At a virtual meeting of the U.S. Food and Drug Administration's Center for Devices and Radiological Health and Patient Engagement Advisory Committee on Thursday, regulators offered updates and new discussion around medical devices and decision support powered by artificial intelligence. One of the topics on the agenda was how to strike a balance between safety and innovation with algorithms getting smarter and better trained by the day. In his discussion of AI and machine learning validation, Bakul Patel, director of the FDA's recently-launched Digital Health Center of Excellence, said he sees huge breakthroughs on the horizon. "This new technology is going to help us get to a different place and a better place," said Patel. You're seeing automated image diagnostics.

Space agencies across the world are working tirelessly to design the best ships and technologies for the chance to claim a stake of the final frontier for their country. Although it may seem like an act of national pride, a professor from Johns Hopkins University warns that space expansion may lead to the extinction of humanity, suggesting it should not be attempted at all. Daniel Deudney recently published a book titled'Dark Skies' that examines space expansionism through geopolitics revealing cosmic habitats could spark totalitarian empires. The political science professor also notes that if these settlements stretch across the solar system, nuclear weapons will become the gold standard in war, along with using asteroids to destroy enemy planets - but other experts feel these arguments are'too pessimistic.' 'I argue that the consequences of what has actually happened in space are much less positive than space enthusiasts and many others believe,' reads'Dark Skies.' 'In sum, this book argues that the large-scale expansion of human activities into space, past and future, should join the lengthening list of catastrophic and existential threats to humanity, and that the ambitious core of space expansionism should be explicitly relinquished.'

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Army will soon operate robots able to destroy enemy armored vehicles with anti-tank missiles, surveil warzones under heavy enemy fire and beam back identified targeting details in seconds due to rapid progress with several new armed robot programs. Several of the new platforms now operate with a Kongsberg-built first-of-its-kind wireless fire control architecture for a robotic armored turret with machine guns, Javelin Anti-Tank Missiles and robot-mounted 30mm cannon selected by the Army to arm its fast-emerging Robotic Combat Vehicles. These now-in-development robotic systems are intended to network with manned vehicles in high-risk combat operations.

Despite recent evidence that physician extenders can streamline radiology workflow and reduce turnaround times, radiologists should rely more on artificial intelligence (AI) for assistance than non-physician providers (NPP), a group of industry experts has said. In an editorial published in the Journal of the American College of Radiology, a team of experts, led by Daniel Ortiz, M.D., with Summit Radiology in Georgia, pointed to the several benefits of AI – not only do the tools save money and streamline workflow, but they will not encroach on a radiologist's responsibilities. For these reasons, they said, radiologists should forego giving physician extenders – the nurse practitioners, physician assistants, and other providers who take on some of a provider's duties – full practice authority. "Although labor costs have been reduced and radiologists can focus more on complex imaging studies and interventional procedures, there are unintended consequences of non-physician practitioners in practice that could diminish physician's role as healthcare providers," the group wrote. "Therefore, we encourage radiologists to consider an alternative to non-physician practitioners in radiology: the incorporation of rapidly evolving artificial intelligence algorithms into daily practice." Their concern was spawned by a recently passed Georgia law that allows advanced practice registered nurses to order CR, MRI, and other imaging exams under certain circumstances.

U.S. Customs and Border Protection wants to apply artificial intelligence to the ingestion and analysis of increasing amounts of data coming out of its efforts to secure the U.S. supply chain. CBP needs to analyze data earlier along the supply chain because currently, it's getting involved too late after products have been manufactured and already begun international transit, Vincent Annunziato, director of the agency's Business Transformation and Innovation Division, said during ACT-IAC's Reimagine Nation ELC 2020 on Monday. The agency is responsible for ensuring importer and exporter compliance with laws and regulations that prevent harmful or counterfeit products from entering or exiting the U.S. The volume of supply chain data CBP is dealing with has skyrocketed since the agency started piloting blockchain to secure various industries like steel and oil, and only AI and machine learning can make sense of it all. "All of this now is starting to play into that AI and machine learning arena because, one, we're getting data that we've never seen before," Annunziato said. "Two … the government is going to look into designing a system that's flexible for the data that's coming in so that, even if you don't have all the appropriate data at the time that you submit it, you can update it as you go along."

