She also did this while she was still a full time undergraduate university student. Normally you'd expect someone with more experience to be doing this kind of low level hardware hacking, but she did all this while still young, before even graduating from university.
GamingChairModel
Reportedly, the app uses an on-device machine learning model to classify the content.
Using your shiny new phone to train AI...
I would think the models are already trained, and then deployed to phones to do on-device inference. The models should be fixed and static by the time they end up on a phone (in large part because phone hardware is just not technically suitable for training, whether pre training or post training).
The federal Stored Communications Act is essentially one party consent. It primarily restricts the ability of a service provider (who isn't a party to those communications) to share things with law enforcement or with others, and governs the manner in which governments can compel disclosure of information about communications. None of it restricts an actual party to the communication, though.
True, although that's kinda on the parents for naming their kid after a character whose arc isn't finished in popular culture.
I'm not aware of any state that applies two party consent rules to written communications. As far as I can tell, state wiretap laws only apply to in-person or live voice conversations (like traditional phone calls).
Oregon, for example, is a two party consent state for phone calls but single party consent for electronic communications. Same with Illinois.
Connecticut and Montana don't apply two party rules when the non-recording parties are merely aware that recording is happening, and it seems like a pretty straightforward analysis that everyone knows that the other side keeps text messages on their phone, usually indefinitely.
The nature of email, text messaging, or any other kind of written communication basically makes it impossible to expect the other side not to save a record of the conversation, because that's just how the technology generally works.
Imagine being named Alexa, born in 1998, minding your own damn business when Amazon just completely wrecks your name as you're entering high school.
aren’t just wrong — they’re a bridge too far
Haven't the PR flacks learned by now to sound less obvious about using LLM generated text?
Yes, the rules of Conway's Game of Life is Turing Complete.
The glider shape (the thing moving diagonally out of the window in this meme) can be created non-destructively by a glider gun (another shape that creates gliders), which can be controlled using stored pixels in another configuration within the glider gun, including taking in input from other gliders, such that the whole thing can loop and do everything necessary to be a universal Turing machine. Not super efficiently, but it will still do the things that Turing machines can do, given enough cycles.
The -cel suffix suggests that it's an insult (derived from incel).
Vibe coding is the name for creating computer code by telling an AI to generate the code for you, without necessarily even understanding anything about the code itself.
A thousand yard stare is a description for someone who looks like they're staring off into the distance, as if they're having some kind of PTSD flashback, or just at a loss for words.
This is a photograph of Sam Altman, the CEO of OpenAI, a major generative AI company.
Tokens are the unit of measure of how much computing power a particular generative AI query uses.
So basically, it's a meme that ironically takes the position of the vibe coder, mocking someone who is actually unwilling to use the AI code generation tools (calling them a codecel and criticizing them for saying something anti-vibe-coding or vibephobic), by giving the blank stare of Sam Altman's soulless eyes, and calling it a billion token stare, but basically doing it ironically to make fun of generative AI enthusiasts by lobbing a really stupid criticism and making the critic look bad.
The mechanism happens in the way LLMs generate text sequentially. So as the LLM generates text, each time the LLM encounters a choice between two or more equally probable words and has to pick one, it chooses from a known pseudorandom key instead of its previous practice of actually relying on a source of randomness.
If you run the text through a checker script that tries to predict the next word, and takes note of every choice made in any given fork in the probabilistic flowchart, you can see that whole sequence of choices and compare to whether it matches a known key.
You'd need to know the model itself well enough to know when the text reaches a particular fork, and what the choices are in that fork, and the key itself, so even OpenAI/Google/Anthropic may not be able to run this watermark detection on their competitors' models. But they'd be able to know it themselves, for their own models.
But it's not the company itself putting those assets up for sale. It's the court-approved or court-appointed bankruptcy trustee of that company, executing transactions approved by the court. That's why bankruptcy has to go through court, but it also provides a level of immunity (commonly called "exculpation" in bankruptcy cases) to the individuals actually doing the things ordered by the court.
If it's illegal to do, a court shouldn't be ordering it, so the affected people should have to fight the court order before it happens. But it's not illegal to sell anonymized user data like this, and not illegal to sell employee work-related data, either, in the U.S.
Wait can tokens just be directly compared like that? My impression is that token cost can vary by 2 orders of magnitude, depending on model, because the actual work of computation varies by that much between models.