Stanford University researchers unveiled an AI model they say can analyze decades of property records in just a few days at little expense to weed out racist language, and they will offer the tool for free across the state and around the country.
I mean they literally do analyze text. They’re great at it. Give it some text and it will analyze it really well. I do it with code at work all the time.
Because they are two completely different tasks. Asking them to recall information from their training is a very bad use. Asking them to analyze information passed into them is what they are great at.
Give it a sample of code and it will very accurately analyse and explain it. Ask it to generate code and the results are wildly varied in accuracy.
I’m not assuming anything you can literally go and use one right now and see.
The person you’re replying to is correct though. They do not understand, they do not analyse. They generate (roughly) the most statistically likely answer to your prompt, which may very well end up being text representing an accurate analysis. They might even be incredibly reliable at doing so. But this person is just pushing back against the idea of these models actually understanding or analysing. Its slightly pedantic, sure, but its important to distinguish in the world of machine intelligence.
I literally quoted the word for that exact reason. It just gets really tiring when you talk about AIs and someone always has to make this point. We all know they don’t think or understand in the same way we do. No one gains anything by it being pointed out constantly.
You said “they literally do analyze text” when that is not, literally, what they do.
And no, we don’t “all know” that. Lay persons have no way of knowing whether AI products currently in use have any capacity for genuine understanding and reasoning, other than the fact that the promotional material uses words like “understanding”, “reasoning”, “thought process”, and people talking about it use the same words. The language we choose to use is important!
I never made any “AI bad” arguments (in fact, I said that they may be incredibly well suited to this) I just argued for the correct use of words and you hallucinated.
LLMs arent “bad” (ignoring, of course, the massive content theft necessary to train them), but they are being wildly misused.
“Analysis” is precisely one of those misuses. Grand Theft Autocomplete can’t even count, ask it how many 'e’s are in “elephant” and you’ll get an answer anywhere from 1 to 3.
This is because they do not read or understand, they produce strings of tokens based on a statistical likelihood of what comes next. If prompted for an analysis they’ll output something that looks like an analysis, but to determine whether it is accurate or not a human has to do the work.
Get the general vibe of a text (sentiment analysis)
Generate plausible text
Semantics aside, they’re very different skills that require different setups to accomplish. Just because counting is an easier task than analysing text for humans, doesn’t mean it’s the same it’s the same for a LLM. You can’t use that as evidence for its inability to do the “harder” tasks.
You forgot to put caveats on all the things you claim LLMs can do, but only one of them doesn’t need them.
Why would you think that LLMs can do sentiment analysis when they have no concept of context or euphemism and are wholly incapable of distinguishing sarcasm from genuine sentiment?
Why would you think that their translations are of any use given the above?
The human capacity for reason is greatly overrated. The overwhelming majority of conversation is regurgitated thought, which is exactly what LLMs are designed to do.
I don’t really dispute that but at least we are able to apply formal analytical methods with repeatable outcomes. LLMs might (and do) achieve a similar result but they do so without any formal approach that can be reviewed, which has its drawbacks.
LLMs neither understand nor analyze text. They are statistical models of the text they were trained on. A map of language.
And, like any map, they should not be confused for the territory they represent.
If you admit that they have issues with facts, why would you assume that the randomly generated facts their “analysis” produces must be accurate?
I mean they literally do analyze text. They’re great at it. Give it some text and it will analyze it really well. I do it with code at work all the time.
Because they are two completely different tasks. Asking them to recall information from their training is a very bad use. Asking them to analyze information passed into them is what they are great at.
Give it a sample of code and it will very accurately analyse and explain it. Ask it to generate code and the results are wildly varied in accuracy.
I’m not assuming anything you can literally go and use one right now and see.
The person you’re replying to is correct though. They do not understand, they do not analyse. They generate (roughly) the most statistically likely answer to your prompt, which may very well end up being text representing an accurate analysis. They might even be incredibly reliable at doing so. But this person is just pushing back against the idea of these models actually understanding or analysing. Its slightly pedantic, sure, but its important to distinguish in the world of machine intelligence.
I literally quoted the word for that exact reason. It just gets really tiring when you talk about AIs and someone always has to make this point. We all know they don’t think or understand in the same way we do. No one gains anything by it being pointed out constantly.
You said “they literally do analyze text” when that is not, literally, what they do.
And no, we don’t “all know” that. Lay persons have no way of knowing whether AI products currently in use have any capacity for genuine understanding and reasoning, other than the fact that the promotional material uses words like “understanding”, “reasoning”, “thought process”, and people talking about it use the same words. The language we choose to use is important!
No it’s not. It’s pedantic and arguing semantics. It is essentially useless and a waste of everyone’s time.
It applies a statistical model and returns an analysis.
I’ve never heard anyone argue when you say they used a computer to analyse it.
It’s just the same AI bad bullshit and it’s tiring in every single thread about them.
I never made any “AI bad” arguments (in fact, I said that they may be incredibly well suited to this) I just argued for the correct use of words and you hallucinated.
LLMs arent “bad” (ignoring, of course, the massive content theft necessary to train them), but they are being wildly misused.
“Analysis” is precisely one of those misuses. Grand Theft Autocomplete can’t even count, ask it how many 'e’s are in “elephant” and you’ll get an answer anywhere from 1 to 3.
This is because they do not read or understand, they produce strings of tokens based on a statistical likelihood of what comes next. If prompted for an analysis they’ll output something that looks like an analysis, but to determine whether it is accurate or not a human has to do the work.
LLMs cannot:
LLMs can
Semantics aside, they’re very different skills that require different setups to accomplish. Just because counting is an easier task than analysing text for humans, doesn’t mean it’s the same it’s the same for a LLM. You can’t use that as evidence for its inability to do the “harder” tasks.
You forgot to put caveats on all the things you claim LLMs can do, but only one of them doesn’t need them.
Why would you think that LLMs can do sentiment analysis when they have no concept of context or euphemism and are wholly incapable of distinguishing sarcasm from genuine sentiment?
Why would you think that their translations are of any use given the above?
https://www.inc.com/kit-eaton/mother-of-teen-who-died-by-suicide-sues-ai-startup/90994040
The human capacity for reason is greatly overrated. The overwhelming majority of conversation is regurgitated thought, which is exactly what LLMs are designed to do.
I don’t really dispute that but at least we are able to apply formal analytical methods with repeatable outcomes. LLMs might (and do) achieve a similar result but they do so without any formal approach that can be reviewed, which has its drawbacks.