For Russia and Vladimir Putin, it is clear that planetary domination and artificial intelligence (AI) are inextricably intertwined. "Artificial intelligence is the future, not only for Russia but for all humankind," he said via live video feed as schools started this month. "Whoever becomes the leader in this sphere will become the ruler of the world." Putin isn't an outlier in his thinking; he is simply vocalizing to match the intensity a race that China, Russia, and the US are already running, to acquire smart military power. Each nation has formally recognized the critical importance of intelligent machines to the future of their national security, and each sees AI-related technologies such as autonomous drones and intelligence processing software as tools for augmenting human soldier capital.

Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you! Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics.

The creators of a new artificial intelligence programme hope it could one day save democracy. Are we ready for robots to take over politics? "Siri, who should I vote for?" It has stock, non-committal answers for anything that sounds remotely controversial. But the next generation of digital helpers, powered by advances in artificial intelligence (AI), might not be so reticent.

Principal component analysis (PCA) is very popular to perform dimension reduction. The selection of the number of significant components is essential but often based on some practical heuristics depending on the application. Only few works have proposed a probabilistic approach able to infer the number of significant components. To this purpose, this paper introduces a Bayesian nonparametric principal component analysis (BNP-PCA). The proposed model projects observations onto a random orthogonal basis which is assigned a prior distribution defined on the Stiefel manifold. The prior on factor scores involves an Indian buffet process to model the uncertainty related to the number of components. The parameters of interest as well as the nuisance parameters are finally inferred within a fully Bayesian framework via Monte Carlo sampling. A study of the (in-)consistence of the marginal maximum a posteriori estimator of the latent dimension is carried out. A new estimator of the subspace dimension is proposed. Moreover, for sake of statistical significance, a Kolmogorov-Smirnov test based on the posterior distribution of the principal components is used to refine this estimate. The behaviour of the algorithm is first studied on various synthetic examples. Finally, the proposed BNP dimension reduction approach is shown to be easily yet efficiently coupled with clustering or latent factor models within a unique framework.

Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art models, not towards a purely comparative end, but as a means to get insight about their inductive abilities. To assess the strengths and weaknesses of each model, we create simple tasks that exhibit first, atomic properties of binary relations, and then, common inter-relational inference through synthetic genealogies. Based on these experimental results, we propose new research directions to improve on existing models.

We envision a world where devices, machines, automobiles, and things are much more intelligent, simplifying and enriching our daily lives. They will be able to perceive, reason, and take intuitive actions based on awareness of the situation, improving just about any experience and solving problems that to this point we've either left to the user, or to more conventional algorithms. Artificial intelligence (AI) is the technology driving this revolution. You may have heard this vision or may think that AI is really about big data and the cloud, and yet Qualcomm's solutions already have the power, thermal, and processing efficiency to run powerful AI algorithms on the actual device -- which brings several advantages. AI is a pervasive trend that is rapidly accelerating thanks to vast amounts of data and progress in both algorithms and the processing capacity of modern devices.

When will AI actually exceed human performance in marketing and sales? To be more direct--when will virtual assistants take over human marketers and sales reps? According to a survey by the Future of Humanity Institute at the University of Oxford, AI will take over human jobs in phases, depending on specializations and degrees of precision required. For example, intelligent assistants will take over translational tasks by 2024, while it may take as many as forty-five years for AI to replace humans in tasks that require surgical precision based on intuitions. In short, marketers and sales reps will either be assisted by or compete against intelligent AI-driven assistants for the major part of the century.

Afew years ago the cosmologist Max Tegmark found himself weeping outside the Science Museum in South Kensington. He'd just visited an exhibition that represented the growth in human knowledge, everything from Charles Babbage's difference engine to a replica of Apollo 11. What moved him to tears wasn't the spectacle of these iconic technologies but an epiphany they prompted. "It hit me like a brick," he recalls, "that every time we understood how something in nature worked, some aspect of ourselves, we made it obsolete. Once we understood how muscles worked we built much better muscles in the form of machines, and maybe when we understand how our brains work we'll build much better brains and become utterly obsolete." Tegmark's melancholy insight was not some idle hypothesis, but instead an intellectual challenge to himself at the dawn of the age of artificial intelligence. What will become of humanity, he was moved to ask, if we manage to create an intelligence that outstrips our own?

Early diagnosis can help patients make lifestyle changes which may help slow the progression of Alzheimer's. A devastating chronic neurodegenerative disease, Alzheimer's disease (AD) currently affects around 5.5 million people in the United States alone. Causing progressive mental deterioration, it ultimately advances to impact basic bodily functions such as walking and swallowing. Looking for a way to help, researchers at the University of Bari and Istituto Nazionale di Fisica Nucleare in Italy have developed new machine learning AI technology that may help identify Alzheimer's a decade before doctors usually can, by way of non-invasive MRI brain scans. An early diagnosis -- before any of the symptoms a doctor might recognize become apparent -- could give patients a chance to make changes to their lifestyle which may slow Alzheimer's progression.

The anonymous street artist Banksy has donated £205,000 from the sale of a protest piece to anti-arms campaigners. The artwork, Civilian Drone Strike, was on display at the Stop the Arms Fair art exhibition in east London. It depicts drones destroying a cartoon image of a house, while a child and her dog look on in distress. The exhibition was held alongside the world's largest arms fair, the Defence and Security Equipment International - both exhibitions closed on Friday. The art exhibition claimed to highlight "the inhumanity of the arms trade" through art, with work by more than 1,600 exhibitors from 54 countries.