In 2017, China laid out a three-step roadmap to become the world leader in AI by 2030. It hopes to make the industry worth 1 trillion yuan, or $147.7 billion, within the next decade. Already, it has announced billions in funding for innovative startups and launched programmes to entice researchers. It might not be long before it gains an edge over the US. That said, the US continues to pave the way, as is the case with many new fields of technology. In 2019, President Donald Trump signed an executive order announcing the American AI Initiative, which orders funds, programmes and data to be directed towards the research and commercialisation of AI.

Japan has passed a bill to build "super cities" which address societal issues using emerging technologies such as AI. The bill, passed on Wednesday, aims to accelerate the sweeping change of regulations across various fields to support the creation of such futuristic cities. Addressing issues such as depopulation and an aging society will be the focus of the super cities. Technologies including big data and AI will be key to successfully tackling the challenging problems. Large amounts of data will be collected and organised from across administrative organisations. Local governments will be selected for the ambitious projects which will launch forums with the national government and private companies to take forward the plans. Draft plans will be created from this deep public-private collaboration that will subsequently be submitted to the state government if approved by local residents.

IIT-Ropar, one of the eight new IITs established by the Ministry of Human Resource Development (MHRD), Government of India, and TSW, the executive education division of Times Professional Learning (a part of The Times of India Group), have launched a Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The programme will be coordinated by The Indo-Taiwan Joint Research Centre (ITJRC) on Artificial Intelligence (AI) and Machine Learning (ML), at IIT-Ropar. Supported by the Ministry of Science and Technology, Taiwan, ITJRC is a bilateral centre for collaborative research in disruptive technologies like AI and ML. The programme, with its focus on Artificial Intelligence and Deep Learning, has an eligibility criterion of a minimum of 2 years of work experience in the IT industry. Though an engineering degree is a desirable prerequisite for this programme, one does not need a coding or mathematics background to be eligible.

Google warned on Thursday that the EU's definition of artificial intelligence was too broad and that Brussels must refrain from over-regulating a crucial technology. The search and advertising giant made its argument in feedback to the European Commission, the EU's powerful regulator that has reached out to big tech as it draws up ways to set new rules for AI. The EU has not decided yet on how to regulate AI, but is putting most of its focus on what it calls "high risk" sectors, such as healthcare and transport. It's plans, to be spearheaded by EU commissioners Margrethe Vestager and Thierry Breton, are not expected until the end of the year. "A clear and widely understood definition of AI will be a critical foundational element for an effective AI regulatory framework," the company said in its 45-page submission.

Unnecessary restrictions on blood donors should be removed to maximize the blood and plasma available for use. With a vaccine for coronavirus disease 2019 (COVID-19) likely more than a year away, we must identify effective therapies for patients now. One promising approach is the use of plasma from patients who have recovered from COVID-19 (1, 2). To facilitate this strategy, the U.S. Food and Drug Administration (FDA) recently revised some of the restrictions on blood donation, including a decrease in deferral time for men who have sex with men (MSM) to 3 months (3). This is a positive change to an outdated guideline, but it does not go far enough.

Science's COVID-19 coverage is supported by the Pulitzer Center. Timothy Sheahan, a virologist studying COVID-19, wishes he could keep pace with the growing torrent of new scientific papers related to the pandemic. But there have just been too many--more than 5000 papers a week. "I'm not keeping up," says Sheahan, who works at the University of North Carolina, Chapel Hill. A loose-knit army of data scientists and software developers is pressing hard to change that.

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might be directed to address those risks. A key property of hypothetical AI technologies is introduced, called \emph{prepotence}, which is useful for delineating a variety of potential existential risks from artificial intelligence, even as AI paradigms might shift. A set of \auxref{dirtot} contemporary research \directions are then examined for their potential benefit to existential safety. Each research direction is explained with a scenario-driven motivation, and examples of existing work from which to build. The research directions present their own risks and benefits to society that could occur at various scales of impact, and in particular are not guaranteed to benefit existential safety if major developments in them are deployed without adequate forethought and oversight. As such, each direction is accompanied by a consideration of potentially negative side effects.

Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications. While KGs have become a mainstream technology, the RDF/SPARQL-centric toolset for operating with them at scale is heterogeneous, difficult to integrate and only covers a subset of the operations that are commonly needed in data science applications. In this paper, we present KGTK, a data science-centric toolkit to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications. We illustrate KGTK with real-world scenarios in which we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet, in our own work.

We propose an adaptation of the Random Forest algorithm to estimate the conditional distribution of a possibly multivariate response. We suggest a new splitting criterion based on the MMD two-sample test, which is suitable for detecting heterogeneity in multivariate distributions. The weights provided by the forest can be conveniently used as an input to other methods in order to locally solve various learning problems. The code is available as \texttt{R}-package \texttt{drf}.

The partnership launched Thursday after a virtual meeting between national technology ministers. It was nearly two years after the leaders of Canada and France announced they were forming a group to guide the responsible adoption of AI based on shared principles of "human rights, inclusion, diversity, innovation and economic growth."