This feels like a very @SwiftOnSecurity story but I’m going to tell it.
Chat bots (not just LLM driven) are surprisingly old. In the mid 90s, a mark up language for string-driven bots called AIML was released. A small community of early hackers and devs got really into it. I was part as a teen.
@hacks4pancakes I’m an AI sceptic because in my ‘Artificial Intelligence’ course I only attended two classes in 3rd year Uni, then spent all day drinking at Wollongong Uni Bar before the exam, and still got a High Distinction.
I feel that if that strategy works, there’s something suspect at the heart of the discipline.
I had the opposite experience. The AI course in my final year had an exam with questions of the form ‘in lecture three, I made a brief reference to a system called Flibblefloozle. Describe it in detail.’ With no questions on any of the (quite useful) conceptual material. It was also taught by someone who liked to get his teaching finished early in the week, so schedule both lectures back to back starting at 9am on a Monday.
After the exam (before the results) I changed my course preferences for the next term to not do the second AI course and instead do the course about SAT problems and how SAT solvers work. Which turned out to be far more useful than I expected.
I did use machine learning in my PhD, but for problems it’s actually suited to (prefetching, where things go fast if you get it right and where you don’t lose much if you get it wrong).
@david_chisnall @troberts @hacks4pancakes
In the late 80's I worked for EDS in Research and Development. We learned and used "Artificial Intelligence" and "Machine Learning" techniques, including automated reasoning.
What I found was that
1. It's hard to get funding for such work.
2. People ask for "AI" when they have no idea what they want or how to accomplish it. "Mix in the AI and magically produce great results!" is what they want.
3. Expectations are always unreasonable.
@david_chisnall @troberts @hacks4pancakes
People generally expect "AI" systems to be (1) as reliable and (2) as maintainable as conventional procedural code. Like, when it identifies black people in photographs as gorillas, most people think that it must be a simple coding mistake that they can assign some intern to track down and fix the "if" statement. No, it's always much more complex than that, with many difficult to understand and explain dependencies. ...
@JeffGrigg @david_chisnall @troberts @hacks4pancakes
This corroborates your post in part: https://www.wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/
@audubonballroon @david_chisnall @troberts @hacks4pancakes
Yep! The moment the problem happened, I said that this would be the result. Not the slightest bit surprising at all, to me. 💢
It's fundamental to how AI works.
And it's fundamental to how managers and others think about such things and react to them. And it's about all the programmers can do, to comply with the (inevitable predictable) demands of their superiors. 😢
Lesley Carhart 
Dread Pirate (Tom) Roberts
David Chisnall (*Now with 50% more sarcasm!*)
Jeff Grigg
Audubon Ballroon (he/him)