You now have access to a treasure trove of government info through your smart speaker if you live in the UK. The British government has made over 12,000 pieces of Gov.uk information available through Alexa and Google Assistant, saving you the trouble of wading through official pages. Some of them are simple questions like the next bank holiday, while others are more involved questions such as obtaining a passport. Not everything is available, so you can't completely depend on a voice assistant just yet. However, there are promises of expansion.

Much of the discussion of nation-state competition in artificial intelligence (AI) focuses on relatively easily quantifiable phenomena including funding, technological advances, access to data and computational power, and the speed of AI industrialization. However, a central element of AI leadership is something much less tangible: control over the norms and values that shape the development and use of AI around the world. The U.S. government has overlooked this dimension of AI development for years, but the last couple months indicate the beginnings of a change of course. If the U.S. hopes to maintain global AI leadership, the government must continue to stake out a comprehensive positive vision, or we may find that the future of AI is a world few of us want to live in. Until recent months, the U.S. government had remained relatively quiet on the topics of AI values and ethics.

Intelligence should be "distributed" where "knowledge" is created and "decisions" are made A few years ago, it was hard to find anyone to have a serious discussion about Artificial Intelligence (AI) outside academic institutions. Like any new major technology trend, the new wave of making AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions. Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had excepted. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.

In this paper we present a set of key demarcations, particularly important when discussing ethical and societal issues of current AI research and applications. Properly distinguishing issues and concerns related to Artificial General Intelligence and weak AI, between symbolic and connectionist AI, AI methods, data and applications are prerequisites for an informed debate. Such demarcations would not only facilitate much-needed discussions on ethics on current AI technologies and research. In addition sufficiently establishing such demarcations would also enhance knowledge-sharing and support rigor in interdisciplinary research between technical and social sciences.

In this paper, we propose an extension to an existing algorithm (instance-MIR) which tackles the multiple instance regression (MIR) problem, also known as distribution regression. The MIR setting arises when the data is a collection of bags, where each bag consists of several instances which correspond to the same and unique real-valued label. The goal of a MIR algorithm is to find a mapping from the instances of an unseen bag to its target value. The instance-MIR algorithm treats all the instances separately and maps each instance to a label. The final bag label is then taken as the mean or the median of the predictions for that given bag. While it is conceptually simple, taking a single statistic to summarize the distribution of the labels in each bag is a limitation. In spite of this performance bottleneck, the instance-MIR algorithm has been shown to be competitive when compared to the current state-of-the-art methods. We address the aforementioned issue by computing the kernel mean embeddings of the distributions of the predicted labels, for each bag, and learn a regressor from these embeddings to the bag label. We test our algorithm (instance-kme-MIR) on five real world datasets and obtain better results than the baseline instance-MIR across all the datasets, while achieving state-of-the-art results on two of the datasets.

We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum annealer, the D-Wave 2000Q, is used to sample from this model. This algorithm joins a growing family of algorithms that use a quantum annealing subroutine in deep learning, and provides a framework to test the advantages of quantum-assisted learning in GANs. Fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum annealing sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies, and is also applied to the LSUN dataset bedrooms class for the Chimera topology. Evaluated using the Fr\'{e}chet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset.

KIEV - Ukraine's election has catapulted Volodymyr Zelenskiy, a 41-year-old stand-up comedian and television star with no political experience, into the nation's top job. As leader of a country dependent on international aid and battling separatists, Zelenskiy will have to deal with Russian President Vladimir Putin, deep economic problems and possibly rebellious elites. Here is a look at the main challenges facing Ukraine's sixth president: Voters expect the new commander-in-chief to end a five-year war with Moscow-backed separatists in the industrial east. The conflict has claimed some 13,000 lives since 2014 and is a huge burden on the economy and society. Despite numerous attempts to staunch the bloodletting, the conflict regularly claims the lives of soldiers and civilians, and a solution is nowhere in sight.

On the floor of the New York Auto Show this week, Genesis showed off its sweet little Mint concept, an electric two-seater with a very abbreviated sedan body. The Hyundai luxury arm does not, however, have any plans to put the adorable thing into production--perhaps because, as we learned this week, getting world-changing tech into the market takes a fair amount of elbow grease. Elon Musk's Boring Company is slowly making its way through the necessary paperwork to make its DC to Baltimore Loop concept a real, live thing. Uber is rounding up the oodles of cash it needs to develop self-driving vehicles. "Flying taxi" engineers are trying to get their concepts past now-nervous aviation regulators.

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Artificial Intelligence (AI) has been omnipresent and the latest in the block is Military. In recent times, AI has become a critical part of modern warfare. Compared with the conventional systems, military establishments churning enormous volumes of data are capable to integrate AI on a more unified process. Ensuring operational efficiency, AI improves self-regulation, self-control and self-actuation of combat systems, credit to its inherent computing coupled with accurate decision-making capabilities. Taking into account the enormous capability Artificial intelligence (AI) holds in the modern-day warfare, many of the world's most powerful countries have increased their investments into military and self-security.