The Algorithm: AI-generated art raises tricky questions about ethics, copyright, and security
Thanks to his distinctive style, Rutkowski is now one of the most commonly used prompts in the new open-source AI art generator Stable Diffusion, which was launched late last month—far more popular than some of the world’s most famous artists, like Picasso. His name has been used as a prompt around 93,000 times.
But he’s not happy about it. He thinks it could threaten his livelihood—and he was never given the choice of whether to opt in or out of having his work used this way.
The story is yet another example of AI developers rushing to roll out something cool without thinking about the humans who will be affected by it.
Stable Diffusion is free for anyone to use, providing a great resource for AI developers who want to use a powerful model to build products. But because these open-source programs are built by scraping images from the internet, often without permission and proper attribution to artists, they are raising tricky questions about ethics, copyright, and security.
Artists like Rutkowski have had enough. It’s still early days, but a growing coalition of artists are figuring out how to tackle the problem. In the future, we might see the art sector shifting toward pay-per-play or subscription models like the one used in the film and music industries. If you’re curious and want to learn more, read my story.
And it’s not just artists: We should all be concerned about what’s included in the training data sets of AI models, especially as these technologies become a more crucial part of the internet’s infrastructure.
In a paper that came out last year, AI researchers Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe analyzed a smaller data set similar to the one used to build Stable Diffusion. Their findings are distressing. Because the data is scraped from the internet, and the internet is a horrible place, the data set is filled with explicit rape images, pornography, malign stereotypes, and racist and ethnic slurs.
A website called Have I Been Trained lets people search for images used to train the latest batch of popular AI art models. Even innocent search terms get lots of disturbing results. I tried searching the database for my ethnicity, and all I got back was porn. Lots of porn. It’s a depressing thought that the only thing the AI seems to associate with the word “Asian” is naked East Asian women.
Not everyone sees this as a problem for the AI sector to fix. Emad Mostaque, the founder of Stability.AI, which built Stable Diffusion, said on Twitter he thought the ethics debate around these models to be “paternalistic silliness that doesn’t trust people or society.”
But there’s a big safety question. Free open-source models like Stable Diffusion and the large language model BLOOM give malicious actors tools to generate harmful content at scale with minimal resources, argues Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI expert at Boston Consulting Group.
The sheer scale of the havoc these systems enable will constrain the effectiveness of traditional controls like limiting how many images people can generate and restricting dodgy content from being generated, Gupta says. Think deepfakes or disinformation on steroids. When a powerful AI system “gets into the wild,” Gupta says, “that can cause real trauma … for example, by creating objectionable content in [someone’s] likeness.”
We can’t put the cat back in the bag, so we really ought to be thinking about how to deal with these AI models in the wild, Gupta says. This includes monitoring how the AI systems are used after they have been launched, and thinking about controls that “can minimize harms even in worst-case scenarios.”
There’s no Tiananmen Square in the new Chinese image-making AI
Inside the conference where researchers are solving the clean-energy puzzle
The Advanced Research Projects Agency for Energy (ARPA-E) funds high-risk, high-reward energy research projects, and each year the agency hosts a summit where funding recipients and other researchers and companies in energy can gather to talk about what’s new in the field.
As I listened to presentations, met with researchers, and—especially—wandered around the showcase, I often had a vague feeling of whiplash. Standing at one booth trying to wrap my head around how we might measure carbon stored by plants, I would look over and see another group focused on making nuclear fusion a more practical way to power the world.
There are plenty of tried-and-true solutions that can begin to address climate change right now: wind and solar power are being deployed at massive scales, electric vehicles are coming to the mainstream, and new technologies are helping companies make even fossil-fuel production less polluting. But as we knock out the easy wins, we’ll also need to get creative to tackle harder-to-solve sectors and reach net-zero emissions. Here are a few intriguing projects from the ARPA-E showcase that caught my eye.
“I heard you have rocks here!” I exclaimed as I approached the Quaise Energy station.
