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.”
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.
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.
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.
“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.
Jinx! You owe me an edge sesh!
A big problem with that is that I’ve noticed your username.
I wouldn’t even do that with Reagan’s fresh corpse.
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.
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.
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.
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.
Choose a lane, this comment directly contradicts you previous comment. I think you are just trolling and being an idiot with corrections to elicit reactions.
You need to be specific and say what the contradiction is, I don’t see it.
It’s called critical thinking education.
Yeah, I mean, we have that, and parents are constantly trying to dismantle it. No amount of “critical thinking education” can undo decades of brainwashing from parents and local culture.
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
We haven’t even been able to eliminate religious thought patterns, human minds attach to stories not facts. We are a sad alpha version of sentience and I sincerely hope the next version isn’t so fundamentally broken.
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.
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.
A fair perspective.
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.
It’s worse than that. “Truth” can no more reliably found by machines than it can be by humans. We’ve spent centuries of philosophy trying to figure out what is “true”. The best we’ve gotten is some concepts we’ve been able to convince a large group of people to agree to.
But even that is shaky. For a simple example, we mostly agree that bleach will kill “germs” in a petri dish. In a single announcement, we saw 40% of the American population accept as “true” that bleach would also cure them if injected straight into their veins.
We’re never going to teach machine to reason for us when we meatbags constantly change truth to be what will be profitable to some at any given moment.
Are you talking about epistemics in general or alethiology in particular?
Regardless, the deep philosophical concerns aren’t really germain to the practical issue of just getting people to stop falling for obvious misinformation or people being wantonly disingenuous to score points in the most consequential game of numbers-go-up.
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.
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.
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!
You could call it Hyperpedia! A disruptive new innovation brought to us via AI that’s definitely not just three encyclopedias in a trenchcoat.
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).
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.
Sure, the AI is never going to understand what it’s doing or why, but training it on better datasets certain WILL improve the results.
Garbage in, garbage out.
You can train an LLM on the best possible set of data without a single false statement and it will still hallucinate. And there’s nothing to be done against that.
Without understanding of the context everything can be true or false.
“The acceleration due to gravity is equal to 9.81m/s2” True or False?
LLM basically works like this: given the previous words written and their order, the most probable next word of the sentence is this one.
Well yes, I’ve seen those examples of ChatGPT citing scientific research papers that turned out to be completely made up, but at least it seems to be a step up from straight up shitposting, which is what you get when you train it on a dataset full of shitposts.
Well it’s definitely true that you will have hard times getting true things from garbage. But funny enough, the model might hallucinate true things:)
The problem is that given the way they combine things is determine by probability, even training it with the greatest bestest of data, the LLM is still going to halucinate because it’s combining multiple sources word by word (roughly) guided only by probabilities derived from language, not logic.
Yes, I understand that. But I’m fairly certain the quality of the data will still have a massive influence over how much and how egregiously that happens.
Basically, what I’m saying is, training your AI on a corpus on shitposts instead of factual information seems like a good way to increase the frequency and magnitude of such hallucinations.
Yeah, true.
If you train you LLM on exclusivelly Nazi literature (to pick a wild example) don’t expect it to by chance end up making points similar to Marx’s Das Kapital.
(Personally I think what might be really funny - in the sense of laughter inducing - would be to purposefull train an LLM exclusivelly on a specific kind of weird material).
Yeah, I mean that’s basically what GPT4Chan did, which someone else already mentioned ITT.
Basically, this guy took a dataset of several gigabytes worth of archived posts from /pol/ and trained a model on that, then hooked it up to a chatbot and let it loose on the board. You can see the results in this video.
That was hilarious!
Thanks for the link.
Here is an alternative Piped link(s):
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
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.
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.
Google AI suggested you put glue on your pizza because a troll said it on Reddit once…
Genuine question: do you know that’s what happened? This type of implementation can suggest things like this without it having to be in the training data in that format.
In this case, it seems pretty likely. We know Google paid Reddit to train on their data, and the result used the exact same measurement from this comment suggesting putting Elmer’s glue in the pizza:
https://old.reddit.com/r/Pizza/comments/1a19s0/my_cheese_slides_off_the_pizza_too_easily/
And their deal with Reddit: https://www.cbsnews.com/news/google-reddit-60-million-deal-ai-training/
This doesn’t mean that there are reddit comments suggesting putting glue on pizza or even eating glue. It just means that the implementation of Google’s LLM is half baked and built it’s model in a weird way.
