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.

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

It’s FDA-approved for weight loss

Sorry, but this makes clear that you aren’t in science. You should avoid trying to shit on studies if you don’t know how to interpret them. Both of the things you mentioned actually support the existence of a true effect.

First, if the treatment has an effect, you would expect a greater rate of relapse after the treatment is removed, provided that it treats a more final pathway rather than the cause: People in the placebo group have already been relapsing at the typical rate, and people receiving treatment–whose disease has been ramping up behind the dam of a medication preventing it from showing–are then expected to relapse at a higher rate after treatment is removed. The second sixth-month period was after cessation of the curcumin or place; it was a follow-up for treatment-as-usual.

Second, people drop out of a study nonrandomly for two main reasons: side effects and perceived lack of treatment efficacy. The placebo doesn’t have side effects, so when you have a greater rate of dropout in your placebo group, that implies the perceived treatment efficacy was lower. In other words, the worst placebo participants are likely the extra dropouts in that group, and including them would not only provide more degrees of freedom, it would theoretically strengthen the effect.

This is basic clinical trials research knowledge.

Again, I have no skin in the game here. I don’t take curcumin, nor would I ever. I do care about accurate depictions of research. I’m a STEM professor at an R1 with three active federal grants funding my research. The meme is inaccurate.

Why are you completely ignoring the second paper I linked, which doesn’t suffer from any of the limitations you mentioned?

The meme says no trial was successful. Any trial with any small difference is a successful trial.

I’m not saying the study is good, just that the meme isn’t true.

Also, you can level almost every single one of those criticisms against many studies for SSRIs and they’d hit just as hard. The exception being sample size.

Not true:

https://www.sciencedirect.com/science/article/pii/S0165032714003620

https://www.cghjournal.org/article/S1542-3565(06)00800-7/fulltext

I found more, too.

Edit: I have no skin in this game. I don’t take turmeric and won’t ever because of the risk of lead. I’m just pointing out that the meme is inaccurate. The person who replied to me pointed out some flaws in the first study (not the second), but none of the flaws mentioned makes the meme accurate. Even the shitty first study I linked found a significant condition difference in its primary endpoint at 8 weeks. Yeah, it’s got flaws (which the second doesn’t), but a successful trial with heavy limitations and conflicts of interest is nonetheless a successful trial, making this meme inaccurate. The second study I linked is stronger.

Also, the limitations in the first trial are standard for many clinical trials. For example:

https://onlinelibrary.wiley.com/doi/abs/10.1111/jsr.12201

https://www.sciencedirect.com/science/article/pii/S0924977X14001266

I could list 100 more with the same limitations of the first study I linked above. High dropout, small sample sizes, funding by an industry with a conflict of interest etc. are standard for clinical trial studies.

That’s not actually the abstract; it’s a piece from the discussion that someone pasted nicely with the first page in order to name and shame the authors. I looked at it in depth when I saw this circulate a little while ago.

It did, actually: https://fred.stlouisfed.org/series/LES1252881500Q

Ah, I would consider that fluff, which is okay in my book. I don’t use it for writing, personally, but what I tell my students is that if it’d be fine for a friend to do the thing and not get coauthorship, it’s fine to use AI for that (provided you acknowledge it, as you would a friend who provides some helpful comments on a draft). Proofing and suggesting minor stylistic things fall under that umbrella IMO.

I’m in science. It isn’t difficult to get an English speaking coauthor. Going to an LLM is easier and faster, sure, but if someone can’t understand the output then they have no idea if their text is being translated correctly.

It can’t write much of substance. The only people using it in science for anything more than fluff are people who don’t speak English well or who have no business writing papers. I sympathize with the former, but I don’t understand why those folks wouldn’t just either publish in a language they speak or get an English-speaking coauthor to help write in English. I wouldn’t ever use it to write an article. Even editing, it tends to butcher scientific nuance.

It is good at writing fluff though, which is helpful for things like letters of recommendation for undergraduates.

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Sync does formatting correctly. I came to Lemmy only because I like Sync so much. I paid for the lifetime version of it with Reddit and will probably pay for the lifetime version of this eventually. To each their own wrt how Lemmy is viewed, I guess.

There’s an ad for something, with the bell. We both use the free version of Sync.

At least it comes at a discount right now

I’m thinking of shorting it. My friend is definitely shorting it.