The ethics of Artificial Intelligence (AI) is arguably the most interesting debate policymakers are yet to have. It is also one of the most urgent. The pace of algorithmic innovation and scale of deployment mean it is no longer sustainable for businesses to decide ethical dilemmas in isolation. Politicians and regulators must engage. This is because, after decades of false starts, AI is finally approaching critical mass.

This week, I talk to Brendan McCord, who wrote the Pentagon's AI strategy and is now a Special Government Employee at the National Security Commission on AI. Brendan talks about what he believes the US needs to do to stay competitive with China and promote an alternative vision of AI-powered security and prosperity to the world.

Shanghai is on course to build itself into an artificial intelligence nexus with global influence by fostering research and development platforms, industrial clusters and pilot projects, the city's Party secretary Li Qiang said on Thursday. Speaking at the opening ceremony of the second World Artificial Intelligence Conference in Shanghai, Li stressed the importance of "openness", "innovation" and'inclusiveness" in achieving the long-term AI ambitions of the metropolis. Efforts to catapult Shanghai to the AI front line include establishing cutting-edge innovation platforms that can accommodate various application scenarios, piloting some of the latest AI research in the city and making breakthroughs in AI data sharing, technology promotion and market access, he added. Li said that Shanghai has chosen AI as a strategic priority and has taken frequent measures to accelerate AI's development by forging a benign ecosystem featuring comprehensive production factors, openness and coordination. Earlier achievements in pushing the AI revolution forward include setting up fundamental research platforms in realms like brain science and integrated circuits, expanding industrial clusters that can house a growing number of industry titans and local startups and accelerating construction of a national pilot program, he said.

The head of the Pentagon's Joint Artificial Intelligence Center recently declared that deepfakes pose a very real threat to national security. Speaking at an AI conference on Thursday, Lt. General Jack Shanahan told the crowd that disinformation tactics, including deepfake videos, could have a serious impact on the upcoming election unless the government seriously invests in ways to counter them, according to C4ISRNET. The warning paints a troubling picture of the future, where democracy and national security could be compromised by convincing, AI-generated media. DARPA, the Pentagon's research division, has already spent tens of millions of dollars on anti-deepfake technology. But, as Shanahan notes, trying to catch up with the tech driving these AI-generated videos is a bit of a cat-and-mouse game.

President Trump's personal assistant, Madeline Westerhout, resigned from the White House amid allegations she shared private information about the president to reporters. President Trump on Friday expressed disappointment over what he described as the "unfortunate" departure this week of his personal assistant Madeleine Westerhout over "hurtful" comments she made to reporters at a recent off-the-record gathering, saying Westerhout explained to him she was "drinking" at the time. "She told me she was very upset," Trump told reporters before boarding Marine One. She said she was drinking." On Thursday, it was reported that Westerhout abruptly left her job after it was determined she shared private information about the president and his family with reporters near Bedminster, N.J., where Trump was on vacation. According to Trump, "She was with reporters and everything she said was off the record." But some of her comments made their way back to the White House. "I think the press was very dishonest," he said. "Because it was supposed to be off the record." The president didn't cite what Westerhout said – but said her comments were a "little bit hurtful." "You don't say things like she said," she said. According to Politico, Westerhout shared intimate details about the president's family – including about daughters Ivanka and Tiffany -- during the dinner with reporters "Tiffany is great," Trump said Friday, in response to those reports. Westerhout served as a gatekeeper to the president, having a desk outside the Oval Office. Trump said: "She's a very good person.

It's a frightening and far too common scenario: One in four Americans age 65 and older falls each year, according to the Centers for Disease Control and Prevention. These accidents, which comprise 2.8 million injuries, account for an emergency room visit every 11 seconds. Falls are the leading cause of fatal injury for this population, numbering more than 27,000 deaths every year. No family wants to envision the scenario, even a minor one. After all, older adults often face a tough recovery, and they may avoid social engagements or exercise due to fear of falling again.

Z Advanced Computing, Inc. (ZAC) of Potomac, MD announced on August 27 that it is funded by the US Air Force, to use ZAC's detailed 3D image recognition technology, based on Explainable-AI, for drones (unmanned aerial vehicle or UAV) for aerial image/object recognition. ZAC is the first to demonstrate Explainable-AI, where various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," said Dr. Saied Tadayon, CTO of ZAC. "For complex tasks, such as drone vision, you need ZAC's superior technology to handle detailed 3D image recognition." "You cannot do this with the other techniques, such as Deep Convolutional Neural Networks, even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," continued Dr. Bijan Tadayon, CEO of ZAC.

Software star-up, Z Advanced Computing, Inc. (ZAC), has received funding from the U.S. Air Force to incorporate the company's 3D image recognition technology into unmanned aerial vehicles (UAVs) and drones for aerial image and object recognition. ZAC's in-house image recognition software is based on Explainable-AI (XAI), where computer-generated image results can be understood by human experts. ZAC – based in Potomac, Maryland – is the first to demonstrate XAI, where various attributes and details of 3D objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," says Dr. Saied Tadayon, CTO of ZAC. "You cannot do this with the other techniques, such as deep Convolutional Neural Networks (CNNs), even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," adds Dr. Bijan Tadayon, CEO of ZAC.

Former FDA Commissioner Dr. Scott Gottlieb stressed the need for modernizing the clinical trials process in a speech to the Bipartisan Policy Center in January of this year.1 He is quoted as saying, "digital technologies are one of our most promising tools for making healthcare more efficient." Improving efficiency in clinical trial development is only one potential enhancement that can result from the use of machine learning. Machine learning and artificial intelligence (AI) are often used interchangeably, but that assumption is incorrect. Machine learning is the subset of AI that is related to the development of algorithms that can make accurate predictions of future outcomes via pattern recognition and rules-based logic. Such use of logic and algorithms can improve patient selection, provide predictive long-term outcomes, and reduce the time and cost in the execution of clinical trials.

Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power. Consequently, there is a growing need for the development of data-driven and, in particular, deep learning methods for various tasks such as cancer diagnosis, detection, prognosis, and prediction. Despite recent successes, however, designing high-performing deep learning models for nonimage and nontext cancer data is a time-consuming, trial-and-error, manual task that requires both cancer domain and deep learning expertise. To that end, we develop a reinforcement-learning-based neural architecture search to automate deep-learning-based predictive model development for a class of representative cancer data. We develop custom building blocks that allow domain experts to incorporate the cancer-data-specific characteristics. We show that our approach discovers deep neural network architectures that have significantly fewer trainable parameters, shorter training time, and accuracy similar to or higher than those of manually designed architectures. We study and demonstrate the scalability of our approach on up to 1,024 Intel Knights Landing nodes of the Theta supercomputer at the Argonne Leadership Computing Facility.