MajinBlayze

@MajinBlayze@lemm.ee

• ⁨Comment⁩ on ⁨The Writers Strike Is Over: WGA Votes to Lift Strike Order After 148 Days⁩ ⁨⁨1⁩ ⁨year⁩ ago⁩:

  Didn’t see anything in the article about steamer revenue. Did that get figured out?

• ⁨Comment⁩ on ⁨⁩ ⁨⁨1⁩ ⁨year⁩ ago⁩:

  Title is weird

• ⁨Comment⁩ on ⁨join the dicks cord sever⁩ ⁨⁨1⁩ ⁨year⁩ ago⁩:

  But it’s it federated?

• ⁨Comment⁩ on ⁨Has AI made any breakthroughs in other fields? Or how close are we to that happening?⁩ ⁨⁨1⁩ ⁨year⁩ ago⁩:

  To be clear, I’m not trying to make the argument that it can only produce exactly what it’s seen, I recognize that this argument is frankly overstated in media. (The interviews with Adam Conover are great examples; he’s not wrong per se, but he does oversimplify things to the point that I think a lot of people misunderstand what’s being discussed)

  The ability to recombine what it’s seen in different ways as an emergent property is interesting and provocative, but isn’t really what OP is asking about.

  A better example of how LLMs can be useful in research like what OP described would be asking it to coalesce information from multiple existing studies about what properties correlate with superconducting in order to help accelerate research in collaboration with actual material scientists. This is all research that could be done without LLMs, or even without ML, but having a general way to parse and filter these kinds of documents is still incredibly powerful, and will be a sort of force multiplication for these researchers going forward.

• ⁨Comment⁩ on ⁨Has AI made any breakthroughs in other fields? Or how close are we to that happening?⁩ ⁨⁨1⁩ ⁨year⁩ ago⁩:

  It’s important to be clear what kind of actual system you’re using when you say “AI”.

  If you’re talking about something like ChatGPT, you’re using an LLM, or “Large Language Model”. It’s goal is to produce something that reasonably looks like a human wrote it. It has reviewed a ridiculous amount of human text, and has a metric assload of weights associating the relationships between these words.

  If the LLM sees your question and associates a particular compound with superconductors, it’s because it’s seen these things related in other writings (directly or indirectly).

  It’s important not to ascribe more intent behind what your seeing than exists. It can’t understand what a superconductor is or how materials can achieve the state, it’s just really good at relaying related words in a convincing manner

  That’s not to say it isn’t cool or useful, or that ML(Machine Learning) can’t be used to help find answers to these kinds of questions.