The very fact that objects in your sight may obscure your view of things that lie further ahead is blindingly obvious to people. But Peiyun Hu, a Ph.D. student in CMU's Robotics Institute, said that's not how self-driving cars typically reason about objects around them. Rather, they use 3D data from lidar to represent objects as a point cloud and then try to match those point clouds to a library of 3D representations of objects. The problem, Hu said, is that the 3D data from the vehicle's lidar isn't really 3D -- the sensor can't see the occluded parts of an object, and current algorithms don't reason about such occlusions. "Perception systems need to know their unknowns," Hu observed.

Stéphane Fymat, the head of that new business, said Honeywell expects the hardware and software market for urban air taxis, drone cargo delivery, and other drone businesses to reach $120 billion by 2030 and Honeywell's market opportunity would be about 20% of that. He declined to say how much of that market Honeywell was targeting to capture, adding only that the unit has hundreds of employees with many engineers. Honeywell doesn't build drones itself but provides autonomous flight controls systems and aviation electronics. The new business creation comes as the coronavirus pandemic creates a surge of interest in drone deliveries; Fymat said it's accelerating the drone cargo delivery programs of some of its partners. Some of Honeywell's customers include Intel-backed Volocopter, Slovenia-based small aircraft maker Pipistrel, which is developing an electric vertical take-off and landing aircraft for cargo delivery, and UK-based Vertical Aerospace, which has test flown a prototype vehicle last year that can carry 250 kilograms and fly at 80 kilometers an hour.

UC Berkeley AI researchers are using an iPhone X and Apple's ARKit to train a robotic arm how to grasp an object. ARKit creates point clouds from data generated by moving an RGB camera around an object for two minutes. Robotic grasping is a particular robotics subfield focused on the challenge of teaching a robot to pick up, move, manipulate, or grasp an object. The Dexterity Network, or Dex-Net, research project at UC Berkeley's Autolab dates back to 2017 and includes open source training data sets and pretrained models for robotic grasping in an ecommerce bin-picking scenario. The ability for robots to quickly learn how to grasp objects has a big impact on how automated warehouses like Amazon fulfillment centers can become.

A free facial recognition tool that allows people to find pictures of themselves or others from around the internet has drawn criticism from privacy campaigners. PimEyes describes itself as a privacy tool to help prevent misuse of images. But Big Brother Watch said it could "enable state surveillance, commercial monitoring and even stalking on a scale previously unimaginable". It comes as Amazon decides to pause its use of facial recognition for a year. Polish website PimEyes was set up in 2017 as a hobby project, and commercialised last year.

Amazon announced on Wednesday that it would freeze for one year the use of its facial recognition technology by law enforcement agencies. Amazon announced on Wednesday that it would freeze for one year the use of its facial recognition technology by law enforcement agencies. Amazon announced on Wednesday a one-year moratorium on police use of its facial-recognition technology, yielding to pressure from police-reform advocates and civil rights groups. It is unclear how many law enforcement agencies in the U.S. deploy Amazon's artificial intelligence tool, but an official with the Washington County Sheriff's Office in Oregon confirmed that it will be suspending its use of Amazon's facial recognition technology. Researchers have long criticized the technology for producing inaccurate results for people with darker skin.

Microsoft will not sell facial recognition technology to U.S. police departments until there is a national law to regulate this technology. On Thursday, Microsoft president Brad Smith said on Washington Post Live the company will cease the selling of this technology until a law "grounded in human rights" is put in place. "This is a moment in time that really calls on us to listen more to learn more and, most importantly, to do more," Smith said. The move follows Amazon's Wednesday announcement to suspend police use of its facial recognition technology, Rekognition, for one year after several studies found bias in the software that disproportionately targets people of color. Similarly, in a letter to Congress, IBM CEO Arvind Krishna also said the company would not sell facial recognition services to most customers.

This week, three of the leading developers of facial-recognition technology announced they would stop, or at least pause, selling this technology to police. The decision stems from evidence of racial bias inherent in these tools. For the researchers who first uncovered the deep-seated issues, it's a watershed moment. Will facial-recognition technology continue to grow unchecked? Or will this week's announcements result in lasting change?

IBM announced this week that it would stop selling its facial recognition technology to customers including police departments. The move prompted calls for other tech firms, like Amazon and Microsoft, to do the same. IBM announced this week that it would stop selling its facial recognition technology to customers including police departments. The move prompted calls for other tech firms, like Amazon and Microsoft, to do the same. IBM will no longer provide facial recognition technology to police departments for mass surveillance and racial profiling, Arvind Krishna, IBM's chief executive, wrote in a letter to Congress.

Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning -- a discipline within artificial intelligence -- to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica. The University of Cagliari-based team set out to create an AI-managed "buy and hold" (B&H) strategy -- a system of deciding whether to take one of three possible actions -- a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance.

New AI technologies are helping scientists to sort through the wealth of COVID-19 papers -- hopefully hastening the research process.Credit: Adapted from Getty The COVID-19 literature has grown in much the same way as the disease's transmission: exponentially. But a fast-growing set of artificial-intelligence (AI) tools might help researchers and clinicians to quickly sift through the literature. Driven by a combination of factors -- including the availability of a large collection of relevant papers, advances in natural-language processing (NLP) technology and the urgency of the pandemic itself -- these tools use AI to find the studies that are most relevant to the user, and in some cases to extract specific findings from the results. Beyond the current pandemic, such tools could help to bridge fields by making it easier to identify solutions from other disciplines, says Amalie Trewartha, one of the team leads for the literature-search tool COVIDScholar, at the Lawrence Berkeley National Laboratory in Berkeley, California. The tools are still in development, and their utility is largely unproven.