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.

There have been many different attempts to create a J.A.R.V.I.S type of AI system to act as a personal assistant, able to interact with you and automate things. These have been from high profile people like Mark Zuckerberg (Facebook) creating a version on J.A.R.V.I.S in his home, voiced by Morgan Freeman. This system has linked simple things like lights, music and toasters which all have IoT devices that you can link together, to more elaborate items such as a t-shirt dispenser, motors to open the curtains and face recognition door system. The brains behind are based on a chat bot and mobile app that the user can integrate with. Other J.A.R.V.I.S examples and developments range from simple lights and desktop interactions, to Amazon Alexa being used to control interactions with apps called J.A.R.V.I.S.

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.

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.

Understanding how different factors affect the supply chain remains a top priority for research firms around the globe. This unwavering drive represents the continued interest in advancing today's capabilities with state-of-the-art technology and adaptability. From artificial intelligence to refocusing on procurement, the state of supply chain continued to explode throughout 2016, and you need to understand why. Artificial intelligence (AI) is among the most well-recognized ideas in science fiction. However, it's true applications are becoming more apparent daily.

Frustrated with automated answering machines before you finally get to speak with a customer service representative? When it comes to insurance, you'll just as likely end up dealing with a robot as a human within three years, according to a survey by Accenture Plc. About two-thirds of insurers already use artificial intelligence-based "virtual assistants," the consulting firm said in the report, which was published on Wednesday. Of the executives who took part in the survey, 85 percent said they plan to invest "significantly" in AI in the next three years. "It's coming pretty quickly," John Cusano, global head of Accenture's insurance practice, said in a telephone interview.

Determinantal Point Processes (DPPs) are probabilistic models that arise in quantum physics and random matrix theory and have recently found numerous applications in computer science. DPPs define distributions over subsets of a given ground set, they exhibit interesting properties such as negative correlation, and, unlike other models, have efficient algorithms for sampling. When applied to kernel methods in machine learning, DPPs favor subsets of the given data with more diverse features. However, many real-world applications require efficient algorithms to sample from DPPs with additional constraints on the subset, e.g., partition or matroid constraints that are important to ensure priors, resource or fairness constraints on the sampled subset. Whether one can efficiently sample from DPPs in such constrained settings is an important problem that was first raised in a survey of DPPs by \cite{KuleszaTaskar12} and studied in some recent works in the machine learning literature. The main contribution of our paper is the first resolution of the complexity of sampling from DPPs with constraints. We give exact efficient algorithms for sampling from constrained DPPs when their description is in unary. Furthermore, we prove that when the constraints are specified in binary, this problem is #P-hard via a reduction from the problem of computing mixed discriminants implying that it may be unlikely that there is an FPRAS. Our results benefit from viewing the constrained sampling problem via the lens of polynomials. Consequently, we obtain a few algorithms of independent interest: 1) to count over the base polytope of regular matroids when there are additional (succinct) budget constraints and, 2) to evaluate and compute the mixed characteristic polynomials, that played a central role in the resolution of the Kadison-Singer problem, for certain special cases.