Take the example earlier of the rounding error. That makes perfect sense for a pure LLM. It's not doing any maths, just trying to predict the most likely next token. So, if it's seen the string "7.393" near to the string "7.3874" and the string "rounded" in its training data then it may say that's the most likely result. But I think you can now tell most frontier models to look for maths and when it finds an equation to generate a bit of code to solve it rather than just using the LLM.
From the maths it's resolved for me so far it was a suprise to see that. Rounding is basic logic and numbers, and numbers or letters, it's just pattern matching logic as far as I understand it. It is good at finding errors i.e. when you know something isn't right (I'm using a 3D CAD model to verify the chatbot/AI/LLM output) and challenge it, the model and conversation improves.
Certainly it shows how you can't reliably use it to simply do work that you don't have some understanding of or an ability to spot errors in, but if the original Apple was a bicycle for the brain I'm finding current LLMs more like a motorbike (or an Avinox e-MTB.. ha).
The half dozen AIs i've used are saving me a colossal amount of time. They can produce a days work in minutes with a handful of decent prompts.
Unfortunately it takes me almost as long to error check and edit the output, and make sure it's not lying.
It's also bloody useless at context dependent language.
So, yeah, in an eight hour day, it saves me about 20 minutes. Colossal.
I'm so pleased about it.
Hell, it even makes mistakes summarizing a 1000 word meeting minutes, bad enough that the entire context is broken.
Sam Altman has admitted being wrong in his predictions about AI job losses.
"I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened... that is an area where my intuitions were just off.”
It hasn't made headlines but this seems pretty damning, one of the key AI chiefs admitting that his product can't really do what he's been promising afterall
My boss gets loads of AI emails, he uses AI to collate and summarise them and uses it again to write replies... He thinks AI basically talking to itself all day long is a huge benefit to the company. As a fairly plain speaking person, I found it handy to chuck all my thoughts into it and get a management level proposal out of it.
@jameso, interested to hear how you're applying AI and CAD models as while I get loads of people trying to sell me AI CAD add-ons, I'm yet to find any real useful crossover in that area.
Meanwhile,
“I’m delighted to be wrong about this, I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened,” Altman said.
I think by now is a key part of that. " he's not saying it can't do what he said, just that it hasn't done it yet.
“I’m delighted to be wrong about this, I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened,” Altman said.
I think by now is a key part of that. " he's not saying it can't do what he said, just that it hasn't done it yet.
That's true, he does leave open the possibility that it might deliver in the future. But I'm not sure he has much choice, considering he still needs investors pumping money in, and they aren't going to do that if he openly admits that the tech isn't capable of living up to the promises.
I think there's a good chance that this is one of those things that's always "2-3 years away" from delivering. The AI CEO's have been very aggressive with their predictions but their initial timelines are running out and there's no real sign of the jobs apocalypse, and this is the first time I've seen them strike a more cautious tone so it's an interesting development.
It'll be fascinating to see how this plays out over the next 12 months. What happens when an industry which has attracted hundreds of billions in investment simply fails to deliver on its promises, I don't know.
I do not get the same result What am I missing about that image?
The explanation at the bottom of it?
