A Proton rocket launched from the Baikonur Cosmodrome in Kazakhstan today, taking the European Robotic Arm (ERA) payload to the International Space Station. The 11-meter long robot has been folded and attached to the Multipurpose Laboratory Module, also called'Nauka', that will be its home base when it reaches the ISS. The rocket put Nauka and the ERA into orbit at 16:08pm GMT, ten minutes after liftoff, at an altitude of nearly 200 kilometres above the Earth. The ISS already has two robotic arms, which are used to berth spacecraft and transfer payloads and astronauts, but neither arm can each the Russian segment, the European Space Agency said. Instead, the ERA will'walk' around the Russian parts of the orbital complex, handling components up to 8000 kilograms, and transport astronauts when it eventually reaches the station.

Russia is launching a new module for the International Space Station (ISS), after more than a decade of delays. The Nauka module is set to lift off from Baikonur Cosmodrome in Kazakhstan on top of a Proton-M rocket at around 1500 GMT today, along with a new robotic arm for the station created by the European Space Agency. The ISS is composed of modules and equipment from different space agencies including Europe, Japan and Canada, but the bulk of the station is composed of two main sections, a Russian segment and a US segment. At 13 metres long and weighing more than 20 tonnes, Nauka, also called the Multipurpose Laboratory Module, will be among the largest in Russia's half. After launch, Nauka will take eight days to reach the ISS.

Fox News correspondent Lucas Tomlinson has the details from the Pentagon on'Special Report' Russian President Vladimir Putin inspected the country's newly unveiled "Checkmate" warplane on Tuesday. The prototype of the Sukhoi fifth-generation stealth fighter was revealed at the MAKS-2021 International Aviation and Space Salon, Reuters reported. The show opened Tuesday in Zhukovsky, outside Moscow. Fifth-generation refers to the jet's stealth characteristics, a capability to cruise at supersonic speed as well as artificial intelligence to assist the pilots, among other advanced features. "What we saw in Zhukovsky today demonstrates that the Russian aviation has a big potential for development and our aircraft making industries continue to create new competitive aircraft designs," Putin said in a speech at the show.

Meteorologists are aiming to use AI to help them get an edge in early detection and disaster relief in response to natural hazards and disasters, which according to scientists have become more frequent and unpredictable due to the impact of climate change. In response, the International Telecommunication Union (ITU) together with the World Meteorological Organization (WMO) and UN Environment, have launched a Focus Group on AI for Natural Disaster Management, according to a recent account from MyITU. ITU scientists see that Al shows great potential to support data collection and monitoring, the reconstruction and forecasting of extreme events, and effective and accessible communication before and during a disaster. The ITU, founded in 1865 to facilitate international connectivity in communications networks, is today a UN specialized agency with 193 member countries and a membership of over 900 companies, universities, and international and regional organizations. The group recently held its first Focus Group on AI workshop meeting.

All the sessions from Transform 2021 are available on-demand now. In early June, border officials "quietly deployed" the mobile app CBP One at the U.S.-Mexico border to "streamline the processing" of asylum seekers. While the app will reduce manual data entry and speed up the process, it also relies on controversial facial recognition technologies and stores sensitive information on asylum seekers prior to their entry to the U.S. The issue here is not the use of artificial intelligence per se, but what it means in relation to the Biden administration's pre-election promise of civil rights in technology, including AI bias and data privacy. When the Democrats took control of both House and Senate in January, onlookers were optimistic that there was an appetite for a federal privacy bill and legislation to stem bias in algorithmic decision-making systems. This is long overdue, said Ben Winters, Equal Justice Works Fellow of the Electronic Privacy Information Center (EPIC), who works on matters related to AI and the criminal justice system.

"The Russian military seeks to be a leader in weaponizing AI technology," Lt. Gen. Michael Groen, director of the Pentagon's Joint Artificial Intelligence Center, told National Defense. The JAIC -- which has been working to facilitate AI adoption across the Defense Department since 2018 -- recently commissioned a report by CNA, a research organization based in Arlington, Virginia, to examine Russia's developments. The report -- titled "Artificial Intelligence and Autonomy in Russia" -- identified more than 150 AI-enabled military systems in various stages of development, Groen said in an email in June. Key areas of interest include autonomous air, underwater, surface and ground platforms. The nation wants to use AI for electronic warfare, intelligence, surveillance, reconnaissance and strategic decision-making processes as leaders pursue information dominance on the battlefield, Groen said.

Artificial intelligence is a fifth more accurate at predicting whether individuals are likely to become long-term recipients of benefits. A new method of predicting welfare dependency, developed by Dr. Dario Sansone from the University of Exeter Business School and Dr. Anna Zhu from RMIT University, could save governments billions in welfare costs as well as help them make earlier interventions to prevent long-term economic disadvantage and social exclusion. Their study found that machine learning algorithms, which improve through several iterations and use of big data, are 22% more accurate at predicting the proportion of time individuals are on income support than the standard early warning systems. The researchers were able to apply the off-the-shelf algorithms to the entire population of people enrolled in the Australian social security system between 2014 and 2018. This included demographic and socio-economic data of anyone who received a welfare payment from Australia's social security system Centrelink, whether on the grounds of unemployment, disability, having children, or being a carer, a student or of pensionable age.

Many governments are increasingly approaching artificial intelligence with an almost religious zeal. By 2018 at least 22 countries around the world, and also the EU, had launched grand national strategies for making AI part of their business development, while many more had announced ethical frameworks for how it should be allowed to develop. The latest is Ireland, which has just announced its national artificial intelligence strategy, "AI – Here for Good". It aims to become "an international leader in using AI to benefit our economy and society, through a people-centred, ethical approach to its development, adoption and use". This is to be obtained via eight policy commandments, including increasing trust in and understanding of AI by using an "AI ambassador" – a veritable AI high priest – to spread the message around the country.

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.

Abstract-- This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs). The uncertainty propagation over a task-allocation process is done by using the Unscented transform that uses the Sigma-Point sampling mechanism. It has great potential to be employed for generic task-allocation schemes, in the sense that there is no need to modify an existing task-allocation method that has been developed without considering the uncertainty in the situational awareness. The proposed framework was tested in a simulated environment where the decision-maker needs to determine an optimal allocation of multiple locations assigned to multiple mobile flying robots whose locations come as random variables of known mean and covariance. The simulation result shows that the proposed stochastic task allocation approach generates an assignment with 30% less overall cost than the one without considering the uncertainty.