autotldr@lemmings.world
on 12 Jun 11:30
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This is the best summary I could come up with:
Even Apple CEO Tim Cook isn’t sure the company can fully stop AI hallucinations.
In an interview with The Washington Post, Cook said he would “never claim” that its new Apple Intelligence system won’t generate false or misleading information with 100 percent confidence.
These features will let you generate email responses, create custom emoji, summarize text, and more.
Recent examples of how AI can get things wrong include last month’s incident with Google’s Gemini-powered AI overviews telling us to use glue to put cheese on pizza or a recent ChatGPT bug that caused it to spit out nonsensical answers.
The voice assistant will turn to ChatGPT when it receives a question better suited for the chatbot, but it will ask for your permission before doing so.
In the demo of the feature shown during WWDC, you can see a disclaimer at the bottom of the answer that reads, “Check important info for mistakes.”
The original article contains 334 words, the summary contains 153 words. Saved 54%. I’m a bot and I’m open source!
It’s kind of funny how AI has the exact same problems some humans have.
I always thought AI wouldn’t have that kind of problems, because they would be carefully fed accurate information.
Instead they are taught from things like Facebook and the thing formerly known as Twitter.
What an idiotic timeline we are in. LOL
I thought the main issue was that AI don’t really know how to say I don’t know or second guess themselves, as it would take a lot more robust architecture with multiple feedback loops. Like a brain.
Anyway, LLM’s aren’t the only AI that do this. So them being trained on Facebook data certainly isn’t the whole issue.
Yeah it’s the old garbage in, garbage out problem, the AI algorithms don’t really understand what they are outputting.
I think at this point voice recognition and text generation AI would be more useful as something like a phone assistant. You could tell it complex things like “Mute my phone for the next 2 hours” or “Notify me if I receive an email from John Smith.” Those sort of things could be easily done by AI algorithms that A) Understand your voice and B) Are programmed to know all the features of the OS. Hopefully with a known dataset like a phone OS there shouldn’t be hallucination problems, the AI could just act as an OS concierge.
The narrow purpose models seem to be the most successful, so this would support the idea that a general AI isn't going to happen from LLMs alone. It's interesting that hallucinations are seen as a problem yet are probably part of why LLMs can be creative (much like humans). We shouldn't want to stop them, but just control when they happen and be aware of when the AI is off the tracks. A group of different models working together and checking each other might work (and probably has already been tried, it's hard to keep up).
Seems Siri and Alexa could already do things like that without needing LLMs trained on Facebook shit.
NeoNachtwaechter@lemmy.world
on 12 Jun 12:25
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Instead they are taught from things like Facebook and the thing formerly known as Twitter.
Imagine they would teach in our schools to inform yourself about all the important things, and therefore you should read as many toilet walls as newspapers…
MentalEdge@sopuli.xyz
on 12 Jun 12:56
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There’s also the fact that they can’t tell reality apart from fiction in general, because they don’t understand anything in the first place.
LLMs have no way of differentiating fantasy RPG elements from IRL things. So they can lose the plot on what is being discussed suddenly, and for seemingly no reason.
LLMs don’t just “learn” facts from their training data. They learn how to pretend to be thinking, they can mimic but not really comprehend. If there were facts in the training data, it can regurgitate them, but it doesn’t actually know which facts apply to which subjects, or when to not make some up.
What weirds me out is that the things it has issues with when generating images/video are basically a list of things lucid dreamers check on to see if they’re awake or dreaming.
Hands. Are your hands… Hands? Do they make sense?
Written language. Does it look like normal written language?
(3. Turn the lights off/4. Pinch your nose and breath through it) - these two not so much
How did I get here? Where was I before this? Does the transition make sense?
Mirrors. Are they accurate?
Displays on digital devices. Do they look normal?
Clocks. Digital and analog… Do they look like they’re telling time? Even if they do, look away and check again.
(9. Physics, try to do something physically impossible, like poking your finger through your palm. 10. Do you recognize people/do they recognize you) - on two more that aren’t relevant.
But still… It’s kinda remarkable.
Also, Nvidia launched their earth 2 earth simulator recently. So, simulation theory confirmed, I guess.
Also, check your cell phone. Despite how ubiquitous they are in our daily lives, I don’t think I’ve seen a single cell phone in my dreams. Or any other phone, for that matter.
And now that I think about it, I’ve definitely had a dream of being in my living room where there’s a TV, but I don’t remember the TV actually being in the dream.
The problem with AI hallucinations is not that the AI was fed inaccurate information, it's that it's coming up with information that it wasn't fed in the first place.
As you say, this is a problem that humans have. But I'm not terribly surprised these AIs have it because they're being built in mimicry of how aspects of the human mind works. And in some cases it's desirable behaviour, for example when you're using an AI as a creative assistant. You want it to come up with new stuff in those situations.
It's just something you need to keep in mind when coming up with applications.
AdrianTheFrog@lemmy.world
on 12 Jun 16:33
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Not in the case of the google search AI. It quotes directly from unreliable sources.
Exactly, which is why I've objected in the past to calling Google Overview's mistakes "hallucinations." The AI itself is performing correctly, it's giving an accurate overview of the search result it's being told to create an overview for. It's just being fed incorrect information.
technocrit@lemmy.dbzer0.com
on 12 Jun 15:49
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It’s not the exact same problems humans have. It’s completely different. Marketers and hucksters just use anthropomorphic terminology to hype their dysfunctional programs.
Right? In all science fiction, artificial intelligence starts out better than us, and the only question is whether it can capture some idiosyncratic element of “being human.” Instead, AI has started out dumber than us, and we’re all standing around saying “uh what is this good for?”
That’s what it comes by not really understanding what you’re doing. Most of the AI models I work with are the state of the art just because they happen to work.
In my case, when I solve a PDE using finite difference schemes, there are precise mathematical conditions that guarantee you if the method is going to be stable or not. When I do the same using AI, I can’t tell if my method is going to work or not unless I run it. Moreover, I’ve had it sometimes fail and sometimes succeed.
It’s just the way it is for now. Some clever people have to step in and sort things out, because our knowledge is not keeping up with technological resources.
DudeDudenson@lemmings.world
on 12 Jun 12:21
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I mean companies world wide just jumped in the AI bandwagon like a lot of people did with the NFT one. Mostly because AI actually has solid use cases and can make a big difference in broad situations.
Just since people are just slapping AI in everything it’s gonna end up being another fad to raise stock prices, like firing people last year.
Let’s just hope when all of the hype blows over and the general public thinks of AI as the marketing buzzword that never works quite right we’ll keep AI in the things it’s actually useful for
AI interest has come and gone. Some decades ago, people would slap the AI label to expert systems. If we go further back, one would call AI to solving problems in blocks world. It’s eventually going to fade away, just like all the previous waves did.
nieceandtows@programming.dev
on 12 Jun 12:31
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If Apple can stop AI hallucination, any other AI company can also stop AI hallucination. Which is something they could have already done instead of making AI seem a joke on purpose. AI hallucinations are a sort of phenomena that nobody has control over. Why would Tim Cook have unique control over it?
Unless Apple became the first to figure out how, then they suddenly have a huge leg up on the rest. Which is kinda how Apple has been making their bread for most of their successes in my lifetime
nieceandtows@programming.dev
on 12 Jun 13:11
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eh. I don’t think Apple’s gonna be a pioneer in AI. If anybody can do it, it would be openai figuring it out first. Happy to be proven wrong tho.
Yeah. When Apple says it’s coming into a market, they mean they have already perfected it.
Zorsith@lemmy.blahaj.zone
on 12 Jun 15:53
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(Or let other companies polish up a feature/concept for a few years, slap a coat of Space Gray on it, and release it as a revolutionary “new” feature for apple)
Imgonnatrythis@sh.itjust.works
on 12 Jun 13:02
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I only trust moguls and political figures that are 100% sure of everything. I really like the confidence and it makes me feel like they deserve big paychecks and special rights because they must be so smart to have have no room for the doubt like the rest of us spineless imps. This guy is displaying weakness and should be shamed!
I bet Tim Apple is going to fire his ass.
KingThrillgore@lemmy.ml
on 12 Jun 13:06
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He’s only being honest for the sake of the shareholders.
DarkThoughts@fedia.io
on 12 Jun 13:33
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I doubt anyone can for as long as "AI" is synonymous with LLMs. LLMs are just inherently unreliable because of how they work.
