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
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).
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.
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.
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.
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?”
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…
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
deleted by creator
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.
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.
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.
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?”
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…