'Michael – we are bigger than US Steel". Over the holiday season, I said this to my friend Jeremy Geelan when I was comparing the Mobile industry to the IoT. The term Internet of Things was coined by the British technologist Kevin Ashton in 1999, to describe a system where the Internet is connected to the physical world via ubiquitous sensors. Languishing depths of academia(at least here in Europe …) – IoT has it's netscape moment early in 2014 when Google acquired Nest Mobile is huge and has dominated the Tech landscape for the last decade. So, 50 billion by 2020 is a massive number by a factor, and no one doubts that number any more.

This paper characterizes the conditional distribution properties of the finite sample ridge regression estimator and uses that result to evaluate total regression and generalization errors that incorporate the inaccuracies committed at the time of parameter estimation. The paper provides explicit formulas for those errors. Unlike other classical references in this setup, our results take place in a fully singular setup that does not assume the existence of a solution for the non-regularized regression problem. In exchange, we invoke a conditional homoscedasticity hypothesis on the regularized regression residuals that is crucial in our developments.

We consider the problem of \textit{best arm identification} with a \textit{fixed budget $T$}, in the $K$-armed stochastic bandit setting, with arms distribution defined on $[0,1]$. We prove that any bandit strategy, for at least one bandit problem characterized by a complexity $H$, will misidentify the best arm with probability lower bounded by $$\exp\Big(-\frac{T}{\log(K)H}\Big),$$ where $H$ is the sum for all sub-optimal arms of the inverse of the squared gaps. Our result disproves formally the general belief - coming from results in the fixed confidence setting - that there must exist an algorithm for this problem whose probability of error is upper bounded by $\exp(-T/H)$. This also proves that some existing strategies based on the Successive Rejection of the arms are optimal - closing therefore the current gap between upper and lower bounds for the fixed budget best arm identification problem.

A pdf version of this document created using latex can be downloaded by clicking here. Polymorphic malware detection is challenging due to the continual mutations miscreants introduce to successive instances of a particular virus. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we argue that these methods can be usefully applied to malware detection. Unfortunately, gene classification tools are usually optimized for and restricted to an alphabet of four letters (nucleic acids). Consequently, we have selected the Strand gene sequence classifier, which offers a robust classification strategy that can easily accommodate unstructured data with any alphabet including source code or compiled machine code. To demonstrate Stand's suitability for classifying malware, we execute it on approximately 500GB of malware data provided by the Kaggle Microsoft Malware Classification Challenge (BIG 2015) used for predicting 9 classes of polymorphic malware.

One of Britain's leading computer scientists has criticised the "staggering sexism" in the industry, citing a visit to an artificial intelligence laboratory where a prototype of an "enhanced human" was entirely male. Ursula Martin, a professor of computer science at Oxford University, said that despite attempts to redress the problem, there was still an anti-female bias. She said that universities had attempted to encourage more women to enrol on science and mathematics courses, admitting that the institutions "did not always get it right" but they did try to remove obstacles.

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data.

This paper proposes a relational learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is, partial configurations of the players on the court) and temporal (that is, the order of events and positions) aspects of the game. We analyze both the men's and women's final match from the 2014 FIVB Volleyball World Championships, and are able to identify several interesting and relevant strategies from the matches.

A photo shows the logo of Renren.com. Will robots take over our world? These questions, which once seemed irrelevant, now frequently come into our minds with the advancement of Artificial Intelligence (AI). A recent report shows that there are almost no active users left on Renren, as advertising accounts keep pushing uninteresting contents and the system keeps recommending other people's posts that were so "yesterday". Some have jokingly said this must be what will happen to our world after it is taken by AI.

Recently I visited the Innorobo show in Paris, a gathering of robot companies from around the world, which brought together industrial robots, service robots, toy robots, family robots, and many other robot types, all under one roof. One of the key highlights was SoftBank Robotics launching Pepper Partners Europe, inviting developers and companies in Europe to build applications for SoftBank's flagship robot, Pepper. This initiative is part of SoftBank's expansion of Pepper outside Japan, where more than 3,000 Pepper robots have already been deployed at over 1,000 companies. SoftBank Robotics, largely composed of the French robotics pioneer Aldebaran (of which Softbank owns 95%), has more than 500 employees globally, with the majority based in Paris at the Aldebaran facility. SoftBank has big ambitions for Pepper, and with more than 20,000 Pepper robots deployed worldwide in both consumer and enterprise environments, SoftBank is easily the leading player in this space.

Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive. Users asked each AI questions using normal language, not the robotic commands you're used to using with digital assistants.