It wasn’t about speed, it was about reducing spending on wages

The one with the most power in your region

Pretty sure some of that furniture was structural

RLHF and boosting

I don’t really see how those avoid the problem. I can see how they produce much better training results, but ultimately a trained LLM is a neural net that has fixed set of parameters that represent a function over n-dimensional space (I.e. the information in the context window), plus a noise term. If you turn the temperature down to zero, they are perfectly deterministic, no? That’s the mean that they are producing values around…

Yeah. Agree in theory. I’m a bit sus on the adversarial AI approach in practice, because most LLMs seem to produce a lot of sycophancy, and I think that’s a far too easy trap to fall in to.

I do think there are some practical uses, such as reducing availability bias by asking it “is there anything I’ve forgotten to consider in this thing I wrote?” AFTER you’ve done the thinking and writing. But I think the temptation to use it for generative purposes is pretty huge, and likely to suck a lot of people in.

GenAI is an averager (as all empirical models are), so it tends towards predicting the mean of whatever it’s trained on (in a given context). But it also has noise added, so some variance comes back, but there’s no guaranteed that that mean+noise produces something meaningful/true/valuable.

But, GenAI is very good at producing syntactically correct language. This is a problem because it lulls the reader into a sense that the author knows what it’s talking about, when it doesn’t. When a junior scientist produces text, the awkward language alerts the reviewer to the poor thinking (same with a junior coder producing weak code). With an LLM, you don’t get that - it produces the impression of knowledge without any actual understanding.

This, combined with the lure of efficiency and the feeling of effectiveness, make it a honey pot for quick but sloppy thinking. I think this is what connects 2 to 1, especially in domains where it’s hard to get external validation from other people who can understand what you’re trying to mean, and not just say.

As for your point 3: I agree. People don’t spot flaws already, and that’s part of why we saw the replication crisis in behavioural science. Adding LLMs to the mix will just make things like that more likely.

I guess that depends how much real damage Trump has done to the underlying economic come B-day.

What is that?

Yeah… Ask him what advice he would have given out around 2007

You can ask your pension find if they’re aware of the bubble. They might have an option with low-AI investment

1. They are true believers
2. They know they’ll get a bail out because they are too big to fail

The AI bubble is propping up the American bond market too (countering all of the other crap Trump had been doing to undermine the economy). When the bubble pops, the bond market is going to suffer massively as well.

Yeah… That shit with ICANN and Autistici kinda raises some red flags now though…

Sorry, but that’s just not true. Researchers might be experts in teir field generally, but day-to-day work is very rarely focused on well-known parts of their domain, because the whole point is to obtain new knowledge.

Source: I’m a climate scientist, have been in and around academia for nearly 20 years.

I’m a climate scientist, I’ve been a web dev, and I’ve worked with complexity in climate, risk, and management spaces. So, compared to those I guess. I’ve worked with some software devs who are well aware of complexity concepts, but I’ve never really seen any of them take a complexity-first approach.

I fully acknowledge that I have a particular perspective, but I struggle to believe that most software devs in generic workplaces are better at complexity thinking than the ones I’ve worked with, given the content we were working with.

Using it for learning stuff that’s already well known (e.g. beginner level coding) is fine.

Using it for “knowledge work” is a bad idea idea, because AI pushes toward the mean, while research by definition is focused on new edges of knowledge. This is is exactly the space where AI will introduce biases and untruths that will be very hard to spot. Using it also reduces critical thinking ability which is critical in this space.

AI is going to tank academia. It’s already going down hill after decades of corporate-style management.

It was shit when it came out

The bit we should regret is that we let the rich corporates steal it.

In my experience most software developers have very little understanding of complexity, and mostly ignore it or try to design around it, nor with it. Which is mostly just a function of them needing to work to get by, not to contribute something meaningful to society.