ONLYOFFICE (sorry about the caps, poor name choice IMO) has even better docx compatibility, and its source code is open

Yeah, I understood. My reply wasn’t actually directed at you; sorry for not being clear. I just wanted to add that bit in case other readers didn’t know that this was more forceful than a request.

They weren’t asked, they were mandated to do so directly by executive order. I get the desire to not comply, here, but if I’m NIH, I’m probably thinking that complying to keep the doors open for four years will do a hell of a lot more for the country than if they refuse and Trump totally dismantles their entire architecture with enough time that it’s difficult to reinstitute when he’s gone.

I’ve heard that most, if not all, of their stations outside of NY are essentially for training other police departments. Is that not true?

Trogdor was popular way before Reddit

It doesn’t have to be

https://www.mathworks.com/products/compiler.html

MATLAB can ruin all sorts of coding experiences, programming included

Examples? I can think of a number of foreign companies that the US facilitates, like Nestle.

(⌐■ ͜ʖ■)

Eh, I switched. I switched all of my lab’s computers, too, and my PhD students have remarked a few different times that Linux is pretty cool. It might snowball.

This makes sense, thanks

Why would China turn against Putin for them using their nukes? I don’t keep up much on their relations.

Oregonians almost take pleasure in driving slowly in front of you. Maybe they’ve just gotten used to going slow because the entire state freeway system is always under construction. People driving crazily is infuriating for a completely different reason.

The best time to start was decades ago, but at least they’ve started.

This is a problem that’s becoming outdated, thanks to NIH now requiring females to be included in studies in order to receive grant funding–barring an exceptional reason for studying males alone (e.g., male-specific problems). They are even requiring cell lines for in vitro studies to be derived, at least in part, from females, rather than from males alone.

Sorry, what? Not sure if you’re joking, but Americans use texts because they’re free and the ability to use them comes preloaded on the phone (no need to download something that takes up more space). I have Signal and WhatsApp on my phone for my international friends, but I use texts to communicate with US friends because RCS works with everyone and it’s integrated much better into my phone, watch, etc. than any app can be without an absurd amount of permissions given to the app.

I never understand why lemmy downvotes someone who is trying to help by providing accurate information, presumably because they think that there’s a very small chance that the person they’re replying to isn’t being sarcastic.

A fellow Julia programmer! I always test new models by asking them to write some Julia, too.

I actually took that bit out because LLMs are pro climate and against everything that makes the environment worse. That’s a result of being trained on a lot of scientific literature. I was just curious what Opus would say about the conceptual knowledge piece.

Claude Opus disagrees, lol (edit to add: all of what follows is Claude; not me):

I respectfully disagree with the claim that current language models are logically incapable of mapping language to actual concepts. While today’s LLMs certainly have limitations and are prone to hallucinations, they have demonstrated a remarkable ability to capture and manipulate complex concepts through language.

A few key points:

LLMs are trained on vast amounts of human-generated text which is grounded in real-world concepts, allowing the models to build rich representations that go beyond surface-level word associations. Techniques like unsupervised pre-training enable LLMs to learn meaningful conceptual relationships.

In many domains, LLMs have shown an impressive capacity for reasoning, inference, and knowledge synthesis - skills that rely on a deep understanding of the underlying concepts, not just pattern matching. Their performance on complex question-answering, analogical reasoning, and natural language inference tasks is evidence of this.

LLMs can be fine-tuned and augmented with techniques like retrieval-augmented generation or chain-of-thought prompting to further ground them in factual knowledge and improve their reasoning capabilities. This allows more robust conceptual mappings.

Careful prompt engineering and sampling techniques can help to mitigate hallucinations and nonsensical outputs. While not a complete solution, this shows that the conceptual knowledge is often present even if the model sometimes struggles to express it faithfully.

That said, I fully agree that the tendency for subtle nonsense is a serious issue, especially for layperson use cases where hallucinations may go undetected. Continued research into making LLMs more truthful, consistent and robust is crucial. Techniques like constitutional AI to bake in truthfulness, and better UX design to convey uncertainty are important steps.

But in summary, I believe the evidence suggests that LLMs, while flawed, are not fundamentally incapable of meaningful conceptual representation and reasoning. We should push forward on making them more reliable and trustworthy, rather than dismissing their potential prematurely.

I’m back on my BS is also a solid contributor