CosmoNova@lemmy.world
on 12 Jun 13:36
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I’m not exaggerating when I say there’s only like a dozen true experts for generative AI on the planet and even they’re not completely sure what’s going on in that blackbox. And as far as I’m aware Tim Cook isn’t even one of them. How would he know?
I would expect that Apple has hired some of those experts and they told him.
technocrit@lemmy.dbzer0.com
on 12 Jun 15:45
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These programs are averaging massive amounts of data into a massive averaging function. There’s no way that a human could ever understand what’s going on inside that kind function. Humans can’t hold millions of weights/etc in their head and comprehend what it means. Otherwise, if humans could do this, there would be no point in doing this kind of statistics with computers.
The AI is confidently wrong, that’s the whole problem. If there was an easy way to know if it could be wrong we wouldn’t have this discussion
Ashyr@sh.itjust.works
on 12 Jun 16:19
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While it can’t “know” its own confidence level, it can distinguish between general knowledge (12” in 1’) and specialized knowledge that requires supporting sources.
At one point, I had a chatGPT memory designed for it to automatically provide sources for specialized knowledge and it did a pretty good job.
AdrianTheFrog@lemmy.world
on 12 Jun 16:31
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this paper tries to do that: arxiv.org/pdf/2404.04689
there are also several other techniques I think
Kecessa@sh.itjust.works
on 12 Jun 14:51
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I’m 100% sure they can’t because what they call AI isn’t intelligence.
Who said anything about human intelligence? AIs have a different kind of intelligence, an artificial kind. I’m tired of pretending they don’t
Ever heard of the Turing test? Ever since AIs could pass it it became not a thing. Before that, playing Go was the mark of AI.
Any time an AI achieves a new thing people move goalposts. So I ask you: what does AI need to achieve to have intelligence?
homicidalrobot@lemm.ee
on 12 Jun 16:39
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The same thing actually passing a turing test would require. You’ve obviously read the words “Turing test” somewhere and thought you understood what it meant, but no robot we’ve ever produced as a species has passed the turing test. It EXPLICITLY requires that intelligence equal to (or indistinguishable from) HUMAN intelligence is shown. Without a liar reading responses, no AI we’ll produce for decades will pass the turing test.
No large language model has intelligence. They’re just complicated call and response mechanisms that guess what answer we want based on a weighted response system (we tell it directly or tell another machine how to help it “weigh” words in a response). Obviously with anything that requires massive amounts of input or nuance, like language, it’ll only be right about what it was guided on, which is limited to areas it is trained in.
We don’t have any novel interactions with AI. They are regurgitation engines, bringing forward sentences that aren’t theirs piecemeal. Given ten messages, I’m confident no major LLM would pass a Turing test.
The Turing test is flawed, because while it is supposed to test for intelligence it really just tests for a convincing fake. Depending on how you set it up I wouldn’t be surprised if a modern LLM could pass it, at least some of the time. That doesn’t mean they are intelligent, they aren’t, but I don’t think the Turing test is good justification.
For me the only justification you need is that they predict one word (or even letter!) at a time. ChatGPT doesn’t plan a whole sentence out in advance, it works token by token… The input to each prediction is just everything so far, up to the last word. When it starts writing “As…” it has no concept of the fact that it’s going to write “…an AI A language model” until it gets through those words.
Frankly, given that fact it’s amazing that LLMs can be as powerful as they are. They don’t check anything, think about their answer, or even consider how to phrase a sentence. Everything they do comes from predicting the next token… An incredible piece of technology, despite it’s obvious flaws.
petrol_sniff_king@lemmy.blahaj.zone
on 13 Jun 03:42
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The Turing test is flawed, because while it is supposed to test for intelligence it really just tests for a convincing fake.
This is just conjecture, but I assume this is because the question of consciousness is not really falsifiable, so you just kind of have to draw an arbitrary line somewhere.
Like, maybe tech gets so good that we really can’t tell the difference, and only god knows it isn’t really alive. But then, how would we know not to give the machine legal rights?
For the record, ChatGPT does not pass the turing test.
ChatGPT is not designed to fool us into thinking it’s a human. It produces language with a specific tone & direct references to the fact it is a language model. I am confident that an LLM trained specifically to speak naturally could do it. It still wouldn’t be intelligent, in my view.
The Turing Test says that any person could have any conversation with a machine and there’s no chance you could tell it’s a machine. It does not say that one person could have one conversation with a machine and not be able to tell.
Current text generation models out themselves all the damn time. It can’t actually understand the underlying concepts of words. It just predicts what bit of text would be most convincing to a human based on previous text.
Playing Go was never the mark of AI, it was the mark of improving game-playing machines. It doesn’t represent “intelligence”, only an ability to predict what should happen next based on a set of training data.
It’s worth noting that after Lee Se Dol lost to Alphago, researchers found a fairly trivial Go strategy that could reliably beat the machine. It was simply such an easy strategy to counter that none of the games in the training data had included anyone attempting that strategy, so the algorithm didn’t account for how to counter it. Because the computer doesn’t know Go theory, it only knows how to predict what to do next based on the training data.
Detecting the machine correctly once is not enough. You need to guess correctly most of the time to statistically prove it’s not by chance. It’s possible for some people to do this, but I’ve seen a lot of comments on websites accusing HUMAN answers of being written by AIs.
If the current chat bots improve to reliably not be detected, would that be intelligence then?
KataGo just fixed that bug by putting those positions into the training data. The reason it wasn’t in the training data is because the training data at first was just self-play games. When games that are losses for the AI from humans are included, the bug is fixed.
petrol_sniff_king@lemmy.blahaj.zone
on 13 Jun 03:35
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When games that are losses for the AI from humans are included, the bug is fixed.
You’re not grasping the fundamental problem here.
This is like saying a calculator understands math because when you plug in the right functions, you get the right answers.
The AI grasps the strategic aspects of the game really well. To the point that if you don’t let it “read” deeply into the game tree, but only “guess” moves (that is, only use the policy network) it still plays at a high level (below professional, but strong amateur)
petrol_sniff_king@lemmy.blahaj.zone
on 13 Jun 08:34
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How does it “understand the strategic aspects of the game really well” if it can’t solve problems it hasn’t seen the answers to?
Current AIs pass it, since most people can’t reasonably tell between AI and human-written stuff every time
ChairmanMeow@programming.dev
on 12 Jun 19:27
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It’s dead simple to see if you’re talking to an LLM. The latest models don’t pass the Turing test, not even close. Asking them simple shit causes them to crap themselves really quickly.
Ask ChatGPT how many r’s there are in “veryberry”. When it gets it wrong, tell it you’re disappointed and expect a correct answer. If you do that repeatedly, you can get it to claim there’s more r’s in the word than it has letters.
Can you show the question you asked that led to this and which model was used? I just tested in several models, even slightly older ones and they all answered precisely. Of course if you follow up and tell it the right answer is wrong you can make it say stuff like this, but not one got it wrong out of the gate.
ChairmanMeow@programming.dev
on 13 Jun 05:59
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My point is that telling it a right answer is wrong often causes LLMs to completely shit the bed. They used to argue with you nonsensically, now they give you a different answer (often also wrong).
The only question missing at the start was "How many r’s are there in the word ‘veryberry’. I think raspberry also worked when I tried it. This was ChatGPT4-O. I did mark all the answers as bad, so perhaps they’ve fixed this one by now.
Still, it’s remarkably trivial to get an LLM to provide a clearly non-human response.
<img alt="" src="https://lemmy.world/pictrs/image/702f0340-a5ed-4701-b8a9-89356f9d719f.jpeg">
Fair enough, but it does somewhat undercut your message that every model I’ve tested including quite old ones answer this question correctly on the first try. This image is ChatGPT-4o.
ChairmanMeow@programming.dev
on 13 Jun 06:43
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Perhaps it was being influenced by the chat history. But try asking how many r’s in raspberry, it does get that consistently wrong for me. And you can ask it those followup questions to easily get it to spout nonsense, and that was mostly my point; figuring out if you’re talking to an LLM is fairly trivial.
zbyte64@awful.systems
on 12 Jun 19:38
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Ever heard of the Turing test? Ever since AIs could pass it it became not a thing.
