Boosting is an extremely successful idea, allowing one to combine multiple low accuracy classifiers into a much more accurate voting classifier. In this work, we present a new and surprisingly simple Boosting algorithm that obtains a provably optimal sample complexity. Sample optimal Boosting algorithms have only recently been developed, and our new algorithm has the fastest runtime among all such algorithms and is the simplest to describe: Partition your training data into 5 disjoint pieces of equal size, run AdaBoost on each, and combine the resulting classifiers via a majority vote. In addition to this theoretical contribution, we also perform the first empirical comparison of the proposed sample optimal Boosting algorithms. Our pilot empirical study suggests that our new algorithm might outperform previous algorithms on large data sets.

It was 6:28 am when I woke up to a text from a friend in Shanghai, China. "Hey, Amanda--is this you?" he wrote via WeChat. I hadn't even had my morning coffee yet. I pulled my phone closer to get a better look. "Yes, it's me," I typed back.

We provide a new multi-task benchmark for evaluating text-to-image models. We perform a human evaluation comparing the most common open-source (Stable Diffusion) and commercial (DALL-E 2) models. Twenty computer science AI graduate students evaluated the two models, on three tasks, at three difficulty levels, across ten prompts each, providing 3,600 ratings. Text-to-image generation has seen rapid progress to the point that many recent models have demonstrated their ability to create realistic high-resolution images for various prompts. However, current text-to-image methods and the broader body of research in vision-language understanding still struggle with intricate text prompts that contain many objects with multiple attributes and relationships. We introduce a new text-to-image benchmark that contains a suite of thirty-two tasks over multiple applications that capture a model's ability to handle different features of a text prompt. For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly. Rather than subjectively evaluating text-to-image results on a set of prompts, our new multi-task benchmark consists of challenge tasks at three difficulty levels (easy, medium, and hard) and human ratings for each generated image.

The United States will likely face a "great depression" if China seizes Taiwan's semiconductor industry, a hedge fund chief has said. Speaking at the Bloomberg New Economy Forum in Singapore last week, Citadel CEO Ken Griffin said the U.S. GDP would take a hit of between 5% to 10%, causing "an immediate Great Depression." "The United States has no ability to produce anywhere near the number of semiconductors it needs to run its economy," he was quoted as saying by Fortune. "If we lose access to Taiwanese semiconductors, the hit to U.S. GDP is probably in the order of magnitude of 5% to 10%. While it is unclear when, or if, that scenario will happen, Griffin said America's recent export controls, which include measures to cut China off from semiconductor chips and chip-making equipment, amounts to the U.S. "playing with fire." "You can argue that by depriving the Chinese of access to semiconductors, we up the risk that they seize Taiwan," Griffin added. In October, the Biden administration imposed sweeping export controls that banned U.S. companies from selling advanced semiconductors or equipment used to fabricate newer chips to China. Only companies that acquire a license from the Commerce Department will be allowed to export semiconductors and chip-making equipment to Chinese companies, according to The Wall Street Journal. In addition, the Biden administration also banned international companies from exporting chips that were built using U.S. technology. American citizens and green-card holders were also banned from working on certain technology for Chinese companies and entities. China has prioritized the development of semiconductor chips that are used in a variety of technology equipment, including artificial intelligence products. Commerce Secretary Gina Raimondo this month also said China will likely use advanced semiconductor technology "for surveillance." "China is crystal clear," she said, adding, "They will use this technology for surveillance.

The achievements in face recognition and identification are often applied in a maleficent way. Researchers work on a masking mechanism that does not break the image continuity and misleads face recognition systems with fake faces. Currently, access rights in social networks are defined per image, which friends are allowed to see. But our faces appear in many photos, even when we do not want this. Researchers suggest the "My Face My Choice" principle.

The face of a Connecticut farmer thought to be a vampire when he died of tuberculosis in the 19th century has been seen for the first time since his corpse was mutilated and tossed into a grave. The disease turns people's skin a pale yellow, their eyes become red and swollen and they sometimes have bloodstains around their mouth from coughing, which was believed to be signs of the undead about 200 years ago. The man's skeleton, buried in a casket with'JB55' engraved on it, was used to performed a DNA analysis that was fed to a machine learning system to predict what he may have looked like before being riddled with the disease. The results showed he had fair skin, brown or hazel eyes, brown or black hair and some freckles. The man, a farmer who lived in Connecticut, died of tuberculosis in the 19th century, which led people to believe he was a vampire.

The role of artificial intelligence (AI) in regulation is changing rapidly, as the technology matures and is increasingly applied across a broad range of industries. While AI has the potential to improve regulatory outcomes by complementing human decision-making, there are also risks associated with its use. As AI technologies become more ubiquitous, it is important for regulators to understand both the opportunities and challenges posed by AI. AI can be used for a variety of tasks in regulation, from automating repetitive tasks to providing recommendations on how to respond to complex situations. Automation can free up resources that can be redirected to other activities, such as risk management or policy analysis. Moreover, by analyzing large data sets, AI can identify patterns that humans may not be able to detect.

Adobe's Lightroom software uses new AI technology to select objects, people, clothing and facial features in an attempt to make photo editing faster and more powerful. The technology can remove some of the drudgery of photo editing, picking out portions of an image pixel by pixel. The new technology is available in Lightroom, Lightroom Classic and the smartphone versions of the photo cataloging and editing software, the company said Tuesday at its Max conference. The new tools for selecting objects and people expand earlier AI tools that select subjects and skies. By signing up, you agree to our Terms of Use and acknowledge the data practices in our Privacy Policy.

"On the internet, nobody knows you're a dog." So says the dog sitting at a computer in Peter Steiner's 1993 New Yorker cartoon. The cartoon captured a radical change in the nature of human interactions that was just beginning in 1993, a change both exhilarating for its possibilities, and terrifying for the same reason. Over the past quarter century, we've all learned the dog's lesson. A random stranger on the internet could be anybody, anywhere.

Tesla's CEO Elon Musk is set to unveil its prototype humanoid robots at an event on Sept. 30, hoping to expand beyond self-driving cars that have not yet become reality despite his repeated promises. While robots are widely used for specialist tasks at factories, other companies have struggled to create commercially viable human-like robots, despite decades-long development efforts. "This market is very, very challenging market because you buy this big expensive robot, but it actually cannot do much," Heni Ben Amor, a robotics professor at Arizona State University, said. Tesla's humanoid robots, Optimus, will be initially used in manufacturing and logistics for boring and repetitive work, thus addressing a labor shortage. For the longer term, Musk said the robot could be used in homes, even becoming a "buddy" or a "catgirl" sex partner.