The leaders of the Senate Homeland Security and Government Affairs Committee are looking to get the federal workforce – particularly program managers and acquisition specialists – on board with artificial intelligence. A new bill, the Artificial Intelligence Training for the Acquisition Workforce Act, would set up a training program for federal workers to learn more about AI technology, from its scientific underpinnings to risks associated with its use. "Federal employees must be aware of the ethical implications, risks, and benefits associated with AI," Sen. Gary Peters (D-Mich.), the chair of the committee and a co-sponsor of the bill, said in a statement. "This important legislation will help protect our national security, help us remain competitive in the long run, and make sure AI technology is used properly." The bill tasks the Office of Management and Budget with establishing training to help the federal workforce understand the science behind AI, how AI can benefit government programs as well as the risks associated with AI, including privacy violations and inherent bias in algorithms that power AI programs.

The US military continues to experiment with artificial intelligence technologies. According to network sources, the US Northern Command has completed a series of tests as part of the Global Information Dominance Experiment (GIDE) by building an information system based on AI algorithms and cloud computing, which is capable of predicting the development of events for several days in advance. The source notes that the use of this technology will lead to major changes in strategy during military and government operations. GIDE's machine learning system is able to track the slightest changes in data, indicating the possibility of any problems, in real time. For example, if satellite images record the preparation of a submarine from a port of a hostile country, then the AI algorithm will note this fact, assuming that the boat is likely to leave the berth.

Get the day's biggest stories sent direct to your inbox so you never miss a thing Artificial Intelligence (AI) has become a staple of our daily lives, from Siri to Google Assistant which can control our phones, computers and even homes. The world of media has explored the advancement and potential dangers of rapidly advancing AI for decades, films such as Blade Runner and 2001: A Space Odyssey have touched on the themes of what happens when AI grows beyond human control. But how much does this affect our perception of AI and its involvement in our daily lives? Using data from Google Search Trends and Linkfluence, new research from Ebuyer has revealed that globally almost 3 million people had searched negative themes around AI online. The research discovered that the biggest search queries included "Can artificial intelligence be dangerous?"

Deep neural networks have been applied to a wide range of problems across different application domains with great success. Recently, research into combinatorial optimization problems in particular has generated much interest in the machine learning community. In this work, we develop deep learning models to predict the chromatic number and maximum clique size of graphs, both of which represent classical NP-complete combinatorial optimization problems encountered in graph theory. The neural networks are trained using the most basic representation of the graph, the adjacency matrix, as opposed to undergoing complex domain-specific feature engineering. The experimental results show that deep neural networks, and in particular convolutional neural networks, obtain strong performance on this problem.

This paper discusses recent research progress in robotic grasping and manipulation in the light of the latest Robotic Grasping and Manipulation Competitions (RGMCs). We first provide an overview of past benchmarks and competitions related to the robotics manipulation field. Then, we discuss the methodology behind designing the manipulation tasks in RGMCs. We provide a detailed analysis of key challenges for each task and identify the most difficult aspects based on the competing teams' performance in recent years. We believe that such an analysis is insightful to determine the future research directions for the robotic manipulation domain.

Researchers are using artificial intelligence (AI) techniques to calibrate some of NASA's images of the Sun. Launched in 2010, NASA's Solar Dynamics Observatory (SDO) has provided high-definition images of the Sun for over a decade. The Atmospheric Imagery Assembly, or AIA, is one of two imaging instruments on SDO and looks constantly at the Sun, taking images across 10 wavelengths of ultraviolet light every 12 seconds. This creates a wealth of information of the Sun like no other, but like all Sun-staring instruments--AIA degrades over time, and the data needs to be frequently calibrated, NASA said in a statement. To overcome this challenge, scientists decided to look at other options to calibrate the instrument, with an eye towards constant calibration.

U.S. Northern Command (NORTHCOM) recently conducted a series of tests known as the Global Information Dominance Experiments, or GIDE, which combined global sensor networks, artificial intelligence (AI) systems, and cloud computing resources in an attempt to "achieve information dominance" and "decision-making superiority." According to NORTHCOM leadership, the AI and machine learning tools tested in the experiments could someday offer the Pentagon a robust "ability to see days in advance," meaning it could predict the future with some reliability based on evaluating patterns, anomalies, and trends in massive data sets. While the concept sounds like something out of Minority Report, the commander of NORTHCOM says this capability is already enabled by tools readily available to the Pentagon. General Glen VanHerck, Commander of NORTHCOM and North American Aerospace Defense Command (NORAD), told reporters at the Pentagon this week that this was the third test of GIDE, conducted in conjunction with all 11 combatant commands "collaborating in the same information space using the same exact capabilities." The experiment largely centered around contested logistics and information advantage, two cornerstones of the new warfighting paradigm recently proposed by the Vice Chairman of the Joint Chiefs of Staff.

The Pentagon is stealing a page from Minority Report with an experimental artificial-intelligence program that can look'days in advance' and predict possible attacks on vulnerable locations. The Global Information Dominance Experiments, or GIDE, use machine learning to sift through vast amounts of data to notice tiny changes that humans might miss - such as the number of cars increasing or decreasing in a parking lot - which might indicate an evolving threat. The program can then alert human agents who can take a closer look at the location. The latest experiment - GIDE 3 - focused on'contested logistics', in a scenario where lines of communication in the Panama Canal were compromised, military officials said. General Glen VanHerck, commander of the US Northern Command (NORTHCOM), said GIDE combines artificial intelligence and cloud computing resources with data from sources around the world to'achieve information dominance' and'decision-making superiority.'

Shameek Kundu, Head of Financial Services at TruEra, looks at whether AI's credibility gap could hold back the banking industry. Artificial Intelligence (AI) is widely seen as key to the banking industry's transformation. Industry surveys, including one from the Bank of England, suggest that two in every three financial institutions have adopted AI in some form. In most banks, neither the 2020 budget restrictions nor the failure of some AI systems during COVID-19 appear to have slowed down AI-related recruitment or technology spending. But is the reality of AI adoption in banking living up to the hype?

The media ecosystem propagated by inhuman recommendation algorithms can easily be weaponized by bad faith actors, and there exist clear examples in which such tactics have been utilized for anti-democratic ends. In the 2016 United States presidential election, Russian operatives created fake social media accounts with the goal of exacerbating political polarization and increasing social discord. These accounts propagated provocative and misleading statements that intentionally targeted delicate subjects, specifically issues regarding race. Controversial and highly engaging content like this is exactly what the recommendation algorithms favor, and there is no doubt the technology helped circulate Russian disinformation, which ultimately affected the results of the election.