Collaborating Authors


Air Force Betting on New Robotic Wingman


The next year will be pivotal for the Air Force's effort to acquire a new class of autonomous drones, as industry teams compete for a chance to build a fleet of robotic wingmen that will soon undergo operational experimentation. The "Skyborg" program is one of the service's top science-and-technology priorities under the "Vanguard" initiative to deliver game-changing capabilities to its warfighters. The aim is to acquire relatively inexpensive, attritable unmanned aircraft that can leverage artificial intelligence and accompany manned fighter jets into battle. "I expect that we will do sorties where a set number are expected to fly with the manned systems, and we'll have crazy new [concepts of operation] for how they'll be used," Assistant Secretary of the Air Force for Acquisition, Technology and Logistics Will Roper said during an online event hosted by the Mitchell Institute for Aerospace Studies. The platforms might even be called upon to conduct kamikaze missions.

'Attacking at speed': Army Project Convergence and breakthrough lightning-fast war

FOX News

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. The U.S. military recently conducted a live-fire full combat replication with unmanned-to-unmanned teaming guiding attacks, small reconnaissance drones, satellites sending target coordinates to ground artillery and high-speed, AI-enabled "networked" warfare. This exercise was a part of the Army's Project Convergence 2020, a weapons and platform combat experiment which, service leaders say, represents a massive transformation helping the service pivot its weapons use, tactics and maneuver strategies into a new era. Taking place at Yuma Proving Grounds, Arizona, Project Convergence involved live-fire war experiments aligned in three distinct phases, intended to help the Army cultivate its emerging modern Combined Arms Maneuver strategy.

Singapore urges need for international organisations to 'reform' in digital age


Singapore has called on global organisations such as the United Nations (UN) and World Trade Organisation (WTO) to reform, so international rules are in line with cybersecurity and other key digital developments. The Asian nation also underscores the need for unified cooperation against COVID-19, which it notes has accelerated "self-defeating" sentiments worldwide including protectionism and xenophobia. Continued international cooperation was key to overcoming the impact of the pandemic as well as to rebuilding, and nations needed to build greater trust and learn from each other, said Singapore's Minister for Foreign Affairs Vivian Balakrishnan, in the country's national statement at the UN General Assembly's General Debate of the 75th session held Saturday. Delivered via video message, Balakrishnan said in his speech: "The world is facing a period of prolonged turmoil. The multilateral system is confronted by nationalism, xenophobia, the rejection of free trade and global economic integration, and the bifurcation of technology and supply chains. Caught by the sudden onslaught of COVID-19, most businesses lacked or had inadequate security systems in place to support remote work and now have to deal with a new reality that includes a much wider attack surface and less secured user devices. "But, these threats are not new.

Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform


Artificial intelligence (AI) is surpassing human performance in a growing number of domains. However, there is limited evidence of its economic effects. Using data from a digital platform, we study a key application of AI: machine translation. We find that the introduction of a new machine translation system has significantly increased international trade on this platform, increasing exports by 10.9%. Furthermore, heterogeneous treatment effects are consistent with a substantial reduction in translation costs.

Declaration of the United States of America and the United Kingdom of Great Britain and Northern Ireland on Cooperation in Artificial Intelligence Research and Development


Recommending priorities for future cooperation, particularly in R&D areas where each partner shares strong common interest (e.g., interdisciplinary research and intelligent systems) and brings complementary challenges, regulatory or cultural considerations, or expertise to the partnerships; Promoting research and development in AI, focusing on challenging technical issues, and protecting against efforts to adopt and apply these technologies in the service of authoritarianism and repression. We intend to establish a bilateral Government-to-Government dialogue on the areas identified in this vision and explore an AI R&D ecosystem that promotes the mutual wellbeing, prosperity, and security of present and future generations. Signed in London and Washington on 25 September 2020, in two originals, in the English language.

Programming Fairness in Algorithms


"Being good is easy, what is difficult is being just." "We need to defend the interests of those whom we've never met and never will." Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way with minimal use of mathematics, in the hope that everyone can get something out of reading this. Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories -- indeed, this is their designated purpose in life. This is reflected in the name for these algorithms which are often referred to as discriminative algorithms (splitting data into categories), in contrast to generative algorithms (generating data from a given category). When we use supervised machine learning, this "discrimination" is used as an aid to help us categorize our data into distinct categories within the data distribution, as illustrated below. Whilst this occurs when we apply discriminative algorithms -- such as support vector machines, forms of parametric regression (e.g.

Scientists use reinforcement learning to train quantum algorithm


Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash. "In one traditional example, you can find the shortest route for a salesman who needs to visit a few cities once by enumerating all possible routes, but given a couple thousand cities, the number of possible routes far exceeds the number of stars in the universe; even the fastest supercomputers cannot find the shortest route in a reasonable time."

How will AI and Machine Learning (ML) Affect Cyber Security?


The internet is increasingly becoming a part of our lives, growing every second. A new change takes place every day, rendering the prevailing system obsolete. Adjusting to this change is not always easy. The risks associated with the internet are many and affect the security of the users to a great extent. With the advent of Artificial Intelligence and Machine Learning, every process is being automated.

Inside the Army's futuristic test of its battlefield artificial intelligence in the desert


After weeks of work in the oppressive Arizona desert heat, the U.S. Army carried out a series of live fire engagements Sept. 23 at Yuma Proving Ground to show how artificial intelligence systems can work together to automatically detect threats, deliver targeting data and recommend weapons responses at blazing speeds. Set in the year 2035, the engagements were the culmination of Project Convergence 2020, the first in a series of annual demonstrations utilizing next generation AI, network and software capabilities to show how the Army wants to fight in the future. The Army was able to use a chain of artificial intelligence, software platforms and autonomous systems to take sensor data from all domains, transform it into targeting information, and select the best weapon system to respond to any given threat in just seconds. Army officials claimed that these AI and autonomous capabilities have shorted the sensor to shooter timeline -- the time it takes from when sensor data is collected to when a weapon system is ordered to engaged -- from 20 minutes to 20 seconds, depending on the quality of the network and the number of hops between where it's collected and its destination. "We use artificial intelligence and machine learning in several ways out here," Brigadier General Ross Coffman, director of the Army Futures Command's Next Generation Combat Vehicle Cross-Functional Team, told visiting media.

Future Tense Newsletter: Make the Future Great Again


Politics are in the air, like that ominous reddish glow suffocating much of the West in recent weeks on account of all those tragic wild fires. This coming week we get our first presidential debate. A chance for Donald Trump and Joe Biden to shake hands and have a respectful, reasoned exchange of views on the future of the unfairly maligned Section 230 of the Communications Decency Act; the need to reform the Stored Communications Act; the wisdom of replicating Europe's General Data Privacy Regulation; the merits of taking antitrust action against Google for its manipulation of search results or against Amazon for its treatment of third-party sellers on its platform. Maybe we will even see the candidates reflect humbly on humanity's place in the universe, in light of the breaking news from Venus. The debate will probably be all tense, no future--maybe not as heated as a debate between 2016 Lindsey Graham and 2020 Lindsey Graham, but close.