Tech company Continental is to acquire a minority stake in EasyMile SAS, a French autonomous driving company developing driverless electric shuttles. These shuttles will play a key role in the addressing the'first/last-mile' challenge in the future of mobility. In addition to Continental's equity investment in EasyMile, the companies plan to cooperate closely in the fields of environmental sensors, braking systems and driving safety technologies. This opens the door for Continental to numerous possible applications within EasyMile shuttles worldwide, while incorporating tried and tested large-scale production techniques from the automotive field. The first example is that the EasyMile EZ10 fleet will be running on Continental EcoContact tires with optimized tread geometry, ensuring low rolling resistance and rolling noise.

At a Wednesday event in London, Harry Shum, executive vice president for Microsoft's AI and Research Group, explained the firm's plans to improve its research in artificial intelligence (AI), as well as to use the technology to help the blind and solve environmental issues. The company formed a new incubation hub called Microsoft Research AI (MSR AI) to focus on some of the problems and challenges in the AI space. The MSR AI team will work on the theoretical application of AI, as well as its practical implementation in the real world, a Microsoft blog post said. According to the post, fields of study like machine learning, perception, and natural language processing have fractured and moved in different directions. A goal of the MSR AI team will be to reunite these fields of study and develop a more integrated approach to AI.

Cognisess is a predictive people analytics company that supports companies in selecting, recruiting and managing employees by eliminating bias and making the recruitment process fairer, more efficient and more accurate. Global recruitment faces two disruptive challenges in the next decade. Globalisation will become even more complex and the speed of technological change will become more rapid. Globalisation is entering a new period of tension and competition as geo-politics such as the Trump administration and Brexit re-cast the old order of world trade towards a new operating system for the global economy. Meanwhile disruptive technologies are cleaving into long standing business sectors that have enjoyed many decades of certainty and, in some cases, hegemony.

Artificial Intelligence will have a profound impact on the IT industry. The Machine Learning algorithms and models that bring AI to the forefront get only better with data. If these algorithms can learn from existing medical reports, and help doctors with diagnosis, the same can be used to improve IT operations. After all, enterprise IT deals with humongous data acquired from servers, operating systems, applications and users. These datasets can be used for creating ML models that assist system administrators, DevOps teams and IT support departments.

The era of big data has witnessed a paradigm shift into analytics. Today, it's no longer sufficient to simply gather data from social media, IoT, and wearable devices, and be unable to manage or filter it. It is more about delivering the right data to the right person, at the right time. This trend is growing crucial as data is multiplying every day and pouring in from various devices and smart machines including wearables, electronic gadgets, and other devices. Such factors call for the treatment of vast pools of structured and unstructured data with care and precision. This is precisely where invisible analytics come in.

We present a parallel AND/OR Branch-and-Bound scheme that uses the power of a computational grid to push the boundaries of feasibility for combinatorial optimization. Two variants of the scheme are described, one of which aims to use machine learning techniques for parallel load balancing. In-depth analysis identifies two inherent sources of parallel search space redundancies that, together with general parallel execution overhead, can impede parallelization and render the problem far from embarrassingly parallel. We conduct extensive empirical evaluation on hundreds of CPUs, the first of its kind, with overall positive results. In a significant number of cases parallel speedup is close to the theoretical maximum and we are able to solve many very complex problem instances orders of magnitude faster than before; yet analysis of certain results also serves to demonstrate the inherent limitations of the approach due to the aforementioned redundancies.

One of the basic motivations for solving DCOPs is maintaining agents' privacy. Thus, researchers have evaluated the privacy loss of DCOP algorithms and defined corresponding notions of privacy preservation for secured DCOP algorithms. However, no secured protocol was proposed for Max-Sum, which is among the most studied DCOP algorithms. As part of the ongoing effort of designing secure DCOP algorithms, we propose P-Max-Sum, the first private algorithm that is based on Max-Sum. The proposed algorithm has multiple agents preforming the role of each node in the factor graph, on which the Max-Sum algorithm operates. P-Max-Sum preserves three types of privacy: topology privacy, constraint privacy, and assignment/decision privacy. By allowing a single call to a trusted coordinator, P-Max-Sum also preserves agent privacy. The two main cryptographic means that enable this privacy preservation are secret sharing and homomorphic encryption. In addition, we design privacy-preserving implementations of four variants of Max-Sum. We conclude by analyzing the price of privacy in terns of runtime overhead, both theoretically and by extensive experimentation.

Sponsored Search Auctions (SSAs) are one of the most successful applications of microeconomic mechanisms, with a revenue of about $72 billion in the US alone in 2016. However, the problem of designing the best economic mechanism for sponsored search auctions is far from being solved, and, given the amount at stake, it is no surprise that it has received growing attention over the past few years. The most common auction mechanism for SSAs is the Generalized Second Price (GSP). However, the GSP is known not to be truthful: the agents participating in the auction might have an incentive to report false values, generating economic inefficiency and suboptimal revenues in turn. Superior, efficient truthful mechanisms, such as the Vickrey-Clarke-Groves (VCG) auction, are well known in the literature. However, while the VCG auction is currently adopted for the strictly related scenario of contextual advertising, e.g., by Google and Facebook, companies are reluctant to extend it to SSAs, fearing prohibitive switching costs. Other than truthfulness, two issues are of paramount importance in designing effective SSAs. First, the choice of the user model; not only does an accurate user model better target ads to users, it also is a critical factor in reducing the inefficiency of the mechanism. Often an antagonist to this, the second issue is the running time of the mechanism, given the performance pressure these mechanisms undertake in real-world applications. In our work, we argue in favor of adopting the VCG mechanism based on the cascade model with ad/position externalities (APDC-VCG). Our study includes both the derivation of inefficiency bounds and the design and the experimental evaluation of exact and approximate algorithms.

Among the myriad of desirable properties discussed in the context of forgetting in Answer Set Programming (ASP), strong persistence naturally captures its essence. Recently, it has been shown that it is not always possible to forget a set of atoms from a program while obeying this property, and a precise criterion regarding what can be forgotten has been presented, accompanied by a class of forgetting operators that return the correct result when forgetting is possible. However, it is an open question what to do when we have to forget a set of atoms, but cannot without violating this property. In this paper, we address this issue and investigate three natural alternatives to forget when forgetting without violating strong persistence is not possible, which turn out to correspond to the different possible relaxations of the characterization of strong persistence. Additionally, we discuss their preferable usage, shed light on the relation between forgetting and notions of relativized equivalence established earlier in the context of ASP, and present a detailed study on their computational complexity.

We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in the spirit of common domain adaptation works, we derive a second bound (recently introduced in Germain et al., 2016) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence--expressed as a ratio-- controls the tradeoff between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.