verginer
Joined 10 years ago
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Member since
Oct 30
Recent submissions
Comment · 6 months ago
Comment · 9 months ago
I finally found a job for my Raspberry Pi 1 Model B from 2012. It’s been sitting in a drawer for years, but about a 2 years ago added it to my Tailscale network as an exit node.
It’s a single-core 700MHz ARMv6 chip with 512MB of RAM. It's a fossil—a Pi 5 is 600x faster (according to the video). But for the 'low-bandwidth' task of routing some banking traffic or running a few changedetection watches via a Hetzner VPS (where the actual docker image runs), it’s rock solid. There’s something deeply satisfying about giving 'e-waste' a second life as a weekend project.
Comment · 2 years ago
Good observation, but they also acknowledge:
> there are considerations inherent in our use of the case insensitivity shortcut, which trusts the YouTube search engine to provide all matching results, and which oversamples IDs with letters, rather than numbers or symbols, in their first ten characters. We do not believe these factors meaningfully affect the quality of our data, and as noted above a more direct “brute force” method - even for the purpose of generating a purely random sample
In short I do believe that the sample is valuable, but it is not a true random sample in the spirit that the post is written, there is a heuristic to have "more hits"
Comment · 2 years ago
Admittedly, I did not read the paper linked. But my point is not about google doing something funny. Even if we assume that ids are truly random and uniformly distributed this does not mean that the sampling method doesn't have to be iid. This problem is similar to density estimation where Rejection sampling is super inefficient but converges to the correct solution, but MCMC type approaches might need to run multiple times to be sure to have found the solution.
Comment · 2 years ago
The author notes that they used "cheats". Depending on what these do the iid assumption of the samples being independent could be violated. If it is akin to snowball sampling it could have an "excessive" success rate thereby inflating the numbers.
> Jason found a couple of cheats that makes the method roughly 32,000 times as efficient, meaning our “phone call” connects lots more often
This "use LLMs as psychometric/political polling substitutes" idea seems to have jumpstarted a weird cottage industry of "synthetic" surveys. The model is pattern-matching on superficial visual cues and dressing it up as insight (I have a long beared and hence I vote for the green party).
Nate Silver put it well recently: [AI polls are fake polls][1].
An LLM inferring personality from a photo is even further down that chain of abstraction. That's not profiling, it's stereotyping with extra steps.
[1]: https://www.natesilver.net/p/ai-polls-are-fake-polls