canihasaccount

Deflecting blame by subtle ethnic discrimination. Nice.

MySpace was huge before Facebook, and it killed off a lot of blogs. Late 90s and early 2000s were truly the wild web IMO. I had a geocities page with its own forum before MySpace made me abandon it due to inactivity.

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

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.

The professor probably would have responded that his response was another part of the lesson: don't trust those above you in a business setting.

Desoxyn would like a word.

Edit to add: more commonly prescribed amphetamines are neurotoxic, too. Whether they are neurotoxic at clinical doses is still debated.

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.

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 thinking of shorting it. My friend is definitely shorting it.

No, there is no coursework past a master's thesis. For the last typically ~3-4 years of graduate training, everything that you're doing is original research. If your research isn't good enough or done correctly, you will never get a PhD. You also have to defend your dissertation. Getting a PhD from a reputable university does mean that what you say, specifically related to your research area, is correct.

Unless you're in university administration, academia is not well paid. University administrators who are well paid are usually EdDs (essentially, university-focused MBAs) who didn't take the normal academic route of research first.