ChatGPT has meltdown and starts sending alarming messages to users::AI system has started speaking nonsense, talking Spanglish without prompting, and worrying users by suggesting it is in the room with them
God I hate websites that autoplay unrelated videos and DONT LET ME CLOSE THEM TO READ THE FUCKING ARTICLE
Firefox. Ad block. Even works on mobile.
It’s so ridiculous we have to do this.
Its being trained on us. Of course its acting unexpectedly. The problem with building a mirror is proding the guy on the other end doesnt work out.
To be honest this is the kind of outcome I expected.
Garbage in, garbage out. Making the system more complex doesn’t solve that problem.
The development of LLMs is possibly becoming self defeating, because the training data is being filled not just with human garbage, but also AI garbage from previous, cruder LLMs.
We may well end up with a machine learning equivalent of Kessler syndrome, with our pool of available knowledge eventually becoming too full of junk to progress.
I mean, surely the solution to that would be to use curated/vetted training data? Or at the very least, data from before LLMs became commonplace?
God I hope all those CEOs and greedy fuckheads that fired hundreds of thousands of people wayyyyy too soon to replace them with this get their pants shredded by the fallout.
Naturally they’ll get their golden parachutes and land on their feet even richer than before, but it’s nice to dream lol
I really hope so. I still have to see a meaningful use case for these kind of LLMs that just get fed with all kinds of data. LLMs “on premise” that are used for specific jobs are fine, but this…I really hope a Kessler-Like syndrome blows it out the water, for countless reasons…
The solution is paying intelligent people to interact with it and give honest feedback.
Like, I’m sure you can pay grad students $15/hr to talk to one about their subject matter.
But with as many as they’d need, it would get expensive.
So they train with low quality social media comments, or using copywritten text without paying the owners.
It’s not that we can’t do it, it’s just expensive. So a capitalist society wont.
If we had an FDR style president, this would be a great area for a new jobs program.
It appears, that with the increase in popularity of machine learning, the percentage of people who properly source and sanitize their training data has steeply decreased.
As you stated, a MLAI can only be as good as the data it was trained on, and is usually way worse. The popularity and application of MLAIs built with questionable practices scare me, though, at least their fuckups will keep me employed and likely more busy than ever.
LLM’s are not “machine learning”, they are neural-networks.
Different category.
ML is small potatoes, ttbomk.
Decision-tree stuff.
Neural-nets are black-boxes, with back-propagation training of the neural-net to get closer to ( layer by layer, training-instance by training-instance ) the intended result.
ML is what one does on one’s own machine with some python libraries,
ChatGPT ( 3, 3.5, or 4, don’t know which ) cost something like $100,000,000 to rent the machines required for mixing the training-data & the model ( I’m assuming about $20/hr per machine, so an OCEAN of machines, to do it )
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I imagine it more as a parent child relationship.
We’re trailer park trash with no higher education, believe in ghosts, angels and gods in the sky, refuse to ever believe we could be wrong … and now we’ve just had a baby with no one to help us raise it.
We’re going to raise a highly intelligent psychopath
Someone probably found a way to hack or poison it.
Another theory, Reddit just recently sold data access to an unnamed AI company, so maybe that’s where the data went.
I’ve found the sexism on Reddit to be on par with the racism. Goodness help you if you’re a female of color, unless you’ve been working the same job for multiple decades, or don’t want kids, then you’ll be an inspiration to that community.
Reddit is, alas, not the only forum exhibiting such hate.
… sure … but you don’t prepare a kid for racism with a sheltered upbringing in a pretend world where discrimination doesn’t exist. You point out bad behaviour and tell them why it’s not OK.
My son is three years old, he has two close friends - one is an ethnic minority (you could live an entire year in my city without even walking past a single person of their ethnic background on the street). His other close friend is a girl. My kid is already witnessing (but not understanding) discrimination against both of his two closest friends in the playground and we’re doing what we can to help him navigate that. Things like “I don’t like him he looks funny” and “she’s a girl, she can’t ride a bicycle”.
Large Language Model training is exactly the same - you need to include discrimination in your training set. That’s a necessary step to train a model that doesn’t discriminate. Reddit has worse discrimination than some other place and that’s a good thing.
The worst behaviour is easier to recognise and can help you learn to recognise more subtle discrimination such as “I don’t want to play with that kid” which is not an obviously discriminatory statement, but definitely could be discrimination (and you should probably investigate before agreeing with the person).
Reminds me of Tay, the Microsoft chat bot that learned from Twitter and became racist in a day https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist
They’re only getting redditors comment data, not CoD multiplayer transcripts.
We call just about anything “AI” these days. There is nothing intelligent about large language models. They are terrible at being right because their only job is to predict what you’ll say next.
