Most college students relax over their winter break, eating good food and de-stressing from the previous semester. But when I first talked with Joshua Browder, a Stanford University sophomore, he was busy finishing up schoolwork. I was lucky he had time to talk in the middle of his busy schedule. He's the founder of DoNotPay, a chatbot that helps overturn traffic tickets, and in a few days would be flying to London to meet with government officials about using his technology. Then off to Munich to speak at an international design and innovation conference.

"Microsoft Monday" takes a look back at the past week of news related to Microsoft. This week, "Microsoft Monday" includes details about a Surface Pro 4 price drop, a foldable device patent, the acquisition of Simplygon, a game like Halo Mega Bloks potentially launching, a warning about Windows 7 being dangerous and much more! If you live in the U.S. and you are looking to buy a Surface Pro 4, then you may be happy to hear that the price has been temporarily dropped. The price drop is for the Core i5 version with 128GB storage and 4GB RAM. Until January 28th, the price of this version is $899.

For as long as humans have had consciousness -- two or two hundred millennia depending on who you ask -- human scholars have made great efforts to understand and define what that means. The most facile and purely conceptual description of consciousness might be that it is an awareness of the self within the context of the world. But without an understanding of the underlying mechanism, consciousness keeps chasing its tail. This is, in part, why neuroscientists have successfully interjected themselves in the ongoing conversation about consciousness by pointing to physical phenomena within the brain. But linking the metaphysical to the physical still results in the sort of quasi-scientific, quasi-philosophical overreach that gets academics laughed out of faculty lounges and labeled eccentric.

Singapore bank POSB has launched online chat functionality that understands and reacts to its customers' spoken language. It is the latest bank to harness artificial intelligence (AI) to automate customer interactions with the POSB digibank Virtual Assistant, which is available via Facebook Messenger. Download our most popular articles for IT leaders to learn about skills of great CIOs, tech and digital strategy at the board-level, salary survey results, finding funding for digital initiatives and digital opportunities for CIOs. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

PARIS – In a city hit by chronic pollution and traffic problems, Paris officials are experimenting with a self-driving shuttle linking two train stations in the French capital. Two electric-power EZ10 minibuses, which can carry up to six seated passengers, were put into service Monday and will be tested until early April between the Lyon and Austerlitz stations in Paris. The GPS-guided vehicle is free and will be running seven days a week. "To respond to the pollution emergency in big urban zones it is urgent to innovate with new transportation systems that are more environmentally friendly," said Catherine Baratti-Elbaz, head of the local district where the test is taking place. Jean-Louis Missika, a Paris deputy mayor in charge of innovation, said self-driving vehicles "will change the urban landscape in a spectacular fashion" within the next 20 years.

Most of the previous approaches to lyrics-to-audio alignment used a pre-developed automatic speech recognition (ASR) system that innately suffered from several difficulties to adapt the speech model to individual singers. A significant aspect missing in previous works is the self-learnability of repetitive vowel patterns in the singing voice, where the vowel part used is more consistent than the consonant part. Based on this, our system first learns a discriminative subspace of vowel sequences, based on weighted symmetric non-negative matrix factorization (WS-NMF), by taking the self-similarity of a standard acoustic feature as an input. Then, we make use of canonical time warping (CTW), derived from a recent computer vision technique, to find an optimal spatiotemporal transformation between the text and the acoustic sequences. Experiments with Korean and English data sets showed that deploying this method after a pre-developed, unsupervised, singing source separation achieved more promising results than other state-of-the-art unsupervised approaches and an existing ASR-based system.

In the past twenty years, various large corpora of computer-understandable reasoning knowledge have been developed (Harrison et al., 2014). Apart from axioms, definitions, and conjectures, such corpora include proofs derived in the selected logical foundation with sufficient detail to be machine-checkable. This is either given in the form of premises-conclusion pairs (Sutcliffe, 2009) or as procedures and intermediate steps (Wenzel, 1999). The development of many of these formal proofs required dozens of person-years, their sizes are measured in tens of thousands of human-named theorems and the complete proofs contain billions of low-level inference steps. These formal proof libraries are also interesting for AIbased methods, with tasks such as concept matching, theory exploration, and structure formation (Autexier & Hutter, 2015). Furthermore, the AI methods can be augmented by automated reasoning: progress in the development of efficient first-order automated theorem provers (ATPs) (Kovács & Voronkov, 2013) allows applying them not only as tools that redo the formal proofs, but also to find the missing steps (Urban, 2006). Together with proof translations from the richer logics of the interactive systems to the simpler logics of the ATPs this becomes a commonly used tool in certain interactive provers (Blanchette et al., 2016). Many significant proof developments covering both mathematics and computer science have been created using such technologies. Examples include the formal proof of the Kepler conjecture (Hales et al., 2015), or the proof of correctness of the seL4 operating system kernel (Klein et al., 2010).

We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional non-stationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time but having different covariance matrices. We characterize the sample complexity of graphical model selection for such processes by analyzing a particular selection method, which is based on sparse neighborhood regression. Our results indicate, similar to the case of i.i.d. samples, accurate GMS is possible even in the high- dimensional regime if the underlying conditional independence graph is sufficiently sparse.

We consider the problem of reconstructing a signal from multi-layered (possibly) non-linear measurements. Using non-rigorous but standard methods from statistical physics we present the Multi-Layer Approximate Message Passing (ML-AMP) algorithm for computing marginal probabilities of the corresponding estimation problem and derive the associated state evolution equations to analyze its performance. We also give the expression of the asymptotic free energy and the minimal information-theoretically achievable reconstruction error. Finally, we present some applications of this measurement model for compressed sensing and perceptron learning with structured matrices/patterns, and for a simple model of estimation of latent variables in an auto-encoder.

With 1 to 10 Gigabytes per second bandwidth; 1 millisecond latency and supporting a density of 100 or more devices in any given room size location, it is truly instant, always-on, and able to download the equivalent of a whole movie in a few seconds. This is the new competitive level of performance for the 21st century economy. It enables real-time mission critical rapid response such as connected self-driving cars to enabling the Internet of things sensors and mobile devices that will drive connected buildings, smart cities, savings energy, enabled new skills and services, changing how markets and work gets done.