Steps that either would not work between architectures or that Rosetta would handle (thereby answering your own question)

What? They would compile the code that they control for the M-series architectures. The target architecture is M-series ARM chips running on MacOS.

Rosetta is for translating x86 to ARM. Which wouldn’t be necessary because there are no x86 binaries involved at all.

I don’t understand why Rosetta has to be involved in any way at all.

Apple provides APIs for programming low level GPU instructions, including AI workloads, on their chips, using the Metal API. Anyone interested in using Apple hardware to its full potential can just write Mac-native software, same as any other MacOS native software directly compiled as binaries to run on Mac M series chips. No Rosetta required for translating x86 instructions to ARM, when the whole thing compiles for Apple’s native instructions in the first place.

In 2011, Motorola split into two companies, each with rights to the Motorola name and logo.

Motorola Mobility took the business lines related to cell phones. It got bought by Google, and then Lenovo.

Motorola Solutions took the business lines related to everything else, including (and especially) radios for law enforcement, fire departments, militaries, and surveillance video tech.

Yup. My main living data is on a NAS, my on-site backup is an external USB enclosure with a SATA hard drive hooked up to a Mac Mini, all so that I can leverage a Backblaze account to back up the external hard drive with unlimited data.

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.

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).

True, although that’s kinda on the parents for naming their kid after a character whose arc isn’t finished in popular culture.

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.

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.

It needs to be a system and a contractual framework that complies with the medical privacy regulations of the place where it’s being used (in the US, HIPAA). I have some doubts about whether specific companies or people will actually comply, or the enforcement of those laws by certain governmental authorities, but I don’t think it’s conceptually impossible to have this kind of tech be compliant with those types of regulations.

on average peoples’ computers

Average people don’t have self-administered computers anymore.

People have work laptops administered by their work IT departments, or they have phones and tablets running iOS/Android. Some children have school-administered Chromebooks, and may never own a normal laptop running a traditional desktop OS.

The main people operating self-administered traditional computers are PC gamers (a dying breed during the current price crisis), freelance/independent workers in the fields who still benefit from a mouse and keyboard for productive work and aren’t provided a computer by an employer, and tech nerds.

Anybody who believed that quantum computing posed a risk to symmetric encryption was fundamentally misunderstanding how encryption works and what quantum computing might be good at one day.

Asymmetric cryptography is primarily used for the secure exchanging of symmetric keys: use a public/private key pair to exchange secure messages of what symmetric key to use for their session, and then both sides switch to the symmetric key for actual communication of a real payload.

A public/private key pair is two keys that have some interesting mathematical relationship, such that it is easy to confirm that someone possesses the right private key using the public key or to encrypt something that only the correct private key can decrypt. And that mathematical relationship, relating to the product of two very large prime numbers, is at the core of modern asymmetric cryptography.

Quantum computing may make number factorization much, much easier. So once a product of two large primes becomes possible to factor, the public/private key pairs might not be as secure anymore.

But none of this has anything to do with symmetric encryption, or hash functions. Quantum doesn’t move the needle on that particular math.

The real risk, though, is for an adversary to eavesdrop on an encrypted key exchange (which uses asymmetric cryptography) and then the message itself (which uses symmetric cryptography) and then be able to take the two steps of getting the secret symmetric key from the intercepted key exchange over a compromised asymmetric protocol, and being able to decrypt the symmetric portion of the communication too.