Billions are flowing towards LLMS, and Sam Altman will overpromise AGI is just around the corner and the days of jobs are gone to fill his coffers to anyone that will listen.
Additionally if we begin to use these things in real production environments where mistakes matters, knowing the exact limitations is key.
None of this takes away from the fact that these are exciting times.
So instead of everyone having some enthusiasm and some skepticism, you get a bifurcation where whole classes of people act as the skeptics and others as the enthusiasts. I view the strong skeptics as more “in the wrong” because they often don’t use LLMs much. If you are an actual enthusiastic user, you simply can’t get good performance without a very strong dose of skepticism towards everything LLMs output.
I'm very curious. If you don't mind taking the time to elaborate, will you give a few examples of such skepticism/naysaying? Thank you.
How do we harness LLMs’ potential while rigorously mitigating harm?
so while we're on HN going back and forth about how outright lies by the system indight the whole thing for everybody, we should be careful to note that it's not for everybody, or rather, it's a known limitation so don't trust it to cite real cases for you as a lawyer, but using it to help you figure out what mens rea means in a practical sense by asking it questions about the concept, totally.
Honestly, hallucinations happen so rarely for me because of the kinds of things I ask it, that it doesn't happen enough for me to not believe it's answers in low-stakes situations, or situations on the level of horoscope generation, and I'm sure I'm not alone in treating ChatGPT that way, despite evidence to the contrary.
You are applauding it as pushback for pushback's sake, but it's an article about limitations in biplane construction, published after we'd already landed on the moon.
I think most of the issue comes from the challenge of informational coherence. Once incoherence enters the context, the intelligence drops massively. You can have a lot of context and LLMs can maintain coherence— but not if the context itself is incoherent.
And, informationally, it is just a matter of time before a little incoherence gets into a thread.
This is why agents have so much potential—being able to separate out separate threads of thought in different context windows reduces the likelihood of incoherence emerging (vs one long thread).
Actually, maybe “cybernetic ecologies” are closer to what I mean than “agents.” See Anthropic’s “Building Effective Agents.” https://www.anthropic.com/research/building-effective-agents
As a non-expert, part of my definition of intelligence is that the system can detect incoherence, a.k.a reject bullshit. LLMs today can't do that and will happily emit bullshit in response.
Maybe the "gates" in the "workflows" discussed in the Anthropic article are a practical solution to that. But that still just seems like inserting human intelligence into the system for a specific engineering domain; not a general solution.