1098

You know how Google's new feature called AI Overviews is prone to spitting out wildly incorrect answers to search queries? In one instance, AI Overviews told a user to use glue on pizza to make sure the cheese won't slide off (pssst...please don't do this.)

Well, according to an interview at The Vergewith Google CEO Sundar Pichai published earlier this week, just before criticism of the outputs really took off, these "hallucinations" are an "inherent feature" of  AI large language models (LLM), which is what drives AI Overviews, and this feature "is still an unsolved problem."

you are viewing a single comment's thread
view the rest of the comments
[-] givesomefucks@lemmy.world 335 points 6 months ago

They keep saying it's impossible, when the truth is it's just expensive.

That's why they wont do it.

You could only train AI with good sources (scientific literature, not social media) and then pay experts to talk with the AI for long periods of time, giving feedback directly to the AI.

Essentially, if you want a smart AI you need to send it to college, not drop it off at the mall unsupervised for 22 years and hope for the best when you pick it back up.

[-] Excrubulent@slrpnk.net 155 points 6 months ago

No he's right that it's unsolved. Humans aren't great at reliably knowing truth from fiction too. If you've ever been in a highly active comment section you'll notice certain "hallucinations" developing, usually because someone came along and sounded confident and everyone just believed them.

We don't even know how to get full people to do this, so how does a fancy markov chain do it? It can't. I don't think you solve this problem without AGI, and that's something AI evangelists don't want to think about because then the conversation changes significantly. They're in this for the hype bubble, not the ethical implications.

[-] dustyData@lemmy.world 75 points 6 months ago

We do know. It's called critical thinking education. This is why we send people to college. Of course there are highly educated morons, but we are edging bets. This is why the dismantling or coopting of education is the first thing every single authoritarian does. It makes it easier to manipulate masses.

[-] Excrubulent@slrpnk.net 58 points 6 months ago

"Edging bets" sounds like a fun game, but I think you mean "hedging bets", in which case you're admitting we can't actually do this reliably with people.

And we certainly can't do that with an LLM, which doesn't actually think.

[-] reagansrottencorpse@lemmy.ml 10 points 6 months ago

Jinx! You owe me an edge sesh!

[-] Excrubulent@slrpnk.net 10 points 6 months ago* (last edited 6 months ago)

A big problem with that is that I've noticed your username.

I wouldn't even do that with Reagan's fresh corpse.

[-] explore_broaden@midwest.social 5 points 6 months ago

I think that’s more a function of the fact that it’s difficult to verify that every one of the over 1M college graduates each year isn’t a “moron” (someone very bad about believing things other people made up). I think it would be possible to ensure a person has these critical thinking skills with a concerted effort.

[-] Excrubulent@slrpnk.net 3 points 6 months ago

The people you're calling "morons" are orders of magnitude more sophisticated in their thinking than even the most powerful modern AI. Almost every single one of them can easily spot what's wrong with AI hallucinations, even if you consider them "morons". And also, by saying you have to filter out the "morons", you're still admitting that a lot of whole real assed people are still not reliably able to sort fact from fiction regardless of your education method.

[-] explore_broaden@midwest.social 3 points 6 months ago

No I still agree that we are far from LLMs being ‘thinking’ enough to be anywhere near this. But if we had a bunch of models similar to LLMs that could actually think, or if we really needed to select a person, I do think it would be possible to evaluate a bunch of the models/people to determine which ones are good at distinguishing fake information.

All I’m saying is I don’t think the limitation is actually our ability to select for capability in distinguishing fake information, I think the only limitation is fundamental to how current LLMs work.

[-] Excrubulent@slrpnk.net 3 points 6 months ago

Yes, my point wasn't that it could never be achieved but that LLMs are in a completely different category, which we agree on I think. I was comparing them to humans who have trouble with critical thinking but can easily spot AI's hallucinations to illustrate the vast gulf.