(Disclosure: I work on LLM’s)
While you’re not wrong, how is this different to many existing techniques and compositional models that are used practically everywhere in tech?
Similarly, it’s probably safe to assume that the LLM’s prediction isn’t the only system in use. There will be lots of auxiliary services giving an orchestrator information to reason with. In this instance, if you have a system that is trying to figure out what to say next, with several knowledge stores and feedback services telling you “you were just discussing this” or “you can access the weather from here” is that all that different from “intelligence”?
At a given point, it’s arguing semantics. Are any AI techniques true intelligence? Probably not, but then again, we don’t really know what true intelligence is.
how is this different to many existing techniques and compositional models that are used practically everywhere in tech?
It’s not. LLM is just a statistical model. Nothing special about it. Nothing different what we’ve already been doing for a while. This only validates my statement that we call just about anything “AI” these days.
We don’t even know what true intelligence is, yet we are quick to make claims that this is “AI”. There is no consciousness here. There is no self awareness. No emotion. No ability to reason or deduct. Anyone who thinks otherwise is just fooling themselves.
It’s a buzz word to get people riled up. It’s completely disingenuous.
I think the point of the Turing test is to avoid thorny questions about the definition of intelligence. We cant precisely define intelligence, but we know that normally functioning humans are intelligent. Therefore, if we talk to a computer and it is indistinguishable from a human in a conversation, then it is intelligent by definition.
So, by your definition, no AI is AI, and we don’t know what AI is, since we don’t know what the I is?
While I hate that AI is just a buzzword for scam artists and tech influencers nowadays, dismissing a term seems a bit overkill. It also seems overkill when it’s not something that academics/scholars seem particularly bothered by.
There is no consciousness here. There is no self awareness. No emotion. No ability to reason or deduct.
Of all of these qualities, only the last one—the ability to reason or deduct—is a widely-accepted prerequisite for intelligence.
I would also argue that contemporary LLMs demonstrate the ability to reason by correctly deriving mathematical proofs that do not appear in the training datasets. How would you be able to accomplish such a feat without some degree of reasoning?
The worrisome thing is that LLMs are being given access to controlling more and more actions. With traditional programming sure there are bugs all but at least they’re consistent. The context may make the bug hard to track down, but at the end of the day, the code is being interpreted by the processor exactly as it was written. LLMs could just go haywire for impossible to diagnose reasons. Deploying them safely in utilities where they have control over external systems will require a lot of extra non LLM safe guards that I do not see getting added enough, and that is concerning.
What is intelligence?
Even if we don’t know what it is with certainty, it’s valid to say that something isn’t intelligence. For example, a rock isn’t intelligent. I think everyone would agree with that.
Despite that, LLMs are starting to blur the lines and making us wonder if what matters of intelligence is really the process or the result.
A LLM will give you much better results in many areas that are currently used to evaluate human intelligence.
For me, humans are a black box. I give them inputs and they give me outputs. They receive inputs from reality and they generate outputs. I’m not aware of the “intelligent” process of other humans. How can I tell they are intelligent if the only perception I have are their inputs and outputs? Maybe all we care about are the outputs and not the process.
If there was a LLM capable of simulating a close friend of yours perfectly, would you say the LLM is not intelligent? Would it matter?
Things we know so far:
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Humans can train LLMs with new data, which means they can acquire knowledge.
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LLMs have been proven to apply knowledge, they are acing examns that most humans wouldn’t dream of even understanding.
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We know multi-modal is possible, which means these models can acquire skills.
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We already saw that these skills can be applied. If it wasn’t possible to apply their outputs, we wouldn’t use them.
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We have seen models learn and generate strategies that humans didn’t even conceive. We’ve seen them solve problems that were unsolvable to human intelligence.
… What’s missing here in that definition of intelligence? The only thing missing is our willingness to create a system that can train and update itself, which is possible.
If you look at efficacy though on academic tests or asking it some fact question and you compare that to asking a random person instead of always getting the ‘right’ answer, which we expect computers/calculators to do, would LLMs be comparable or better? Surely someone has some data on that.
E: It looks like in certain domains at least LLMs beat out human counterparts. https://stanfordmimi.github.io/clin-summ/
The person that commented below kinda has a point. While I agree that there’s nothing special about LLMs an argument can be made that consciousness (or maybe more ego?) is in itself an emergent mechanism that works to keep itself in predictable patterns to perpetuate survival.
Point being that being able to predict outcomes is a cornerstone of current intelligence (socially, emotionally and scientifically speaking).
If you were to say that LLMs are unintelligible as they operate to provide the most likely and therefore most predictable outcome then I’d agree completely.
AI in science fiction has a meltdown and starts a nuclear war or enslaves the humane race.
“AI” in reality has a meltdown and just starts talking gibberish.
