To design an intelligent and human-centered control system [1] that adaptively adjusts relevant parameters in time to meet the human driver's needs and to provide a basic control law for the advanced vehicle dynamics control system [2][3] or driver assistance system [4][5], driver behaviors, driving styles or characteristics should be recognized and predicted. For example, to improve vehicle's fuel economy and reduce the emission, we can design different control strategies for driving styles. To achieve these goals, recognition and prediction of driving styles and characteristics precisely is the primary work. Drivers and their factors have been discussed from the viewpoint of application in vehicle dynamics [6][7], physical attributes of human drivers, and modeling driver [8][9]. For the recognition and prediction of driving characteristics or driver types, including physical characteristics/states (e.g., fatigue, drunk, and drowsiness), psychical characteristics (e.g., nervous, relaxed) and driving styles (e.g., aggressive, moderate), a lot of investigations have been conducted in recent years. In general, the basic idea to identify and predict driving behaviors or styles is based on driver model, called indirect or model-based method. The model-based method, firstly, requires to establish a driver model that can describe driver's

Between the University of Washington, a thriving tech community, and strong research institutions, like the Allen Institute for Artificial Intelligence (AI2), many of the rapid developments in AI are playing out in Seattle. Perhaps that's why the White House has selected the Emerald City for its first public workshop on artificial intelligence. The Office of Science and Technology Policy will co-host the first of four events on artificial intelligence at the University of Washington May 24. The workshop, put on by the UW's Tech Policy Lab and School of Law, will explore issues such as policy, logistical applications, and safety, as they relate to AI. Speakers include AI2 CEO and UW Professor of Computer Science and Engineering Oren Etzioni, White House Deputy U.S. CTO Edward Felten, Microsoft Principal Researcher Kate Crawford, and other industry experts. The workshops are intended "to spur public dialogue on artificial intelligence and machine learning and identify challenges and opportunities related to this emerging technology," writes Felton in a White House blog post.

Sabrina Knight's second-grade students at a Brooklyn public school receive lessons in coding. Some school districts in the United States are attempting to expand computer science education while the Obama administration is pushing to bring the subject to every public school in the nation. On a recent Friday afternoon at a Brooklyn public school, the children of Sabrina Knight's second-grade class listened intently as she used a peanut butter and jelly sandwich to talk about algorithms. Moments later, a student volunteer walked back and forth across the room to demonstrate looping, a technical term used in the field of computer programming. "Thumbs up if you got it," Knight said, as a flurry of 7- and 8-year-old hands and thumbs shot up in the air.

When Bob Greifeld became Nasdaq's CEO in 2003, he was presented with outdated tools and a company that was bleeding cash and rapidly losing market share. Thirteen years later, Nasdaq has the largest market share for options and equities of any exchange in the US. Through acquisitions and partnerships, Greifeld has shaped Nasdaq into what he primarily views as a cutting-edge technology company. During a recent interview with Business Insider, Greifeld discussed his vision for the future of Nasdaq and how it fits into the exchange industry. What follows is that portion of our interview, edited for length and clarity.

Artificial intelligence is quickly becoming one of the most powerful tools in the tech industry, and while AI can be used for harmless tasks like defeating world Go champions, it also has the potential for misuse. A malevolent AI would be like a computer virus on steroids, and while there are currently no known cases, researchers Federico Pistono and Roman Yampolskiy from the University of Louisville in Kentucky believe that we should already be preparing for them. Pistono and Yampolskiy have published a research paper called "Unethical Research: How to Create a Malevolent Artificial Intelligence." In it, they explain that it is entirely possible for a malevolent AI to be created in the right environment, and they lay out what sort of warning signs the cyber security industry should be looking out for. First and foremost, Pistono and Yampolskiy say that any organization interested in creating a malevolent AI would resist any form of oversight on their research.

When Bob Greifeld became Nasdaq's CEO in 2003, he was presented with outdated tools and a company that was bleeding cash and rapidly losing market share. Thirteen years later, Nasdaq has the largest market share for options and equities of any exchange in the US. Through acquisitions and partnerships, Greifeld has shaped Nasdaq into what he primarily views as a cutting-edge technology company. During a recent interview with Business Insider, Greifeld discussed his vision for the future of Nasdaq and how it fits into the exchange industry. What follows is that portion of our interview, edited for length and clarity.

It happens hundreds of times a day: We press snooze on the alarm clock, we pick a shirt out of the closet, we reach for a beer in the fridge. In each case, we conceive of ourselves as free agents, consciously guiding our bodies in purposeful ways. But what does science have to say about the true source of this experience? In a classic paper published almost 20 years ago, the psychologists Dan Wegner and Thalia Wheatley made a revolutionary proposal: The experience of intentionally willing an action, they suggested, is often nothing more than a post hoc causal inference that our thoughts caused some behavior. The feeling itself, however, plays no causal role in producing that behavior. This could sometimes lead us to think we made a choice when we actually didn't or think we made a different choice than we actually did.

Deep conditional generative models are developed to simultaneously learn the temporal dependencies of multiple sequences. The model is designed by introducing a three-way weight tensor to capture the multiplicative interactions between side information and sequences. The proposed model builds on the Temporal Sigmoid Belief Network (TSBN), a sequential stack of Sigmoid Belief Networks (SBNs). The transition matrices are further factored to reduce the number of parameters and improve generalization. When side information is not available, a general framework for semi-supervised learning based on the proposed model is constituted, allowing robust sequence classification. Experimental results show that the proposed approach achieves state-of-the-art predictive and classification performance on sequential data, and has the capacity to synthesize sequences, with controlled style transitioning and blending.

Tensor factorization is a powerful tool to analyse multi-way data. Compared with traditional multi-linear methods, nonlinear tensor factorization models are capable of capturing more complex relationships in the data. However, they are computationally expensive and may suffer severe learning bias in case of extreme data sparsity. To overcome these limitations, in this paper we propose a distributed, flexible nonlinear tensor factorization model. Our model can effectively avoid the expensive computations and structural restrictions of the Kronecker-product in existing TGP formulations, allowing an arbitrary subset of tensorial entries to be selected to contribute to the training. At the same time, we derive a tractable and tight variational evidence lower bound (ELBO) that enables highly decoupled, parallel computations and high-quality inference. Based on the new bound, we develop a distributed inference algorithm in the MapReduce framework, which is key-value-free and can fully exploit the memory cache mechanism in fast MapReduce systems such as SPARK. Experimental results fully demonstrate the advantages of our method over several state-of-the-art approaches, in terms of both predictive performance and computational efficiency. Moreover, our approach shows a promising potential in the application of Click-Through-Rate (CTR) prediction for online advertising.

We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number $K$ of clusters of sizes linearly growing with the global population of items $n$. Every pair of items is labeled independently at random, and label $\ell$ appears with probability $p(i,j,\ell)$ between two items in clusters indexed by $i$ and $j$, respectively. The objective is to reconstruct the clusters from the observation of these random labels. Clustering under the SBM and their extensions has attracted much attention recently. Most existing work aimed at characterizing the set of parameters such that it is possible to infer clusters either positively correlated with the true clusters, or with a vanishing proportion of misclassified items, or exactly matching the true clusters. We find the set of parameters such that there exists a clustering algorithm with at most $s$ misclassified items in average under the general LSBM and for any $s=o(n)$, which solves one open problem raised in \cite{abbe2015community}. We further develop an algorithm, based on simple spectral methods, that achieves this fundamental performance limit within $O(n \mbox{polylog}(n))$ computations and without the a-priori knowledge of the model parameters.