In both cases I think there are almost certainly more barriers in the way than an education. The quest for a truthful AI will be as contentious as the quest for truth in humans, meaning all the same claim-counterclaim culture-war propaganda tug of war will happen, which I think is the main reason for people being miseducated against critical thinking. In a vacuum it might be a simple technical and educational challenge, but the reason this is a problem in the first place is that we don't exist in a political vacuum.

load more comments (2 replies)
[-] RidcullyTheBrown@lemmy.world 5 points 6 months ago

What does this have to do with AI and with what OP said? Their point was obviously about limitations of the software, not some lament about critical thinking

[-] scarabic@lemmy.world 2 points 6 months ago

Humans aren't great at reliably knowing truth from fiction too

You’re exactly right. There is a similar debate about automated cars. A lot of people want them off the roads until they are perfect, when the bar should be “until they are safer than humans,” and human drivers are fucking awful.

Perhaps for AI the standard should be “more reliable than social media for finding answers” and we all know social media is fucking awful.

[-] Excrubulent@slrpnk.net 1 points 6 months ago* (last edited 6 months ago)

The problem with these hallucinated answers that makes them such a sensational story is that they are obviously wrong to virtually anyone. Your uncle on facebook who thinks the earth is flat immediately knows not to put glue on pizza. It's obvious. The same way It's obvious when hands are wrong in an image or someone's hair is also the background foliage. We know why that's wrong; the machine can't know anything.

Similarly, as "bad" as human drivers are we don't get flummoxed because you put a traffic cone on the hood, and we don't just drive into tue sides of trucks because they have sky blue liveries. We don't just plow through pedestrians because we decided the person that is clearly standing there just didn't matter. Or at least, that's a distinct aberration.

Driving is a constant stream of judgement calls, and humans can make those calls because they understand that a human is more important than a traffic cone. An autonomous system cannot understand that distinction. This kind of problem crops up all the time, and it's why there is currently no such thing as an unsupervised autonomous vehicle system. Even Waymo is just doing a trick with remote supervision.

Despite the promises of "lower rates of crashes", we haven't actually seen that happen, and there's no indication that they're really getting better.

Sorry but if your takeaway from the idea that even humans aren't great at this task is that AI is getting close then I think you need to re-read some of the batshit insane things it's saying. It is on an entirely different level of wrong.

[-] scarabic@lemmy.world 2 points 6 months ago

A fair perspective.

[-] RootBeerGuy@discuss.tchncs.de 54 points 6 months ago

I let you in on a secret: scientific literature has its fair share of bullshit too. The issue is, it is much harder to figure out its bullshit. Unless its the most blatant horseshit you've scientifically ever seen. So while it absolutely makes sense to say, let's just train these on good sources, there is no source that is just that. Of course it is still better to do it like that than as they do it now.

[-] givesomefucks@lemmy.world 34 points 6 months ago

The issue is, it is much harder to figure out its bullshit.

Google AI suggested you put glue on your pizza because a troll said it on Reddit once...

Not all scientific literature is perfect. Which is one of the many factors that will stay make my plan expensive and time consuming.

You can't throw a toddler in a library and expect them to come out knowing everything in all the books.

AI needs that guided teaching too.

load more comments (8 replies)
load more comments (2 replies)
[-] Zarxrax@lemmy.world 45 points 6 months ago

I'm addition to the other comment, I'll add that just because you train the AI on good and correct sources of information, it still doesn't necessarily mean that it will give you a correct answer all the time. It's more likely, but not ensured.

[-] RidcullyTheBrown@lemmy.world 15 points 6 months ago

Yes, thank you! I think this should be written in capitals somewhere so that people could understand it quicker. The answers are not wrong or right on purpose. LLMs don't have any way of distinguishing between the two.

[-] Leate_Wonceslace@lemmy.dbzer0.com 31 points 6 months ago

it's just expensive

I'm a mathematician who's been following this stuff for about a decade or more. It's not just expensive. Generative neural networks cannot reliably evaluate truth values; it will take time to research how to improve AI in this respect. This is a known limitation of the technology. Closely controlling the training data would certainly make the information more accurate, but that won't stop it from hallucinating.

The real answer is that they shouldn't be trying to answer questions using an LLM, especially because they had a decent algorithm already.

[-] Aceticon@lemmy.world 6 points 6 months ago* (last edited 6 months ago)

Yeah, I've learned Neural Networks way back when those thing were starting in the late 80s/early 90s, use AI (though seldom Machine Learning) in my job and really dove into how LLMs are put together when it started getting important, and these things are operating entirelly at the language level and on the probabilities of language tokens appearing in certain places given context and do not at all translate from language to meaning and back so there is no logic going on there nor is there any possibility of it.

