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"The researchers, Tero Karras, Samuli Laine, and Timo Aila, came up with a new way of constructing a generative adversarial network, or GAN. GANs employ two dueling neural networks to train a computer to learn the nature of a data set well enough to generate convincing fakes. When applied to images, this provides a way to generate often highly realistic fakery. The same Nvidia researchers have previously used the technique to create artificial celebrities (read our profile of the inventor of GANs, Ian Goodfellow)."

"The system used historical data, including arrest records and electronic police reports, to forecast crime and help shape public safety strategies, according to company and city government materials. At no point did those materials suggest any effort to clean or amend the data to address the violations revealed by the DOJ. In all likelihood, the corrupted data was fed directly into the system, reinforcing the department's discriminatory practices."

"That's interesting work suggesting that Russian troll activity was significantly more ambitious on an international scale than previously thought. It also suggests a way of spotting this kind of meddling as it is happening by looking for the kind of forensic fingerprint the team identified. Of course, finding trolls is a cat-and-mouse game. For the organizations responsible for Russian troll activity, it ought to be a straightforward matter to change the pattern of activity in a way that does not create the same signature."

"Neural networks can find telltale patterns in a person's gait that can be used to recognize and identify them with almost perfect accuracy, according to new research published in IEEE Transactions on Pattern Analysis and Machine Intelligence. The new system, called SfootBD, is nearly 380 times more accurate than previous methods, and it doesn't require a person to go barefoot in order to work. It's less invasive than other behavioral biometric verification systems, such as retinal scanners or fingerprinting, but its passive nature could make it a bigger privacy concern, since it could be used covertly."

"The reason to look at humans is because there are certain things that humans do much better than deep-learning systems. That doesn't mean humans will ultimately be the right model. We want systems that have some properties of computers and some properties that have been borrowed from people. We don't want our AI systems to have bad memory just because people do. But since people are the only model of a system that can develop a deep understanding of something-literally the only model we've got-we need to take that model seriously."

"It's like turning the clock back to a more earnest time on the web, when the novelty of having a voice or being able to connect with anyone still filled us with a sense of boundless opportunity and optimism. It harkens back to the late 1990s and early 2000s-before social media, before smartphones-when going online was still a valuable use of time to seek community."

"A data trust is a legal entity that collects and manages people's personal data on their behalf. Though the structure and function of these trusts are still being defined, and many questions remain, data trusts are notable for offering a potential solution to long-standing problems in privacy and security."

"Raji says her investigation into the data has made her gravely concerned about deep-learning-based facial recognition. "It's so much more dangerous," she says. "The data requirement forces you to collect incredibly sensitive information about, at minimum, tens of thousands of people. It forces you to violate their privacy. That in itself is a basis of harm. And then we're hoarding all this information that you can't control to build something that likely will function in ways you can't even predict. That's really the nature of where we're at.""

"Data collection-as Parscale's comment suggested-is perhaps the most powerful thing the Trump 2020 app does. On signing up, users are required to provide a phone number for a verification code, as well as their full name, email address, and zip code. They are also highly encouraged to share the app with their existing contacts. This is part of a campaign strategy for reaching the 40 to 50 million citizens expected to vote for Trump's reelection: to put it bluntly, the campaign says it intends to collect every single one of these voters' cell-phone numbers. This strategy means the app also makes extensive permission requests, asking for access to location data, phone identity, and control over the handset's Bluetooth function."

"This time the model could look at both the surrounding words and the content of the image to fill in the blank. Through millions of repetitions, it could then discover not just the patterns among the words but also the relationships between the words and the elements in each image."

"But Leuthardt, for one, expects he will live to see it. "At the pace at which technology changes, it's not inconceivable to think that in a 20-year time frame everything in a cell phone could be put into a grain of rice," he says. "That could be put into your head in a minimally invasive way, and would be able to perform the computations necessary to be a really effective brain-computer interface.""

"PhotoGuard works by altering select pixels in an image such that they will disrupt an AI's ability to understand what the image is. Those "perturbations," as the research team refers to them, are invisible to the human eye but easily readable by machines. The "encoder" attack method of introducing these artifacts targets the algorithmic model's latent representation of the target image - the complex mathematics that describes the position and color of every pixel in an image - essentially preventing the AI from understanding what it is looking at."

"Late last year, IBM took the record for the largest quantum computing system with a processor that contained 433 quantum bits, or qubits, the fundamental building blocks of quantum information processing. Now, the company has set its sights on a much bigger target: a 100,000-qubit machine that it aims to build within 10 years."

"In a Copenhagen suburb, a fifth-grade classroom is having its weekly cake-eating session, a common tradition in Danish public schools. While the children are eating chocolate cake, the teacher pulls up an infographic on a whiteboard: a bar chart generated by a digital platform that collects data on how they've been feeling. Organized to display the classroom's weekly "mood landscape," the data shows that the class averaged a mood of 4.4 out of 5, and the children rated their family life highly. "That's great!" the teacher exclaims, raising two thumbs up in the air. She then moves to an infographic on sleep hygiene. Here the data shows the students struggling, and the teacher invites them to think of ways to improve their sleeping habits. After briefly talking among themselves, the children suggest "less screen time at night," "meditation before sleep," and "having a hot bath." They collectively make a commitment to implement these strategies. At next week's cake time, they will be asked whether or not they followed through."

"Abbott's experience mirrors the situation faced by Africans who don't speak English. Many language models like ChatGPT do not perform well for languages with smaller numbers of speakers, especially African ones. But a new venture called Lelapa AI, a collaboration between Abbott and a biomedical engineer named Pelonomi Moiloa, is trying to use machine learning to create tools that specifically work for Africans."

"According to the website Lexica, which tracks over 10 million images and prompts generated by Stable Diffusion, Rutkowski's name has been used as a prompt around 93,000 times. Some of the world's most famous artists, such as Michelangelo, Pablo Picasso, and Leonardo da Vinci, brought up around 2,000 prompts each or less. Rutkowski's name also features as a prompt thousands of times in the Discord of another text-to-image generator, Midjourney. Rutkowski was initially surprised but thought it might be a good way to reach new audiences. Then he tried searching for his name to see if a piece he had worked on had been published. The online search brought back work that had his name attached to it but wasn't his. "It's been just a month. What about in a year? I probably won't be able to find my work out there because [the internet] will be flooded with AI art," Rutkowski says. "That's concerning.""

"In the fall of 2020, gig workers in Venezuela posted a series of images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles-including some you really wouldn't want shared on the Internet. In one particularly revealing shot, a young woman in a lavender T-shirt sits on the toilet, her shorts pulled down to mid-thigh. The images were not taken by a person, but by development versions of iRobot's Roomba J7 series robot vacuum. They were then sent to Scale AI, a startup that contracts workers around the world to label audio, photo, and video data used to train artificial intelligence."