>So, why are you still working for them?

>If the answer is the salary, say it louder, and do not force yourself to make excuses.

>If the answer is “someone worse would replace me”, say it too, and louder too.

>If the answer is “I can change things from the inside”, then show me what you changed. Haugen tried the inside. Coxon tried the inside. At the end, unfortunately, the inside did not move.

>

Okay, but what if I think that both sides are LARPing their personal science fiction stories and reality will end up much more mundane (but still incredibly beneficial) than the utopias and dystopias everyone is cooking up?

This is presupposing a moral dilemma that just doesn't exist in my opinion.

that is generally a very healthy attitude in the AI space anyway in my opinion.

Some of our R&D departments haven't actually finished an interesting project in years because they keep jumping from trend to trend wanting to try out all the latest shit all the time.

If you give them a wide open problem statement, they'll start talking a lot of semi intelligible gibberish.

My guess is that this happens because that's not what they are evaluated on anymore for these kinds of tasks. The generated code is evaluated (in this case the lean code). So talking a bit of gibberish in the language part so you have more test time compute is not punished.

I think it's just the splash screen they didn't properly localize.

They are not "further along" the AI discussion, they are a bunch of wackos LARPing as scientists living out their personal sci-fi scenarios.

I currently run pi agent in Lima on a Mac with only the code project folder mounted and an extension that prevents pi agent from reading the contents of .env files directly.

Yeah, there probably are some freak situations where this isn't safe enough, but I don't really see any realistic ways this is going to end up badly. Am I overlooking some obvious security holes?

What we perceive as "effort worth taking" instead of "dull occupational therapy" is very prone to change with technology.

If you would argue that modern photographers need to take the time to physically develop their photos and use chemicals to get their effects rather than applying photoshop filters, you'd not be taken very seriously - in the 80s and 90s it would have been a very different discussion where people saw photoshop as "taking the helicopter to the summit of Mt Everest".

Same even with paper writing. I still had old school teachers in the 90s and early 2000s who insisted that writing anything on a computer was a "shortcut" that would encourage worse writing because you could undo stuff etc. They did all their handouts and worksheets on their old typewriters.

There is a discussion to be had on AI in maths, but I don't think it's this one. I think mathematicians should be talking about what the future of their field is supposed to look like in a time where AI will be able to find the proofs. Maybe maths will turn into a more "experimental" science, where you already know the proof of a theorem, but you want to find a particularly elegant way that helps humans understand it or find other ways to apply the knowledge. Or rewrite old theories from different angles based on all the new proofs generated by AI. I don't know, but I think there's a lot of mathematics to do out there for humans even in a time with AI.

So in my opinion it currently falls into a kind of void. If your use case is worth predicting and you put a data scientist on it, you're better off just training cheaper ARIMA models.

And in reality you can use it in much broader applications than just words. I once threw it onto session data of an online shop with just the visited item_ids one after another for each individual session. (the session is the sentence, the item_id the word) You end up with really powerful embeddings for the items based on how users actually shop. And you can do more by adding other features into the mix. By adding "season_summer/autumn/winter/spring" into the session sentences based on when that session took place you can then project the item_id embeddings onto those season embeddings and get a measure for which items are the most "summer-y" etc.

And you can see the influx of people on r/physics and the like who are convinced they've solved dark matter/quantum gravity/... because ChatGPT kept agreeing with them when they presented their ideas to it. Just recently there was a post by a guy who essentially "rediscovered" 17th century physics with the help of ChatGPT but was convinced his formula would explain dark matter because ChatGPT told him so.

I fear this will push even more people into deep rabbit holes they won't be able to get out of because they think this neutral AI has confirmed their suspicions/ideas/observations.

IF they are correct and they have an RT superconductor, then it's a highly political decision on who gets sent some samples. Like, all their former colleagues and collaborators will be pissed if they don't get any. But you also would want to send some samples to someone who you know has 1) all the necessary equipment to confirm your claim, 2) has the necessary publication history to make him/her a trustworthy expert on high temperature superconductivity that others would believe, 3) would not want to slow down your progress just so he/she can gain an advantage.

And with something with such huge potential for applications as RT superconductivity, you then also get vested national interests where your national physics society or research ministry might be pissed off if you don't involve enough researchers from the country which funded your research.

Honestly, if I was in their shoes I'd be delaying me sending out my samples into the world as well.

Of course this all hinges on whether they actually have a room-temperature superconductor or not. If they don't actually have one then the delay tactic could also just be in hopes for this all to blow over without them having to prove their findings.

This way papers are peer replicated in an emerging manner because the knowledge is passed from one group to another and they use parts of that knowledge to then apply it to their own research. You have to see this from a more holistic picture. Individual papers don't mean too much, it's their overlap that generates scientific consesus.

In contrast, requiring some random reviewer to instead replicate my full paper would be an impossible task. He/she would not have the required equipment (because there's only 2 lab setups in the whole world with the necessary equipment), he/she would probably not have the required knowledge (because mine and his research only partially overlap - e.g. we're researching the same materials but I use angle-resolved photoemission experiments and he's doing electronic transport) and he/she would need to spend weeks first adapting the growth recipee to the point where his sample quality is the same as mine.

Peer replication is completely unfeasible in experimental fields of science. The current process of peer review is alright, people just need to learn that single papers standing by themselves don't mean too much. The "peer replication" happens over time anyway when others use the same tools, samples, techniques on related problems and find results in agreement with earlier papers.