⢀⣠⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠀⠀⣠⣤⣶⣶ ⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠀⢰⣿⣿⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣧⣀⣀⣾⣿⣿⣿⣿ ⣿⣿⣿⣿⣿⡏⠉⠛⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⣿ ⣿⣿⣿⣿⣿⣿⠀⠀⠀⠈⠛⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠿⠛⠉⠁⠀⣿ ⣿⣿⣿⣿⣿⣿⣧⡀⠀⠀⠀⠀⠙⠿⠿⠿⠻⠿⠿⠟⠿⠛⠉⠀⠀⠀⠀⠀⣸⣿ ⣿⣿⣿⣿⣿⣿⣿⣷⣄⠀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣴⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⣿⠏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠠⣴⣿⣿⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⡟⠀⠀⢰⣹⡆⠀⠀⠀⠀⠀⠀⣭⣷⠀⠀⠀⠸⣿⣿⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⠃⠀⠀⠈⠉⠀⠀⠤⠄⠀⠀⠀⠉⠁⠀⠀⠀⠀⢿⣿⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⢾⣿⣷⠀⠀⠀⠀⡠⠤⢄⠀⠀⠀⠠⣿⣿⣷⠀⢸⣿⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⡀⠉⠀⠀⠀⠀⠀⢄⠀⢀⠀⠀⠀⠀⠉⠉⠁⠀⠀⣿⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⣧⠀⠀⠀⠀⠀⠀⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢹⣿⣿ ⣿⣿⣿⣿⣿⣿⣿⣿⣿⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿
“On two occasions I have been asked, ‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.” - Charles Babbage
The business people adopting AI: “who cares what it’s trained on? It’s intelligent right? It’ll just sort through the garbage and magically come up with the right answers to everything”
Of course modern UX design is very much based on getting the right answer with the wrong inputs (autocorrect, etc).
I believe Robustness was the term I learned years ago: the ability of a system to gracefully handle user error, make it easy to recover from or fix, clearly communicate what was wrong etc.
Of course, nothing is ever perfect and humans are very creative at fucking up, and a lot of companies don’t seem to take UX too seriously. Particularly when the devs get tunnel vision and forget about user error being a thing…
Oh no, the AI are inbreeding.
As junk web pages written by AI proliferate, the models that rely on that data will suffer.
Good.
Model degeneration is an already well-known phenomenon. The article already explains well what’s going on so I won’t go into details, but note how this happens because the model does not understand what it is outputting - it’s looking for patterns, not for the meaning conveyed by said patterns.
Frankly at this rate might as well go with a neuro-symbolic approach.
The issue with your assertion is that people don’t actually work a similar way. Have you ever met someone who was clearly taught "garbage’?
The issue with your assertion is that people don’t actually work a similar way.
I’m talking about LLMs, not about people.
I know you are, but the argument that an LLM doesn’t understand context is incorrect. It’s not human level understanding, but it’s been demonstrated that they do have a level of understanding.
And to be clear, I’m not talking about consciousness or sapience.
but it’s been demonstrated that they do have a level of understanding.
Citation needed
Here you go
A better mathematical system of storing words does not mean the LLM understands any of them. It just has a model that represents the relation between words that it uses.
If I put 10 minus 8 into my calculator I get 2. The calculator doesn’t actually understand what 2 means, or what subtracting represents, it just runs the commands that gives the appropriate output.
That’s a bad analogy, because the calculator wasn’t trained using an artificial neural network literally designed by studying biological brains (aka biological neutral networks).
And “understand” doesn’t equate to consciousness or sapience. For example, it is entirely and factually correct to state that an LLM is capable of reasoning. That’s not even up for debate. The accuracy of an LLM’s reasoning capability is one of the fundamental benchmarks used for evaluating its quality.
But that doesn’t mean it’s “thinking” in the way most people consider.
Edit: anyone up voting this CileTheSane clown is in the same boat of not comprehending how LLMs work.
I know you are, but the argument that an LLM doesn’t understand context is incorrect
Emphasis mine. I am talking about the textual output. I am not talking about context.
It’s not human level understanding
Additionally, your obnoxiously insistent comparison between LLMs and human beings boils down to a red herring.
Not wasting my time further with you.
[For others who might be reading this: sorry for the blatantly rude tone but I got little to no patience towards people who distort what others say, like the one above.]
I got little to no patience towards people who distort what others say,
My original reply was meant to be tongue-in-cheek, but I guess I forgot about Poe’s law. I’m not a layman, for the record. I’ve worked with AI for over a decade
Not wasting my time further with you.
Ditto. Have a nice day.
I’m autistic and sometimes I feel like an ai bot spewing out garbage in social situations. If I do what people normally do and make it sound believable, maybe no one will notice.
I’d be very wary of extrapolating too much from this paper.
The past research along these lines found that a mix of synthetic and organic data was better than organic alone, and a caveat for all the research to date is that they are using shitty cheap models where there’s a significant performance degrading in the synthetic data as compared to SotA models, where other research has found notable improvements to smaller models from synthetic data from the SotA.
Basically this is only really saying that AI models across multiple types from a year or two ago in capabilities recursively trained with no additional organic data will collapse.
It’s not representative of real world or emerging conditions.
Water is wet
Is it wet or does it make other things wet?
AI making itself sick and worthless after flooding the internet with trash just gives me a warm glow.
Garbage in; Garbage out.
Shit-fueled ouroboros
Recycle the garbage that comes out… Still more garbage out.
Well, you’ve got a timestamped copy of much of the Web that existed up until latent-diffusion models at archive.org. That may not give you access to newer information, but it’s a pretty whopping big chunk of data to work with.
Hopefully archive.org have measures in place to stop people from yanking all their data too quickly. As least not without a hefty donation or something. As a user it can chug a bit, and I’m hoping that’s the rate-limiting I’m talking about and not that they’re swamped.
provenance requires some way to filter the internet into human-generated and AI-generated content, which hasn’t been cracked yet
It doesn’t need to be filtered into human / AI content. It needs to be filtered into good (true) / bad (false) content. Or a “truth score” for each.
We don’t teach children to read by just handing them random tweets. We give them books that are made specifically for children. Our filtering mechanism for good / bad content is very robust for humans. Why can’t AI just read every piece of “classic literature”, famous speeches, popular books, good TV and movie scripts, textbooks, etc?
That’s what smaller models do, but it doesn’t yield great performance because there’s only so much stuff available. To get to gpt4 levels you need a lot more data, and to break the next glass ceiling you’ll need even more.
Then these models are stupid. Humans don’t start as a blank slate. They have an inherent aptitude for language and communication. These models should start out with basics of language, so they don’t have to learn it from the ground up. That’s the next step. Right now they’re just well read idiots.
Then these models are stupid
Yup that is kind of the point. They are math functions designed to approximate human tasks.
These models should start out with basics of language, so they don’t have to learn it from the ground up. That’s the next step. Right now they’re just well read idiots.
I’m not sure what you’re pointing at here. How they do it right now, simplified, is you have a small model designed to cut text into tokens (“knowledge of syllables”), which are fed into a larger model which turns tokens into semantic information (“knowledge of language”), which is fed to a ridiculously fat model which “accomplishes the task” (“knowledge of things”).
The first two models are small enough that they can be trained on the kind of data you describe, classic books, movie scripts etc… A couple hundred billion words maybe. But the last one requires orders of magnitude more data, in the trillions.
