Artificial intelligence is increasingly embedded in our consumer and business lives, and it is poised to transform how societies function in the years to come. Yet universities are not adequately preparing students for a changing world. To better prepare students for a changing world, AI needs to be increasingly embedded into higher education. For students, AI will inevitably impact their careers. Those interested in careers in AI could pursue a wide range of exciting new career possibilities focused on data science, machine learning or advanced statistics.

We study the online learning problem where a forecaster is trying to predict each day the next bit in a sequence, such as whether the stock market will go up or down. Every morning, for T days, he solicits the opinions of a number n of experts, who each make up or down predictions. Based on their predictions, the forecaster makes a choice between up and down, then buys or sells accordingly. The goal of the forecaster is to make as few mistakes as possible given that the bit sequence may be generated adversarially. This is a classical learning problem that has been studied in a large body of literature starting with the development of Blackwell approachability [Bla56] and Hannan consistency [Han57], and continued in learning theory under the paradigm of combining expert advice [LW94, Vov90]. One of the best known approaches is the Weighted-Majority algorithm [LW94], which keeps track of weights for all the experts and changes them in every round depending on the quality of their predictions. The average number of mistakes made by the forecaster when using such an algorithm can be bounded by the number of mistakes made by the best expert plus log n/T.

We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic representations of stationary processes, based on techniques with strong asymptotic consistency properties. The models generate new data by concatenating points from the training data sequences in a context-sensitive manner, together with some additive driving noise. We present novel EM-type maximum-likelihood algorithms for data-driven bandwidth selection in KDE-MMs. Additionally, we augment the KDE-MMs with a hidden state, yielding a new model class, KDE-HMMs. The added state variable captures non-Markovian long memory and signal structure (e.g., slow oscillations), complementing the short-range dependences described by the Markov process. The resulting joint Markov and hidden-Markov structure is appealing for modelling complex real-world processes such as speech signals. We present guaranteed-ascent EM-update equations for model parameters in the case of Gaussian kernels, as well as relaxed update formulas that greatly accelerate training in practice. Experiments demonstrate increased held-out set probability for KDE-HMMs on several challenging natural and synthetic data series, compared to traditional techniques such as autoregressive models, HMMs, and their combinations.

Deep neural networks bring in impressive accuracy in various applications, but the success often relies on the heavy network architecture. Taking well-trained heavy networks as teachers, classical teacher-student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable student network with significantly fewer parameters can achieve a considerable accuracy which is comparable to that of teacher network. However, beyond accuracy, robustness of the learned student network against perturbation is also essential for practical uses. Existing teacher-student learning frameworks mainly focus on accuracy and compression ratios, but ignore the robustness. In this paper, we make the student network produce more confident predictions with the help of the teacher network, and analyze the lower bound of the perturbation that will destroy the confidence of the student network. Two important objectives regarding prediction scores and gradients of examples are developed to maximize this lower bound, so as to enhance the robustness of the student network without sacrificing the performance. Experiments on benchmark datasets demonstrate the efficiency of the proposed approach to learn robust student networks which have satisfying accuracy and compact sizes.

In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. In the standard setting, the agent learns from stochastic feedback in the form of real-valued rewards. In many applications, however, numerical reward signals are not readily available -- instead, only weaker information is provided, in particular relative preferences in the form of qualitative comparisons between pairs of alternatives. This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction. The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits. To this end, we provide an overview of problems that have been considered in the literature as well as methods for tackling them. Our taxonomy is mainly based on the assumptions made by these methods about the data-generating process and, related to this, the properties of the preference-based feedback.

Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big data analytics and machine learning, this work contributes in three ways. First, we utilize the call detail records (CDR) data to detect anomalies in the network. For authentication and verification of anomalies, we use k-means clustering, an unsupervised machine learning algorithm. Through effective detection of anomalies, we can proceed to suitable design for resource distribution as well as fault detection and avoidance. Second, we prepare anomaly-free data by removing anomalous activities and train a neural network model. By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data. Lastly, we use an autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Through simple visualization, we show that anomaly free data better generalizes the learning models and performs better on prediction task.

New research proves that eyes might in fact be windows to the soul. Over the past few years, eye tracking technology has emerged as a field of much academic and corporate interest. With major acquisitions by Apple (SMI) and Oculus (The Eye Tribe), it's clear that major international companies regard eye tracking technology as an vital facet of Industry 4.0 -- particularly in its integration with virtual and augmented reality technologies (VR/AR), which involve persistent interaction with the human eye. In the study, researchers tracked 42 participants' eye movements while going about their day on a university campus, and matrixed these findings against user questionnaires. The results assert that machine learning can in fact deduce important personality traits with appropriate datasets -- with the algorithm reliably identifying four of the "Big Five" human personality traits: agreeableness, conscientiousness, extroversion, and neuroticism. In a statement from UniSA, Senior Lecturer of Psychology Dr. Tobias Loetscher explained that this research establishes a meaningful link between our eye motions and our innate and learned characteristics: People are always looking for improved, personalised [sic] services.

President Donald Trump's July 19 executive order establishing the President's National Council for the American Worker is directed at preparing Americans for the workplace of the future. Although short on specifics, the order sends a powerful message about the need for revitalizing educational opportunities if Americans are to thrive in the era of big data, robots and artificial intelligence. The president's intent is to lay the groundwork for tackling a national "skills crisis." His order accepts that Americans need additional skills to fill the current 6.7 million job vacancies. In fact, the executive order gives official imprimatur to what many in industry and academia have feared for some time: "The economy is changing at a rapid pace because of the technology, automation, and artificial intelligence," and existing programs have "prepared Americans for the economy of the past."

The graph represents a network of 2,637 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 28 July 2018 at 09:16 UTC. The requested start date was Saturday, 28 July 2018 at 00:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 3-day, 9-hour, 56-minute period from Tuesday, 24 July 2018 at 14:03 UTC to Saturday, 28 July 2018 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

A pair of Canadian students making a simple, inexpensive prosthetic arm have taken home the grand prize at Microsoft's Imagine Cup, a global startup competition the company holds yearly. SmartArm will receive $85,000, a mentoring session with CEO Satya Nadella, and some other Microsoft goodies. But they were far from the only worthy team from the dozens that came to Redmond to compete. The Imagine Cup is an event I personally look forward to, because it consists entirely of smart young students, usually engineers and designers themselves (not yet "serial entrepreneurs") and often aiming to solve real-world problems. In the semi-finals I attended, I saw a pair of young women from Pakistan looking to reduce stillbirth rates with a new pregnancy monitor, an automated eye-checking device that can be deployed anywhere and used by anyone, and an autonomous monitor for water tanks in drought-stricken areas.