I'm not famous or anything. I've written some academic papers and had a couple blog posts trend on HN, which are surely in the training set.

It was able to identify me based on my style (at least according to its explanation). The way I approached the topic and some of the notation I used point to a particular academic lineage, and the general style reflected my previous blog posts.

That said, I gave it part of an (unpublished) personal essay, and it had no idea. But I have no writing in that style that's published, so it makes sense. Still impressed.

Military aircraft don't use ADS-B out a lot of the time. Spy planes are obviously not going to transmit their locations. A civilian plane with an electrical failure might stop transmitting ADS-B out. Being able to identify planes via satellite is a whole separate capability.

[1] In all the planes I've flown, ADS-B is configured to transmit whenever the master electrical switch is turned on, but it can be configured to be turned on and off at will. See this video on a mid-air collision involving an eccentric character who liked to fly with ADS-B out turned off: https://youtu.be/G5y3JiOEnVs?si=rs5gNMurZ9ssUloS. If I recall correctly, he had his ADS-B wired to his nav lights so he could turn it on and off at will.

Here's a recent study on YouTube from the same author as this TikTok study finding left-leaning bias in US recommendations: https://academic.oup.com/pnasnexus/article/2/8/pgad264/72424....

Here's a somewhat older study from Twitter itself where they determined that their recommendations were biased toward right-leaning accounts: https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter....

IMO the interesting question is not whether an individual platform is biased and what its biases are, but rather how we might regulate recommendations given that there is always a risk of bias.

How could we regulate this? I can think of two ways:

- Results-based enforcement. i.e., companies are free to use whatever recommendation model they like, but have to recommend content within ideological bounds. i.e., you can't bias toward one partisanship more than X%. There's some precedent for this with the equal-time rule (https://en.wikipedia.org/wiki/Equal-time_rule) and FCC fairness doctrine (https://en.wikipedia.org/wiki/Fairness_doctrine).

- Algorithm-based enforcement. i.e., there are limits on the algorithm itself. Perhaps you have to present your algorithm to a government agency and provide a proof that it obeys certain properties. But the enforcement here is analytical rather than empirical.

With the current methodology, the bot either watches the whole video, a fixed duration of it, or a random duration before swiping. The bot doesn't organically watch or swipe based on its interests like a human user would.

Very similar methodology to an earlier study the government cited in their case against TikTok: https://networkcontagion.us/wp-content/uploads/NCRI-Report_-...

There are a number of issues with these studies, one being that the way the sock puppet bots interact with content is not exactly organic. Typically they search for content in a conditioning phase, followed by random scrolling during which the recommended videos are collected and classified by an LLM. Modern recommendation algorithms famously work by examining how long and how users engage with content, and there's none of that going on here. Still, the methodology itself and the use of LLMs to classify content is clever and probably about the best we can get.

Also, even if there _is_ a bias, it doesn't tell us why. Are the recommendations intentionally spiked, or is this simply the recommendation strategy that maximizes profit? (Or that the recommendation model thinks will maximize profit?) It's very difficult to tell, which is part of what makes these models dangerous and also part of what makes them difficult to regulate.

On a sidenote, TikTok (and presumably other content platforms) _really_ does not like these studies, as demonstrated by them nerfing search functionality after the second study above was released to prevent researchers using these techniques in the future. I haven't read the study in detail yet, but it will be interesting to see how the team at NYU Abu Dhabi adapted their methodology.

Comments URL: https://news.ycombinator.com/item?id=30303795

Points: 6

# Comments: 0

Shocker---you might be required to be vaccinated in order to live in a facility for ultra-sick kids, many of whom are immunocompromised. I guess that's an "insult to the Canadian anthem" somehow.

English -> French seemed to work best, with the AI output have a very similar timbre to my real voice. Not hyperrealistic for me, but decent enough given I gave it a ~20s sample.

French -> English was less good in terms of the timbre and pitch of the voice---way higher than my real voice. It did have a bit of a Canadian accent, though, which is funny because I speak French with a Quebec accent. Maybe that's what I would sound like if I had a Canadian accent in English?

Where it seems like we're pretty far from the theoretical min is connections within the continental US. Latency is pretty bad (e.g. I get like 1.5x - 4x the theoretical min latency from my Fiber connection in the SF Bay Area, depending on endpoint). I assume part of this is indirect connections (you don't have a direct fiber connection between every pair of cities, because that would be dumb) and some of it is routing overhead (a connection to Asia goes a long distance, but often has way fewer hops).

Note that this is theoretical min latency based on the speed of light through fiber*, which is a bit higher than (about 5/3x) the speed of light through air. New fiber optic tech might help this at some point.

Comments URL: https://news.ycombinator.com/item?id=29614581

Points: 5

# Comments: 0

For anyone unfamiliar with the CSGO skins market and the explosion of illegal gambling sites a few years ago, I highly recommend this video [1].

[1] https://www.youtube.com/watch?v=qSkXFUw8tTU

I had no idea, but schizophrenic breaks and other psychological symptoms are not uncommon at meditation retreats, and staff are often untrained on how to deal with it.

Sorry this happened to you OP, and I hope you're able to get effective help.