Flashtoo

> They didn't physically ship any of it, they sold it and rebought in Europe.

The Netherlands physically moved 27 tons of gold from the US and Canada (mostly New York) to the Netherlands this year.

> Some emerging research suggests Earth’s magnetism matters, as well. Being barefoot on the ground versus 30 stories high in a downtown skyscraper puts you in a completely different magnetic environment.

Really? My instinctive reaction to this statement is that it's quackery. Would be very interested to see headlines appear about this if it's true.

The methane will eventually break down and be part of new plants, but in the meantime it will heat the planet much more than if it hadn't been there. Think of it this way: industrial scale cattle farming essentially makes and feeds biochemical reactors that turn hydrocarbons into a gaseous form that maximizes their global heating effect. Consider the counterfactual where we don't stuff billions of fart machines full of corn.

> they maintain the websites for social status

Are you implying that's a bad thing? Doing things for social status has been an integral part of society for millennia. It's a legitimate motivation that can benefit both the recipient of the status and the rest of society. In this specific case, if you assume that the author makes content that is useful to you only because of the social status reward, taking away that reward means the author will stop making content that is useful to you.

What do you get out of that? The utility of having the end product (a generated game for an obsolete console) seems pretty small, and personally I would think the fun is in learning about the inner workings of the time capsule that is 90s hardware. Would you mind explaining?

One I've started noticing is the "quotation marks around a phrase awkwardly trying to bundle a concept" thing. Like all LLM cliches it's something that has been used in writing for a long time, but I've seen it so much more recently. I think lots of people have picked this up from seeing LLMs use it. But like you said, who knows.

> It's literally the free market at work--it's really simple, if you don't believe in whatever he says, don't speculate!

This, too, is actually addressed in the article - the whole point is that you're being forced to participate/speculate through this rapid inclusion in indices and mutual funds, because he's had the indices change the rules for him. So no, it's not that simple.

Same, but recently I discussed the LLM flood on Reddit with some friends IRL and they were surprised I had left Reddit, as they simply hadn't noticed significant bot activity. Mind you, these are very AI-aware people. I have little hope that normies will catch on and actually mind enough to leave.

This is correct.

These are good practices to keep in mind when setting up GenAI solutions, but I'm not convinced that this part of the job will allow "data scientist" as a profession to thrive. Here's my pessimistic take.

Data scientists were appreciated largely because of their ability to create models that unlock business value. Model creation was a dark magic that you needed strong mathematical skills to perform - or at least that's the image, even if in reality you just slap XGBoost on a problem and call it a day. Data scientists were enablers and value creators.

With GenAI, value creation is apparently done by the LLM provider and whoever in your company calls the API, which could really be any engineering team. Coaxing the right behavior out of the LLM is a bit of black magic in itself, but it's not something that requires deep mathematical knowledge. Knowing how gradients are calculated in a decoder-only transformer doesn't really help you make the LLM follow instructions. In fact, all your business stakeholders are constantly prompting chatbots themselves, so even if you provide some expertise here they will just see you as someone doing the same thing they do when they summarize an email.

So that leaves the part the OP discusses: evaluation and monitoring. These are not sexy tasks and from the point of view of business stakeholders they are not the primary value add. In fact, they are barriers that get in the way of taking the POC someone slapped together in Copilot (it works!) and putting that solution in production. It's not even strictly necessary if you just want to move fast and break things. Appreciation for this kind of work is most present in large risk-averse companies, but even there it can be tricky to convince management that this is a job that needs to be done by a highly paid statistician with a graduate degree.

What's the way forward? Convince management that people with the job title "data scientist" should be allowed to gatekeep building LLM solutions? Maybe I'm overestimating how good the average AI-aware software engineer is at this stuff, but I don't see the professional moat.

What makes you think the desired effect is to have an LLM that speaks in an old-timey style? The training process is the whole point.