AlphaAndOmega0

I'm a psychiatry resident, and I have ADHD. The myth is, in fact, a myth.

More importantly, the dose of desoxyn (methamphetamine) used therapeutically for ADHD is *much* lower than what's consumed recreationally. That's really it. Use low doses, as prescribed, and the risks are minimal/comparable to most alternative stimulants.

It's like asking why a society where everyone has cups of tea twice a day has a different relationship to caffeine than a frat house where they're drinking 3 cans of Red Bull a night.

Agreed. I find that particularly annoying, and I also seem to find that the spatial arrangement or stereo effect is muted for most instruments (or the model simply doesn't use that feature as well as a good human musician).

I'm a psychiatry resident who has been into ML since... at least 2017. I even contemplated leaving medicine for it in 2022 and studied for that, before realizing that I'd never become employable (because I could already tell the models were getting faster than I am).

You would be sorely mistaken to think I'm utterly uninformed about LLM-research, even if I would never dare to claim to be a domain expert.

I'm still impressed by the progress in interpretability, I remember being quite pessimistic that we'd achieve even what we have today (and I recall that being the consensus in ML researchers at the time). In other words, while capabilities have advanced at about the pace I expected from the GPT-2/3 days, mechanistic interpretability has advanced even faster than I'd hoped for (in some ways, we are very far from completely understanding the ways LLMs work).

I'm a psychiatry resident who finds LLM research fascinating because of how strongly it reminds me of our efforts to understand the human brain/mind.

I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one.

I would be curious to know more precise numbers. My intuition suggests that when Sony sells millions of them, the number diverted for non-gaming purposes is maybe thousands or tens of thousands.

The programming workspace of the future of the future will have three employees:

A man, a dog and an instance of Claude.

The dog writes the prompts for Claude, the man feeds the dog, and the dog stops the man from turning off the computer.

Given that Gemini 3 Pro already did solid on that test, what exactly did they improve? Why would they bother?

I double checked and tested on AI Studio, since you can still access the previous model there:

>You should drive. >If you walk there, your car will stay behind, and you won't be able to wash it.

Thinking models consistently get it correct and did when the test was brand new (like a week or two ago). It is the opposite of surprising that a new thinking model continues getting it correct, unless the competitors had a time machine.

Previous models from competitors usually got that correct, and the reasoning versions almost always did.

This kind of reflexive criticism isn't helpful, it's closer to a fully generalized counter-argument against LLM progress, whereas it's obvious to anyone that models today can do things they couldn't do six months ago, let alone 2 years back.

Gary Marcus successfully predicted all ten of the one AI Winters.

He also claimed that LLMs were a failure because of prompts that GPT 3.5 couldn't parse, after the launch of GPT-4,which handled them with aplomb.

Nanobots are fantasy? Nobody told your cells or bacteria I guess. We have an existence proof right there.

GPS jamming for incoming drones?

>Interactive Human Simulator is a bold way to describe spinning up a few GPT calls with mood sliders, but sure, let’s call it anthropology. Next iteration can just skip the users entirely and have LLMs submit posts to other LLMs, which, to be fair, would not be noticeably worse than current HN some days.

My sides

Humans with certain amnestic syndromes are incapable of learning. That doesn't make them unintelligent or incapable of thought.