I love the feeling of having a new way to think about the world. I especially love when there's some vague idea that gets formalized into a concrete concept. Information theory is a prime example of this. Information theory gives us precise language for describing a lot of things. How uncertain am I? How much does knowing the answer to question A tell me about the answer to question B? How similar is one set of beliefs to another? I've had informal versions of these ideas since I was a young child, but information theory crystallizes them into precise, powerful ideas. These ideas have an enormous variety of applications, from the compression of data, to quantum physics, to machine learning, and vast fields in between. Unfortunately, information theory can seem kind of intimidating. I don't think there's any reason it should be. In fact, many core ideas can be explained completely visually! Before we dive into information theory, let's think about how we can visualize simple probability distributions. We'll need this later on, and it's convenient to address now. As a bonus, these tricks for visualizing probability are pretty useful in and of themselves! Sometimes it rains, but mostly there's sun! Let's say it's sunny 75% of the time. It's easy to make a picture of that: Most days, I wear a t-shirt, but some days I wear a coat. Let's say I wear a coat 38% of the time. It's also easy to make a picture for that! What if I want to visualize both at the same time?

Worldwide spending on cybersecurity likely topped 75 billion last year, researchers at Gartner estimated, with companies more wary than ever of the risks posed by data breaches and other digital attacks. And along with rising costs, the sheer volume of digital security data has also increased dramatically: IBM estimated in a recent study that the average organization sees more than 200,000 pieces of security event data per day and that more than 10,000 security-related research papers are published every year. "Security researchers are getting hit with a firehose," says Caleb Barlow, vice president of IBM Security. "Once they get done with today, they've got another deluge of data coming tomorrow." To help companies handle that flood of data, IBM says it's training its Watson artificial intelligence platform--previously known for using its natural language processing power to beat humans on Jeopardy--to parse cybersecurity information, from automated network-level threat reports to blog posts from security professionals. "It's just gonna think just like a forensics investigator," says Barlow.

I can't remember the last time I called someone on the phone to place an order for pizza. Papa John's told investors during its first-quarter earnings call that 55% of its total sales now come through digital. Sixty percent of those digital transactions came from mobile devices. Competing pizza chains Domino's and Yum! Brands' Pizza Hut are also investing heavily in e-commerce. Both have invested in ordering methods for smartwatches, connected cars, and video game systems.

Everywhere you turn these days there are more and more automated processes appearing all the time. From automatic vacuum cleaners to self-order counters at restaurants, to cars that automatically park themselves, robots are all around us in one way or another and physics is no different. In using the latest artificial intelligence to do the same tasks as people, we are not only saving time and money but saving on resources too. A recent physics experiment developed by physicists from The Australian National University (ANU) and the University of New South Wales at the Australian Defence Force Academy (UNSW ADFA) was shown to be completed by artificial intelligence (AI) just as a human would. The test was to create a replica of "Laser Beam" experiment that won the 2001 Nobel Prize and produced an extremely cold gas trapped in a laser beam (known as Bose-Einstein condensate) and the incredible AI literally taught itself how to do the experiment, from start to finish, in under one hour!

Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is also author of the books Credit Risk Management: Basic Concepts, published by Oxford University Press in 2008; and Analytics in a Big Data World published by Wiley in 2014. His research is summarized at www.dataminingapps.com.

At its 10th annual I/O developer conference, Google CEO Sundar Pichai and his lieutenants doubled down on artificial intelligence as the next big phase of computing. Machine learning was a common thread in Google's latest products and launches - Google Home, Google's Assistant, Duo and Allo, and Instant Apps. As the event unfolded, it outlined Pichai's vision of what kind of company Google wants to be. Every decade, a new era of computing arrives that pretty much shapes everything we do. During the event, Pichai noted that saying that Google aims to be more assistive and provide a more ambient experience.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree. It's no surprise then that she needed some coaching, especially since feedback is so critical to student success.

One of the major issues in stochastic gradient descent (SGD) methods is how to choose an appropriate step size while running the algorithm. Since the traditional line search technique does not apply for stochastic optimization algorithms, the common practice in SGD is either to use a diminishing step size, or to tune a fixed step size by hand, which can be time consuming in practice. In this paper, we propose to use the Barzilai-Borwein (BB) method to automatically compute step sizes for SGD and its variant: stochastic variance reduced gradient (SVRG) method, which leads to two algorithms: SGD-BB and SVRG-BB. We prove that SVRG-BB converges linearly for strongly convex objective functions. As a by-product, we prove the linear convergence result of SVRG with Option I proposed in [10], whose convergence result is missing in the literature. Numerical experiments on standard data sets show that the performance of SGD-BB and SVRG-BB is comparable to and sometimes even better than SGD and SVRG with best-tuned step sizes, and is superior to some advanced SGD variants.

Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them. These broadcasters are likely to gain greater attention if their posts can remain visible for a longer period of time among their followers' most recent feeds. Then when to post? In this paper, we study the problem of smart broadcasting using the framework of temporal point processes, where we model users feeds and posts as discrete events occurring in continuous time. Based on such continuous-time model, then choosing a broadcasting strategy for a user becomes a problem of designing the conditional intensity of her posting events. We derive a novel formula which links this conditional intensity with the visibility of the user in her followers' feeds. Furthermore, by exploiting this formula, we develop an efficient convex optimization framework for the when-to-post problem. Our method can find broadcasting strategies that reach a desired visibility level with provable guarantees. We experimented with data gathered from Twitter, and show that our framework can consistently make broadcasters' post more visible than alternatives.

Recent work in distance metric learning has focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures. Here, we introduce the problem of learning these transformations when the underlying constraint generation process is nonstationary. This nonstationarity can be due to changes in either the ground-truth clustering used to generate constraints or changes to the feature subspaces in which the class structure is apparent. We propose and evaluate COMID-SADL, an adaptive, online approach for learning and tracking optimal metrics as they change over time that is highly robust to a variety of nonstationary behaviors in the changing metric. We demonstrate COMID-SADL on both real and synthetic data sets and show significant performance improvements relative to previously proposed batch and online distance metric learning algorithms.