My current approach is a "model marketplace" (https://accomplice.ai/models) where the most popular open source text-to-image models (VQGAN+CLIP, Disco Diffusion, DALL-E Mega coming soon…), sit alongside the most popular open source style transfer models, and then finally I have the ability for a user to finetune their own models using a simple drag-and-drop tool (https://accomplice.ai/no-code-model-training).

Using this approach a user has enough models to try or train that they can have a higher hit rate. For example, Accomplice currently has finetuned models for photo realistic people (https://accomplice.ai/models/f58bfa91-bb18-406f-a0e1-db00fcf...), watercolor backgrounds (https://accomplice.ai/models/91b8a080-faca-4ff4-8b11-64b0789...), etc…

So theoretically if there were a searchable marketplace of 100s of different finetuned models people could choose from, they would use it much like an iStockPhoto and be able to create the kind of images they want instead of just downloading them.

But it's of course a constant work in progress. Slowly growing though and lots of promising stuff ahead!

What does that link do?

The confirmation link should only be going to accomplice.ai unless Sendgrid is doing some link tracking that I've just forgotten about. Could you forward that email to adam at accomplice dot ai if you get a chance. Thanks for letting me know!

Same thing happened to me multiple times across multiple platforms: SendGrid, Mandrill, MailJet, MailGun. I always turn off the tracking (enabled by default on all of them), but magically its back on a few weeks/months later. I've given up finding a solution and just revisit my settings every few months to check on it.

The two “african american” ones look south or maybe southeast asian (and the one of those that is a “young...girl” looks like a, maybe young, adult.)

All the ones without a racial/ethnic prompt are white, and disproportionately blue eyed (again, including sclerae.)

(It indicate “diverse”, and yet all of the examples read white or Asian, though the unlabeled darker-skinned male figure in the group of six at the top is ambiguous enough to be plausibly be something else.)

The “beautiful woman with curly red hair” has rather radical facial asymmetry, and straight to slightly wavy hair.

In one sense it's kind of like a much "smarter" photoshop filter, where it can make your own art/photos look more like what you want (ex: Van Gogh, Dali, Picasso, or combinations of those, or something completely weird/new/different).

You could also train the models on your own work and have it generate art in your own style that could inspire you or could be useful to you either as a base to work from or that you could take interesting elements from to create new art.

Similar things can be done in music, by the way, and that would be really useful to musicians too.

Poets could use something like this to create poetry, novel writers to write novels, etc..

This is really an improvement on the collaboration potential between humans and computers -- which is probably why it's called "Accomplice".

i.e. The ability to easily take your logo and stylize it: https://accomplice.ai/@adam/iterations/2bcc90ad-3237-486a-8d...

Create a photorealistic avatar whenever you need it: https://accomplice.ai/@adam/iterations/988b7d54-dc39-43b1-b5...

Easily remove the background of a photo: https://accomplice.ai/models/97746c4b-c6f0-49cb-ae1b-859716b...

Upscale a photo: https://accomplice.ai/models/bd4619ee-8202-4cf0-a04e-291820f...

Etc etc. AI can make all this stuff easier. And you have a sense of ownership over what you create. All in one place where you can collaborate on all of it with your team. I feel like that's valuable. It's certainly a tool I've always wanted.

But, also, as a bit of an aside – if the Googles and OpenAIs of the world are just going to bite every artist's style anyway with a mostly black box service and training set… it feels like the option for an artist to train/finetune their own model, promote it and possibly make money off of that is worth trying.

Rule 1 of working in this field, recruit a mid level member of the other company's research group biannually to get the latest gossip.

It's impossible to keep a 1 page or shorter "algorithm" secret, when the creators are geniuses and they hop jobs every year or so.

Fellas with more IQ than games in a baseball season, they just don't forget.

It's less cynical, more incentive alignment.

- being open is kind of just how things in ML generally work right now, it's in stark contrast to things like chemistry or physics where paywalls are pretty common

- it's a matter of clout, ML is moving ridiculously quickly, with work from just 5 years ago being considered outdated in terms of capability, if you don't publish, someone else will and they'll get the credit. This likely also matters for the researchers since they get credit too. In a sense this is just publish or perish culture from academia.

- it's also somewhat about hiring, which is related to the clout. By putting out this kind of research, they're attracting talented engineers to consider working for them. This of course is pretty relevant to the rest of their business, especially given how heavily Google leans on AI to handle moderation.

Yes they can actually.

If you are a shareholder you can either sue (unlikely to succeed) or vote against the board. That's pretty much the only recourse.

Or you could sell or just threaten to sell your shares.

Buying more of the company's shares to take it over is another option.

Good luck doing that with Google.

You're saying it's Google who have done this research. In a way that's true. But really it is Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet and Mohammad Norouzi who did it, with material support from Google.

It's likely that some or all of these people would have refused to do the work they do if Google kept it all as their secret sauce.

And moreover, there are excellent reasons why they wouldn't want to. It's not just the obvious that if it all were secret, they wouldn't be able to use it in their non-Google career advancement. It's also that research without the freedom to talk is far more difficult and frustrating.

On paper, scientific papers are supposed to document the whole of the discovery/innovation. So you might think that an insider, who got to read all the secret Google research papers AND all the public ones would have an advantage. But problem is, even the best written papers with full code and comments inevitably leave out things, especially of the "why this and not that" type.

If you're a researcher in the free world, you can just ask. Especially if you have a public track record of great papers yourself, they will WANT to talk to you. You can learn so much more from the interactive process of back and forth questions than you can from a static piece of information like a scientific paper.

If you work for a secretive and command-driven organization, you need to be careful about what you reveal of your own research when you ask. You can't talk freely. The thought of having to justify your communication to some old-school IBM lawyer type, is going to chill even the most enthusiastic reseacher. It's easier to just stay in your own corporate bubble, and focus on the things your corporation does well since at least you can talk freely to your colleagues (although in really paranoid organizations like the NSA or old IBM, even that may not be true). But then at best you specialize, at worst you fall behind.

The other reason is that the leaders at Google at the time believed that we would achieve the singularity faster if Jeff Dean periodically sent ideas back 10 years in time to Doug Cutting.

2. Deploying models in a cost effective way is hard

3. Lessons learned from building this model can indeed be monetized and many of them may be kept secret.