In place of the Turing test we have a new test that informs us whether an individual can properly identify a stochastic parrot
People can mean different things. Intelligence can mean a calculator doing a sum, and it can mean the way humans talk to each other. AI can do some intelligent things without people agreeing that it’s intelligent in the latter sense.
heavy@sh.itjust.works
on 12 Jun 15:51
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No, really, if you understood how the language models work, you would understand it’s not really intelligence. We just tend to humanize it because that’s what our brains do.
There’s a lot of great articles that summarize how we got to this stage and it’s pretty interesting. I’ll try to update this post with a link later.
I think LLMs are useful (and fun) and have a place, but intelligence they are not.
I’m still waiting for the definition of intelligence that won’t have the same moving of goalposts the Turing Test did
Barbarian@sh.itjust.works
on 12 Jun 16:59
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I’m happy with the Oxford definition: “the ability to acquire and apply knowledge and skills”.
LLMs don’t have knowledge as they don’t actually understand anything. They are algorithmic response generators that apply scores to tokens, and spit out the highest scoring token considering all previous tokens.
If asked to answer 10*5, they can’t reason through the math. They can only recognize 10, * and 5 as tokens in the training data that is usually followed by the 50 token. Thus, 50 is the highest scoring token, and is the answer it will choose. Things get more interesting when you ask questions that aren’t in the training data. If it has nothing more direct to copy from, it will regurgitate a sequence of tokens that sounds as close as possible to something in the training data: thus a hallucination.
assassin_aragorn@lemmy.world
on 12 Jun 17:12
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This can be intuitively understood if you’ve gone through difficult college classes. There’s two ways to prepare for exams. You either try to understand the material, or you try to memorize it.
The latter isn’t good for actually applying the information in the future, and it’s most akin to what an LLM does. It regurgitates, but it doesn’t learn. You show it a bunch of difficult engineering problems, and it won’t be able to solve different ones that use the same principle.
The human could be described in very similar terms. People think we’re magic or something, but we to are just a weighted neural network assembling outputs based strictly on training data built from reinforcement. We are just for the moment much much better with massive models. Of course that is reductive but many seem to forget that brains suffer similarly when outside of training data.
rottingleaf@lemmy.zip
on 13 Jun 07:20
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That’s an obsolete description of what a mammal’s brain is.
Artificial neural nets no, but neural networks in general yes. Just because the computer version isn’t like the real thing doesn’t mean that humans do not use a type of neural network.
I’m slightly confused. Which part needs an academic paper? I’ve made three admittedly reductive claims.
Human brains are neural networks.
Its outputs are based on training data built from reinforcement.
We have a much more massive model than current artificial networks.
First, I’m not trying to make some really clever statement. I’m just saying there is a perspective where describing the human brain can generally follow a similar description. Nevertheless, let’s look at the only three assertions I make here. Given that the term neural network is given its namesake from the neurons that make up brains, I assume you don’t take issue with this. The second point, I don’t know if linking to scholarly research is helpful. Is it not well established that animals learn and use reward circuitry like the role of dopamine in neuromodulation? We also have… education, where we are fed information so that we retain it and can recount it down the road.
I guess maybe it is worth exploring the third, even though, I really wasn’t intending to make a scholarly statement. Here is an article in Scientific American that gives the number of neural connections around 100 trillion. Now, how that equates directly to model parameters is absolutely unclear, but even if you take glial cells where the number can be as low as 40-130 billion according to The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting, that number is in the same order of magnitude of current models’ parameters. So I guess, if your issue is that AI models are actually larger than the human brain’s, I guess maybe there is something cogent. But given that there is likely at least a 1000:1 ratio of neural connections to neurons, I just don’t think that is really fair at all.
Barbarian@sh.itjust.works
on 14 Jun 13:57
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So, first of all, thank you for the cogent attempt at responding. We may disagree, but I sincerely respect the effort you put into the comment.
The specific part that I thought seemed like a pretty big claim was that human brains are “simply” more complex neural networks and that the outputs are based strictly on training data.
Is it not well established that animals learn and use reward circuitry like the role of dopamine in neuromodulation?
While true, this is way too reductive to be a one to one comparison with LLMs. Humans have genetic instinct and body-mind connection that isn’t cleanly mappable onto a neural network. For example, biologists are only just now scraping the surface of the link between the brain and the gut microbiome, which plays a much larger role on cognition than previously thought.
Another example where the brain = neural network model breaks down is the fact that the two hemispheres are much more separated than previously thought. So much so that some neuroscientists are saying that each person has, in effect, 2 different brains with 2 different personalities that communicate via the corpus callosum.
There’s many more examples I could bring up, but my core point is that the analogy of neural network = brain is just that, a simplistic analogy, on the same level as thinking about gravity only as “the force that pushes you downwards”.
To say that we fully understand the brain, to the point where we can even make a model of a mosquito’s brain (220,000 neurons), I think is mistaken. I’m not saying we’ll never understand the brain enough to attempt such a thing, I’m just saying that drawing a casual equivalence between mammalian brains and neural networks is woefully inadequate.
For what it’s worth, in spite of my poor choice of words and general ignorance on many topics, I agree with everything you said here, and find these fascinating topics. Especially that of our microbiome which I think by mass is larger than our brains; so who’s really doing the thinking around here?
Barbarian@sh.itjust.works
on 14 Jun 16:04
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Even the question of “who” is a fascinating deep dive in and of itself. Consciousness as an emergent property implies that your gut microbiome is part of the “who” doing the thinking in the first place :))
They’re wrong that brains are the same as LLMs, but neural networks are just mimicking our brains. That’s how they work. The difference between an LLM and a thinking mind is in structure and complexity, and in computational power. We don’t yet have the knowledge of how to structure a mind out of the pieces of neural nets we’ve built so far.
Okay, I disagree that our brains “suffer similarly when outside of training data”. The capacity of a mind to infer meaning and dynamically problem solve is qualitatively different from an LLM. We can see something completely new to us and immediately start making connections and inferences. LLMs don’t make inferences because they don’t understand meaning.
However, I agree that our brains are effectively just organic neural networks. That’s just definitionally true, because neural networks are biomimicry, and we can tell we’ve got it right because they successfully mimic pieces of our brain. An LLM is effectively like the language planning centre of our brain, but that planning centre just gives us phrases. We have to pass those phrases through our consciousness, our context engine, to determine if they really mean what we want. When someone is choosing their words carefully, they are doing this. If we aren’t careful sometimes we shit out some words without really thinking and we sound dumb, just like an LLM.
petrol_sniff_king@lemmy.blahaj.zone
on 13 Jun 03:50
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I think the definition is “whichever is more emotionally important to you.” So, in your case, they would be very, very intelligent.
Ultraviolet@lemmy.world
on 12 Jun 16:50
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“Hallucination” is an anthropomorphized term for what’s happening. The actual cause is much simpler, there’s no semantic distinction between true and false statements. Both are equally plausible as far as a language model is concerned, as long as it’s semantically structured like an answer to the question being asked.
No that’s not it at all. People know that they don’t know some things. LLMs do not.
sugar_in_your_tea@sh.itjust.works
on 13 Jun 16:03
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Exactly, the LLM isn’t “thinking,” it’s just matching inputs to outputs with some randomness thrown in. If your data is high quality, a lot of the time the answers will be appropriate given the inputs. If your data is poor, it’ll output surprising things more often.
It’s a really cool technology in how much we get for how little effort we put in, but it’s not “thinking” in any sense of the word. If you want it to “think,” you’ll need to put in a lot more effort.
Your brain is also “just” matching inputs to outputs using complex statistics, a huge number of interconnects and clever digital-analog mixed ionic circuitry.
sugar_in_your_tea@sh.itjust.works
on 14 Jun 03:06
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At a super high level, sure. But human brains also have tens of thousands of years (perhaps hundreds of thousands) to develop, so it’s not like a newborn baby is working off a blank slate, there’s a ton of evolutionary circuitry in there that influences things.
That’s why an algorithm that is based on human data will never quite work like a human. That doesn’t mean it’s not intelligent, it just requires a different set of requirements. That’s why I think the Turing test is a bad metric, since an LLM could just find “proper” responses given a bunch of existing conversations without having to reason about the conversation.
Real intelligence, imo, would need to be able to learn to solve puzzles without seeing similar puzzles. That’s more the domain of other “AI” fields like neural networks and machine learning. But each field approaches problems in a different, limited way, so general AI will be quite complicated unless we find a new approach.
