Facebook Perspectives has launched in the UK, becoming the company's latest politically-focused online tool. Mark Zuckerberg recently accepted that the platform has a responsibility to combat the spread of "fake news", and the Facebook CEO wants to clean up the site ahead of the UK general election next month. Perspectives is a new interactive prompt that will appear in your News Feed, directly beneath articles about the general election. Clicking or tapping on it will take you to another Facebook page called Political Party Positions, in which you'll be able to compare the major political parties' perspectives on a number of issues, including housing, energy, the economy and foreign affairs. Aware of the intense scrutiny Perspectives will be subjected to, the company has been keen to stress the tool's apparent impartiality.

After acquiring Dropcam back in 2014, Nest waited another year to launch its own home monitoring camera. It wasn't much of an improvement over the old Dropcam Pro, and aside from releasing an outdoor version with weatherproof body, Nest has focused on improving its software ever since. But now the smart home company is back with a second-gen connected camera -- the Nest Cam IQ -- which features serious upgrades in the hardware department and an inflated price tag to match. The $299/£299 Nest Cam IQ looks a lot like last year's Nest Cam Outdoor. In contrast to the very first camera, it has cute and curvy, all-white polycarbonate shell and a fancy, patented hinge that allows you to orientate the lens any way you want it.

Kai-Fu Lee sees America as destined to lose to China in the race for leadership in AI. Kai-Fu Lee watched the U.S. beat China to global internet leadership during the dot-com bubble from the inside. Now with what he sees as an even greater technological revolution taking place in the fast-growing field of artificial intelligence, Lee doesn't expect China to take a backseat a second time. "China started slow, and American companies went international," Lee says during a May visit to Forbes Media's headquarters. "But simple math says China has a larger GDP. The market will be bigger."

The Protestant Church in Germany has unveiled a robotic priest called BlessU-2 to mark 500 years since the Reformation. The machine delivers various blessings in eight languages. It's being presented to church-goers in the town of Wittenberg, which was the place most closely associated with Martin Luther and the birth of Protestantism.

Many government agencies are already capturing the potential of artificial intelligence technologies, using them to relieve, replace, and augment humans in completing job-related tasks. For many people, artificial intelligence (AI) conjures images of humanoid robots and talking computers straight out of a science fiction film. The phrase is less frequently associated with a government clerk. But the cognitive and automation technologies behind AI could fundamentally transform the way public-sector employees work--eliminating some jobs, redesigning countless others, and even creating entirely new professions within government.¹ AI is already supporting government efficiency, helping to reduce backlogs and costs for routine tasks, predict fraudulent transactions, and identify criminal suspects via facial recognition.

Chatbots are about to change the world in more ways than we can imagine. Already, bots around the globe can complete a diverse set of varying tasks. From ordering pizza online to mashing faces together in Project Murphy, chatbots are about to become a normal element in everyday life. As the scope of chatbots becomes broader every day, there are new applications popping up constantly. Education has traditionally been known as a sector where innovation moves slowly.

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.

Partially observable environments present an important open challenge in the domain of sequential control learning with delayed rewards. Despite numerous attempts during the two last decades, the majority of reinforcement learning algorithms and associated approximate models, applied to this context, still assume Markovian state transitions. In this paper, we explore the use of a recently proposed attention-based model, the Gated End-to-End Memory Network, for sequential control. We call the resulting model the Gated End-to-End Memory Policy Network. More precisely, we use a model-free value-based algorithm to learn policies for partially observed domains using this memory-enhanced neural network. This model is end-to-end learnable and it features unbounded memory. Indeed, because of its attention mechanism and associated non-parametric memory, the proposed model allows us to define an attention mechanism over the observation stream unlike recurrent models. We show encouraging results that illustrate the capability of our attention-based model in the context of the continuous-state non-stationary control problem of stock trading. We also present an OpenAI Gym environment for simulated stock exchange and explain its relevance as a benchmark for the field of non-Markovian decision process learning.

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.