A man engaged in internet-related reputation management has said that former Justice Minister Katsuyuki Kawai requested the deletion of personal computer data shortly after an alleged vote-buying scandal involving him and his wife, Anri, came to light last year. According to a statement of confession made by the man, read out by prosecutors during a hearing Monday as part of the trial of Anri Kawai at Tokyo District Court, Katsuyuki Kawai, 57, a lawmaker in the House of Representatives, met with the man at the former minister's residence in a dormitory for Diet members in Tokyo on Nov. 3 last year. At the time, Katsuyuki Kawai told the man that staff at his office may have taken some data without authorization, and that he wanted to delete any data that could cause a problem if it was leaked, the man said in the statement. On that day, Katsuyuki Kawai allegedly deleted PC data at his residence using data deletion software purchased by the man, according to the statement. The man also said he visited Kawai's office in Tokyo and the couple's offices in Hiroshima Prefecture the following day to delete PC data there.

A smart city is a municipality that uses information and communication technologies (ICT) to increase operational efficiency, share information with the public and improve both the quality of government services and citizen welfare. While the exact definition varies, the overarching mission of a smart city is to optimize city functions and drive economic growth while improving quality of life for its citizens using smart technology and data analysis. Value is given to the smart city based on what they choose to do with the technology, not just how much technology they may have. Several major characteristics are used to determine a city's smartness. A smart city's success depends on its ability to form a strong relationship between the government -- including its bureaucracy and regulations -- and the private sector.

'Hillbilly Elegy' author J.D. Vance responds to suggestion on'Tucker Carlson Tonight' The idea, mooted by some Democrats and liberals, of a South Africa-style Truth and Reconciliation Commission after President Trump's term of office of complete would be less about reconciliation than "revenge," author J.D. Vance told "Tucker Carlson Tonight" Monday. Former Labor Secretary Robert Reich tweeted Saturday that such a commission would "erase Trump's lies, comfort those who have been harmed by his hatefulness, and name every official, politician, executive, and media mogul whose greed and cowardice enabled this catastrophe." "This is torn from a page in a George Orwell novel ... because who can protest'truth and reconciliation'," stated Vance, the author of "Hillbilly Elegy." Vance added that the idea would not only damage the country, but shows how "whiny" liberal Democrats still are about Hillary Clinton's 2016 election loss. "Instead of trying to win the next election and moving on with the life of American democratic politics, they want to go backward and punish everybody," Vance said.

NVIDIA today announced that the Italian inter-university consortium CINECA -- one of the world's most important supercomputing centers -- will use the company's accelerated computing platform to build the world's fastest AI supercomputer. The new "Leonardo" system, built with Atos, is expected to deliver 10 exaflops of FP16 AI performance to enable advanced AI and HPC converged application use cases. Featuring nearly 14,000 NVIDIA Ampere architecture-based GPUs and NVIDIA Mellanox HDR 200 Gb/s InfiniBand networking, Leonardo will propel Italy as the global leader in AI and high performance computing research and innovation. Leonardo is procured by EuroHPC, a collaboration between national governments and the European Union to develop a world-class supercomputing ecosystem and exascale supercomputing in Europe, and funded by the European Commission through the Italian Ministry of University and Research. "The EuroHPC technology roadmap for exascale in Europe is opening doors for rapid growth and innovation in HPC and AI," said Marc Hamilton, vice president of solutions architecture and engineering at NVIDIA.

We've launched 9,600 satellites since 1957. For the first few decades, no one thought about what would happen once they reached the end of their lives. By the time space agencies decided to do something, it had become a problem. "A vast majority of objects in orbit are effectively stranded there," says Stijn Lemmens, a space debris analyst at the European Space Agency. "And they have a lifetime of hundreds, thousands of years."

Artificial intelligence is the new oil, and the governments or the countries that get the best datasets will unquestionably develop the best AI, the Joint Artificial Intelligence Center's chief technology officer said Oct. 15. Speaking on a panel about AI superpowers at the Politico AI Summit, Nand Mulchandani said AI is a very large technology and industry. "It's not a single, monolithic technology," he said. "It's a collection of algorithms, technologies, etc., all cobbled together to call AI." The United States has access to global datasets, and that's why global partnerships are so incredibly important, he said, noting the Defense Department launched the AI partnership for defense at the JAIC recently to have access to global datasets with partners, which gives DOD a natural advantage in building these systems at scale.

The choices that technologists, policymakers, and communities make in the next few years will shape the relationship between machines and humans for decades to come. The rapidly increasing applicability of AI has prompted a number of organizations to develop high-level principles on social and ethical issues such as privacy, fairness, bias, transparency, and accountability. Building on those broader principles, the AI Policy Forum, a global effort convened by the MIT Stephen A. Schwarzman College of Computing, will provide an overarching policy framework and tools for governments and companies to implement in concrete ways. "Our goal is to help policymakers in making practical decisions about AI policy," says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. "We are not trying to develop another set of principles around AI, several of which already exist, but rather provide context and guidelines specific to a field of use of AI to help policymakers around the world with implementation." "Moving beyond principles means understanding trade-offs and identifying the technical tools and the policy levers to address them.

"Most professional scientists aim to be the first to publish their findings, because it is through dissemination that the work realises its value." So wrote mathematician James Ellis in 1987. By contrast, he went on, "the fullest value of cryptography is realised by minimising the information available to potential adversaries." Ellis, like Alan Turing, and so many of the driving forces in the development of computers and the Internet, worked in government signals intelligence, or SIGINT. Today, this covers COMINT (harvested from communications such as phone calls) and ELINT (from electronic emissions, such as radar and other electromagnetic radiation).

When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.

Despite great interest in recent research, in particular in China [1, 2] micromanagement of sensors by centralized command and control drives possible inefficiencies and risk into operations. Tactical decision making and execution by headquarters usually fail to achieve the speed necessary to meet rapid changes. Collaborative radars with C2 must provide decision superiority despite the attempts of an adversary to disrupt OODA cycles at all level of operations. Artificial intelligence can make a contribution for the purposes of coordinated conduct of the action, by improving the response time to threats and optimizing the allocation and the distribution of tasks within elementary smart radars. In order to address this problem, Thales and the private research lab NukkAI have been collaborating to introduce novel approaches for netted radars. Thales provided the simulation modeling the multi-radar target allocation problem and NukkAI proposed two novel reward-based learning approaches for the problem. In this paper, we present these two approaches: Evolutionary Single-Target Ordering (ESTO), which is based on evolution strategies and an RL approach based on Actor-Critic methods. To make the RL method tractable in practice, we introduce a simplification of the problem that we prove to be equivalent to solving the initial formulation. We evaluate our solutions on diverse scenarios of the aforementioned simulation.