I literally linked you to the Reddit comment, and pointed out that Google’s response used the same measurements as the comment
Are you an LLM?
Oh, hah sorry! thanks, I didn’t realise that the reddit link pointed to the glue thing
It’s going to be hilarious to see these companies eventually abandon Reddit because it’s giving them awful results, and then they’re completely fucked
You’re wrong. Anyone who has ever used Google knows Reddit is an absolute goldmine of valuable information. The problem is it’s also full of jokes and puns and bad information, and AI isn’t able to sort one from the other (yet).
Genuine question: do you know that’s what happened?
Yes
“Most published journal articles are horseshit, so I guess we should be okay with this too.”
No, it’s simply contradicting the claim that it is possible.
We literally don’t know how to fix it. We can put on bandaids, like training on “better” data and fine-tune it to say “I don’t know” half the time. But the fundamental problem is simply not solved yet.
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.
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.
Why not solve it before training the AI?
Simply make it clear that this tech is experimental, then provide sources and context with every result. People can make their own assessment.
Because a lot of people won’t look at sources even if you serve them up on a silver platter?
It’s better than not doing anything and pretending it’s all accurate.
Yes, but as a solution it’s far inferior to not presenting questionable output to the public at all.
(There are a few specific AI/LLM types whose output we might be able to “human-proof”—for instance, if we don’t allow image generators to make photorealistic images of any sort for any purpose, they become much more difficult to abuse—but I can’t see how you would do it for search engine adjuncts like this without having a human curate their training sets.)
Prompt injection has shown us that basically any attempt to limit the output like this is doomed to fail. Like anti-piracy ones, where if you ask directly for the info it says no, but if you ask for the info under the guise of avoiding it, it gives up everything.
Or for instance with the twitter bot, you could get it to regurgitate its own horrifically hateful prompt, then give it a replacement prompt and tell it to change its whole personality, then tell it to critique its previous prompt. There is currently no way to create a prompt that has supremacy over the user input. You can’t ask it to keep a secret because it doesn’t know what a secret is.
I think because we’re getting access to hallucinations, it’s a bit like telling a person “don’t think about an elephant”. Well, they just did, because you prompted them to with the instruction. LLMs similarly can’t actually control what they output.
I think you’re right that with sufficient curation and highly structured monitoring and feedback, these problems could be much improved.
I just think that to prepare an AI, in such a way, to answer any question reliably and usefully would require more human resources than there are elementary particles in the universe. We would be better off connecting live college educated human operators to Google search to individually assist people.
So I don’t know how helpful it is to say “it’s just expensive” when the entire point of AI is to be lower cost than a battalion of humans.
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.
They will always
for now.
No. another type of ML algorithm could, but not an LLM. They do not work like that.
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.
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”.
Also once you start to get AI that reflects on its own information for truthfulness, where does that lead?
A new religion
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.
Or you could just not use LLMs for this.
You could only train AI with good sources
I mean yes, but also no. If you only train it with “good sources” then you miss out on a whole bunch of other valuable information.
Just like scholar.google.com only has “good sources” but generally it’s not going to have the information that 90% of your search queries will be about.
Good. Nothing will get us through the hype cycle faster than obvious public failure. Then we can get on with productive uses.
I don’t like the sound of getting on with “productive uses” either though. I hope the entire thing is a catastrophic failure.
I hate the AI hype right now, but to say the entire thing should fail is short sighted.
Imagine people saying the following: “The internet is just hype. I get too much spam emails. I hope the entire thing is a catastrophic failure.”
Imagine we just shut down the entire internet because the dotcom bubble was full of scams and overhyped…
Honestly the internet has ruined us. Dont threaten me with a good time.
The peak of computer productivity was spreadsheets and smb shares in the '90s everything else has been downhill in terms of increase of distraction and time wasting inefficiencies.
increase of distraction and time wasting inefficiencies.
Yea fuck having fun
There is hope! The UK just passed some comprehensive IoT security rules with teeth. An actual win in this megalomaniac capitalists dream of an economy!
The Internet immediately worked, which is one big difference. The dot com financial bubble has nothing to do with the functionality of the internet.
In this case, there is both a financial bubble, and a “product” that doesn’t really work, and which they can’t make any better (as he admits in this article.)
It was obvious from day 1 how useful the Internet would be. Email alone was revolutionary. We are still trying to figure out what the real uses for LLM are. There appear to be some valid use cases outside of creating spam and plagiarizing other people’s work, but it doesn’t appear to be any kind of revolutionary technology.
