- cross-posted to:
- fuck_ai@lemmy.world
- cross-posted to:
- fuck_ai@lemmy.world
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If scientists made AI, then it wouldn’t be an issue for AI to say “I don’t know”.
But capitalists are making it, and the last thing you want is it to tell an investor “I don’t know”. So you tell it to make up bullshit instead, and hope the investor believes it.
It’s a terrible fucking way to go about things, but this is America…
It is made by scientists. The problem is that said scientists are paid by investors mostly, or by grants that come from investors.
Just put this into GPT 4.
What’s your view of the fizbang Raspberry blasters?
Gpt ‘I’m not familiar with “fizbang Raspberry blasters.” Could you provide more details or clarify what they are?’
It’s a drink making machine from china
Gpt ‘I don’t have any specific information on the “fizbang Raspberry blasters” drink making machine. If it’s a new or niche product, details might be limited online.’
So, in this instance is didn’t hallucinate, i tried a few more made up things and it’s consistent in saying it doesn’t know of these.
Explanations?
Chatgpt and gpt4 are two different things. Gpt4 is like the engine and chatgpt is like a car. In early version they were pretty much the same thing, but nowadays they have implemented so much in chatgpt.
On top of that chatgpt4 is constantly trained for these scenarios, it is no longer a base model.
Oh ok thanks i thought this thread was about AI LLMs in general.
Weird i was downvoted for demonstrating the very thing that apparently (according to these very learned comments) AI can’t do, actually doing it well. Seems like irrational bubble hate to me, common on reddit but getting more so on Lemmy it seems. “that guys asking topic based questions that make our comments look poorly thought out and potentially wrong, burn him”
This is a thing that is true of all LLMs, but it seems like you’re misunderstanding the core issue. It CAN give outputs like that sometimes. What we CAN’T do is force it to give outputs like that ALL the time.
It will answer “I don’t know” if its predictive text model guesses that the most common response to this would be “I don’t know”. To do that, to simplify a little, you could imagine that it reads your question, compares that to all the text in its training data, and tries to find the conversation that looks most like the question you asked, then answers whatever the person in the training data answered. But your exact question wasn’t in its training data, so if you took that mental model, and instead had it compare to 1000 similar looking things in its training model and average them, then it would hopefully do a better job of coming up with something at least close to what you actually asked. Now take it to a million, or a billion.
When we’re asking questions about the real world, we would prefer for it to answer based on knowledge about the real world. But what if it “matches” data from a work of fiction? Or just someone who doesn’t know what they’re talking about? Or true information, but about a different subject?
It doesn’t know anything. It doesn’t understand anything you say. It just looks at patterns that it learned from the training data and tries to guess what words are most likely to be said in that case. In other words, “here’s one case where it didn’t hallucinate” and “it will never hallucinate” are not the same thing at all.
Edit: To clarify, it doesn’t search its training data to answer your question, so asking “was this in the training data” is impossible. By the time you interact with it, the data is long gone. It was just used for training.
Ok very long and detailed response, i was responding to the initial comments that explicitly said if you give ai a made up thing it will definitely hallucinate. Which i demonstrated to be false in (multiple times). I’m not suggesting it doesn’t hallucinate a lot of the time still, but the comments were making out its 100% broken, and it clearly works for many queries very effectively, despite its limited applications. Im just suggesting we don’t throw the baby out with the bathwater.
I think the trouble is, what baby are we throwing out with the bathwater in this case? We can’t prevent LLMs from hallucinating (but we can mitigate it somewhat with carefully constructed prompts). So, use cases where we’re okay with that are fair game, but any use case (or in this case, law?) that would require the LLM never hallucinates aren’t attainable, and to get back earlier, this particular problem has nothing to do with capitalism.
Yes, i agree
Stop asking a language model for accurate information and problem solved. ChatGPT is not supposed to be a knowledge bank, that’s purely incidental for the amount of training data.
Stop asking a language model for accurate information and problem solved
Hey chatgpt, when did jol’s wife get pregnant and by whom?
/s
Unless they used that bitche’s only fans in the training data, it will definitely not know that.
It doesn’t need to know the real answer to produce a confident sounding answer
And if that answer contains Elon Musk, the world is going to believe it no matter what.
And by the time the system can actually research the facts, the internet is so full of LLM generated nonsense neither human or AI can verify the data.
Just ask ChatGPT what it thinks for some non-existing product and it will start hallucinating.
This is a known issue of LLMs and DL in general as their reasoning is a black box for scientists.
Asking chatgpt for information is like asking for accurate reports from bards and minstrels. Sure, sometimes it fits, but most of it is random stuff stitched together to sound good.
No surprise, and this is going to happen to everybody who uses neural net models for production. You just don’t know where your data is, and therefore it is unbelievably hard to change data.
So, if you have legal obligations to know it, or to delete some data, then you are deep in the mud.
I think of ChatGPT as a “text generator”, similar to how Dall-E is an “image generator”.
If I were openai, I would post a fictitious person disclaimer at the bottom of the page and hold the user responsible for what the model does. Nobody holds Adobe responsible when someone uses Photoshop.I would post a fictitious person disclaimer
… or you could read the GDPR and learn that such excuses are void.
You just wasted a lot of my time. What did I do to deserve this?
… said the sparrow and flew out of the library.
There we go. Now that people have calmed their proverbial tits about these thinking machines, we can start talking maturely about the strengths and limitation of the LLM implementations and find their niche in our tools arsenal.
I can’t wait until the AI bubble finally pops.
I’ve got bad news for you though: there will be another new bubble almost immediately. There’s a whole industry based around tech hype cycles and they are constantly throwing shit at the wall to see what sticks. Eventually something will when there’s space for it. It will be just as insufferable as LLMs are, and crypto was before that, and… I actually forget what was before that. Uber? You won’t be able to escape it, because it will dominate the attention economy.
There’s definitely a niche for it, more so than for other fruitless hypes like blockchain or IoT. We really need to be able to offload tasks which need autonomous decisions of simple to average complexity to machines. We can’t continuously scale up the population to handle those. But LLMs aren’t the answer to that, unfortunately. They’re just party tricks if the current limitations cannot be overcome.
The technology has to follow the legal requirements, not the other way around.
That should be obvious to everyone that’s not an evangelist.