Deconceptualist@lemm.ee
on 12 Jun 15:09
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As others are saying it’s 100% not possible because LLMs are (as Google optimistically describes) “creative writing aids”, or more accurately, predictive word engines. They run on mathematical probability models. They have zero concept of what the words actually mean, what humans are, or even what they themselves are. There’s no “intelligence” present except for filters that have been hand-coded in (which of course is human intelligence, not AI).
“Hallucinations” is a total misnomer because the text generation isn’t tied to reality in the first place, it’s just mathematically “what next word is most likely”.
Probably not of what a human is, but thought process is needed for better text generarion and is therefore emergent in their neural net
Deconceptualist@lemm.ee
on 12 Jun 16:43
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Ok, maybe there’s a possibility someday with that approach. But that doesn’t reflect my understanding or (limited) experience with the major LLMs (ChatGPT, Gemini) out in the wild today. Right now they confidently advise ingesting poison because it’s grammatically sound and they found it on some BS Facebook post.
If ML engineers can design an internal concept of what constitutes valid information (a hard problem for humans, let alone machines) maybe there’s hope.
QuantumSoul@lemmy.dbzer0.com
on 12 Jun 20:08
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Ethical and healthy is a whole harder problem lol. Having reasoning and thinking will come before
The problem is they have many different internal concepts with conflicting information and no mechanism for determining truthfulness or for accuracy or for pruning bad information, and will sample them all randomly when answering stuff
QuantumSoul@lemmy.dbzer0.com
on 12 Jun 23:08
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Indeed
_number8_@lemmy.world
on 12 Jun 15:40
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all we know about ourselves is what’s in our memories. the way normal writing or talking works is just picking what words sound best in order
That’s not the whole story. “The dog swam across the ocean.” is a grammatically valid sentence with correct word order. But you probably wouldn’t write it because you have a concept of what a dog actually is and know its physiological limitations make the sentence ridiculous.
The LLMs don’t have those kind of smarts. They just blindly mirror what we do. Since humans generally don’t put those specific words together, the LLMs avoid it too, based solely on probability. If lots of people started making bold claims about oceanfaring canids (e.g. as a joke), then the LLMs would absolutely jump onboard with no critical thinking of their own.
Have you heard of music theory and psychoacoustics (frankly even painting with oil you’ll use that)? Where we hear something dependent on what we expect and what we actually get, both in time, in length, in color, in amplitude etc.
Religion is about the same, it uses the concepts of impossible, unreachable and transcendent. Kicking something left and then back into place is not the same as not touching it.
I was wondering, are people working on networks that train to create a modular model of the world, in order to understand it / predict events in the world?
I imagine that that is basically what our brains do.
Deconceptualist@lemm.ee
on 12 Jun 16:37
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Yeah I’m sure folks are working on it, but I’m not knowledgeable or qualified on the details.
eestileib@sh.itjust.works
on 12 Jun 18:38
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Many attempts, some well-funded.
They have been successful in very limited domains. For example, the F-35 integrated sensor suite.
Not really anything properly universal, but a lot of task specific models exists with integration with logic engines and similar stuff. Performance varies a lot.
You might want to take a look at wolfram alpha’s plugin for chatgpt for something that’s public
captain_aggravated@sh.itjust.works
on 12 Jun 18:49
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Remember the game people used to play that was something like “type my girlfriend is and then let your phone keyboards auto suggestion take it from there?” LLMs are that.
cheese_greater@lemmy.world
on 12 Jun 16:07
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Tim Apple
AdrianTheFrog@lemmy.world
on 12 Jun 16:14
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They can’t. AI has hallucinations. Google has shown that AI can’t even rely on external sources, either.
At least LLMs will. The only real fix we’ve seen was running it through additional specialized LLMs to try to massage out errors, but that just increases costs and scale for marginally low results.
NutWrench@lemmy.world
on 12 Jun 16:58
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If you want to have good AI, you need to spend money and send your AI to college. Have real humans interact with it, correct it’s logic, make sure it understands sarcasm and logical fallacies.
Or, you can go the cheap route: train it on 10 years of Reddit sh*tposts and hope for the best.
What you can do is try to filter out the garbage, but it’s basically trying to find gold in food waste.
baatliwala@lemmy.world
on 12 Jun 18:40
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Stupid headline, it’s like Tim Cook saying he’s not 100% sure Apple can stop batteries in their devices from exploding. You do as much as you can to prevent it but it might happen anyway because that’s just how it is.
cybersandwich@lemmy.world
on 12 Jun 19:53
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Of course you are getting downvoted, because you are right and not being a reactionary douche like your average lemmizen.
It’s insane how many people already take AI as more capable/accurate than other medium. I’m not against AI, but I’m definitely against how much of a bubble of being worshipped that some people have it in.
flop_leash_973@lemmy.world
on 12 Jun 21:33
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Well yeah, its using the same dataset as MS copilot.
Spitting out inaccurate (I wish the media would stop feeding into calling it something that sounds less bad like hallucinations) answers is nothing something that will go away until the LLM gains the ability to decern context.
Because everything they output is a hallucination. Just because sometimes those hallucinations are true to life doesn’t mean jack shit. Even a broken clock is right twice a day.
“Only feed it accurate information.”
Even that doesn’t work because it just mixes and matches every element of its input to generate a new, novel output. Which would inevitably be wrong.
I’m 100% sure he can’t. Or at least, not from LLMs specifically. I’m not an expert so feel free to ignore my opinion but from what I’ve read, “hallucinations” are a feature of the way LLMs work.
One can have an expert system assisted by ML for classification. But that’s not an LLM.
chonglibloodsport@lemmy.world
on 13 Jun 08:41
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Everything these AIs output is a hallucination. Imagine if you were locked in a sensory deprivation tank, completely cut off from the outside world, and only had your brain fed the text of all books and internet sites. You would hallucinate everything about them too. You would have no idea what was real and what wasn’t because you’d lack any epistemic tools for confirming your knowledge.
That’s the biggest reason why AIs will always be bullshitters as long as their disembodied software programs running on a server. At best they can be a brain in a vat which is a pure hallucination machine.
Voroxpete@sh.itjust.works
on 13 Jun 12:10
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Yeah, I try to make this point as often as I can. The notion that AI hallucinates only wrong answers really misleads people about how these programs actually work. It couches it in terms of human failings rather than really getting at the underlying flaw in the whole concept.
LLMs are a really interesting area of research, but they never should have made it out of the lab. The fact that they did is purely because all science operates in the service of profit now. Imagine if OpenAI were able to rely on government funding instead of having to find a product to sell.
First of all I agree with your point that it is all hallucination.
However I think a brain in a vat could confirm information about the world with direct sensors like cameras and access to real-time data, as well as the ability to talk to people and determine things like who was trustworthy. In reality we are brains in vats, we just have a fairly common interface that makes consensus reality possible.
The thing that really stops LLMs from being able to make judgements about what is true and what is not is that they cannot make any judgements whatsoever. Judging what is true is a deeply contextual and meaning-rich question. LLMs cannot understand context.
I think the moment an AI can understand context is the moment it begins to gain true sentience, because a capacity for understanding context is definitionally unbounded. Context means searching beyond the current information for further information. I think this context barrier is fundamental, and we won’t get truth-judging machines until we get actually-thinking machines.
kenkenken@sh.itjust.works
on 13 Jun 09:00
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To be 100 percent sure is a hallucination. Probably he tried to say that he is less than 80 percent sure.
boatsnhos931@lemmy.world
on 13 Jun 11:39
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Tim Cook…go take your meds and watch Price is Right
Because it’s all a corporation and a huge part of the corporate capitalist system is infinite growth. They want returns, BIG ones. When? Right the fuck now. How do you do that? Well AI would turn the world upside down like the dot-com boom. So they dump tons of money into AI. So… it’s the AI done? Oh no no no, we’re at machine leaning AI is pretty far down the road actually, what we’re firing the AI department heads and releasing this machine leaning software as 100% all the way done AI?
It’s all the same reasons section 8 housing and low cost housing don’t work under corporate capitalism. It’s profitable to take government money, it’s profitable to have low rent apartments. That’s not the problem, the problem is THEY NEED THE GROWTH NOW NOW NOW!!! If you have a choice between owning a condo where you have high wage renters, and you add another $100 to rent every year, you get more profit faster. No one wants to invest in a 10% increase over 5 years if the can invest in 12% over 4 years. So no one ever invests in low rent or section 8 housing.
crystalmerchant@lemmy.world
on 13 Jun 15:38
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Of course they can’t. Any product or feature is only as good as the data underneath it. Training data comes from the internet, and the internet is full of humans. Humans make and write weird shit so so the data that the LLM ingests is weird, this creates hallucinations.
