Bob Gourley is the co- founder of the Artificial Intelligence cybersecurity and due diligence consultancy OODA LLC . He is the publisher of CTOvision.com Bob's background is as an all source intelligence analyst and an enterprise CTO.

Military pilots may soon have a new kind of wingman to depend upon: not flesh-and-blood pilots but fast-flying, sensor-studded aerial drones that fly into combat to scout enemy targets and draw enemy fire that otherwise would be directed at human-piloted aircraft. War planners see these robotic wingmen as a way to amplify air power while sparing pilots' lives and preventing the loss of sophisticated fighter jets, which can cost more than $100 million apiece. "These drone aircraft are a way to get at that in a more cost-effective manner, which I think is really a game-changer for the Air Force," says Paul Scharre, director of the technology and national security program at the Center for a New American Security, a think tank in Washington, D.C. Unlike slow-moving drones such as the Reaper and the Global Hawk, which are flown remotely by pilots on the ground, the new combat drones would be able to operate with minimal input from human pilots. To do that, they'd be equipped with artificial intelligence systems that give them the ability not only to fly but also to learn from and respond to the needs of the pilots they fly alongside. "The term we use in the Air Force is quarterbacking," says Will Roper, assistant secretary of the U.S. Air Force for acquisition, technology and logistics and one of the experts working to develop the AI wingmen.

The interconnectedness of technology, coupled with the growing number of mobile devices, quickly evolving technologies, and more prominent use of Wi-Fi has resulted in a severe uptick of cyber attacks. To ward off impending threats, we're increasingly turning to machine learning for help. Machine learning has the potential to offer better, more efficient solutions than what's currently available on the market to prevent cybercrime. In this article, we'll give a deep-dive into how machine learning is currently improving cybersecurity. Machine learning can be used to monitor and detect breaches in a certain network, and can also help generate an automated response to an attack.

Deep-learning techniques have made it easier and easier for anyone to forge convincing misinformation. Two researchers at the United Nations decided to find out. In a new paper, they used only open-source tools and data to show how quickly they could get a fake UN speech generator up and running. They used a readily available language model that had been trained on text from Wikipedia and fine-tuned it on all the speeches given by political leaders at the UN General Assembly from 1970 to 2015. Thirteen hours and $7.80 later (spent on cloud computing resources), their model was spitting out realistic speeches on a wide variety of sensitive and high-stakes topics from nuclear disarmament to refugees.

Business competition is increasingly defined not just by product quality or delivery logistics, but also by unique data assets and the ability to capitalize on them. It should be no surprise, then, that as companies invest heavily in AI strategy and execution, data scientists find themselves in heavy demand. The data scientist star has actually been in ascendance for some time. In 2012, the Harvard Business Review infamously declared the position "the sexiest job of the 21st century." More recently, the U.S. Bureau of Labor Statistics (BLS) rosily projected six-figure salaries and thousands of new positions for the years ahead.

As the Trump administration prepares to expand retaliatory tariff hikes of up to 25% to another $300 billion of Chinese products, Beijing has sought to highlight China's capacity to endure and overcome hardship. Yi told Bloomberg Television in an interview broadcast Friday that he expected the meeting with Mnuchin to be "difficult." But he said China's central bank, the People's Bank of China, had plenty of room to maneuver to help keep the economy growing despite the pounding the country's export manufacturers are taking as the toll from higher tariffs mounts. Speaking Thursday in France, Trump said he plans to make a decision about ramping up tariffs on China after speaking with Xi at the summit in Osaka at the month's end. "I will make that decision I would say over the next two weeks -- probably right after the G-20," he said. The Trump administration began slapping tariffs on imports of Chinese goods nearly a year ago, accusing China of resorting to predatory tactics to give Chinese companies an edge in advanced technologies such as artificial intelligence, robotics and electric vehicles.

The use of AI to create high-resolution fake images and videos has raised concerns about the use of disinformation as a political tool. Over the past few years, the application of artificial intelligence to create faked images, audio, and video has sparked a great deal of concern among policymakers and researchers. A series of compelling demonstrations -- the use of AI to create believable synthetic voices, to imitate the facial movements of a president, to swap faces in faked porn -- illustrate the rapid speed at which the technology is advancing. Machine learning, the subfield of artificial intelligence that underlies much of the technology's modern progress, studies algorithms that improve through the processing of data. Machine learning systems acquire what is known in the field as a representation, a concept of the task to be solved, which can then be used to generate new iterations of the thing that has been learned.

The government on Friday adopted a draft bill to realize its "supercity" initiative to create cities that make use of artificial intelligence, big data and other advanced technologies. In such cities, autonomous driving, cashless payments, goods delivery by drone and novel services using sophisticated technologies will be introduced in an integrated manner. Initially, the government planned to submit to the Diet a bill to revise the national strategic special zone law by the end of March. But it was unable to do that because of difficulty obtaining support from the Cabinet Legislation Bureau for related deregulation. The bill is unlikely to pass the Diet before the end of the ongoing ordinary session, set for June 26.

NHS chief Simon Stevens today called on tech firms to help the health service become a world leader in the use of artificial intelligence (AI) and machine learning. He also asked staff to work with us and share ideas on reforms to the payment systems that would help encourage and facilitate quicker adoption and expansion. The technology can help speed up diagnosis of cancer and other diseases and deliver more convenient care by revolutionising outpatient services. Speaking at the Reform Health Conference today, NHS chief executive Simon Stevens announced a global call for evidence from technologists for how the NHS can best incentivise the use of carefully targeted AI across the NHS from April 2020 and beyond. The NHS boss challenged tech innovators to come forward with proposals for how the NHS can harness innovative solutions that can free up staff time and cut the time patients wait for results.

The Food and Drug Administration announced Tuesday that it is developing a framework for regulating artificial intelligence products used in medicine that continually adapt based on new data. The agency's outgoing commissioner, Scott Gottlieb, released a white paper that sets forth the broad outlines of the FDA's proposed approach to establishing greater oversight over this rapidly evolving segment of AI products. It is the most forceful step the FDA has taken to assert the need to regulate a category of artificial intelligence systems whose performance constantly changes based on exposure to new patients and data in clinical settings. These machine-learning systems present a particularly thorny problem for the FDA, because the agency is essentially trying to hit a moving target in regulating them. The white paper describes criteria the agency proposes to use to determine when medical products that rely on artificial intelligence will require FDA review before being commercialized.