Alex J. Champandard Profile picture
Nov 5 9 tweets 3 min read
The Chrome team is cutting support for the superior JPEG-XL codec in its browser — even before they enabled it!

The decision was made in secret under the direction of a single person who has conflicts of interests, and promotes the inferior AVIF alternative.
AVIF is based on VP10 codec, like a successor to WEBP which is based on the VP8 codec. Google owns & controls VP10, so has interests in promoting it instead of superior alternatives.

This means there'll be ~50% more energy used, and thus carbon, for internet bandwidth. 🙄
Most of our internal pipelines for data processing in #ML are based on JPEG-XL. For instance, the FFHQ dataset with "visually lossless" compression takes now 84Gb instead of 995Gb. Transfers faster, loads faster, trains faster.
In the browser too, seeing the same thing. We can do 1440x1440 preview JXL images vs 720x720 JPG with about similar size and better visual quality.

Cutting JXL support from Chrome, and sentencing the whole downstream ecosystem to that, is a huge profit-based mistake IMHO. 🤯
@nathan_wasson It'd be amazing if you could do a follow-up article on the conflicts of interest, as Google's response/excuse you got is clearly a smoke screen for the real decision.

The person who developed VP8-VP10 seems to be promoting an inferior solution based on his codec!
In retrospect, it seems there's are two factors that allowed JPEG-XL to be railroaded like this:
1) Branding, it sounds too close to crappy JPG.
2) Adoption, downstream libraries aren't there.

(Like Ubuntu 22.04 default repository, or even built-in Pillow support. @jonsneyers)
That does not justify Google's dubious and conflicted decision that ruins the web, but it means it'll be a bit harder to drum up support to fight them on this issue...
To be fair, JPEG-XL is ~2 years younger than AVIF. The spec of AVIF was standardized in Feb 2019 and JPEG-XL's bitstream was frozen in Dec 2020.

This explains the lag in adoption, and gives Google the shady opportunity to sabotage the codec before it establishes itself. 😒
Both AVIF and JPEG-XL are royalty free and safe from a patents perspective.

That battle has been won at least!

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More from @alexjc

Nov 5
It's been 36h since this thread, with many constructive discussions since!

One front I failed on goes something like this:
"I thought you were an AI coder. How come you want CoPilot to be withdrawn? Do you want to cancel large models?"

Let's unpack! 👇
First, there's no risk of CoPilot service being terminated and the technology abandoned. I don't want to see that and that's not the objective of their lawsuit either.

Second, I think medium- to large-models are absolutely worth pursuing technologically!
I'll be honest: part of AI ethics today feels misguided, hard to ever define, disconnected from reality. That goes hand in hand with the ethics washing.

However, I resonate with other parts and that's where I draw my line: consent. This goes hand in hand with following the law.
Read 20 tweets
Nov 4
"The Right To Read Is The Right To Mine" was a campaign from ~2012-2015 to convince the public & legislators that machines should bypass copyright for data-mining.

IMHO we're at the next stage of this campaign, now for generative systems — should they act outside copyright?
Articles like this one are at the tail end of the first pro-mining campaign and precursors to this new generative campaign?

It tries to establish that "reading by robots doesn’t count" and "infringement is for humans only".

ilr.law.uiowa.edu/print/volume-1… (via @GradySimon)
Of course, this view obfuscates the situation! Think systemically:

Robots that are mining or learning are operated by companies that operate under copyright (both for benefit and liability).

Robots that are generating are operated by human users also operating under copyright.
Read 4 tweets
Nov 4
If you're working at a generative company, and worried about the lawsuit against GitHub for their generative model, please take some comfort in the fact that I think they made *many* missteps — with either a serious lack of due care, or the intent to break the law.
For instance, Google announced they had a similar code model and they didn't release it. They used it internally & measured a 6% improvement on productivity while they understand the legal and ethical implications.

(Could also be that Google wanted to see others get sued first?)
I will compile my best advice for companies who, understandably, want to continue their work in a promising/competitive field, but also don't want to spend all their money on lawyers!

Stay tuned...
Read 4 tweets
Nov 3
Reading through the GitHub CoPilot litigation submitted; although it was pulled off quickly — it's a solid piece of work!

My assessment is that the defendants, GitHub, Microsoft and OpenAI are in a very bad position...
githubcopilotlitigation.com
The documents show how Codex and CoPilot act like databases; they have three different examples of JS code that is recited verbatim — with mistakes — from licensed sources.

Including this debug code below isPrime(n):
The documents then proceeds to cast doubt on the claim of FairUse, that even if it was applicable here, it wouldn't help circumvent (a) the breach of contract, (b) the privacy issues, and (c) the DMCA.
Read 17 tweets
Nov 3
You know how hands & fingers are particularly difficult to generate?

Wouldn't it be funny if people having important conversations online (in the near future) used hand gestures in front of their faces, so both sides know it's not a #DeepFake.

Anchor: I'm sorry to ask Mr. President, but before this TV interview can proceed please make a creative gesture with your hands.

Pres: What?

Anchor: Well, in the last election multiple candidates were caught using DeepFakes to make them look & sound smarter than they are.
Bank: Sir, we need to authenticate you by online video because of climate lockdown #37.

Customer: OK, let's do it!

B: Make the vulcan hand sign, flip it a round, turn it into a finger gun, and pretend to shoot in the air.

C: Wait, what!

B: Yes, because #DeepFakes.
Read 4 tweets
Nov 3
In NVIDIA's new paper on #Diffusion Models, they show how more denoisers (for each stage) and more embeddings (text, image) helps with quality!

TL;DR: If you buy more GPUs, you get correct spelling too.
deepimagination.cc/eDiffi/ #AI #ML
With so many different labs rushing to research and deploy this kind of technology, this will quickly turn into a race for more efficiency as different providers compete on costs too.
The paper is a bit evasive on the dataset (LAION?) — I presume for legal reasons. But the good news is that it's "only" 1B text/image pairs... although they are highly filtered.

IMHO there's much more room to improve quality with the current datasets.
Read 5 tweets

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