JackbyDev@programming.dev
on 13 Jun 22:58
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That’s like saying you can’t be 100% sure you never have fake news at the top of search query results. It’s just a fact.
threaded - newest
This is the best summary I could come up with:
Even Apple CEO Tim Cook isn’t sure the company can fully stop AI hallucinations.
In an interview with The Washington Post, Cook said he would “never claim” that its new Apple Intelligence system won’t generate false or misleading information with 100 percent confidence.
These features will let you generate email responses, create custom emoji, summarize text, and more.
Recent examples of how AI can get things wrong include last month’s incident with Google’s Gemini-powered AI overviews telling us to use glue to put cheese on pizza or a recent ChatGPT bug that caused it to spit out nonsensical answers.
The voice assistant will turn to ChatGPT when it receives a question better suited for the chatbot, but it will ask for your permission before doing so.
In the demo of the feature shown during WWDC, you can see a disclaimer at the bottom of the answer that reads, “Check important info for mistakes.”
The original article contains 334 words, the summary contains 153 words. Saved 54%. I’m a bot and I’m open source!
It’s kind of funny how AI has the exact same problems some humans have.
I always thought AI wouldn’t have that kind of problems, because they would be carefully fed accurate information.
Instead they are taught from things like Facebook and the thing formerly known as Twitter.
What an idiotic timeline we are in. LOL
I thought the main issue was that AI don’t really know how to say I don’t know or second guess themselves, as it would take a lot more robust architecture with multiple feedback loops. Like a brain.
Anyway, LLM’s aren’t the only AI that do this. So them being trained on Facebook data certainly isn’t the whole issue.
Yeah it’s the old garbage in, garbage out problem, the AI algorithms don’t really understand what they are outputting.
I think at this point voice recognition and text generation AI would be more useful as something like a phone assistant. You could tell it complex things like “Mute my phone for the next 2 hours” or “Notify me if I receive an email from John Smith.” Those sort of things could be easily done by AI algorithms that A) Understand your voice and B) Are programmed to know all the features of the OS. Hopefully with a known dataset like a phone OS there shouldn’t be hallucination problems, the AI could just act as an OS concierge.
The narrow purpose models seem to be the most successful, so this would support the idea that a general AI isn't going to happen from LLMs alone. It's interesting that hallucinations are seen as a problem yet are probably part of why LLMs can be creative (much like humans). We shouldn't want to stop them, but just control when they happen and be aware of when the AI is off the tracks. A group of different models working together and checking each other might work (and probably has already been tried, it's hard to keep up).
Yeah the hallucinations could be very useful for art and creative stepping stones. But not as much for factual information.
Seems Siri and Alexa could already do things like that without needing LLMs trained on Facebook shit.
Imagine they would teach in our schools to inform yourself about all the important things, and therefore you should read as many toilet walls as newspapers…
There’s also the fact that they can’t tell reality apart from fiction in general, because they don’t understand anything in the first place.
LLMs have no way of differentiating fantasy RPG elements from IRL things. So they can lose the plot on what is being discussed suddenly, and for seemingly no reason.
LLMs don’t just “learn” facts from their training data. They learn how to pretend to be thinking, they can mimic but not really comprehend. If there were facts in the training data, it can regurgitate them, but it doesn’t actually know which facts apply to which subjects, or when to not make some up.
True, and they are so darn good at it, that it can be somewhat confusing at times.
But the current AIs are not the ones we read about in SciFi.
I’d argue that referring to it as “AI” is a stretch since it’s all A and no I.
This is why I strictly refer to these things as LLMs. That’s what they are.
What weirds me out is that the things it has issues with when generating images/video are basically a list of things lucid dreamers check on to see if they’re awake or dreaming.
Hands. Are your hands… Hands? Do they make sense?
Written language. Does it look like normal written language?
(3. Turn the lights off/4. Pinch your nose and breath through it) - these two not so much
How did I get here? Where was I before this? Does the transition make sense?
Mirrors. Are they accurate?
Displays on digital devices. Do they look normal?
Clocks. Digital and analog… Do they look like they’re telling time? Even if they do, look away and check again.
(9. Physics, try to do something physically impossible, like poking your finger through your palm. 10. Do you recognize people/do they recognize you) - on two more that aren’t relevant.
But still… It’s kinda remarkable.
Also, Nvidia launched their earth 2 earth simulator recently. So, simulation theory confirmed, I guess.
Also, check your cell phone. Despite how ubiquitous they are in our daily lives, I don’t think I’ve seen a single cell phone in my dreams. Or any other phone, for that matter.
And now that I think about it, I’ve definitely had a dream of being in my living room where there’s a TV, but I don’t remember the TV actually being in the dream.
Weird.
The problem with AI hallucinations is not that the AI was fed inaccurate information, it's that it's coming up with information that it wasn't fed in the first place.
As you say, this is a problem that humans have. But I'm not terribly surprised these AIs have it because they're being built in mimicry of how aspects of the human mind works. And in some cases it's desirable behaviour, for example when you're using an AI as a creative assistant. You want it to come up with new stuff in those situations.
It's just something you need to keep in mind when coming up with applications.
Not in the case of the google search AI. It quotes directly from unreliable sources.
Exactly, which is why I've objected in the past to calling Google Overview's mistakes "hallucinations." The AI itself is performing correctly, it's giving an accurate overview of the search result it's being told to create an overview for. It's just being fed incorrect information.
It’s not the exact same problems humans have. It’s completely different. Marketers and hucksters just use anthropomorphic terminology to hype their dysfunctional programs.
Right? In all science fiction, artificial intelligence starts out better than us, and the only question is whether it can capture some idiosyncratic element of “being human.” Instead, AI has started out dumber than us, and we’re all standing around saying “uh what is this good for?”
That’s what it comes by not really understanding what you’re doing. Most of the AI models I work with are the state of the art just because they happen to work.
In my case, when I solve a PDE using finite difference schemes, there are precise mathematical conditions that guarantee you if the method is going to be stable or not. When I do the same using AI, I can’t tell if my method is going to work or not unless I run it. Moreover, I’ve had it sometimes fail and sometimes succeed.
It’s just the way it is for now. Some clever people have to step in and sort things out, because our knowledge is not keeping up with technological resources.
I mean companies world wide just jumped in the AI bandwagon like a lot of people did with the NFT one. Mostly because AI actually has solid use cases and can make a big difference in broad situations.
Just since people are just slapping AI in everything it’s gonna end up being another fad to raise stock prices, like firing people last year.
Let’s just hope when all of the hype blows over and the general public thinks of AI as the marketing buzzword that never works quite right we’ll keep AI in the things it’s actually useful for
AI interest has come and gone. Some decades ago, people would slap the AI label to expert systems. If we go further back, one would call AI to solving problems in blocks world. It’s eventually going to fade away, just like all the previous waves did.
If Apple can stop AI hallucination, any other AI company can also stop AI hallucination. Which is something they could have already done instead of making AI seem a joke on purpose. AI hallucinations are a sort of phenomena that nobody has control over. Why would Tim Cook have unique control over it?
Unless Apple became the first to figure out how, then they suddenly have a huge leg up on the rest. Which is kinda how Apple has been making their bread for most of their successes in my lifetime
eh. I don’t think Apple’s gonna be a pioneer in AI. If anybody can do it, it would be openai figuring it out first. Happy to be proven wrong tho.
Oh I’m not suggesting the will or are able to, I’m coming from a strategic standpoint
Yeah. When Apple says it’s coming into a market, they mean they have already perfected it.
(Or let other companies polish up a feature/concept for a few years, slap a coat of Space Gray on it, and release it as a revolutionary “new” feature for apple)
Like the revolutionary space gray USB-C port?
I’m sure Tesla can do it within the decade! /s
You mean xAI?
I only trust moguls and political figures that are 100% sure of everything. I really like the confidence and it makes me feel like they deserve big paychecks and special rights because they must be so smart to have have no room for the doubt like the rest of us spineless imps. This guy is displaying weakness and should be shamed!
I bet Tim Apple is going to fire his ass.
He’s only being honest for the sake of the shareholders.
I doubt anyone can for as long as "AI" is synonymous with LLMs. LLMs are just inherently unreliable because of how they work.
