Although Wingcopter hasn't received clearance from the Federal Aviation Administration, the company moved a step closer to achieving that goal last year when it was named one of 10 companies in the United States to meet the FAA's "airworthiness" criteria for drones more complex than the consumer versions available online. The drone software company Flirtey and the delivery firm Zipline also were among the companies edging toward sending delivery aircraft weighing under 89 pounds into the skies.

As the use and power of artificial intelligence increases, the risk for AI to become the hacker, rather than the victim also grows, according to a new report. The problem is two-fold, wrote Bruce Schneier, a fellow at the Berkman Center for Internet and Society at Harvard University, in "The Coming AI Hackers," a recent report from Harvard's Belfer Center for Science and International Affairs. "One, AI systems will be used to hack us. And two, AI systems will themselves become hackers: finding vulnerabilities in all sorts of social, economic, and political systems, and then exploiting them at an unprecedented speed, scale, and scope," Schneier said. We risk a future of AI systems hacking other AI systems, with humans being little more than collateral damage." He pointed to several public-sector examples where this threat is already becoming apparent. In one, researchers used a text-generation program to submit 1,000 comments to a request for input on a Medicaid issue, effectively fooling Medicaid.gov They interact normally but occasionally make a politically charged post. "Persona bots will break the'notice-and-comment' rulemaking process by flooding government agencies with fake comments," Schneier said, potentially affecting public opinion. "It's not that being persuaded by an AI is fundamentally more damaging than being persuaded by another human, it's that AIs will be able to do it at computer speed and scale." Governments tend to use AI to make their processes more efficient. For instance, in the United Kingdom, a Stanford University student built a bot to automatically determine eligibility and fill out applications for services such as government housing. AI is also used to inform military targeting decisions, the report added. "As AI systems get more capable, society will cede more -- and more important -- decisions to them," Schneier said. "They already influence social outcomes; in the future they might explicitly decide them.

The UK government has announced that basic self-driving cars with automated lane-keeping system (ALKS) could arrive on British roads by the end of 2021. The new designation will allow trials of such systems, allowing Britain to develop its own self-driving regulations and catch up with other countries like the US and Japan. The UK's Department of Transportation (DfT) said that it will "set out" (describe) how ALKS-equipped vehicles can be legally defined as self-driving, as long as "there is no evidence to challenge the vehicle's ability to self-drive." In other words, it's creating rules that will allow ALKS systems to be legally used on UK roads. ALKS systems, called "traffic jam chauffeur technology" by the DfT, are designed to help a vehicle stay in its lane and at a safe distance from traffic, with speeds limited to 37 MPH.

Tokyo stocks gained Wednesday, led by issues of companies that released their earnings reports the day before. The 225-issue Nikkei average climbed 62.08 points, or 0.21%, to end at 29,053.97, after shedding 134.34 points Tuesday. The Topix index of all first section issues advanced 5.51 points, or 0.29%, to finish at 1,909.06, following a 14.60-point drop the previous day. The Tokyo market began on a weak note as players took to position-squaring selling ahead of the Golden Week holiday period, starting Thursday. Stocks headed north later in the morning thanks to buying of firms which released their earnings results and forecasts Tuesday such as industrial robot-maker Fanuc and electronics maker Fuji Electric.

Safr from RealNetworks has earned its third Small Business Research Innovation (SBIR) deal from the United States Air Force (USAF) to enable the extension of Safr AI-powered analytics, including facial recognition, to unmanned ground vehicles (UGVs). The UGVs would be used to reduce risks in perimeter protection and domestic emergency medical services (EMS) search and rescue missions. In a news release, the company said the contract will help improve its platform to be able to operate on an NVIDIA Jetson AGX Xavier-based UGV system, with the goal of reducing the risk service members face as the Safr-enhanced UGVs will be able to detect unauthorized persons in restricted areas with face biometrics. "As a USAF military working dog handler, I have employed canines in various environments fulfilling the multi-use role of detection and deterrence. The ability to utilize UGV systems to augment K9 teams during work/rest cycles, or as an additional force, broadens security in-depth and allows operations to continue unhindered," said Air Force Technical Sergeant Dustin Cain, Non-Commissioned Officer in Charge of Police Services, 366th Security Forces Squadron, Mountain Home Air Force Base, Idaho.

At some point in the near future--how near depends on who you ask--autonomous vehicles (AVs) will become a common sight on the roads. Without the need for a driver or human input, AVs, which are also known as self-driving cars, will require sensors and computers working together to read the road and surrounding environment. Most of the advanced driver aids in the wild today use a combination of radar and sonar to deliver warnings on unseen threats and to help stop a vehicle before a collision occurs. Lidar is a technology that can perform similar functions to radar and sonar, but it's a next-generation system that may represent the best option for AVs' ability to "see." As automakers and other companies move through testing and real-world drives, it has become clear that next-generation sensors and tech offer intriguing functionality but are not the silver bullet that many thought they'd be at first.

The future of work promises increased productivity, getting top results with fewer resources, and using AI not to eliminate jobs but to help teams excel. Ofir Paldi and the startup he runs, Shamaym, may have found the key to this future of work: Create a debriefing culture, the foundation for real-time collaboration and improving individual and enterprise productivity. After completing a distinguished service with the Israeli air force, Paldi and a few fellow pilots established a non-profit with the goal of using what they have learned about performance improvement to make a difference and improve the lives of others. They launched a non-profit, "taking the air force's debriefing culture to the civilian world," says Paldi. One of the top performing organizations in the world, the Israeli air force has developed a debriefing process in which pilots share lessons learned and insights that enable all team members to constantly improve their performance.

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

I happened to come across a document, issued by the Department of Justice of the State of Israel, which opens a public consultation, regarding the regulation of self-driving vehicles. Dated February 8, it bears the title, translated of course, of "Towards the Regulation of the Use of Self-Driving Vehicles -- an Application for a Public Position in the Field of Liability and Insurance." The incipit of the paper is a valid invitation to continue reading: "Technological innovation is commonly seen as including one or more of the following components: autonomous, electric, connectivity, and cooperative, when each of these components is based on a large number of technologies with different characteristics. Perceptual change is commonly described as a transition from "transportation as a product" to "transportation as a service." As part of this concept, the transportation system is not a total of privately owned vehicles, but a collection of tools designed to provide transportation services (of human passengers or any other goods) from point A to point B in the most efficient way. From this, the existing relationship between the vehicle owner and the vehicle driver comes to be dismantled. Thus, in contrast to the current de facto situation, where there is often an identity between the vehicle owner and the driver, a separation will be created between the vehicle owner and the vehicle users."

We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic in order to inform decision-makers of future demand and assess the societal value of possible interventions. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. Instead, given daily admissions counts, we model aggregated counts of observed resource use, such as the number of patients in the general ward, in the intensive care unit, or on a ventilator. In order to explain how individual patient trajectories produce these counts, we propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization. We develop an Approximate Bayesian Computation approach that draws samples from the posterior distribution over the model's transition and duration parameters given aggregate counts from a specific location, thus adapting the model to a region or individual hospital site of interest. Samples from this posterior can then be used to produce future forecasts of any counts of interest. Using data from the United States and the United Kingdom, we show our mechanistic approach provides competitive probabilistic forecasts for the future even as the dynamics of the pandemic shift. Furthermore, we show how our model provides insight about recovery probabilities or length of stay distributions, and we suggest its potential to answer challenging what-if questions about the societal value of possible interventions.