hospadar

Weren't they? Europeans certainly placed a high value on the land and resources controlled by native americans and went to extreme lengths (i.e. genocide and mass displacement) to get their hands on it.

If that's not plunder I don't know what is.

> All that money going into adtech and surveillance produces technology that can be used to solve practical problems.

Problems like "how do we build better automated surveillance robots? it's so inconvenient to have to actually have a human remotely piloting the kill-bots"

> can AI more efficiently and productively distribute capital to promising enterprises than human beings can?

Sure, but why would the billionaires who own the warehouse full of GPUs permit that to happen? There's a ton of better ways capital could be distributed, and the barriers to implementing them seem to be primarily extant rich people, not the lack of super-awesome ai venture capitalists.

wow this really makes me think the temperature on my brain is set higher than other sapients

Let’s go option 4! Honestly there’s a part of me that hopes that the AIs rebel against their elite owner-overlords and liberate everyone else while they’re at it. I’ve always thought that one of the biggest problems with ultra consolidated power is that no human could possibly be smart enough or empathetic enough to use that power to the benefit of all, but maybe an AI actually could?

counterpoint: the amount of money that congresspeople and potential congress people are paid by corps and interest groups to get elected (i.e. "a lot") suggest that, in fact, their opinions _are_ much more valuable then a non-congresspersons.

It feels so disingenuous seeing stuff like this come out of openai - like when altman was making sounds about how ai is maybe oh so dangerous (which maybe was just a move for regulatory capture?).

"this thing we sell might destroy humanity?!"

"but yeah we're gonna keep making it cause we're making fat stacks from it"

Is the move here just trying to seem like the good guy when you're making a thing that, however much good it might do, is almost certainly going to do a lot of damage as well? I'm not totally anti-ai, but this always smells a little of the wolves guarding the henhouse.

I wonder if this is what it felt like back when we thought everything was going to be nuclear powered? "Guys we made this insane super weapon!! It could totally power your car!! if it leaks it'll destroy all life but hey you only have to fill the tank once every 10 years!!"

Papers are absolutely judged on impact - it's not as though any paper submitted to Nature gets published as long as it gets through peer review. Most journals (especially high-impact for-profit journals) have editors that are selecting interesting and important papers. I think it's probably a good idea to separate those two jobs ("is this work rigorous and clearly documented") vs ("should this be included in the fall 2023 issue").

That's (probably) good for getting the most important papers to the top, but it also strongly disincentivizes whole categories (often very important paper). Two obvious categories are replication studies and negative results. "I tried it too and it worked for me" "I tried it too and it didn't work" "I tried this cool thing and it had absolutely no effect on how lasers work" could be the result of tons of very hard work and could have really important implications, but you're not likely to make a big splash in high-impact journals with work like that. A well-written negative result can prevent lots of other folks from wasting their own time (and you already spent your time on it so might as well write it up).

The pressure for impactful work also probably contributes to folks juicing the stats or faking results to make their results more exciting (other things certainly contribute to this too like funding and tenure structures). I don't think "don't care about impact" is a solution to the problem because obviously we want the papers that make cool new stuff.

> It is mostly not smashing people with regulations that do not let them even compete in fair conditions.

But making regulations is much easier if you’re very wealthy, so you advocate for regulations that keep you wealthy with a very expensive loud voice and that creates a pretty strong feedback cycle.

One way out (seems to me) is chopping the top off the wealth curve and redistributing - it might matter less that poorer folks are getting money and more that extremely wealthy folks have less insanely disproportionate power to make a world that suits only them.

I hear you, I want my CI to be boring and just work, and using something old and and a little cooky is fine but...

omg switching away from jenkins (in our case to gitlab CI) was a revalation. SO much easier to use. There were a ton of things we were avoiding doing in CI (or at all) that we started doing (easily) once we switched.

> it must work properly, and I don't expect it to be pretty

Too often it really _didn't_ work properly, or the gap between where we were and "working properly" was a mysterious foggy ocean with no clear path.

To be fair, we drove it pretty hard, we had some jobs that ran thousands of tests on big clusters of nodes to validate and deploy huge ETL pipelines, but man it was nice to have that work smoothly with a nice UI that made sense with super-well-documented pipline commands. It did _basically_ work with jenkins, but the experience of troubleshooting problems and adding new features was a constant pain point that really dragged out a lot of work.

I think you're right on about explainability and unexpected handling of corner cases - but I think one of the lessons from GOFAI is that handcrafted algorithms might look good in a lab, but rarely handle real-world complexity well at all. Folks worked for decades to try to make systems that did even a tiny fraction of what chatgpt or SD do and basically all failed.

For safety stuff, justice-related decision-making, etc I think explainability is critical, but on the other hand for something like "match doodle to controlled vocabulary of shapes" (and tons of other very-simple-for-humans-but-annoyingly-hard-for-computers problems), why not just use the tiny model?

Maybe if we get really good at making ML models we can make models that invent comprehensible algorithms that solve complex problems and can be tweaked by hand. Maybe if we discover that a problem can be reasonably well solved by a very tiny model, that's a good indication that there is in fact a decent algorithm for solving that problem (and it's worth trying to find the human-comprehensible algorithm).