Russia's leaders have been paying close attention to artificial intelligence (AI) technologies for several years now. President Vladimir Putin has said on numerous occasions that the leader in the field of AI would become "the master of the world." Until recently, however, Russia remained virtually the only large country without its own AI development strategy. That changed in October 2019, when the country adopted a long-discussed National Strategy for the Development of Artificial Intelligence Through 2030. One of the driving forces behind the strategy was Sberbank president German Gref. The state-owned bank has also developed a road map for developing AI in Russia and coordinated the creation of Russia's AI development strategy, which is largely corporate, involving the internet giants Yandex and Mail.ru

After more than five months of the coronavirus pandemic in the United States, White House briefings and press conferences still lack an American Sign Language interpreter. On Monday, a week after President Trump resumed his daily briefings, the National Association of the Deaf filed a lawsuit against the White House arguing that the continued refusal to include an interpreter violates the Americans With Disabilities Act. "All 50 states' governors have provided in-frame American Sign Language ('ASL') interpretation during public briefings regarding the pandemic," the lawsuit notes. "President Trump, however, does not. He now stands alone in holding televised briefings regarding the Covid-19 pandemic without ever having provided any ASL Interpretation."

Artificial Intelligence (AI) is not the one that is borne by the overwhelming science fiction vision. In the near future, we will see almost every area of life in order to make our activities more effective and interactive. According to China's search engine, Baidu's top researcher, "Reliability of speech technology approaches the point we will only use and do not even think about." Andrew Ng says the best technology is often invisible, and speech recognition will disappear in the background as well. Baidu is currently working on more accurate speech recognition and more efficient sentence analysis, which expects sound technologies to be able to interact with multiple devices such as household appliances.

Robotic machinery that is being used in industries to assemble airplanes and smart phones are vulnerable to cyber attacks say security experts from Trend Micro Inc. And the researchers argue that most of such machinery is susceptible to hacking activities like data steal and remotely altering the movement of robots. Trend Micro's report titled "Robot Automation" says that industrial environments having robotic machinery are exposed to serious consequences like machinery failure, physical damage to operators and sometimes injuries and life loss to them. Technically, robots run with the help of systems driven by operating systems and some vulnerability in them could make cyber criminals to induce malicious codes into them and program them remotely to run as per their likes. For instance, they found App based software produced by ABB LTD from Switzerland to be exhibiting certain flaws that when explored by hackers could bring operational troubles to industrial firms- especially those related to automobile sector.

Richard Harmon, Managing Director of Financial Services at Cloudera, discusses the importance of relevant machine learning models in today's age, and how the financial sector can prepare for future changes. The past six months have been turbulent. Business disruptions and closures are happening at an unprecedented scale and impacting the economy in a profound way. In the financial services sector, S&P Global estimates that this year could quadruple UK bank credit losses. The economic uncertainty in the UK is heightened by Brexit, which will see the UK leave the European Union in 2021.

Microsoft has just provided two new nuggets on information from Flight Simulator, which is about to make its descent onto gamers' systems on August 18 -- now, also on Steam. VIEW GALLERY – 10 IMAGES In a new update, Microsoft explained that Flight Simulator "heralds a new era of data-driven flight simulation". In the update, Microsoft continued: "One of the exciting implications of this is that the data that makes up the world is always improving, and players can look forward to a simulator that evolves over time".The post continued: "Thanks to our partnership with Bing, players will enjoy new, even better terrain, and an ever evolving, machine learning-fueled simulator that grows over time".Microsoft finished up the update on Flight Simulator, adding: "We have so much ahead for Microsoft Flight Simulator, but even on day one simmers will get highly detailed aircraft, a new checklist system, live air traffic, dynamic weather, new aerodynamic modeling, as well as all the many other features we shared in our launch date announcement, all within a beautifully crafted and rendered world. Whether you are new to flight simulation or an aviation pro, the sky is calling in Microsoft Flight Simulator".There will be 3 different …

Technologies powered by artificial intelligence, such as chatbots and personalized shopping suggestions, have become more common in recent years, leading many consumers to embrace artificial intelligence. Such human-centered AI analyzes data through the lens of human behavior, which in turn allows companies to better understand their customer base. As this technology develops and becomes more integrated into our daily lives, the future of human-centered AI is looking brighter than ever. Below, the members of Forbes Technology Council share 13 exciting future uses of human-centered AI to keep an eye on. Because we have the opportunity to teach and train the AI of the future, we have a unique opportunity to define AI for all.

During this CONNECT University session the participants had the opportunity to discover "Elements of AI", a course developed by Reaktor together with the University of Helsinki in order to teach the basics of Artificial Intelligence to citizens and EU staff from a wide range of backgrounds.

Deep neural networks, including reinforcement learning agents, have been proven vulnerable to small adversarial changes in the input, thus making deploying such networks in the real world problematic. In this paper, we propose RADIAL-RL, a method to train reinforcement learning agents with improved robustness against any $l_p$-bounded adversarial attack. By simply minimizing an upper bound of the loss functions under worst case adversarial perturbation derived from efficient robustness verification methods, we significantly improve robustness of RL-agents trained on Atari-2600 games and show that RADIAL-RL can beat state-of-the-art robust training algorithms when evaluated against PGD-attacks. We also propose a new evaluation method, Greedy Worst-Case Reward (GWC), for measuring attack agnostic robustness of RL agents. GWC can be evaluated efficiently and it serves as a good estimate of the reward under the worst possible sequence of adversarial attacks; in particular, GWC accounts for the importance of each action and their temporal dependency, improving upon previous approaches that only evaluate whether each single action can change under input perturbations. Our code is available at https://github.com/tuomaso/radial_rl.

The Managed Care system within Medicaid (US Healthcare) uses Request For Proposals (RFP) to award contracts for various healthcare and related services. RFP responses are very detailed documents (hundreds of pages) submitted by competing organisations to win contracts. Subject matter expertise and domain knowledge play an important role in preparing RFP responses along with analysis of historical submissions. Automated analysis of these responses through Natural Language Processing (NLP) systems can reduce time and effort needed to explore historical responses, and assisting in writing better responses. Our work draws parallels between scoring RFPs and essay scoring models, while highlighting new challenges and the need for interpretability. Typical scoring models focus on word level impacts to grade essays and other short write-ups. We propose a novel Bi-LSTM based regression model, and provide deeper insight into phrases which latently impact scoring of responses. We contend the merits of our proposed methodology using extensive quantitative experiments. We also qualitatively asses the impact of important phrases using human evaluators. Finally, we introduce a novel problem statement that can be used to further improve the state of the art in NLP based automatic scoring systems.