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Is "leakage" just another term for overfitting?
So usually it ends up in overfitting, but is more about having information at training time that it shouldn't.
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This is incredibly simplistic. Search engine results give a lot of context clues about the reliability of their asserted facts and provide a potential spectrum of answers. LLM-generated answers strip all that away, and give a single authoritatively phrased answer. Even if you’re inclined to disbelieve it, the LLM answer gives you no ability to dig in, refine, or compare. It just is. If you ask a chatbot if it’s sure,…
I think you overestimate people’s ability to sniff out bad data on the internet. Also are you suggesting people fact check an AI by asking it if it is correct? That seems absurd.
Is "leakage" just another term for overfitting?
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Humans are not fact machines, we are often wrong. Do humans not have intelligence? What do you even mean by "intelligence" when you say a probabilistic syllable generator "is not intelligence"?
Like clockwork, out come the "but humans" deflections. An LLM is not a human-like intelligence. This is patently obvious, such comparisons are nonsensical and just further the problem of people anthropomorphizing a tool and treating it like an oracle.
> Unfortunately, most scientific fields have succumbed to AI hype, leading to a suspension of common sense. For example, a line of research in political science claimed to predict the onset of civil war with an accuracy2 of well over 90%, a number that should sound facially impossible. (It turned out to be leakage, which is what got us interested in this whole line of research.) This coupled with people acting on its…
"accuracy2" sigh - the 2 is a superscript to a footnote and not a domain specific term. "facially impossible" ... does that really riff on "on the face of it", or is it farcically misspelt? Garbage in, garbage out 8)
In my professional work, I treat chatgpt as a search engine that I feel I can ask questions of in a natural manner. I often find small flaws in technical solutions it offers, but it can still provide useful starting points to investigate. I rarely trust code it generates (at least for the language I mainly work in) as i’ve seen it make some serious mistakes (eg: using keywords in the language that don’t exist)
> I rarely trust code it generates (at least for the language I mainly work in) as i’ve seen it make some serious mistakes (eg: using keywords in the language that don’t exist) It's only a mistake from your perspective. The model just generates text based the probabilities it learned during training. In that respect, there is no such thing as "incorrect" output because the model doesn't operate at that level of abstr…
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That is not writing with the help of GPT 4, that is letting it write for you! I can’t imagine doing anything creative and letting a computer source material for me without having reviewed the material first hand, even if it was accurate. Clearly, this is not where everyone’s head is at, and I suspect your wife’s workflow is more the common case. I’ve said from the outset that in academic settings you should be able t…
You sound like the people who used to know how to fix a car, or sew, or write cursive, or do multiplication times tables in their head, or know how to derive a formula, or check a mathematical proof. Ask anyone below 30 if they can write cursive today, or know their times tables hehe. Ask them if they can derive a formula instead of using Mathematica. Or ask a developer if they know how their pixel shaders work, or w…
Not something I'd trust as a "source of truth". Maybe a neat idea generator. And some of the deep learning algorithms can identify patterns that humans might miss -- patterns that could reveal useful insight. But they're not doing the knowledge work.
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Like clockwork, out come the "but humans" deflections. An LLM is not a human-like intelligence. This is patently obvious, such comparisons are nonsensical and just further the problem of people anthropomorphizing a tool and treating it like an oracle.
You didn't answer the question.
Since we do not possess a definition of intelligence that isn't human-like, it would be meaningless to argue if LLMs are intelligent in general. All that can be said is that they are not intelligent in the way that humans are.
People treating tools like they're infallible has been a problem since computers were invented, but IMHO the biggest difference with AI is how confident and convincing it can be in its output. Much like others here, I already have had to convince, very carefully, many otherwise-decently-intelligent people who believed ChatGPT was correct. Thus I think the biggest success of AI will be the arts, where imprecision is n…
You can see an example of this in the use of LLMs to generate code. In that case, there's a whole SDLC pipeline designed to detect errors: type systems, language compilers and runtimes, tests of various kinds, QA, user feedback, etc. We don't just trust confident software developers to produce correct code.
Even a life-critical function like medical imaging - where imprecision can be fatal - can potentially benefit from this, where AI is used in conjunction with human review. It mainly requires development of some standards of practice - unlike with an average user blindly trusting the output of a model, radiologists would need training on how to use the models in question.