Jonathan HoggMy experience with generative-AI has been that, at its very best, it is subtly wrong in ways that only an expert in the relevant subject would recognise. So I don't worry about us creating super-intelligent AI, I worry about us allowing that expertise to atrophy through laziness and greed. I refuse to use LLMs not because I'm scared of how clever they are, but because I do not wish to become stupider.
I will say one thing for generative AI: since these tools function by remixing/translating existing information, that vibe programming is so popular demonstrates a colossal failure on the part of our industry in not making this stuff easier. If a giant ball of statistics can mostly knock up a working app in minutes, this shows not that gen-AI is insanely clever, but that most of the work in making an app has always been stupid. We have gatekeeped programming behind vast walls of nonsense.
We seem to have largely stopped innovating on trying to lower barriers to programming in favour of creating endless new frameworks and libraries for a vanishingly small number of near-identical languages. It is the mid-2020s and people are wringing their hands over Rust as if it was some inexplicable new thing rather than a C-derivative that incorporates decades old type theory. You know what I consider to be genuinely ground-breaking programming tools? VisiCalc, HyperCard and Scratch.
jarkman@jonathanhogg That's the kind of talk you usually hear just before someone invents themselves a new language. Just saying.
michael@jarkman @jonathanhogg I get the broader point here, but at the same time, as computers have moved to encompass more and more of the human sphere, is it actually reasonable to exect any languge to be actually general purpose?
Perhaps for some uses cases it's the right choice, but when I look at data-science code written by vernacular developers (experts whose expertise is in a domain other than computer science) I feel the freedom from those languages just gives more scope for error/mistake/poor style that will bite them later). Why can't we embrace more DSLs?
Sten Turpin@jonathanhogg @michael @jarkman I once asked a very senior HPC developer at Red Hat what keeps him up at night and he said, paraphrasing and pulling from memory that's about 15 years old now, "we haven't created new computer science since the 1960s and I fear we'll exhaust what we know before we discover anything new," and I think about that a lot these days.
Peter Ludemann@thatsten
The 1960s were mostly math because most CS was done on blackboards (as one of my profs put it) because access to machines was very limited. Also, there was a "Cambrian explosion" of ideas in this new field - and after that, evolution slowed down.