New generation geostationary satellites make solar reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating quality land monitoring products requires correction of the effects of atmospheric scattering and absorption, which vary in time and space according to geometry and atmospheric composition. Many atmospheric radiative transfer models, including that of Multi-Angle Implementation of Atmospheric Correction (MAIAC), are too computationally complex to be run in real time, and rely on precomputed look-up tables. Additionally, uncertainty in measurements and models for remote sensing receives insufficient attention, in part due to the difficulty of obtaining sufficient ground measurements. In this paper, we present an adaptation of Bayesian Deep Learning (BDL) to emulation of the MAIAC atmospheric correction algorithm. Emulation approaches learn a statistical model as an efficient approximation of a physical model, while machine learning methods have demonstrated performance in extracting spatial features and learning complex, nonlinear mappings. We demonstrate stable surface reflectance retrieval by emulation (R2 between MAIAC and emulator SR are 0.63, 0.75, 0.86, 0.84, 0.95, and 0.91 for Blue, Green, Red, NIR, SWIR1, and SWIR2 bands, respectively), accurate cloud detection (86\%), and well-calibrated, geolocated uncertainty estimates. Our results support BDL-based emulation as an accurate and efficient (up to 6x speedup) method for approximation atmospheric correction, where built-in uncertainty estimates stand to open new opportunities for model assessment and support informed use of SR-derived quantities in multiple domains.

This year, NASCIO and the Center for Digital Government, with support from IBM, set out to understand state CIOs' motivations, plans and deterrents around AI adoption. The survey, Delivering on Digital Government: Achieving the Promise of Artificial Intelligence, yielded responses from 45 states. The results reflect state leaders' eagerness to gain efficiencies and free up their workforces for higher-value work, tempered by caution due to concerns around lack of data maturity and privacy policies, as well as a dearth of employees with the necessary skills for AI adoption.

The camera flies past a detachment of robot soldiers on a spaceship. Nearly half of all web traffic is from "bots." That statistic alone should grab your attention, but the one that should worry you more is that "bad" bots are growing in number and reach. Cutting right to the chase: the only way bad bots can be contained is with good or other bad bots. A bot … "is an automated program that is programmed for certain actions and executes them either regularly or reactively. The bot does this without needing human activation. It analyzes the environment and'decides' which actions to take depending on the situation."

The Pentagon is increasingly incorporating artificial intelligence, including for what the military calls "maneuver and fires," the part of fighting wars that involves targeting and shooting people. To help ensure that these machines behave ethically, the Pentagon is looking for an AI ethicist to join its new Joint Artificial Intelligence Center. "We're thinking deeply about the safe and lawful use of AI," says Lt. Gen. Jack Shanahan, in the briefing where he announced the position. Navigating AI ethics for the Pentagon will require safeguarding human and civil rights while keeping pace with AI development in China and Russia, countries whose militaries appear less preoccupied with such rights. Even if the Pentagon develops computer algorithms that are always able to distinguish between enemy targets and noncombatants, there is a risk in developing capabilities that are too effective, says Patrick Lin, a philosophy professor at California Polytechnic State University.

Google's claims of quantum supremacy have some people worried that the internet is now broken. Within hours of the news breaking, US presidential candidate Andrew Yang tweeted that "It means, among many other things, that no code is uncrackable." Although there is reason to believe that quantum computers will pose a risk to encryption, there is still a long way to go before that happens. The internet uses encryption to ensure that sensitive data, such as bank details or private messages, can't be read by prying …

WASHINGTON, D.C. – Today, the U.S. Department of Energy (DOE) announced $13 million in funding for five research projects in artificial intelligence (AI) aimed at improving AI as a tool of scientific investigation and prediction. The projects involve scientists at nine research institutions, including both DOE national laboratories and universities. "Artificial intelligence, including machine learning, provides an extremely powerful way of tackling the most pressing issues facing our scientists today," said U.S. Secretary of Energy Rick Perry. "This research will help us adapt AI to the specific scientific challenges that DOE-supported scientists are addressing today and in the process help sustain U.S. leadership in this critical and growing field." Of the $13 million, $11.1 million is reserved for two three-year projects focused on the development of new AI algorithms and software adapted to specific scientific problems.

WIRE)--RiskSense, Inc., pioneering risk-based vulnerability management and prioritization, today announced that its CEO, Dr. Srinivas Mukkamala will lead an expert panel at the SINET Showcase conference in Washington, DC on November 7, 2019 on the impact of bias in AI-driven security systems. Dr. Srinivas Mukkamala, co-founder and CEO of RiskSense, is a recognized expert on artificial intelligence (AI) and neural networks. He was part of a think tank that collaborated with the U.S. Department of Defense and U.S. Intelligence Community to apply these concepts against cybersecurity problems. Dr. Mukkamala was also a lead researcher for CACTUS (Computational Analysis of Cyber Terrorism against the U.S.) and holds a patent on Intelligent Agents for Distributed Intrusion Detection System and Method of Practicing. Artificial intelligence and machine learning are increasingly being used and trusted by organizations to automate security threat detection.

The Machinery Directive is the core piece of EU legislation for the mechanical engineering industry: it promotes the free movement of machinery within the Internal Market, while setting out the'Essential Health and Safety Requirements' to be observed when placing a machine on the market for the first time. The mechanical engineering companies Orgalim represents really value this Directive: in addition to ensuring a high level of safety for employees and end users, it has provided a stable legal environment for businesses since it came into force over a decade ago. Particularly for small and medium sized enterprises (SMEs), this stability is invaluable. In the last year or two, however, the European Commission has been exploring the possibility of reviewing the Directive – prompted primarily by new developments in technologies like artificial intelligence (AI). AI has climbed up the political agenda in recent years, reflecting public concerns that this technology is somehow different; that, in the most dramatic scenarios, systems deploying AI could start to act autonomously and provoke new, unmanageable dangers.

Cybersecurity is always a top concern for federal IT leaders. Increasingly, artificial intelligence is becoming a key area of interest as well. How can AI-based tools help agencies with their cybersecurity? Such tools can help agencies speed up decision-making and and sift through large volumes of data efficiently. At the Imagine Nation ELC 2019 conference in Philadelphia, we spoke to IT leaders how they think automation will affect cybersecurity.

Sci-fi and science can't seem to agree on the way we should think about artificial intelligence. Sci-fi wants to portray artificial intelligence agents as thinking machines, while businesses today use artificial intelligence for more mundane tasks like filling out forms with robotic process automation or driving your car. When interacting with these artificial intelligence interfaces at our current level of AI technology, our human inclination is to treat them like vending machines, rather than to treat them like a person. Because thinking of AI like a person (anthropomorphizing) leads to immediate disappointment. Today's AI is very narrow, and so straying across the invisible line between what these systems can and can't do leads to generic responses like "I don't understand that" or "I can't do that yet".