disdain for shorts. Kill that shit with fire man.

card_zero4• 19 days ago

So we have that quote from the Oxford guy about explanations: "systems that can explain... You need models that can answer questions like: What matters? What causes what?", and then a mention of simulation of what the world looks like.

Fine, that describes theorizing.

But then a contradictory ending statement: "We're still going to need humans to figure out what questions to ask, what to build, what to create".

So that's moral theorizing. I don't think you can have one without the other. Then there's two more suggestions before the end of the article:

smarter than us
staff of assistants

Both of which are completely gratuitous assumptions. Why should its theories be better than established ones? Is it supposed to be a maverick hermit genius and come up with everything from first principles, or does it in fact participate in the existing world of ideas like a normal person? Then, being a normal person with moral theories, why would it take on the role of assistant rather than theorizing "I don't want to do that for you"?

AI is 'not smart' so what's next in artificial intelligence?

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