Maybe some kind of ML can help do the transformation from the language space to a meaning space were things can be operated on by logic and then back, but LLMs aren't a way to do it as whatever internal representation spaces (yeah, plural) they use in their inners layers aren't those of meaning and we don't really have a way to apply logic to them).

[-] snooggums@midwest.social 3 points 6 months ago

So with reddit we had several pieces of information that went along with every post.

User, community along with up, and downvotes would inform the majority of users as to whether an average post was actually information or trash. It wasn't perfect, because early posts always got more votes and jokes in serious topics got upvotes, bit the majority of the examples of bad posts like glue on food came from joke subs. If they can't even filter results by joke sub, there is no way they will successfully handle saecasm.

Only basing results on actual professionals won't address the sarcasm filtering issue for general topics. It would be a great idea for a serious model that is intended to only return results for a specific set of topics.

[-] Leate_Wonceslace@lemmy.dbzer0.com 4 points 6 months ago

only return results for a specific set of topics.

This is true, but when we're talking about something that limited you'll probably get better results with less work by using human-curated answers rather than generating a reply with an LLM.

[-] snooggums@midwest.social 5 points 6 months ago

Yes, that would be the better solution. Maybe the humans could write down their knowledge and put it into some kind of journal or something!

[-] Excrubulent@slrpnk.net 3 points 6 months ago* (last edited 6 months ago)

You could call it Hyperpedia! A disruptive new innovation brought to us via AI that's definitely not just three encyclopedias in a trenchcoat.

load more comments (2 replies)
[-] vrighter@discuss.tchncs.de 18 points 6 months ago

no, the truth is it's impossible even then. If the result involves randomness at its most fundamental level, then it's not reliable whatever you do.

load more comments (10 replies)
[-] jeeva@lemmy.world 7 points 6 months ago

That's just not how LLMs work, bud. It doesn't have understanding to improve, it just munges the most likely word next in line. It, as a technology, won't advance past that level of accuracy until it's a completely different approach.

[-] rambling_lunatic@sh.itjust.works 5 points 6 months ago

Or you could just not use LLMs for this.

[-] Canary9341@lemmy.ml 2 points 6 months ago

They could also perform some additional iterations with other models on the result to verify it, or even to enrich it; but we come back to the issue of costs.

[-] Excrubulent@slrpnk.net 10 points 6 months ago* (last edited 6 months ago)

Also once you start to get AI that reflects on its own information for truthfulness, where does that lead? Ultimately to determine truth you need to engage with the meaning of the words, and the process inherently involves a process of self-awareness. I would say you're talking about treaching the AI to understand context, and there is no predefined limit to the layers of context needed to understand the truthfulness of even basic concepts.

An AI that is aware of its own behaviour and is able to explore context as far as required to answer questions about truth, which would need that exploration precached in some sort of memory to reduce the overhead of doing this from first principles every time? I think you're talking about a mind; a person.

I think this might be a fundamental barrier, which I would call the "context barrier".

load more comments (1 replies)
[-] red@sopuli.xyz 2 points 6 months ago* (last edited 6 months ago)

The truth is, this is the perfect type of a comment that makes an LLM hallucinate. Sounds right, very confident, but completely full of bullshit. You can't just throw money on every problem and get it solved fast. This is an inheret flaw that can only be solved by something else than a LLM and prompt voodoo.

They will always spout nonsense. No way around it, for now. A probabilistic neural network has zero, will always have zero, and cannot have anything but zero concept of fact - only stastisically probable result for a given prompt.

It's a politician.

load more comments (2 replies)
load more comments (7 replies)
this post was submitted on 27 May 2024
1098 points (98.0% liked)

Technology

60052 readers
2845 users here now

This is a most excellent place for technology news and articles.


Our Rules


  1. Follow the lemmy.world rules.
  2. Only tech related content.
  3. Be excellent to each another!
  4. Mod approved content bots can post up to 10 articles per day.
  5. Threads asking for personal tech support may be deleted.
  6. Politics threads may be removed.
  7. No memes allowed as posts, OK to post as comments.
  8. Only approved bots from the list below, to ask if your bot can be added please contact us.
  9. Check for duplicates before posting, duplicates may be removed

Approved Bots


founded 2 years ago
MODERATORS