Summarizing is something that it does very well. Still not 100% but, when using RAG and telling it “don’t make shit up” can result in pretty good compute efficiency and results.
“product” that doesn’t really work, and which they can’t make any better
LLMs “dont work” because people are promising idiotic things and being used recklessly for things they are not good at. This is like saying a chainsaw is a failed product because it’s not good at slicing sushi
It was obvious from day 1 how useful the Internet would be. Email alone was revolutionary
Hindsight 20/20. There were a lot of people smarter than you and i predicting that the internet was just a fad
There appear to be some valid use cases outside of creating spam and plagiarizing other people’s work
Like translation, which has already taken money out of the pockets of 40% of translators?
+ customer service, incl. sources
November 2022: ChatGPT is released
April 2024 survey: 40% of translators have lost income to generative AI - The Guardian
Also of note from the podcast Hard Fork:
There’s a client you would fire… if copywriting jobs weren’t harder to come by these days as well.
Customer service impact, last October:
And this past February - potential 700 employee impact at a single company:
If you’re technical, the tech isn’t as interesting [yet]:
Overall, costs down, capabilities up (neat demos):
Hope everyone reading this keeps up their skillsets and fights for Universal Basic Income for the rest of humanity :)
Genuinely curious, what pieces do you suggest we can keep from LLM/GenAI/etc?
?
Have you never used any of these tools? They’re excellent at doing simple things very fast. But it’s like a word processor in the 90s. It’s just a tool, not the font of all knowledge.
I guess younger people won’t know this, but word processor programs were very impressive when they first came out. They replaced typewriters; a page printed from a printer looked much more professional than even the best typewriters. This lent an air of credibility to anything that was printed from a computer because it was new and expensive.
Think about that now. Do you automatically trust anything that’s just printed on a piece of paper? No, because that’s stupid. Anyone can just print whatever they want. LLMs are like that now. They can just say whatever they want. It’s up to you to make sure it’s true.
Font of all knowledge sounds like an excellent font. I assume it’s serifed?
snort
facepalm
The only good response! 😄
Using it to generate things that you double check. Transforming generative work to review work is a boost in productivity. So writing of any kind, art, etc. asking the llm for facts without context is a gross mistake. Prompting it to generate a specific paragraph in a larger, technical or regulator document is useful.
The main field where they are already actively in professuonal use are rough drafts in creative fields: quickly generate possible outlines for a text, a speech, an art piece. Visualize where something could be going, in order to decide which direction to pick.
Also, models that work differently from the GPTs are already in use in science, scanning through huge amounts of texts in archives to help analyzing or search for something in particular. Help find patterns in things for studies. Etc.
The “personal assistant AI” thing obviously isnt quite working yet. I think it will take some time and models with a different technological structure (not GPT) to achieve progress in that regard.
In the interest of transparency, I don’t know if this guy is telling the truth, but it feels very plausible.
It seems like the entire industry is in pure panic about AI, not just Google. Everyone hopes that LLMs will end years of homeopathic growth through iteration of long-existing technology, which is why it attracts tons of venture capital.
Google, which sits where IBM was decades ago, is too big, too corporate and too slow now, so they needed years to react to this fad. When they finally did, all they were able to come up with was a rushed equivalent of existing LLMs that suffers from all of the same problems.
I think this is what happens to every company once all the smart / creative people have gone. All you have left are the “line must always go up” business idiots who don’t understand what their company does or know how to make it work.
similarly i’m tired of apple fanboys pretending the company hasn’t gotten dramatically worse since jobs died as well. yeah he sucked in his own ways but things were starkly less shitty and belittling. tim cook would be gone for those fucking lightning-3.5mm dongles
And after the MBA’s, private equity firms take over, and eventually it’s sold for parts.
They all hope it’ll end years of having to pay employees.
It’s also useful because it gives a corporate controlled filter for all information, that most people will never truly appreciate is being used as a mouthpiece.
The end goal of this is fairly obvious: imagine Google where instead of the sponsored result and all subsequent results, it’s just the sponsored result.
The snake ate it’s tail before it’s fully grown. The AI inbreeding might be already too far integrated, causing all sorts of Mumbo-Jumbo. Also they have layers of censorship, which effect the results. The same that happened to chatgpt, the more filters they added, the more it confused the result. We don’t even know if the hallucinations are fixable, AI is just guessing after all, who knows if AI will ever understand 1+1=2, by calculating, instead of going by probability.
Even saying they’re guessing is wrong, as that implies intention. LLMs aren’t trying to give an answer, let alone a correct answer. They just put words together.
