Oh, certainly not. I have been using LLM chatbots extensively this week. If your uncle were not just confident while stating falsities, but uncapable of self-assessment, he would have been murdered.
Title: "The Talker" - https://image.cagle.com/287573/980/-the-talker.png
This reads as "I've actually used LLMs" like it's a rare occurrence giving you a unique insight, when most of us have been using LLMs extensively for almost two years now.
Oh please. Guidelines: «respond to the strongest plausible interpretation».
Does the form "An occasion of extensive use of the main current chatbots for circumstantial reasons in the past few days confirmed and reinforced the assessments raised years ago already" work better for you?
(b) The kids-vs-uncle metaphor is spot on in my experience too, and also illustrates how far these language models have come.
Understanding how these algorithms work, I don't know why we would expect anything different from this?
For me, my expectations are adjusted. I kinda know what to expect it to do, and it does quite well for what I use it for, essentially smart intellisense for coding.
A lot of money is being spent on trying to improve it too. But right now the emperor has very little clothes. It remains to be seen whether they have materialized by the time people open their eyes.
If I want to close the paper division, and my boss wants to close the paper division, does the LLM whisper sweet nothings in our ear and show soothing Powerpoint decks about how in the eyes of an objective observer, the paper division is unprofitable and there would be no career risk in closing it?
It's not a sexual thing I swear, it's a sort of comforting intimacy that needs to be injected into the business process in order to cope with the pressures executives feel. I don't know that anybody has trained an LLM to do that yet.
You bring up a good point though. Very dystopian idea of a new type of enterprise tooling that emerges to soothe and calibrate human knowledge workers. Very Severance Wellness room.
LLM's did a number on that unfortunately. Major subreddits banned all AI content, mostly due to the sheer infestation of really boring Midjourney content sent out by naive users and bots. Without reach, I lost interest, so no more Markov Chains.
[0]: https://www.dota2.com/687
[1]: https://old.reddit.com/r/DotA2/comments/4fw6gm/how_687_would...
> There is no charge associated with your streamed applications, simply click Enable Microphone from the ElastiCache documentation.
It's way funnier when our brains try to make sense of the nonsense.
https://github.com/cemulate/the-mlab
This is a parody of the nLab, a wiki for collaborative work on category theory and higher category theory. As anyone who's visited is probably aware, the jargon can be absolutely impenetrable for the uninitiated -- thus, the idea for this project was born!
This project uses my package nearley-generator, a module that can turn a Nearley grammar into an efficient and controllable fake text generator. The grammar file for this site can be found at /src/grammar/nlab.ne
I wonder if there is combination of temperature and prompt engineering that could make it interesting.
This is the biggest challenge I run into when I see LLM Generated nonsense.
So much stuff that ChatGPT spits out is close enough to true that Brandolini is laughing.
But it's also that it's borrowing on stuff that's already there so it can't really get that 'creative' either.
Before anything LLM existed, I built a site[0] to generate fake "AWS Blog Posts." I trained a markov chain generator on all AWS announcement posts up to that point, copied the html + css of aws's standard blog posts, then glued them all together with some python + JS. It turned out, IMO, pretty funny! People familiar with AWS's blog posts would often get several sentences in before they realized they were looking at word-soup.
When GPT was new, I looked into using that to "upgrade" it. I spent a weekend messing around with Minimaxir's gpt-2-simple generating blog posts based on AWS content. What I found was, ultimately, it was way less fun. The posts were far too realistic to be interesting. They read like totally-real blog posts that just happened to not be true.
I realized then that the humor of those early markov generations was the ridiculousness. The point where, a few words or sentences in, you realized it was all nonsense. LLM's these days are too good for that - the text they generate is sometimes wrong, but rarely nonsense in a humorous way.
Markov chain content was wrong in a "kid's say the darndest things" way, while modern LLMs are wrong in a "My uncle doesn't know basic geography" way.
[0] https://totes-not-amazon.com/ - click any link to get a new one.