Let's see how this would turn out in longterm.
If they approach things this way, and transistor progress continues linearly (relative to the last few years) maybe they can make their first devices that can meet these goals in… 2-3 years?
It's a different set of trade-offs.
* Theoretically; I don't own an iPhone.
Server side means shared resources, shared upgrades and shared costs. The privacy aspect matters, but at what cost?
The cost, so far, is greater.
1. NVAE: A Deep Hierarchical Variational Autoencoder https://arxiv.org/pdf/2007.03898
Also, if we're being nitpicky, diffusion model inference has been proven equivalent to (and is often used as) a particular NF so.. shrug
The appendix goes on to explain, "We apply simple volume-preserving normalizing flows of the form z′ = z + b(z) to the samples generated by the encoder at each level".
To get deterministic results, you fix the seed for your pseudorandom number generator and make sure not to execute any operations that produce different results on different hardware. There's no difference between the approaches in that respect.
A more accurate headline would be - Apple starting to create images using 4 year old techniques.
> short: both Apple and OpenAI are moving beyond diffusion, but while OpenAI is building for its data centers, Apple is clearly building for our pockets.
https://arstechnica.com/apple/2024/11/apple-intelligence-not...
A glance through the comments also shows HNers doing their best too. The mind still boggles as to why this site is so willing to perform mental gymnastics for a corporate.
celias•3d ago