I’m not exaggerating when I say there’s only like a dozen true experts for generative AI on the planet and even they’re not completely sure what’s going on in that blackbox. And as far as I’m aware Tim Cook isn’t even one of them. How would he know?
I would expect that Apple has hired some of those experts and they told him.
These programs are averaging massive amounts of data into a massive averaging function. There’s no way that a human could ever understand what’s going on inside that kind function. Humans can’t hold millions of weights/etc in their head and comprehend what it means. Otherwise, if humans could do this, there would be no point in doing this kind of statistics with computers.
They could make Siri change its voice and Genmoji based on the degree of certainty of the response:
They could sell different voice packages. Revive the ringtone market.
The AI is confidently wrong, that’s the whole problem. If there was an easy way to know if it could be wrong we wouldn’t have this discussion
While it can’t “know” its own confidence level, it can distinguish between general knowledge (12” in 1’) and specialized knowledge that requires supporting sources.
At one point, I had a chatGPT memory designed for it to automatically provide sources for specialized knowledge and it did a pretty good job.
this paper tries to do that: arxiv.org/pdf/2404.04689
there are also several other techniques I think
I’m 100% sure they can’t because what they call AI isn’t intelligence.
Even people hallucinate. Under your definition intelligence doesn’t exist
Wow whoosh. The point is that “AI” isn’t actually “intelligent” like a human and thus can’t “hallucinate” like an intelligent human.
All of this anthropomorphic terminology is just misleading marketing bullshit.
Who said anything about human intelligence? AIs have a different kind of intelligence, an artificial kind. I’m tired of pretending they don’t
Ever heard of the Turing test? Ever since AIs could pass it it became not a thing. Before that, playing Go was the mark of AI.
Any time an AI achieves a new thing people move goalposts. So I ask you: what does AI need to achieve to have intelligence?
The same thing actually passing a turing test would require. You’ve obviously read the words “Turing test” somewhere and thought you understood what it meant, but no robot we’ve ever produced as a species has passed the turing test. It EXPLICITLY requires that intelligence equal to (or indistinguishable from) HUMAN intelligence is shown. Without a liar reading responses, no AI we’ll produce for decades will pass the turing test.
No large language model has intelligence. They’re just complicated call and response mechanisms that guess what answer we want based on a weighted response system (we tell it directly or tell another machine how to help it “weigh” words in a response). Obviously with anything that requires massive amounts of input or nuance, like language, it’ll only be right about what it was guided on, which is limited to areas it is trained in.
We don’t have any novel interactions with AI. They are regurgitation engines, bringing forward sentences that aren’t theirs piecemeal. Given ten messages, I’m confident no major LLM would pass a Turing test.
The chat bots will pass the Turing test in a few years, maybe 5. Would that be intelligence then?
The Turing test is flawed, because while it is supposed to test for intelligence it really just tests for a convincing fake. Depending on how you set it up I wouldn’t be surprised if a modern LLM could pass it, at least some of the time. That doesn’t mean they are intelligent, they aren’t, but I don’t think the Turing test is good justification.
For me the only justification you need is that they predict one word (or even letter!) at a time. ChatGPT doesn’t plan a whole sentence out in advance, it works token by token… The input to each prediction is just everything so far, up to the last word. When it starts writing “As…” it has no concept of the fact that it’s going to write “…an AI A language model” until it gets through those words.
Frankly, given that fact it’s amazing that LLMs can be as powerful as they are. They don’t check anything, think about their answer, or even consider how to phrase a sentence. Everything they do comes from predicting the next token… An incredible piece of technology, despite it’s obvious flaws.
This is just conjecture, but I assume this is because the question of consciousness is not really falsifiable, so you just kind of have to draw an arbitrary line somewhere.
Like, maybe tech gets so good that we really can’t tell the difference, and only god knows it isn’t really alive. But then, how would we know not to give the machine legal rights?
For the record, ChatGPT does not pass the turing test.
ChatGPT is not designed to fool us into thinking it’s a human. It produces language with a specific tone & direct references to the fact it is a language model. I am confident that an LLM trained specifically to speak naturally could do it. It still wouldn’t be intelligent, in my view.
The Turing Test says that any person could have any conversation with a machine and there’s no chance you could tell it’s a machine. It does not say that one person could have one conversation with a machine and not be able to tell.
Current text generation models out themselves all the damn time. It can’t actually understand the underlying concepts of words. It just predicts what bit of text would be most convincing to a human based on previous text.
Playing Go was never the mark of AI, it was the mark of improving game-playing machines. It doesn’t represent “intelligence”, only an ability to predict what should happen next based on a set of training data.
It’s worth noting that after Lee Se Dol lost to Alphago, researchers found a fairly trivial Go strategy that could reliably beat the machine. It was simply such an easy strategy to counter that none of the games in the training data had included anyone attempting that strategy, so the algorithm didn’t account for how to counter it. Because the computer doesn’t know Go theory, it only knows how to predict what to do next based on the training data.
Detecting the machine correctly once is not enough. You need to guess correctly most of the time to statistically prove it’s not by chance. It’s possible for some people to do this, but I’ve seen a lot of comments on websites accusing HUMAN answers of being written by AIs.
If the current chat bots improve to reliably not be detected, would that be intelligence then?
KataGo just fixed that bug by putting those positions into the training data. The reason it wasn’t in the training data is because the training data at first was just self-play games. When games that are losses for the AI from humans are included, the bug is fixed.
You’re not grasping the fundamental problem here.
This is like saying a calculator understands math because when you plug in the right functions, you get the right answers.
The AI grasps the strategic aspects of the game really well. To the point that if you don’t let it “read” deeply into the game tree, but only “guess” moves (that is, only use the policy network) it still plays at a high level (below professional, but strong amateur)
How does it “understand the strategic aspects of the game really well” if it can’t solve problems it hasn’t seen the answers to?
It doesn’t get fed answers in the training data, only positions. If it sees a position, it will eventually learn to solve it by itself
en.m.wikipedia.org/wiki/Turing_test
Here you go since you’ve heard of it but don’t understand it.
Current AIs pass it, since most people can’t reasonably tell between AI and human-written stuff every time
It’s dead simple to see if you’re talking to an LLM. The latest models don’t pass the Turing test, not even close. Asking them simple shit causes them to crap themselves really quickly.
Ask ChatGPT how many r’s there are in “veryberry”. When it gets it wrong, tell it you’re disappointed and expect a correct answer. If you do that repeatedly, you can get it to claim there’s more r’s in the word than it has letters.
<img alt="" src="https://lemmy.world/pictrs/image/ba733fce-8b69-4e55-9e3b-56ab877c19b6.jpeg">
that’s it? you asked one question and that was enough for you?
It’s quite easy to identify an AI when you’re talking to one. To be fair, you need to actually run the Turing test since it removes confirmation bias
xD God damn that was funny.
Here’s what I got:**
<img alt="" src="https://programming.dev/pictrs/image/afd5da0c-595e-4f87-8941-807e862b0402.png">
<img alt="" src="https://programming.dev/pictrs/image/5c831fb3-1dac-49e4-a882-765387280706.png">
Can you show the question you asked that led to this and which model was used? I just tested in several models, even slightly older ones and they all answered precisely. Of course if you follow up and tell it the right answer is wrong you can make it say stuff like this, but not one got it wrong out of the gate.
My point is that telling it a right answer is wrong often causes LLMs to completely shit the bed. They used to argue with you nonsensically, now they give you a different answer (often also wrong).
The only question missing at the start was "How many r’s are there in the word ‘veryberry’. I think raspberry also worked when I tried it. This was ChatGPT4-O. I did mark all the answers as bad, so perhaps they’ve fixed this one by now.
Still, it’s remarkably trivial to get an LLM to provide a clearly non-human response.
<img alt="" src="https://lemmy.world/pictrs/image/702f0340-a5ed-4701-b8a9-89356f9d719f.jpeg"> Fair enough, but it does somewhat undercut your message that every model I’ve tested including quite old ones answer this question correctly on the first try. This image is ChatGPT-4o.
Perhaps it was being influenced by the chat history. But try asking how many r’s in raspberry, it does get that consistently wrong for me. And you can ask it those followup questions to easily get it to spout nonsense, and that was mostly my point; figuring out if you’re talking to an LLM is fairly trivial.
