Chinese autonomous vehicle startup Pony.ai has received a permit from California's Department of Motor Vehicles to test its driverless cars without human safety drivers behind the wheel on specified streets in three cities. Pony has been authorized to test autonomous vehicles with safety drivers in California since 2017, but the new permit will let it test six autonomous vehicles without safety drivers on specific streets in Fremont, Alameda County; Milpitas, Santa Clara County; and Irvine, Orange County. According to the DMV, the vehicles are designed to be driven on roads with speed limits of 45 miles per hour or less, in clear weather and light precipitation. The first testing will be in Fremont and Milpitas on weekdays between 10AM and 3PM. A total of 55 companies have active permits to test driverless vehicles in California according to the DMV, but Pony is only the eighth company to receive a driverless testing permit, joining fellow Chinese companies AutoX, Baidu, and WeRide, along with US companies Cruise, Nuro, Waymo, and Zoox.

On May 10, 40 advocacy groups sent an open letter demanding a permanent ban on the use of Amazon's facial recognition software, Rekognition, by US police. The letter was addressed to Jeff Bezos and Andy Jassy, the company's current and incoming CEOs, and came just weeks before Amazon's year-long moratorium on sales to law enforcement was set to expire. The letter contrasted Bezos's and Jassy's vocal support of Black Lives Matter campaigners during last summer's racial justice protests after the murder of George Floyd with reporting that other Amazon products have been used by law enforcement to identify protesters. On May 17, Amazon announced it would extend its moratorium indefinitely, joining competitors IBM and Microsoft in self-regulated purgatory. The move is a nod at the political power of the groups fighting to curb the technology--and recognition that new legislative battle grounds are starting to emerge.

IS THERE no problem artificial intelligence can't tackle? Methods such as deep learning are finding uses in everything from algorithms that recommend what you should purchase next to ones that predict someone's voting habits. The result is that AI has developed a somewhat mystical reputation as a tool that can digest many different types of data and accurately predict many different outcomes, an ability that could be of particular use for solving previously impenetrable problems within healthcare. However, AI is no panacea. Too often, it is turned to too quickly and in an impulsive way, resulting in claims that it works when it doesn't.

On May 18, Google CEO Sundar Pichai announced an impressive new tool: an AI system called LaMDA that can chat to users about any subject. To start, Google plans to integrate LaMDA into its main search portal, its voice assistant, and Workplace, its collection of cloud-based work software that includes Gmail, Docs, and Drive. But the eventual goal, said Pichai, is to create a conversational interface that allows people to retrieve any kind of information--text, visual, audio--across all Google's products just by asking. LaMDA's rollout signals yet another way in which language technologies are becoming enmeshed in our day-to-day lives. But Google's flashy presentation belied the ethical debate that now surrounds such cutting-edge systems.

President Barack Obama bumped fists with Nathan Copeland during a tour of innovation projects at the White House Frontiers Conference at the University of Pittsburgh in 2016. President Barack Obama bumped fists with Nathan Copeland during a tour of innovation projects at the White House Frontiers Conference at the University of Pittsburgh in 2016. A robotic arm with a sense of touch has allowed a man who is paralyzed to quickly perform tasks like pouring water from one cup into another. The robotic arm provides tactile feedback directly to the man's brain as he uses his thoughts to control the device, a team reports Thursday in the journal Science. Previous versions of the arm required the participant, Nathan Copeland, to guide the arm using vision alone.

Council members in King County, Wash., where Amazon's Seattle headquarters is based, are considering a local ban this month. And in Virginia, where Amazon is building its second headquarters, known as HQ2, state lawmakers just enacted one of the strictest laws in the country, requiring local law enforcement to secure state legislative approval before using any facial recognition system.

An explain-able multi-attribute decision model based on argumentation.

A research team from the Technical University of Munich (TUM) and the Fritz Haber Institute in Berlin is using active machine learning in the search for suitable molecular materials for new organic semiconductors, the basis for organic field effect transistors (OFETs), light-emitting diodes (OLEDs) and organic solar cells (OPVs). To efficiently deal with the myriad of possibilities for candidate molecules, machine learning proves an invaluable tool. It is envisaged that organic semiconductors will enable important future technologies such as portable solar cells or rollable displays. For such applications, improved organic molecules – which make up these materials – need to be discovered. For material discovery tasks of this nature researchers are increasingly utilising machine learning methods, training on data from computer simulations or experiments.

Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution. 1 1. The first two revolutions introduced programmable logic controllers and distributed control systems, which enabled plant-wide data collection and automation. The third revolution--advanced process controls--further abstracted automation into high-level models, allowing for increasingly dynamic plant operation. For more on the latest innovations in process controls, see Stephan Görner, Andy Luse, Naman Maheshwari, Ravi Malladi, Lapo Mori, and Robert Samek, "The potential of advanced process controls in energy and materials," November 23, 2020. AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever.

According to the researchers at Adversa, machine learning systems that process visual data account for most of the work on adversarial attacks, followed by analytics, language processing, and autonomy.