Vladimir Putin has expressed his fears over artificial intelligence by asking Russia's largest technology firm how long it will be until smart robots'eat us'. The Russian president was speaking to Arkady Volozh, chief of internet firm Yandex, during a tour of the company's Moscow headquarters. Volozh was discussing the potential of AI when Putin caused surprise by asking when the technology will'eat us'. The Russian president was speaking to Arkady Volozh, chief of internet firm Yandex, during a tour at the company's Moscow headquarters. He replied: 'I hope never.' After a pause, he then explained that other machines are'better than humans' in certain areas, giving the example of an excavator being better at digging than a person with a shovel.

The I-7 Innovators' Strategic Advisory Board on People-Centered Innovation is the engagement group launched last May during the G7 Summit in Taormina (par. The group is in charge of providing guidance on emerging innovation issues. The creation of this group is an experiment proposed by the Italian G7 Presidency with the goal of driving attention towards the multiple challenges that innovation poses and cannot be faced only at national level. Each country and the EU have designated their own group of experts. We encourage Canada to consider continuing this experiment during their imminent Presidency. How can AI help governments make better decisions and deliver policies and services more effectively?

Perhaps you've bumped into Mildred, Carla or Oscar on your recent travels. They're not real people but avatars of chatbots – concocted by Lufthansa, Avianca and Air New Zealand respectively – or artificial intelligence (AI) powered computer programs accessed on your smartphone that enable you to have a simulated conversation of sorts. Now airports are getting in on the act, and it's all part of a paradigm shift towards self-service and interactions with technologies that offer "personal" information to help us on our way through the terminal. It's a shift confirmed in the findings of the Passenger IT Trends Survey released by Sita, the provider of much of the digital infrastructure that underpins airport and airline communications and operations worldwide. The survey found that face-to-face check-in is now down to 46 per cent of passengers, and since last year's survey, self-service bag-tagging has risen from 31 per cent to 47 per cent. Almost a fifth of passengers now use self-service bag drop, and when it comes to ID control, 57 per cent of passengers would definitely use biometrics instead of a passport or boarding pass across the journey.

One in three employees believe artificial intelligence (AI) will increase the number of jobs available in the future, with millennials especially positive, reveals CCS Insight's latest employee enterprise survey. The survey of more than 650 employees, across five regions, reaffirmed that employees see artificial intelligence as the technology that will cause the most disruption to the workplace over the next few years. More than half of employees expect artificial intelligence to affect their jobs within three years, with 70 percent feeling it will do so within the next decade. The automation of mundane work tasks, the increasing performance of machinery and the use of assistive AI features in productivity and collaboration applications were cited by respondents as the top uses and benefits of the technology in the future. CCS Insight's vice president of enterprise research, Nick McQuire, comments, 'Despite many reports painting a bleak outlook for the impact of AI on the job market in recent months, our survey reveals rather positive attitudes to the technology, both as a job creator and an enabler of work.

E-Spirit has added an artificial intelligence-powered personalization content engine into its FirstSpirit Digital Experience Hub. The Dortmund, Germany-based web content management provider is calling it the FirstSpirit Intelligent Content Engine. FirstSpirit is the company's web content management system that enables its Digital Experience Hub. Michael Gerard, chief marketing officer for e-Spirit, told CMSWire in an interview this week the Intelligent Content Engine helps complete the company's digital experience offering with personalization. He called it a "major component" that supports the expansion of channels used by customers by helping marketers analyze internal and external data coupled with behavioral data.

We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional non-Gaussian setting. Our estimators leverage the score function based second-order Stein's lemma and do not require Gaussian or elliptical symmetry assumptions made in the literature. Moreover, to handle score functions and response variables that are heavy-tailed, our estimators are constructed via carefully thresholding their empirical counterparts. We show that our estimator achieves near- optimal statistical rate of convergence in several settings. We supplement our theoretical results via simulation experiments that confirm the theory.

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.

We study the quantum synchronization between a pair of two-level systems inside two coupledcavities. Using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in this digital-analog block decomposition the fundamental elements of a quantum machine learning protocol, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If we can additionally equip this algorithm with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state and local conditional operations on the agent and register subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experience different loss/decoherence mechanisms, and give us flexibility to choose the synchronization state. Finally, we propose an implementation based on current technologies in superconducting circuits.

We introduce a mixed-effects model to learn spatiotempo-ral patterns on a network by considering longitudinal measures distributed on a fixed graph. The data come from repeated observations of subjects at different time points which take the form of measurement maps distributed on a graph such as an image or a mesh. The model learns a typical group-average trajectory characterizing the propagation of measurement changes across the graph nodes. The subject-specific trajectories are defined via spatial and temporal transformations of the group-average scenario, thus estimating the variability of spatiotemporal patterns within the group. To estimate population and individual model parameters, we adapted a stochastic version of the Expectation-Maximization algorithm, the MCMC-SAEM. The model is used to describe the propagation of cortical atrophy during the course of Alzheimer's Disease. Model parameters show the variability of this average pattern of atrophy in terms of trajectories across brain regions, age at disease onset and pace of propagation. We show that the personalization of this model yields accurate prediction of maps of cortical thickness in patients.

Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix with a maximal sum of its entries. Le Van et al. (Ranked Tiling, 2014) already illustrated its applicability to gene expression analysis and addressed it with a constraint programming (CP) approach combined with large neighborhood search (CP-LNS). In this work, we exhibit some key properties of this NP-hard problem and define a bounding function such that larger problems can be solved in reasonable time. Two different algorithms are proposed in order to exploit the highlighted characteristics of the problem: a CP approach with a global constraint (CPGC) and mixed integer linear programming (MILP). Practical experiments conducted both on synthetic and real gene expression data exhibit the characteristics of these approaches and their relative benefits over the original CP-LNS method. Overall, the CPGC approach tends to be the fastest to produce a good solution. Yet, the MILP formulation is arguably the easiest to formulate and can also be competitive.