361–370 of 422 posts
If they are actually using the engineering principles that their job description hints at, they'll probably be fine. Software Engineering is a growing field, the need for actual Engineers (you know, the kind that get licensed in other fields) is unlikely to shrink any time soon.
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> LLMs just spew words. It just so happens that human beings can decode them into something related, useful, and meaningful surprisingly often. This sentence is inherently contradictory. If LLM output is meaningful more than chance, then it's literally not "just spewing words". Therefore whatever model it is using to generate that meaning must contain some semantic content, even if it's not semantic content that's as…
It's a sufficiently large N shannonizer. Nothing more
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This matches what we know about LLMs and hallucination-avoidance behavior in LLMs. "Wrong answers on SAT" is also the leading hypothesis on why o3 was such an outlier - far more prone to hallucinations than either prior or following OpenAI models. On SAT, giving a random answer is right 20% of the time - more if you ruled at least one obviously wrong answer out. Saying "I don't know" and not answering is right 0% of…
> But another cause of hallucinations is limited self-awareness of modern LLMs… Humans have some awareness of the limits of their knowledge Until you said that I didn’t realize just how much humans “hallucinate“ in just the same ways that AI does. I have a friend who is fluent in Spanish, a native speaker, but got a pretty weak grammar education when he was in high school. Also, he got no education at all in Critical…
There’s such a thing in Spanish and in French. Formal and informal settings is reflected in the language. French even distinguishes between three different level of vocabulary (one for very informal settings (close friends), one for business and daily interactions, and one for very formal settings. It’s all cultural.
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It's a sufficiently large N shannonizer. Nothing more
so are we, maybe.
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I don't see the use-case now, maybe there will be one by GPT-9
Absence of your need isn't evidence of no need.
Maybe you know one.
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The company I work for uses LLM's for digital marketing, the company has over 100M ARR selling products build on top of LLM's with real life measurable impact as measured by KPIs.
> real life measurable impact as measured by KPIs This is making me even more skeptical of your claims. Individual metrics are often very poor at tracking reality.
There is really a balance between spoon-feeding it the answers vs just abandoning it - the spikiness is really apparent where it can totally nail some things but then utterly fail what should be pretty simple (i.e there was a point where I wanted to refactor two similar functions into a shared library and it just couldn't do it)
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The analogy goes down the drain when a criterion for good performance is being objectively right. Like with Reinforcement Learning from Verifiable Rewards.
Nobody that I'd be using this analogy with is currently using LLMs for tasks that are covered by RLVF. They're asking models for factual information about the real world (Google replacement), or to generate text (write a cover letter), not the type of outputs that are verifiable within formal systems—by definition the type of output that RLVF is intended to improve. The actor analogy is still helpful for providing in…
Under the hood we have billions of parameters that defy any simple analogies.
Operations of a network are shaped by human data. But the structure of the network is not like the human brain. So, we have something that is human-like in some ways, but deviates from humans in ways, which are unlikely to be like anything we can observe in humans (and use as a basis for analogy).
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> If a human being talked confidently about something that they were just making up out of thin air by synthesizing based (consciously or unconsciously) on other information they know you wouldn’t call it “hallucination”: you’d call it “bullshit”. I'd recommend you watch https://www.youtube.com/watch?v=u9CE6a5t59Y&t=2134s&pp=ygUYc... which covers the topic of bullshit. I don't think we can call LLM output "bullshit"…
It's precisely because they can't care that they are by definition bullshit machines. See https://link.springer.com/article/10.1007/s10676-024-09775-5
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This isn't a good characterization of it either. I don't think LLMs know the difference. Bullshit implies they are lying. It's possible LLMs are lying but my guess is that they really just can't tell the difference.
They're using the term 'bullshit' as it is understood as a term of art, which doesn't imply lying. It's closer to creating a response without any regard for telling the truth. Bullshitting is often most effective when you happen to be telling the truth, although the bullshitter has no commitment to that.