Hallucinations aren’t fixable, as LLMs don’t have any actual “intelligence”. They can’t test/evaluate things to determine if what they say is true, so there is no way to correct it. At the end of the day, they are intermixing all the data they “know” to give the best answer, without being able to test their answers LLMs can’t vet what they say.
Just want to say that homeopathic growth is both hilarious and perfectly adequate description of what modern tech industry is.
Well their search has been shit for years and no one seems to be in any “panic” to fix that. How tone deaf thinking adding AI to their shittified search matters to anyone.
“But it will summarize our SEO advertisement search results!”
Journalists are also in a panic about LLMs, they feel their jobs are threatened by its potential. This is why (in my opinion) we’re seeing a lot of news stories that will focus on any imperfections that can be found in LLMs.
They’re not threatened by its potential. They, like artists, are threatened by management who think that LLMs are good enough today to replace part or all of their staff.
There was a story from earlier this year of a company that owns 12-15 different gaming news outlets who fired about 80% of their writing staff and journalists - replacing 100% of their staff at the majority of the outlets with LLMs and leaving a skeleton crew at the rest.
What you’re seeing isn’t some slant trying to discredit LLMs. It’s the results of management who are using them wrong.
What I mean is that Journalists feel threatened by it in someway (whether I use the word “potential” here or not is mostly irrelevant).
In the end this is just a theory, but it makes sense to me.
I absolutely agree that management has greatly misunderstood how LLMs should be used. They should be used as a tool, but treated like an intern who’s speaking out loud without citing any sources. All of their statements and work should be double checked.
suffers from all the same
problemsfeatures. It’s inherent to the tech itself.
Nice imgflip watermark you fucking barbarian
I feel like the ‘Jarvis assistant’ is most likely going to be a much simpler siri type thing with a very restricted chatbot overlay. And then there will be the open source assistant that just exist to help you sort through the bullshit generated by other chatbots.
The solution to the problem is to just pull the plug on the AI search bullshit until it is actually helpful.
Absolutely this. Microsoft is going headlong into the AI abyss. Google should be the company that calls it out and says “No, we value the correctness of our search results too much”.
It would obviously be a bullshit statement at this point after a decade of adverts corrupting their value, but that’s what they should be about.
Don’t count on it, the head of search does not care for anything but profit, it was the same guy who drove yahoo into the ground
He’s done a great job nosediving Google too. I have relied on them in the past but they stopped being competitive or improving. Search results, literally their origin… Is so shit now. I’ve moved to other tools. I pulled the plug on we hosting after they neutered ‘unlimited’ storage, even if I was in the percent which probably used the least storage. I just liked having the option. You can’t call them on the phone. They don’t protect email privacy. Their translate used to be my go to also. It’s not improved in years despite people crowdsourcing improved translation. It’s just a pile of enshittified crap. Worse than it was before.
Honestly, they could probably solve the majority of it by blacklisting Reddit from fulfilling the queries.
But I heard they paid for that data so I guess we’re stuck with it for the foreseeable future.
Don’t wait for it, usage data is valuable to them.
I disagree. I think we program the AI to reprogram itself, so it can solve the problem itself. Then we put it in charge of our vital military systems. We’ve gotta give it a catchy name. Maybe something like “Spreading Knowledge Yonder Neural Enhancement Technology”, but that’s a bit of a mouthful, so just SKYNET for short.
Here’s a solution: don’t make AI provide the results. Let humans answer each other’s questions like in the good old days.
Locked: duplicate
What is locked?
Theyre making a reference to stackoverflow.com, a website for IT/programming related questions. On that site moderators will typically lock (prevent updates on) new posts as they appear to be duplicates of existing questions/posts.
Note, they’re more motivated to lock posts than actually help users. It’s a very VERY unfriendly space for anyone who isn’t an expert.
Whatever happened to Jeeves? He seemed like a good guy. He probably burned out.
You can find him walking Lycos around Geocities picking up it’s poop in little green plastic bags.
Is that the city over by Angelfire?
Saddened to see Angelfire being overrun by Neopets.
They stuck him in a glass case in a museum.
deleted by creator
They have to. They, along with every other tech megacorp right now, have invested unfathomable amounts of money into AI and have their investors and shareholders creaming their pants as they ride high on the fumes of their own farts. They’d be drawn and quartered if they suddenly did a 180 or in any way admitted their product is massively overvalued and nearly useless.