In place of the Turing test we have a new test that informs us whether an individual can properly identify a stochastic parrot
People can mean different things. Intelligence can mean a calculator doing a sum, and it can mean the way humans talk to each other. AI can do some intelligent things without people agreeing that it’s intelligent in the latter sense.
No, really, if you understood how the language models work, you would understand it’s not really intelligence. We just tend to humanize it because that’s what our brains do.
There’s a lot of great articles that summarize how we got to this stage and it’s pretty interesting. I’ll try to update this post with a link later.
I think LLMs are useful (and fun) and have a place, but intelligence they are not.
I’m still waiting for the definition of intelligence that won’t have the same moving of goalposts the Turing Test did
I’m happy with the Oxford definition: “the ability to acquire and apply knowledge and skills”.
LLMs don’t have knowledge as they don’t actually understand anything. They are algorithmic response generators that apply scores to tokens, and spit out the highest scoring token considering all previous tokens.
If asked to answer 10*5, they can’t reason through the math. They can only recognize 10, * and 5 as tokens in the training data that is usually followed by the 50 token. Thus, 50 is the highest scoring token, and is the answer it will choose. Things get more interesting when you ask questions that aren’t in the training data. If it has nothing more direct to copy from, it will regurgitate a sequence of tokens that sounds as close as possible to something in the training data: thus a hallucination.
This can be intuitively understood if you’ve gone through difficult college classes. There’s two ways to prepare for exams. You either try to understand the material, or you try to memorize it.
The latter isn’t good for actually applying the information in the future, and it’s most akin to what an LLM does. It regurgitates, but it doesn’t learn. You show it a bunch of difficult engineering problems, and it won’t be able to solve different ones that use the same principle.
The human could be described in very similar terms. People think we’re magic or something, but we to are just a weighted neural network assembling outputs based strictly on training data built from reinforcement. We are just for the moment much much better with massive models. Of course that is reductive but many seem to forget that brains suffer similarly when outside of training data.
That’s an obsolete description of what a mammal’s brain is.
Do you have a better one?
I could find a dozen better ones in google, but I’m not a neurophysiologist.
The important thing here is that neural nets do not describe human brain.
Artificial neural nets no, but neural networks in general yes. Just because the computer version isn’t like the real thing doesn’t mean that humans do not use a type of neural network.
And your experience to say this is?..
.
You’re the one making a radical claim here. What’s your experience?
No, stating a commonly known thing.
In response to your radical claim, I might add, which doesn’t match with the fact that humans haven’t yet emulated the brain of a nematode.
That’s a strong claim. Got an academic paper to back that up?
I’m slightly confused. Which part needs an academic paper? I’ve made three admittedly reductive claims.
First, I’m not trying to make some really clever statement. I’m just saying there is a perspective where describing the human brain can generally follow a similar description. Nevertheless, let’s look at the only three assertions I make here. Given that the term neural network is given its namesake from the neurons that make up brains, I assume you don’t take issue with this. The second point, I don’t know if linking to scholarly research is helpful. Is it not well established that animals learn and use reward circuitry like the role of dopamine in neuromodulation? We also have… education, where we are fed information so that we retain it and can recount it down the road.
I guess maybe it is worth exploring the third, even though, I really wasn’t intending to make a scholarly statement. Here is an article in Scientific American that gives the number of neural connections around 100 trillion. Now, how that equates directly to model parameters is absolutely unclear, but even if you take glial cells where the number can be as low as 40-130 billion according to The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting, that number is in the same order of magnitude of current models’ parameters. So I guess, if your issue is that AI models are actually larger than the human brain’s, I guess maybe there is something cogent. But given that there is likely at least a 1000:1 ratio of neural connections to neurons, I just don’t think that is really fair at all.
So, first of all, thank you for the cogent attempt at responding. We may disagree, but I sincerely respect the effort you put into the comment.
The specific part that I thought seemed like a pretty big claim was that human brains are “simply” more complex neural networks and that the outputs are based strictly on training data.
While true, this is way too reductive to be a one to one comparison with LLMs. Humans have genetic instinct and body-mind connection that isn’t cleanly mappable onto a neural network. For example, biologists are only just now scraping the surface of the link between the brain and the gut microbiome, which plays a much larger role on cognition than previously thought.
Another example where the brain = neural network model breaks down is the fact that the two hemispheres are much more separated than previously thought. So much so that some neuroscientists are saying that each person has, in effect, 2 different brains with 2 different personalities that communicate via the corpus callosum.
There’s many more examples I could bring up, but my core point is that the analogy of neural network = brain is just that, a simplistic analogy, on the same level as thinking about gravity only as “the force that pushes you downwards”.
To say that we fully understand the brain, to the point where we can even make a model of a mosquito’s brain (220,000 neurons), I think is mistaken. I’m not saying we’ll never understand the brain enough to attempt such a thing, I’m just saying that drawing a casual equivalence between mammalian brains and neural networks is woefully inadequate.
For what it’s worth, in spite of my poor choice of words and general ignorance on many topics, I agree with everything you said here, and find these fascinating topics. Especially that of our microbiome which I think by mass is larger than our brains; so who’s really doing the thinking around here?
Even the question of “who” is a fascinating deep dive in and of itself. Consciousness as an emergent property implies that your gut microbiome is part of the “who” doing the thinking in the first place :))
They’re wrong that brains are the same as LLMs, but neural networks are just mimicking our brains. That’s how they work. The difference between an LLM and a thinking mind is in structure and complexity, and in computational power. We don’t yet have the knowledge of how to structure a mind out of the pieces of neural nets we’ve built so far.
Okay, I disagree that our brains “suffer similarly when outside of training data”. The capacity of a mind to infer meaning and dynamically problem solve is qualitatively different from an LLM. We can see something completely new to us and immediately start making connections and inferences. LLMs don’t make inferences because they don’t understand meaning.
However, I agree that our brains are effectively just organic neural networks. That’s just definitionally true, because neural networks are biomimicry, and we can tell we’ve got it right because they successfully mimic pieces of our brain. An LLM is effectively like the language planning centre of our brain, but that planning centre just gives us phrases. We have to pass those phrases through our consciousness, our context engine, to determine if they really mean what we want. When someone is choosing their words carefully, they are doing this. If we aren’t careful sometimes we shit out some words without really thinking and we sound dumb, just like an LLM.
I think the definition is “whichever is more emotionally important to you.” So, in your case, they would be very, very intelligent.
“Hallucination” is an anthropomorphized term for what’s happening. The actual cause is much simpler, there’s no semantic distinction between true and false statements. Both are equally plausible as far as a language model is concerned, as long as it’s semantically structured like an answer to the question being asked.
That’s also pretty true for people, unfortunately. People are deeply incapable of differentiating fact from fiction.
Like how many, five?
No that’s not it at all. People know that they don’t know some things. LLMs do not.
Exactly, the LLM isn’t “thinking,” it’s just matching inputs to outputs with some randomness thrown in. If your data is high quality, a lot of the time the answers will be appropriate given the inputs. If your data is poor, it’ll output surprising things more often.
It’s a really cool technology in how much we get for how little effort we put in, but it’s not “thinking” in any sense of the word. If you want it to “think,” you’ll need to put in a lot more effort.
Your brain is also “just” matching inputs to outputs using complex statistics, a huge number of interconnects and clever digital-analog mixed ionic circuitry.
At a super high level, sure. But human brains also have tens of thousands of years (perhaps hundreds of thousands) to develop, so it’s not like a newborn baby is working off a blank slate, there’s a ton of evolutionary circuitry in there that influences things.
That’s why an algorithm that is based on human data will never quite work like a human. That doesn’t mean it’s not intelligent, it just requires a different set of requirements. That’s why I think the Turing test is a bad metric, since an LLM could just find “proper” responses given a bunch of existing conversations without having to reason about the conversation.
Real intelligence, imo, would need to be able to learn to solve puzzles without seeing similar puzzles. That’s more the domain of other “AI” fields like neural networks and machine learning. But each field approaches problems in a different, limited way, so general AI will be quite complicated unless we find a new approach.
This is some real “what else besides witches floats in water” ass-logic
All properties are transitive.
LLMs aren’t even hallucinating thou. It’s a euphamistic term to make it’s limitations sound human like
Intelligence is whatever does the job and gets it done well.
