> because citizens participating in a democratic process signals that they value the process and want to keep it.

Key word: "signals". The reason you give for voting is actually a symptom of "health" (left undefined here). It's not necessarily a cause. I'm sure there are good reasons to encourage people to vote, but this one doesn't make sense to me.

Wouldn't the sleep stage algorithm use the accelerometer data? Counting steps is useful.

Doesn't the author address this point? They speculate that it could be attained through self play back then using a ton of 1996 compute and time, but it's theoretically possible.

One possibility is that the long right tail is pulling the average up as individuals in the left tail find new jobs and are removed from the average.

It comes up in evidence-based parenting programs. It helps them connect the feeling word to their internal state, which is a step toward building emotional regulation skills. You're also modeling empathy for them. But one issue rarely talked about is how parents tend to label uncomfortable emotions far more than other ones, which can inadvertently reinforce them and cause them to happen more frequently.

If it's the same study as the one that had this same finding that I dug into recently, then that's exactly what the study showed. People lost weight and slowed aging biomarkers, and the study deliberately did not control for this confounder.

I have a pet theory that there's some overlap of writing patterns that A) indicate quality, clear communication and B) those that the LLMs have been converging on. As a result, we're ruining the reputation of some great writing patterns because we've associated it with AI slop.

My pet theory is that this was happening, because labs naturally worked to source highest quality data sources they could get. Even if they initially slurped everything indiscriminately, the obvious next step is to sort them by "quality", and bias towards the high end while trimming from low end.

So obviously, government documents, top publications from liberal arts scholars and STEM > Wikipedia > Reddit (and within, r/AskHistorians > r/all, etc.). The first group is the one most likely to (over)use correct / fancy typography.

I have a related pet theory: the telltale sign of AI writing is being too good for the context it's found in, in terms of vocabulary and style as well as structure.

What is jarring about this is precisely the quality - we associate certain styles with important (or self-important) venues, so it's uncanny to suddenly see random shitposts, copywriting spam, or regular chat replies look like work of NYT bestselling authors or legal scholars - it feels wrong in places we expect basic language, grug-brain reasoning, and lots of typos.

Also, what makes the above much more noticeable, is that AI systems don't have cross-session stylistic memory. From the POV of an LLM, that perfectly fitting, masterfully crafted starter is the first and only time it used it. From the user's POV, it's the tenth time they're seeing it in the span of an hour.

There's also no voice. If I get a text from a friend, I can hear them. Their tones, their mannerisms. I can't hear Claude because it's not a person that I've met and interacted with.

Would that not also apply to most books, newspapers, and internet comments?

People use language in their own distinct fashion. To the point where you can often figure out the writer from a long enough sample. Newspapers and internet comments end up a blend of different voices but in visually distinct boxes.

LLM intentionally randomize output to cover up all the plagiarism, but this largely destroys the consistency that cause writers to feel unique. Larger patterns still emerge but it just feels stale.

That's a misconception. LLMs are trained to suppress regurgitation, not because of plagiarism concerns, but because quoting training data verbatim means the model didn't learn from it, just memorized it.

Models absolutely do have voices. Spend some time using them, and you'll see. I can easily tell Claude-written prose apart - a consequence of using it daily.

You misunderstood, “Larger patterns still emerge” means yes you notice LLM’s but it’s for different kinds of patterns than you get from people.

> not because of plagiarism concerns

I’ve second hand accounts of meetings focused on exactly the problem of how much verbatim text was being spat out and what to do about it. Some versions of specific models are far worse at this but it’s a huge legal liability not to address it.

Models are of course different from humans, because the process by which they were created is different. But what’s undeniable is that humans also love to quote or repeat what others have said — we learn from our own kind, that’s how we gain experience. Yet rarely does anyone hold a person to a word-for-word standard over the course of their entire life, checking how many of their sentences were picked up from other people’s conversations — and then call that infringement. Just as no one right now would say I’m plagiarizing a model’s speaking style because I use em dashes, and no one would consider it misconduct when I reference a famous person’s ideas in everyday conversation. I don’t think models intend to infringe; it’s simply how they learned language. But perhaps, just like humans, they need to learn to provide more citations for what they reference.

Even individual words can be an issue. Humans understand when you want a new story about a magic school there’s issues using Hogwarts as the name.

LLM’s will happily regurgitate multiple paragraphs practically verbatim if you don’t specifically design them not to behave in this way.

Because ultimately when you design a system to best predict the next output based on a given input perfect replication of prior training is the correct response mathematically.

can't say the same for newspapers or internet comments, but when reading books there's usually a voice or a "vibe", and if there isn't one then it's just an unreadable book

AI writing style comes from post-training, which is entirely under the control of the labs. It's not copying it from any public sources.

I didn't say it's copying anything. It's learning styles - all of them - in pre-training. Post-training is just about giving it the right defaults.

Doesn't change my point anyway - even if post-training, labs naturally want the LLM to come off as smart (well, except for Grok). Post-training is where they "untrained" em-dashes and such, due to complaints of users.

I'd heard an interesting theory that in training on customer chats there's a lot of interactions like this:

Agent: _wall of text_

User: yeah sounds great, do it

Which has the predictable outcome of biasing towards wall of text responses.

Continue to write with em dashes. Be the change you wish to see in the world.

I did at the beginning, but there is only so much you can take, and no one cares if you have read books and seen it before. There were a bunch of programmers whom I thought would understand em dashes, but they did not and said no one uses such dashes only normal hyphens.

Em dashes ended up as one of the first victims of llm age

Em dashes were already "doomed" at least half a century ago, because they didn't make it into ASCII character set - which arguably is just because they were seen as redundant on ancient typewriters; that's also why they aren't there on normal keyboards, which was a double whammy: inconvenient for programmers to support, inconvenient for users to type.

In other words, since the very start of computer era, the use of em dashes was always a signal.

At least with a typewriter, if - was sized right you could -- for an emdash.

I have used -- for decades.

It is not because those patterns have no use. It is because LLMs overdo it.

Every point is delivered with grandeur and rhetorical flourish. It is hard to reward 'negative space', a simple writing, A to B, vanilla pattern.

I think it's because it starts writing the final output and doesn't have the ability to go back and correct. It's like a stream of consciousness bandaid

An example of a past technology that there was substantial motivation to control would be napster. It changed overtime, and you could never really control online privacy. Once local models are good enough, I don't really see how you can control that.

You control dogs by making their owners responsible.

If your supposed dogs are really gods, you reintroduced slavery under very unwise circumstances.

In this analogy, the dogs understand how their leashes, fences, etc. work better than their owners. And you need only take a trip to the park to see how many owners let their dogs walk around without a leash.

Not saying you're wrong. But maybe there's a reason Tiktok got so popular. Maybe for many people this way of communicating information is more effective and doesn't feel as boring/tedious.

I wonder if those are more engaging or more effective, they could look similar if untested.

You're claiming that generics tend to be cheaper in Canada than the US. What are you basing this belief on?

Overall generic drugs in the US are indeed cheaper than in Canada, but that is not the case if you don't have insurance coverage. Without that, it might be cheaper for some to order the Canadian generic if they can't afford insurance.

The other factor is not price, but availability. Some generic are in limited supply or are outright not available in the US.

Generics need to be more carefully sourced if outside the U.S. I prefer brand names from reputable manufacturing areas (Singpore, Germany, etc.), and can get them for close to the price for generics, often starting research at PharmacyChecker.com