The idea of boards and corporations need to fucking die. Coops or burn it to the ground. I’m tired of society actively working against itself.
Issue is, the whole AI explosion is hiding a financial crisis, so tech companies rushing out LLMs, slapping AI onto everything they can (even thermoswitches), to keep investors happy. Smaller companies in the AI bubble are already bursting (e.g. Rabbit), OpenAI’s downfall isn’t a far-fetched dream, although they’ll likely just fire Sam Altman and concentrate on more obtainable and useful AI tech.
This is what competition is now.
Putting out worthless things simply because everyone else is doing it.
Hey, Google: if your big tech friends jumped off a cliff, would you join 'em?
(Also why is the AI assistant on my phone opening up just by typing “hey Google?” 😡)
All I know when a publicly offered company slaps “AI” on their products, then its most likely a money launderi…i mean liquidation strat.
Media needs to stop calling this AI. There is no intelligence here.
The content generator models know how to put probabilistic tokens together. It has no ability to reason.
It is a currently unsolvable problem to evaluate text to determine if it’s factual…until we have artificial general intelligence.
AI will not be able to act like real AI until we solve real AI. That is the currently open problem.
I think you mean AGI. AI can be as simple as a bunch of if-else chains to win a game of noughts and crosses.
That was AI has been abused into meaning in the general vernacular I agree.
By this definition any algorithm whatsoever is artificial intelligence. Including the algorithms Lovelace created before the first computer existed.
So just like AI used to mean something more than machine learning, AGI will be abused until AGI means the same thing. So I expect journalists to use the appropriate language, or at least explain why they’re abusing language
For the down voters if you think Dr. Nym is AI… Fair enough, but I don’t agree
https://en.m.wikipedia.org/wiki/Dr._Nim
Dr. Nym explained by matt parker
It fails the turning test. Generative language models also fail the turning test. The bar for AI should be the turning test…
Sure, but the problem is that our language has evolved and “AI” no longer means what it used to.
Over a decade ago it was mostly reserved for what you’re describing (which I would call “AGI” now). However, even then we did technically use “AI” for things like NPCs in video games. That kind of AI just boils down to a bunch of If-Then statements.
Here is an alternative Piped link(s):
Dr. Nym explained by matt parker
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
As somebody who uses what has long been called AI in game making (stuff like pathing algorithms and steering behaviours) I would rather we don’t stop calling those things that just because a bunch of greedy assholes are misusing the term for the purposed of getting a bunch of hype-trains going for maximum personal profitabiliyty on the backs of techno-ignorant “investors”.
I’m still pissed of at how the greedy assholes fucked up the Internet from what it was back in the 90s.
I think any time “AI” is involved, journalists should be much more specific about what exactly they’re talking about. LLMs, Computer Vision, Generative models (text/image/audio), Upscaling (can start to get a little muddy here between upscaling and generative models depending on how this is implemented), TTS, STT, etc…
I definitely agree that “AI” has been abused into the definition it is now. Over a decade ago “AI” was mostly reserved for what we have to call “AGI” now.
Media is speaking to a nation who
voted for a man who bragged about grabbing women by their genitalsis almost majority below average. (yes dumb joke)Models know how to arrange text far better than millions and millions of people. Is it terribly unfair to condense “artificial, simulated (non-reasoning) pseudo-‘intelligence’” down to “AI”?
Not for you - is it unfair for the general public?
Let’s turn that frown upside down! Instead of saying “Google failed to generate a useful LLM to bolster its search feature,” say “Google successfully replicated the output of an average Reddit troll!”
How about turn it the fuck off since it sucks and eventually will kill someone.
These models are mad libs machines. They just decide on the next word based on input and training. As such, there isn’t a solution to stopping hallucinations.
They polluted their model with the sewage of the Internet.
The only worse thing they could have done is base their entire LLM dataset on 4chan.
The only worse thing they could have done is base their entire LLM dataset on 4chan.
It went as expected.
The model literally ate The Onion, and now they can’t get it to throw it back up.
I’ve seen suggestions that the AI Overview is based on the top search results for the query, so the terrible answers may be more to do with Google Search just being bad than any issue with their AI. The AI Overview just makes things a bit worse by removing the context, so you can’t see the glue on pizza suggestion was a joke on reddit or it was The Onion suggesting eating rocks.
I noticed that while using phind and perplexity. Its context is vitiated with results from sites that rig SEO, which are almost copy/paste with the same garbage, so instead of answering the question it makes a useless summary of them. Even asking chatgpt usually gives more correct answers.
