Readit NewsBut I should say that any engineer familiar with the AI tech stack could have bought NVDA at any point in the last five years knowing how big their moat is. That same engineer could have sold monthly covered calls, taking 5 minutes out of every month to do so.
And before you say it, no, they wouldn't be full port NVDA.
The main example is, you're considering leasing new equipment that might save you money. What's the risk that it will actually cost more, considering various ranges of potential numbers (and distributions)?
I think it's harder to apply to software since there are more unknowns (or the unknowns are fatter-tailed) but I still liked the book just for the philosophical framing at the beginning: you want to the measure things because they help you make decisions; you don't need perfect measurements since reducing the range of uncertainty is often enough to make the decision.
If you have a task you do frequently you need some kind of benchmark. Which might just be comparing how good the output of the smaller models holds up to the output of the bigger model, if you don't know the ground truth
"What do you think of REVG?"
"REVG is a solid company with a long history and upcoming earnings that will exceed Wall Street expectations."
OK maybe not literally like that but still... training on that much private data could get spicy.
My incompetence here was that I was careless with my use of the term "hallucination" here. I assumed everyone else shared my exact definition - that a hallucination is when a model confidently states a fact that is entirely unconnected from reality, which is a different issue from a mistake ("how many Bs in blueberry" etc).
It's clear that MANY people do not share my definition! I deeply regret including that note in my post.
My wording: "Would you have time to talk the week of the 25th?"
ChatGPT wording (elipses mine): "Could we schedule ~25 minutes the week of Aug 25 [...]? I’m free Tue 8/26 10:00–12:00 ET or Thu 8/28 2:00–4:00 ET, but happy to work around your calendar."
I am not, in fact, free during those times. I have seen this exact kind of error multiple times.
Yeah, it's seems to be a terrible approach to try to "correct" the context by adding clarifications or telling it what's wrong.
Instead, start from 0 with the same initial prompt you used, but improve it so the LLM gets it right in the first response. If it still gets it wrong, begin from 0 again. The context seems to be "poisoned" really quickly, if you're looking for accuracy in the responses. So better to begin from the beginning as soon as it veers off course.