Our fourth installment of Research for Practice covers two of the hottest topics in computer science research and practice: cryptocurrencies and deep learning. First, Arvind Narayanan and Andrew Miller, co-authors of the increasingly popular open access Bitcoin textbook, provide an overview of ongoing research in cryptocurrencies. This is a topic with a long history in the academic literature that has recently come to prominence with the rise of Bitcoin, blockchains, and similar implementations of advanced, decentralized protocols. These developments--and colorful exploits such as the DAO vulnerability in June 2016--have captured the public imagination and the eye of the popular press. In the meantime, academics have been busy, delivering new results in maintaining anonymity, ensuring usability, detecting errors, and reasoning about decentralized markets, all through the lens of these modern cryptocurrency systems.

Google's offering voice assistants to your next DIY computing project, we review the new BlackBerry phone (yes, it is 2017), and test-ride an electric dirt bike. Raspberry Pi has teamed up with Google to bring voice integration to the Pi with a clever combination of hardware and software. Packed with the same tech that powers Google Home, the companies have released a kit that transforms a regular Raspberry Pi 3 into your very own virtual assistant. The collaboration marks the first time that Google has produced something for hobbyists. The initiative is called "Artificial Intelligence Yourself" (AIY), and Google's project director said that he wants to create more hobbyist uses for Google software.

The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task. The proposed parallel versions of the ACS differ mainly in their implementations of the pheromone memory. The first two use the standard pheromone matrix, and the third uses a novel selective pheromone memory. Computational experiments conducted on several Travelling Salesman Problem (TSP) instances of sizes ranging from 198 to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536 CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the selective pheromone memory achieved speedups up to 16.85x, but in most cases the obtained solutions were of significantly better quality than for the sequential ACS.

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approaches: GP regression on the absolute and squared returns; regression on the envelope of the returns and the absolute returns; and regression on the envelope of the negative and positive returns separately. We use a maximum a posteriori estimate with a Gaussian prior to determine our hyperparameters. We also test the effect of hyperparameter updating at each forecasting step. We use our approaches to forecast out-of-sample volatility of four currency pairs over a 2 year period, at half-hourly intervals. From three kernels, we select the kernel giving the best performance for our data. We use two published accuracy measures and four statistical loss functions to evaluate the forecasting ability of GARCH vs GPs. In mean squared error the GP's perform 20% better than a random walk model, and 50% better than GARCH for the same data.

There is a critically important dialogue going on across the extended global automotive industry about the future evolution of transportation and mobility. This debate is driven by the convergence of a series of industry-changing forces and mega-trends (see figure 1). Innovative technologies are changing how companies develop and build vehicles. Electric and fuel-cell powertrains tend to offer greater propulsion for lower energy investment at lower emission levels.1 New, lightweight materials enable automakers to reduce vehicle weight without sacrificing passenger safety.2 Further breakthroughs are advancing the introduction of autonomous vehicles; increasingly, daily news reports suggest that driverless cars will soon become a commercial reality.3 We have already seen rapid advances in the "connected car"--innovations that integrate communications technologies and the Internet of Things to provide valuable services to drivers.4

The Applied AI Conference is a must-attend event for people who are working, researching, building, and investing in Applied Artificial Intelligence technologies and products. The event is focused on practical applications and current commercialization of AI technologies across industries such as Transportation & Logistics, Internet of Things (IoT), Future of Work (FoW), Financial Technologies (FinTech), CyberSecurity, and Healthcare Technologies (HealthTech). The 2017 conference agenda will provide insights into the present and future impact of AI on your organization, as well as in your daily life. It will also feature concrete ways, tools, and methods to prepare, organize, and tap AI's transformative power. As active early stage investors in Applied AI, BootstrapLabs will provide an overview of the investment and consolidation landscape at the conference.

"Today is the slowest rate of technological change you will ever experience in your lifetime," wrote Shelly Palmer in his e-book Data-Driven Thinking (Digital Living Press, 2016). As one of the world's premier voices on the accelerating pace of digital technology, he is increasingly preoccupied with helping companies and individuals prepare for the dramatic changes he sees coming, particularly in entertainment and media. Palmer started his career at age 12 as a musician, playing the clarinet, saxophone, and flute in the 1970s in venues around New York. He was also an early experimenter with analog and digital synthesizers. He holds patents for two major interactive television technologies, one of which -- a method for syncing broadcast TV with server-based text, known as enhanced television -- was adopted by Monday Night Football and Who Wants to Be a Millionaire? His background also includes writing the theme music for Spin City and Live with Regis and Kathie Lee, and conducting the London Symphony Orchestra. Currently, he is Fox 5 New York's on-air tech and digital media expert and the proprietor of a popular and prescient email newsletter that covers the impact of technology on media and daily life, with a special focus on smart cars and smart homes. For the past decade, as a venture capitalist and CEO of his own consulting firm and marketing agency, the Palmer Group, Palmer has focused his attention on the evolution of advertising, marketing, and related businesses, along with leading-edge technologies such as smart home systems and data analytics. We recently talked with Palmer in New York. Conscious of the intertwined trajectories of trends in technology and media, we sought to explore how artificial intelligence (AI) and the churn in business models could affect advertising, media, and related fields over the next few years.

Google's TensorFlow has been a hot topic in deep learning recently. The open source software, designed to allow efficient computation of data flow graphs, is especially suited to deep learning tasks. It is designed to be executed on single or multiple CPUs and GPUs, making it a good option for complex deep learning tasks. In it's most recent incarnation – version 1.0 – it can even be run on certain mobile operating systems. This introductory tutorial to TensorFlow will give an overview of some of the basic concepts of TensorFlow in Python. These will be a good stepping stone to building more complex deep learning networks, such as Convolution Neural Networks and Recurrent Neural Networks, in the package.

Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far underexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.