Google and the National Oceanic and Atmospheric Administration (NOAA) have signed a three-year deal to use the tech giant's artificial intelligence and machine learning to enhance the agency's environmental monitoring, weather forecasting and climate research, according to a joint announcement released Tuesday. Research under the deal initially focused on developing small-scale artificial intelligence and machine learning systems, and based on the results, NOAA and Google Cloud will focus on executing full-scale prototypes the agency could use across its organization. "Strengthening NOAA's data processing through the use of big data, artificial intelligence, machine learning, and other advanced analytical approaches is critical for maintaining and enhancing the performance of our systems in support of public safety and the economy," NOAA acting administrator Neil Jacobs said in the announcement. "I am excited to utilize new authorities granted to NOAA to pursue cutting-edge technologies that will enhance our mission and better protect lives and property," Jacobs added. Google engineers and data scientists have used artificial intelligence research to develop new methods for understanding and predicting weather.

The healthcare sector, that contains a diverse array of industries with activities ranging from research to manufacturing to facilities management (pharma, medical equipment, healthcare facilities), generated in 2013 something like 153 exabytes (1 exabyte 1 billion gigabytes). It is estimated that by year 2020 the healthcare sector will generate 2,134 exabytes. To put that into perspective data centres globally will have enough space only for an estimated of 985 exabytes by 2020. Meaning that two and a half times this capacity would be required to house all the healthcare data. Big data have four V's volume, velocity (real time will be crucial for healthcare), variety and veracity (noise, abnormality, and biases). Poor data quality costs the US economy $ 3,1 trillion a year. And 1 in 3 business leaders don't trust the information they use to make decisions, and this is true also for the healthcare sector.

The PhiSat-1 satellite contains a new hyperspectral-thermal camera and an AI smart camera chip, Intel said. PhiSat-1 is one of a pair of satellites on a mission to monitor polar ice and soil moisture, while also testing intersatellite communication systems, it said. According to the European Space Agency (ESA), PhiSat-1 (Ф-sat-1) is the first experiment to demonstrate how artificial intelligence can be used for Earth observation, filtering out less than perfect images so that only usable data are returned to Earth. PhiSat-1 was launched in early September on the Arianespace SSMS rideshare mission.

We have officially entered the post-truth era. With the rise of deep-fakes, lying politicians, and Surkovian disinformation campaigns, it's hard to get a handle on what truth even is. For a few months I was deep in a skeptical hole where I had truly lost grip on what I considered "real", and I had to claw my way out by getting real silly and coming up with a formal definition that we might all agree with. I'll define terms shortly and it'll be clear that I'm abstracting away many messy details, but I will try to convince you that this basic structure agrees with many of our informal intuitions and is useful for decision-making -- potentially even serving as an objective function for the automated scientists of the near future. Even better: perhaps it can be used as an objective function for search-systems or newsfeeds, only returning the top results as ranked by their truth-value.

Machine learning (ML) is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. Many businesses, eager to capitalize on advancements in ML, have not scrutinized the security of their ML systems. Today, along with MITRE, and contributions from 11 organizations including IBM, NVIDIA, Bosch, Microsoft is releasing the Adversarial ML Threat Matrix, an industry-focused open framework, to empower security analysts to detect, respond to, and remediate threats against ML systems. During the last four years, Microsoft has seen a notable increase in attacks on commercial ML systems. Market reports are also bringing attention to this problem: Gartner's Top 10 Strategic Technology Trends for 2020, published in October 2019, predicts that "Through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, AI model theft, or adversarial samples to attack AI-powered systems."