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.

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.

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.

Graph Neural Networks(GNNs) are useful deep learning models to deal with the non-Euclid data. However, recent works show that GNNs are vulnerable to adversarial attacks. Small perturbations can lead to poor performance in many GNNs, such as Graph attention networks(GATs). Therefore, enhancing the robustness of GNNs is a critical problem. Robust GAT(RoGAT) is proposed to improve the robustness of GNNs in this paper, . Note that the original GAT uses the attention mechanism for different edges but is still sensitive to the perturbation, RoGAT adjusts the edges' weight to adjust the attention scores progressively. Firstly, RoGAT tunes the edges weight based on the assumption that the adjacent nodes should have similar nodes. Secondly, RoGAT further tunes the features to eliminate feature's noises since even for the clean graph, there exists some unreasonable data. Then, we trained the adjusted GAT model to defense the adversarial attacks. Different experiments against targeted and untargeted attacks demonstrate that RoGAT outperforms significantly than most the state-of-the-art defense methods. The implementation of RoGAT based on the DeepRobust repository for adversarial attacks.

Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.

"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.

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."

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.

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.

Artificial intelligence (AI) involves the simulation of human intelligence through programming machines or creating software to think similar to humans and mimic their actions. In other words, AI research seeks to develop technology that is capable of learning and problem solving the same way that a human would. Though the idea itself can be traced back to antiquity, AI has become increasingly popular in recent years, with ever-evolving applications across many Canadian industries. To this end, read on for IBISWorld's evaluation of how two up-and-coming ventures have the potential to affect the operations of different industries in Canada. In London, ON, a new AI tool called the Chronic Homelessness Artificial Intelligence model (CHAI) analyzes points, such as age, gender, family and shelter history, to assess the chance that a particular individual will become chronically homeless over the next six months.