canihasaccount

Seven paragraphs is too much? I read the full thing before seeing your comment. It's well written and easy to read.

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.

Engagement helps posts in various algorithms, though I'm not sure that Lemmy uses comments for Hot or anything else. More importantly, I think there's truth to the meme that the quickest way to get an answer to your question on the internet isn't to ask the question, it's to tell someone else the wrong answer. People will then chime in with the right answer if they know it. Wrong answers can be useful in that respect.

Tbf, the bill only blocks the DoJ from using a certain pot of money to reschedule or deschedule marijuana. They could still reschedule it even with this bill.

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.

Everyone I know in the US either drinks tap water or, if they're picky, just uses Brita or similar filter for tap water.

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

Fair points. My use of "primary" was a poor choice; I meant something along the lines of "most common among individuals who aren't philosophers, in my experience."

Interesting take! Is lightning conscious, then? The idea of Thor isn't too far off if so, haha.

Not everyone finds it persuasive, yeah. It's an appeal to intuition that many people, though not all, have.