For more than 20 years, the Landsat 7 satellite circled Earth every 99 minutes or so, capturing images of almost all the planet's surface each 16 days. One of many craft that observed the changing globe, it revealed melting glaciers in Greenland, the growth of shrimp farms in Mexico, and the extent of deforestation in Papua New Guinea. But after Landsat 7 ran short on fuel, its useful life effectively ended. In space, regular servicing has not been an option. Now, though, NASA has a potential fix for such enfeebled satellites.

FOR RELEASE Monday, 21 February 2022 09.00 EST / 15.00 CET Updated Index Ranks AI Policies and Practices in 50 Countries Canada, Germany, Italy, and Korea Rank at Top, US Makes Progress as Concerns about China Remain AI POLICY HIGHLIGHTS -2021 - UNESCO AI Recommendation banned social scoring and mass surveillance - EU Introduced comprehensive, risk-based framework - Council of Europe makes progress on AI convention - Continued progress on implementation of OECD Principles, first AI policy framework - G7 leaders endorsed algorithmic transparency to combat AI bias - US opens-up policy process, embraces “democratic values” - EU and US move toward alignment on AI policy - AI regulation in China leaves open questions about independent oversight - UN fails to reach agreement on lethal autonomous weapons - Growing global battle over deployment of facial recognition looms ahead [PRESS RELEASE]

China and the US had a headstart in the AI race, largely attributed to state-sponsored funding in the Research and Development of AI-based technologies and private participation. Though Russia is not considered a frontrunner in the global AI race, it is still a force to be reckoned with. From dominating World War 2 with their superior tanks to ruling the cybersecurity space, Russians have been quick at weaponising new technologies. Vladimir Putin is a steadfast supporter of the use of AI-enabled weapons. The Russian president once said "Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world."

But it falls short of the definition of artificial general intelligence (AGI), which would be a machine capable of understanding the world as well as any human. In the 1950s, researchers including AI pioneer Herbert A. Simon were convinced that AGI would exist within the next few decades. Since then, AGI has proven to be a daunting, perhaps even impossible-to-achieve milestone. Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and AGI is as wide as the gulf between current space flight and faster-than-light travel. Still, others insist that AGI is drawing close within reach.

At first glance, you might think how it's possible for something that doesn't have a mind, body, or soul to have any legal entitlement. After all, algorithms don't have physical attributes, and their existence can't be easily tracked unless you're a tech specialist responsible for creating them. However, while algorithms don't have any physical attributes, they have become smarter over time, mimicking human behaviors and traits to produce actionable results. Initially, algorithms started as simple data sets combined in several valuable ways to create patterns. They generate suggestions and solutions that help guide you, whether you're on social media or search engines, while also providing insights within several fields, including the legal, medical, and marketing fields.

According to a list published recently by the U.S. Government Accountability Office, which audits federal agencies and programs, the U.S. Department of Defense (DoD) is currently involved with more than 685 artificial intelligence (AI) projects. Some of these projects include major weapon systems such as the MQ-9 Unmanned Aerial Vehicle and the Joint Light Tactical Vehicle. For purposes of combat, the DoD is focused on AI abilities that assist in target recognition, battlefield analysis and autonomy on unmanned systems. One example of AI for autonomous systems is the U.S. Navy's Undersea Warfare Decision Support System, which is designed to help plan and execute undersea missions. Generally, AI will help machines perform tasks such as drawing conclusions and making predictions in place of human thinking.

Deep learning which is also known as Deep Neural Network includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. It can be extensively used for cybersecurity to protect companies from threats like phishing, spear-phishing, drive-by attack, a password attack, denial of service, etc. Learn about the top 10 applications of deep learning in cybersecurity. Deep learning, convolutional neural networks, and Recurrent Neural Networks (RNNs) can be applied to create smarter ID/IP systems by analyzing the traffic with better accuracy, reducing the number of false alerts, and helping security teams differentiate bad and good network activities. Notable solutions include Next-Generation Firewall (NGFW), Web Application Firewall (WAF), and User Entity and Behavior Analytics (UEBA). Traditional malware solutions such as regular firewalls detect malware by using a signature-based detection system.

New Zealand is developing an approach to supporting the ethical adoption of AI -- one that is focused on building an AI ecosystem on a foundation of trust, equity and accessibility right from the onset. A crucial part of this approach is to involve key stakeholders in the planning. And that is exactly the reason why the government has designed the system so every New Zealander and every technology expert who matters can contribute. The success of this ITP requires us to form a consensus view on the scope of our ambition and how this can be achieved with actions and initiatives that are sufficiently realistic to bring about meaningful change – both short and longer-term. Wellington published a draft that should jumpstart its pursuit of an ethical AI ecosystem: the Industry Transformation Plan (ITP) which covers its overall digital transformation road map.

An increasingly common methodological issue in the field of social science is high-dimensional and highly correlated datasets that are unamenable to the traditional deductive framework of study. Analysis of candidate choice in the 2020 Presidential Election is one area in which this issue presents itself: in order to test the many theories explaining the outcome of the election, it is necessary to use data such as the 2020 Cooperative Election Study Common Content, with hundreds of highly correlated features. We present the Fuzzy Forests algorithm, a variant of the popular Random Forests ensemble method, as an efficient way to reduce the feature space in such cases with minimal bias, while also maintaining predictive performance on par with common algorithms like Random Forests and logit. Using Fuzzy Forests, we isolate the top correlates of candidate choice and find that partisan polarization was the strongest factor driving the 2020 presidential election. Social science research today often encounters a difficult methodological situation -- larger and larger datasets, which contain high-dimensional features, which are highly correlated [7]. Quite literally, as in the application we discuss in our paper (the 2020 U.S Presidential election), to test the many different theories and potential explanations for why voters decided to remove then President Trump from office, researchers need to use methodologies that can quickly and efficiently reduce the feature space from hundreds of possible features to a smaller set that can then be the focus of further study. In our paper we present a variant of the popular Random Forest, Fuzzy Forests, which we argue is well suited for exactly this type of applied machine learning problem [6]. Fuzzy Forests are ideal for feature selection in large and high-dimensional datasets, where the features are highly correlated.

While Ukraine is yet to become a member of the North Atlantic Treaty Organization (NATO), the country has been accepted as a contributing participant to the NATO Cooperative Cyber Defence Centre of Excellence (CCDCOE). CCDCOE is a NATO-accredited cyberdefense hub that member nations use for research, training, and exercises covering several areas, including technology, strategy, operations, and law. Although this does not make Ukraine a NATO member, it will likely tighten collaboration and allow it to gain access to NATO member nations' cyber-expertise and share its own. "Ukraine's presence in the Centre will enhance the exchange of cyber expertise, between Ukraine and CCDCOE member nations," said Colonel Jaak Tarien, Director of NATO CCDCOE. "Ukraine could bring valuable first-hand knowledge of several adversaries within the cyber domain to be used for research, exercises and training," Minister of Defence of Estonia Kalle Laanet added that Ukraine "has valuable experience from previous cyber-attacks to provide significant value to the NATO CCDCOE."