Apple Research unearthed forgotten AI technique and using it to generate images

I find it fascinating that Apple-centric media sites are stretching so much to position the company in the AI race. The title is meant to say that Apple found something unique that other people missed, when the simplest explanation is they started working on this a while back (2021 paper, afterall) and just released it.

A more accurate headline would be - Apple starting to create images using 4 year old techniques.

Forgotten from like 2021? NVAE[1] was a great paper but maybe four years is long enough to be forgotten in the AI space? shrug

1. NVAE: A Deep Hierarchical Variational Autoencoder https://arxiv.org/pdf/2007.03898

It’s pretty great that despite having large data centers capable of doing this kind of computation, Apple continues to make things work locally. I think there is a lot of value in being able to hold the entirety of a product in hand.

Paper at https://machinelearning.apple.com/research/normalizing-flows

flows make sense here not just for size but cuz they're fully invertible and deterministic. imagine running same gen on 3 iphones, same output. means apple can kinda ensure same input gives same output across devices, chips, runs. no weird variance or sampling noise. good for caching, testing, user trust all that. fits apple's whole determinism dna and more of predictable gen at scale

normalizing flow might be unpopular but definitely not a forgotten technique

I wonder if it’s noticeably faster or slower than the common way on the same set of hardware.

https://github.com/bayesiains/nflows

This subject is fascinating and the article is informative, but I wish that HN had a button like "flag", but specific for articles that seems written by AI (well at least the section "How STARFlow compares with OpenAI’s 4o image generator" sounds like it)

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Apple AI team keeps going against the bitter lesson and focusing on small on-device models.

Let's see how this would turn out in longterm.