Quaise’s booth featured a screen flashing through some fast facts and demonstration videos. And sure enough, laid out on the table were two slabs of rock. They looked a bit worse for wear, each sporting a hole about the size of a quarter in the middle, singed around the edges.
These rocks earned their scorch marks in service of a big goal: making geothermal power possible anywhere. Today, the high temperatures needed to generate electricity using heat from the Earth are only accessible close to the surface in certain places on the planet, like Iceland or the western US.
Geothermal power could in theory be deployed anywhere, if we could drill deep enough. Getting there won’t be easy, though, and could require drilling 20 kilometers (12 miles) beneath the surface. That’s deeper than any oil and gas drilling done today.
Rather than grinding through layers of granite with conventional drilling technology, Quaise plans to get through the more obstinate parts of the Earth’s crust by using high-powered millimeter waves to vaporize rock. (It’s sort of like lasers, but not quite.)
The emergent industrial metaverse
Annika Hauptvogel, head of technology and innovation management at Siemens, describes the industrial metaverse as “immersive, making users feel as if they’re in a real environment; collaborative in real time; open enough for different applications to seamlessly interact; and trusted by the individuals and businesses that participate”—far more than simply a digital world.
The industrial metaverse will revolutionize the way work is done, but it will also unlock significant new value for business and societies. By allowing businesses to model, prototype, and test dozens, hundreds, or millions of design iterations in real time and in an immersive, physics-based environment before committing physical and human resources to a project, industrial metaverse tools will usher in a new era of solving real-world problems digitally.
“The real world is very messy, noisy, and sometimes hard to really understand,” says Danny Lange, senior vice president of artificial intelligence at Unity Technologies, a leading platform for creating and growing real-time 3-D content. “The idea of the industrial metaverse is to create a cleaner connection between the real world and the virtual world, because the virtual world is so much easier and cheaper to work with.”
While real-life applications of the consumer metaverse are still developing, industrial metaverse use cases are purpose-driven, well aligned with real-world problems and business imperatives. The resource efficiencies enabled by industrial metaverse solutions may increase business competitiveness while also continually driving progress toward the sustainability, resilience, decarbonization, and dematerialization goals that are essential to human flourishing.
This report explores what it will take to create the industrial metaverse, its potential impacts on business and society, the challenges ahead, and innovative use cases that will shape the future. Its key findings are as follows:
• The industrial metaverse will bring together the digital and real worlds. It will enable a constant exchange of information, data, and decisions and empower industries to solve extraordinarily complex real-world problems digitally, changing how organizations operate and unlocking significant societal benefits.
• The digital twin is a core metaverse building block. These virtual models simulate real-world objects in detail. The next generation of digital twins will be photorealistic, physics-based, AI-enabled, and linked in metaverse ecosystems.
• The industrial metaverse will transform every industry. Currently existing digital twins illustrate the power and potential of the industrial metaverse to revolutionize design and engineering, testing, operations, and training.
The Download: China’s retro AI photos, and experts’ AI fears
Across social media, a number of creators are generating nostalgic photographs of China with the help of AI. Even though these images get some details wrong, they are realistic enough to trick and impress many of their followers.
The pictures look sophisticated in terms of definition, sharpness, saturation, and color tone. Their realism is partly down to a recent major update of image-making artificial-intelligence program Midjourney that was released in mid-March, which is better not only at generating human hands but also at simulating various photography styles.
It’s still relatively easy, even for untrained eyes, to tell that the photos are generated by an AI. But for some creators, their experiments are more about trying to recall a specific era in time than trying to trick their audience. Read the full story.
Zeyi’s story is from China Report, his weekly newsletter giving you the inside track on tech in China. Sign up to receive it in your inbox every Tuesday.
Read more of our reporting on AI-generated images:
+ These new tools let you see for yourself how biased AI image models are. Bias and stereotyping are still huge problems for systems like DALL-E 2 and Stable Diffusion, despite companies’ attempts to fix it. Read the full story.