AI is whatever makes the dollar sign number get bigger
It’s intelligent in that regard…
As others are saying it’s 100% not possible because LLMs are (as Google optimistically describes) “creative writing aids”, or more accurately, predictive word engines. They run on mathematical probability models. They have zero concept of what the words actually mean, what humans are, or even what they themselves are. There’s no “intelligence” present except for filters that have been hand-coded in (which of course is human intelligence, not AI).
“Hallucinations” is a total misnomer because the text generation isn’t tied to reality in the first place, it’s just mathematically “what next word is most likely”.
arstechnica.com/…/a-jargon-free-explanation-of-ho…
An LLM once explained to me that it didn’t know, it simulated an answer. I found that descriptive.
They do have internal concepts though: lesswrong.com/…/a-chess-gpt-linear-emergent-world…
Probably not of what a human is, but thought process is needed for better text generarion and is therefore emergent in their neural net
Ok, maybe there’s a possibility someday with that approach. But that doesn’t reflect my understanding or (limited) experience with the major LLMs (ChatGPT, Gemini) out in the wild today. Right now they confidently advise ingesting poison because it’s grammatically sound and they found it on some BS Facebook post.
If ML engineers can design an internal concept of what constitutes valid information (a hard problem for humans, let alone machines) maybe there’s hope.
Ethical and healthy is a whole harder problem lol. Having reasoning and thinking will come before
The problem is they have many different internal concepts with conflicting information and no mechanism for determining truthfulness or for accuracy or for pruning bad information, and will sample them all randomly when answering stuff
Indeed
all we know about ourselves is what’s in our memories. the way normal writing or talking works is just picking what words sound best in order
That’s not the whole story. “The dog swam across the ocean.” is a grammatically valid sentence with correct word order. But you probably wouldn’t write it because you have a concept of what a dog actually is and know its physiological limitations make the sentence ridiculous.
The LLMs don’t have those kind of smarts. They just blindly mirror what we do. Since humans generally don’t put those specific words together, the LLMs avoid it too, based solely on probability. If lots of people started making bold claims about oceanfaring canids (e.g. as a joke), then the LLMs would absolutely jump onboard with no critical thinking of their own.
Humans do the same thing. Have you heard of religion?
<img alt="" src="https://lemmy.world/pictrs/image/e0943395-4d67-483d-b391-57c35a0c3f92.jpeg">
Have you heard of music theory and psychoacoustics (frankly even painting with oil you’ll use that)? Where we hear something dependent on what we expect and what we actually get, both in time, in length, in color, in amplitude etc.
Religion is about the same, it uses the concepts of impossible, unreachable and transcendent. Kicking something left and then back into place is not the same as not touching it.
I was wondering, are people working on networks that train to create a modular model of the world, in order to understand it / predict events in the world?
I imagine that that is basically what our brains do.
Yeah I’m sure folks are working on it, but I’m not knowledgeable or qualified on the details.
Many attempts, some well-funded.
They have been successful in very limited domains. For example, the F-35 integrated sensor suite.
Now I know why they crash so often
Not really anything properly universal, but a lot of task specific models exists with integration with logic engines and similar stuff. Performance varies a lot.
You might want to take a look at wolfram alpha’s plugin for chatgpt for something that’s public
Remember the game people used to play that was something like “type my girlfriend is and then let your phone keyboards auto suggestion take it from there?” LLMs are that.
They can’t. AI has hallucinations. Google has shown that AI can’t even rely on external sources, either.
At least LLMs will. The only real fix we’ve seen was running it through additional specialized LLMs to try to massage out errors, but that just increases costs and scale for marginally low results.
If you want to have good AI, you need to spend money and send your AI to college. Have real humans interact with it, correct it’s logic, make sure it understands sarcasm and logical fallacies.
Or, you can go the cheap route: train it on 10 years of Reddit sh*tposts and hope for the best.
Saying anything else would be lying
You mean we can’t teach a bullshit machine to stop bullshitting? I’m shocked.
What you can do is try to filter out the garbage, but it’s basically trying to find gold in food waste.
Stupid headline, it’s like Tim Cook saying he’s not 100% sure Apple can stop batteries in their devices from exploding. You do as much as you can to prevent it but it might happen anyway because that’s just how it is.
Of course you are getting downvoted, because you are right and not being a reactionary douche like your average lemmizen.
It’s insane how many people already take AI as more capable/accurate than other medium. I’m not against AI, but I’m definitely against how much of a bubble of being worshipped that some people have it in.
Well yeah, its using the same dataset as MS copilot.
Spitting out inaccurate (I wish the media would stop feeding into calling it something that sounds less bad like hallucinations) answers is nothing something that will go away until the LLM gains the ability to decern context.
Here’s how you stop AI from hallucinating:
Turn it off.
Because everything they output is a hallucination. Just because sometimes those hallucinations are true to life doesn’t mean jack shit. Even a broken clock is right twice a day.
“Only feed it accurate information.”
Even that doesn’t work because it just mixes and matches every element of its input to generate a new, novel output. Which would inevitably be wrong.
Yeah, just pull the plug. The amount of time we waste talking about this shit for these assholes to play another round of monopoly is unbelievable
I’m 100% sure he can’t. Or at least, not from LLMs specifically. I’m not an expert so feel free to ignore my opinion but from what I’ve read, “hallucinations” are a feature of the way LLMs work.
One can have an expert system assisted by ML for classification. But that’s not an LLM.
Everything these AIs output is a hallucination. Imagine if you were locked in a sensory deprivation tank, completely cut off from the outside world, and only had your brain fed the text of all books and internet sites. You would hallucinate everything about them too. You would have no idea what was real and what wasn’t because you’d lack any epistemic tools for confirming your knowledge.
That’s the biggest reason why AIs will always be bullshitters as long as their disembodied software programs running on a server. At best they can be a brain in a vat which is a pure hallucination machine.
Yeah, I try to make this point as often as I can. The notion that AI hallucinates only wrong answers really misleads people about how these programs actually work. It couches it in terms of human failings rather than really getting at the underlying flaw in the whole concept.
LLMs are a really interesting area of research, but they never should have made it out of the lab. The fact that they did is purely because all science operates in the service of profit now. Imagine if OpenAI were able to rely on government funding instead of having to find a product to sell.
First of all I agree with your point that it is all hallucination.
However I think a brain in a vat could confirm information about the world with direct sensors like cameras and access to real-time data, as well as the ability to talk to people and determine things like who was trustworthy. In reality we are brains in vats, we just have a fairly common interface that makes consensus reality possible.
The thing that really stops LLMs from being able to make judgements about what is true and what is not is that they cannot make any judgements whatsoever. Judging what is true is a deeply contextual and meaning-rich question. LLMs cannot understand context.
I think the moment an AI can understand context is the moment it begins to gain true sentience, because a capacity for understanding context is definitionally unbounded. Context means searching beyond the current information for further information. I think this context barrier is fundamental, and we won’t get truth-judging machines until we get actually-thinking machines.
To be 100 percent sure is a hallucination. Probably he tried to say that he is less than 80 percent sure.
Tim Cook…go take your meds and watch Price is Right
Seeing these systems just making shit up when they’re not sure on the answer is probably the closest they’ll ever come to human behaviour.
We’ve invented the virtual politician.
I don’t know why they’re trying to shove AI down our throats. They need to take their time, allow it to evolve.
Because it’s all a corporation and a huge part of the corporate capitalist system is infinite growth. They want returns, BIG ones. When? Right the fuck now. How do you do that? Well AI would turn the world upside down like the dot-com boom. So they dump tons of money into AI. So… it’s the AI done? Oh no no no, we’re at machine leaning AI is pretty far down the road actually, what we’re firing the AI department heads and releasing this machine leaning software as 100% all the way done AI?
It’s all the same reasons section 8 housing and low cost housing don’t work under corporate capitalism. It’s profitable to take government money, it’s profitable to have low rent apartments. That’s not the problem, the problem is THEY NEED THE GROWTH NOW NOW NOW!!! If you have a choice between owning a condo where you have high wage renters, and you add another $100 to rent every year, you get more profit faster. No one wants to invest in a 10% increase over 5 years if the can invest in 12% over 4 years. So no one ever invests in low rent or section 8 housing.
Of course they can’t. Any product or feature is only as good as the data underneath it. Training data comes from the internet, and the internet is full of humans. Humans make and write weird shit so so the data that the LLM ingests is weird, this creates hallucinations.
That’s like saying you can’t be 100% sure you never have fake news at the top of search query results. It’s just a fact.