Category: artificial intelligence

Getty Images bans upload of AI-generated content

James Vincent, writing for The Verge:

Getty Images has banned the upload and sale of illustrations generated using AI art tools like DALL-E, Midjourney, and Stable Diffusion. It’s the latest and largest user-generated content platform to introduce such a ban, following similar decisions by sites including Newgrounds, PurplePort, and FurAffinity.

Getty Images CEO Craig Peters told The Verge that the ban was prompted by concerns about the legality of AI-generated content and a desire to protect the site’s customers.

Getty Images bans AI-generated content over fears of legal challenges

Getty Images is being appropriately cautious. AI image synthesis tools, being trained on the open internet, can be easily prompted into copyright violations.

Misplaced Faith in Computer Precision

Computers can be fantastically accurate. And humans have a tendency to assume that this accuracy means something.

For example, an automated license plate reader might flag the license plate in front of you as “stolen.” You look at the report, confirm the license plate in front of you, and arrest the driver. You may not consider that the report itself is wrong. Even if the technology works exactly as intended, it doesn’t necessarily mean what you assume it means.

Joe Posnanski suggests this kind of faith in computer precision may be unfairly impacting athletes as well:

Maybe you heard about the truly insane false-start controversy in track and field? Devon Allen — a wide receiver for the Philadelphia Eagles — was disqualified from the 110-meter hurdles at the World Athletics Championships a few weeks ago for a false start.

Here’s the problem: You can’t see the false start. Nobody can see the false start. By sight, Allen most definitely does not leave before the gun.

Checkmate

Allen’s reaction time was 0.099 seconds, just 1/1000th of a second under the “allowable limit” of 0.1 seconds.

Posnanski writes:

World Athletics has determined that it is not possible for someone to push off the block within a tenth of a second of the gun without false starting. They have science that shows it is beyond human capabilities to react that fast. Of course there are those (I’m among them) who would tell you that’s nonsense, that’s pseudoscience, there’s no way that they can limit human capabilities like that. There is science that shows it is humanly impossible to hit a fastball. There was once science that showed human beings could not run a four-minute mile.

The computer can tell you his reaction time was 0.099 seconds. But it can’t tell you what that means.

As we rely more and more on computers to make decisions, especially “artificially intelligent” computers, it will be critical to understand what they are telling us and what they are not.

Creative Commons raises questions about use of CC-licensed works to train AI’s

Creative Commons licenses typically put few constraints on the re-use of copyrighted material. And that flexibility has allowed AI’s to be trained on CC-licensed material, which sometimes surprises copyright holders.

In a new blog post, Creative Commons outlines the issue and states that it will “examine, throughout the year, the intersection of AI and open content.”

Creative Commons Twitter Poll

155 votes in a Twitter poll where the plurality selects “Depends” is… not a lot of guidance.

AI image synthesis models may struggle with copyright

James Vincent, writing for The Verge:

Like most modern AI systems, Stable Diffusion is trained on a vast dataset that it mines for patterns and learns to replicate. In this case, that core of the training data is a huge package of 5 billion-plus pairs of images and text tags known as LAION-5B, all of which have been scraped from the public web. . . .

We know for certain that LAION-5B contains a lot of copyrighted content. An independent analysis of a 12 million-strong sample of the dataset found that nearly half the pictures contained were taken from just 100 domains. The most popular was Pinterest, constituting around 8.5 percent of the pictures sampled, while the next-biggest sources were sites known for hosting user-generated content (like Flickr, DeviantArt, and Tumblr) and stock photo sites like Getty Images and Shutterstock. In other words: sources that contain copyrighted content, whether from independent artists or professional photographers.

Anyone can use this AI art generator — that’s the risk

Vincent points out that Stable Diffusion even sometimes inserts the “Getty Images” watermark in its generated imagery. Not a good look.

AI that learns from the internet

Ben Thompson at Stratechery points out that new deep learning models are not requiring access to curated data in a way that would advantage large companies:

If not just data but clean data was presumed to be a prerequisite, then it seemed obvious that massively centralized platforms with the resources to both harvest and clean data — Google, Facebook, etc. — would have a big advantage.

. . . . .

To the extent that large language models (and I should note that while I’m focusing on image generation, there are a whole host of companies working on text output as well) are dependent not on carefully curated data, but rather on the Internet itself, is the extent to which AI will be democratized, for better or worse.

The AI Unbundling

This means the new AI models are relatively cheap and also more a reflection of internet content itself, “for better or worse.”

4.2 gigabytes of pure knowledge

Andy Salerno created a digital painting with Stable Diffusion, a new open source image synthesis model that allows anyone with a PC and a decent GPU to create almost any image they can describe:

Andy Salerno’s masterpiece created “with literally dozens of minutes of experience”

Salerno’s step-by-step guide is very straightforward and worth the review.

Most remarkable perhaps is that Stable Diffusion is small enough to be used by almost anyone:

4.2 gigabytes.

That’s the size of the model that has made this recent explosion possible.

4.2 gigabytes of floating points that somehow encode so much of what we know.

4.2 Gigabytes, or: How to Draw Anything

Stable Diffusion was trained on about 5 billion images and cost about $600k on GPUs leased from AWS.

Learning from synthetic data

Microsoft trained an excellent 3D face reconstruction model using synthetic data.

Synthetic (i.e. computer generated) data is helpful because it takes a long time for humans to look at many faces and label all of their features. But synthetic data arrives already labeled. And that allows for good and fast training:

Can we keep things simple by just using more landmarks?

In answer, we present the first method that accurately predicts ten times as many landmarks as usual, covering the whole head, including the eyes and teeth. This is accomplished using synthetic training data, which guarantees perfect landmark annotations.

3D Face Reconstruction with Dense Landmarks

More data for AI interpretation of patents

Google has released the Patent Phrase Similarity dataset, intended to help AI models better understand the somewhat odd world of patent language:

The process of using traditional patent search methods (e.g., keyword searching) to search through the corpus of over one hundred million patent documents can be tedious and result in many missed results due to the broad and non-standard language used. For example, a “soccer ball” may be described as a “spherical recreation device”, “inflatable sportsball” or “ball for ball game”.

Announcing the Patent Phrase Similarity Dataset

The dataset was used in the U.S. Patent Phrase to Phrase Matching Kaggle competition with some close-to-human results.