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.

Those are extremely few and far between, and they aren’t evolutionary biologists. Behe, the most famous of them, doesn’t have a PhD in biology, but a PhD in biochemistry. Those are vastly different fields, and understanding the evidence for evolution wouldn’t have been relevant to Behe’s PhD. MDs more commonly don’t believe in evolution because MDs are essentially average folks who can memorize stuff really well. MDs don’t receive training in research or how to conduct it, so they’re pretty poor at understanding primary research most of the time.

Someone with a PhD from a reputable university (essentially, one that funds their PhD programs rather than making students pay, and one that doesn’t incentivize publications directly with bonuses) will be an expert in their subject area. Behe would be able to tell you about the biochemistry of sickle cell anemia. Someone with a PhD speaking on an area outside of their expertise is perhaps more likely than the average person to be correct because they could have read and understood most primary sources even outside of their area, but I wouldn’t say it’s all that much more likely. Basically, PhDs speaking on the topic of their expertise are experts, but they’re not experts in everything.

Personally, my PhD made me like the trope of someone who could tell you everything you want to know about some esoteric subject but wouldn’t know how to make a meal.

Getting a PhD produces highly specialized knowledge, not general knowledge.

Pharmacists don’t get PhDs, they get degrees for practice, like MDs. A PharmD doesn’t require being able to understand or conduct original research like a PhD does. Basically, a PharmD requires a really good memory, not necessarily critical thinking.

Would you, after devoting full years of your adult life to the unpaid work of learning the requisite advanced math and computer science needed to develop such a model, like to spend years more of your life to develop a generative AI model without compensation? Within the US, it is legal to use public text for commercial purposes without any need to obtain a permit. Developers of such models deserve to be paid, just like any other workers, and that doesn’t happen unless either we make AI a utility (or something similar) and funnel tax dollars into it or the company charges for the product so it can pay its employees.

I wholeheartedly agree that AI shouldn’t be trained on copyrighted, private, or any other works outside of the public domain. I think that OpenAI’s use of nonpublic material was illegal and unethical, and that they should be legally obligated to scrap their entire model and train another one from legal material. But developers deserve to be paid for their labor and time, and that requires the company that employs them to make money somehow.

Wow, a real, live, tankie!

Wow, a real, live tankie!

You don’t use first-person pronouns when describing yourself or your actions?