Last year during Tesla's AI Day, the automaker unveiled its Dojo supercomputer. At the time, Tesla claimed the supercomputer was the world's most powerful training machine and would help the automaker teach its vehicles how to drive without any inputs from a human driver. While Tesla officially announced the system last year, the automaker provided more information on its Dojo supercomputer this year at the Hot Chips conference. Dojo's job is to take all of the video Tesla gathers from its fleet of Tesla cars on the road today and process it to learn how cars drive in the real world. The training process is what represents the base for Tesla's Full Self Driving System.

A Chinese language model performs better than OpenAI's GPT-3 and Google's PaLM. Huawei shows a Codex alternative. Large AI models for language, code, and images play a central role in the current proliferation of artificial intelligence. Researchers at Stanford University therefore even want to call such models "foundation models." The pioneer in the development of very large AI models is the U.S. AI company OpenAI, whose GPT-3 language model first demonstrated the usefulness of such AI systems.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out howto print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.

Indian law enforcement is starting to place huge importance on facial recognition technology. Delhi police, looking into identifying people involved in civil unrest in northern India in the past few years, said that they would consider 80 percent accuracy and above as a "positive" match, according to documents obtained by the Internet Freedom Foundation through a public records request. Facial recognition's arrival in India's capital region marks the expansion of Indian law enforcement officials using facial recognition data as evidence for potential prosecution, ringing alarm bells among privacy and civil liberties experts. There are also concerns about the 80 percent accuracy threshold, which critics say is arbitrary and far too low, given the potential consequences for those marked as a match. India's lack of a comprehensive data protection law makes matters even more concerning.

Resilience is the new holy grail in wireless communication systems. Complex radio environments and malicious attacks using intelligent jamming contribute to unreliable communication systems. Early approaches to deal with such problems were based on frequency hopping, scrambling, chirping, and cognitive radio-based concepts, among others. Physical-layer security was increased using known codes and pseudorandom number sequences. However, these approaches are not up to modern standards; they do not improve resilience and are rather easy to attack by means of intelligent jamming.

The Seeker's software allows players to customize how they practice with it. Athletes can catch balls from close to the machine to improve hand-eye coordination. They can also program the robot to throw a ball at a specific spot on the field, or simulate more lifelike conditions by over or under-throwing a ball. Players wear a pager-like tag which allows the robot to track their location on the field, and throw a ball accurately within inches.

Second, the "race" narrative feels like a cudgel to persuade the public or elected officials to move faster with rules and regulations, justify loose ones or expose people to unnecessary risks to "win." The Wall Street Journal reported last week about concerns that the autonomous trucking company TuSimple was taking safety risks with people's lives "in a rush to deliver driverless trucks to market." The Journal reported that a truck fitted with TuSimple technology veered suddenly on an Arizona interstate last spring and careered into a concrete barricade. TuSimple told The Journal that no one was hurt and that safety was its top priority. Apple's autonomous test cars have smacked into curbs near the company's Bay Area headquarters, and earlier this year one nearly crashed into a jogger who had the right of way crossing the street, The Information reported last month.

Thatcham Research, the motor insurers' automotive research centre, welcomed the government's ambition but warned "complete clarity around the driver's legal responsibilities" was needed, along with transparency on how the technology is marketed, "how the dealer describes systems when handing over the keys and how the self-driving system itself communicates with the driver".

In the 1940s, sociologists Kenneth and Mamie Clark placed white and Black dolls in front of young children and asked them to do things like pick the doll that "looks bad" or "is a nice color." The doll test was invented to better understand the evil consequences of separate and unequal treatment on the self-esteem of Black children in the United States. Lawyers from the NAACP used the results to successfully argue in favor of the desegregation of US schools. Now AI researchers say robots may need to undergo similar tests to ensure they treat all people fairly. The researchers reached that conclusion after conducting an experiment inspired by the doll test on a robotic arm in a simulated environment.

Late last week, Google research scientist Fei Xia sat in the center of a bright, open-plan kitchen and typed a command into a laptop connected to a one-armed, wheeled robot resembling a large floor lamp. The robot promptly zoomed over to a nearby countertop, gingerly picked up a bag of multigrain chips with a large plastic pincer, and wheeled over to Xia to offer up a snack. The most impressive thing about that demonstration, held in Google's robotics lab in Mountain View, California, was that no human coder had programmed the robot to understand what to do in response to Xia's command. Its control software had learned how to translate a spoken phrase into a sequence of physical actions using millions of pages of text scraped from the web. That means a person doesn't have to use specific preapproved wording to issue commands, as can be necessary with virtual assistants such as Alexa or Siri.