UPS has become the first drone delivery service to receive full approval from the Federal Aviation Administration. The company's program, called Flight Forward, is operated in partnership with Matternet, which provides drone logistics networking company in Mountain View, California. Previously, UPS's pilots were only allowed to fly the drones within line of sight, but the FAA approval means they'll be able to significantly expand their delivery range. 'This is history in the making, and we aren't done yet,' said David Abney, UPS chief executive officer in a statement. UPS's Flight Forward drone delivery program is the first to earn full approval by the FAA (pictured one of the drones they will use in the program) The program's currently deployed in Raleigh, North Carolina, where UPS's drones have made more than 1,000 flights carrying deliveries around the WakeMed Health & Hospitals campus.

The following article is part of a series on Argonne National Laboratory's efforts to use the predictive power of artificial intelligence, specifically machine learning, to advance discoveries in a broad range of scientific disciplines. High-energy physics and cosmology seem worlds apart in terms of sheer scale, but the invisible components that comprise the field of one inform the composition and dynamics of the other -- collapsing stars, star-birthing nebulae and, perhaps, dark matter. For decades, the techniques by which researchers in both fields studied their domains seemed almost incompatible, as well. High-energy physics relied on accelerators and detectors to glean some insight from the energetic interactions of particles, while cosmologists gazed through all manner of telescopes to unveil the secrets of the universe. " … it would be interesting to know if image classification techniques from machine learning that have been used successfully by Google and Facebook can simplify or shorten the development of algorithms that identify particle signatures in our 3D detectors."

Two Department of Energy Computational Science Graduate Fellowship recipients are leading an effort to address global climate change effects with machine-learning techniques. Priya Donti, a third-year fellow in computer science and public policy at Carnegie Mellon University, and Kelly Kochanski, a fourth-year fellow in Earth surface processes at the University of Colorado Boulder, are on the steering committee (Donti is co-chair) for Climate Change AI. The group's website says it is a coalition of "volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis." Machine learning algorithms identify patterns in known data and use that information to make predictions or to classify previously unseen data. Machine learning is a key component of artificial intelligence (AI).

On Wednesday, Sens. Ron Wyden and Cory Booker and Rep. Yvette Clarke introduced the Algorithmic Accountability Act, indicating policymakers' increasing concern that artificial intelligence is magnifying human bias in tools such as facial recognition, self-driving cars, customer service, marketing, and content moderation. While A.I. has incredible potential to improve our lives, the truth is that it is only capable of reflecting our societal problems right back at us. And because of that, we can't trust it to make important decisions that are susceptible to human prejudice. Even the most enlightened of humans have deep-seated biases. Difficult to identify, they are even harder to correct.

The artificial intelligence race has kept the world watching in rapt attention. Will the People's Republic of China beat the United States of America? If so, what are the implications? Kai-fu Lee, a former executive at Apple, SGI, Microsoft, and Google, argues in his novel AI Superpowers that China will outpace America in terms of AI development thanks to abundant data, eager entrepreneurs, well-educated and trained scientists, and a supportive policy landscape. However, the debate must be reframed: it's not a battle between the United States and China, but it instead appears to be a tug-of-war between seven technology companies--Google, Amazon, Facebook, and Microsoft on the American team and Alibaba, Baidu, and Tencent on the Chinese side.

Robots are taking wages from American workers. A new study from the Federal Reserve Bank has found that the portion of national income give to human employees has dramatically decreased as automation continues to increase. The study suggests that employees that employees feel they have lost their bargaining power when it comes to asking for a raise out of fear they may be replaced by a robot. A new study from the Federal Reserve Bank has found that the portion of national income has dramatically decreased as automation continues to increase. 'Businesses have more options to automate hard-to-fill positions now than in the past,' the study authors write.

On Wednesday, Oct. 2, and Thursday, Oct. 3, 2019, Argonne hosts the fourth U.S. Department of Energy InnovationXLab Summit, focused on artificial intelligence, at the Drake Hotel in downtown Chicago. Attendance is by invitation only but some of the most highly anticipated presentations will be livecast. Don't miss these thought-provoking speakers sharing their visions of how AI, one of Argonne's key initiatives, will shape the world. And if you want to join the XLab conversation on social media, follow @argonne and #DOEFueledAI on Twitter. This week, the lab will be highly visible as a leader in science and technology.

Will the U.S. Army's future artillery be operated by robot gunners? A new Army research project calls for developing autonomous systems that can perform functions such as loading shells into cannon. It also follows another project that aims to replace forward observers, who call in artillery fire, with robots. The research solicitation calls for developing "autonomous robotic systems that are capable of semi- and fully autonomous munitions handling inside a weapons system. Its functions would support loading of projectiles into the cannon breech, setting of charges and propellants, and management of excess case materials inside of the weapons system. A M109A6 Paladin, a self-propelled 155-millimeter howitzer, is operated by a crew of four: a commander, driver, gunner and loader. It is not clear which crewmen could be replaced by robots should this technology come to fruition, though the gunner and loader would seem to be prime candidates. At the same time, given advances in self-driving vehicles, it is conceivable that the driver could be replaced by a robot, which would leave the commander as the human element of the system. But even the Army acknowledges there are risks, such as being hacked. "Potential solutions should consider the need for compact form factors, low electronic signatures, cyber security protections, shock and vibration management, and power supply constraints," said the research solicitation. But even human gunners may end up looking like robots. The Army wants exoskeletons to assist its artillery crews with the physically strenuous task of operating the weapons. The project aims to develop "passive or active exoskeleton capabilities to assist crew-served artillery systems.

Björn Brinne added: "The report is correct that there are a number of urgent challenges that need to be addressed. Many deep learning projects to date have been focused on small pockets of research, which presents issues in relation to repeatability, auditability and scalability which are needed to make a global impact. Also, lack of skills, cost and complexity remain as barriers. "For the NHS, this is a major challenge as budgets and talent are already limited. "There's also the data issue – deploying deep learning models in the health sector requires retraining them when new data comes in, a complex and often costly task. "Additionally, to begin with, the quantity of data available will be limited and the quality of it inconsistent, which could lead to inaccuracies. There are also obvious challenges in the sensitivity of the data that is needed and requirements for consent." "In order to overcome these challenges, deep learning needs to move away from being used as a research tool, and instead become operationalised to make outputs more robust and usable. This will make deep learning accessible for a wider group of users in the medical industry, so that data pools become greater and more varied over time, improving model performance and, by extension, the quality and effectiveness of patient care."

When it comes to future military readiness, 2019 has been the year of artificial intelligence. The Department of Defense launched its AI strategy in February followed by the White House's executive order on "Maintaining American Leadership in Artificial Intelligence" -- both of which indicate accelerated delivery of AI-enabled capabilities and scaling the technology across DOD while cultivating a much-needed tech workforce. Military leaders recognize AI's potentially seismic impact on their mission and operations, and they expect practical applications to proliferate, from threat monitoring to asset tracking to predictive maintenance. Where, when and how those applications evolve from idea to reality is an unfolding story. So too is the global AI landscape, as Russia, China and other countries make substantial investments in such capabilities.