IBM has been making a lot of noise in the datacenter recently. In December of 2017, the company's POWER9 platforms hit the market with a focus on Artificial Intelligence (AI). In March of this year, Rackspace and Google gave the first public peak at the Zaius and Barreleye 2 Open Compute platform at the Open Compute Summit. At last week's Red Hat summit, IBM and Red Hat announced a partnership to extend IBM Cloud to Red Hat's OpenShift Container Platform and Certified Containers. These are all significant, but perhaps the biggest bang of all was the recent announcement that IBM and Nutanix are partnering to deliver hyperconverged platforms based on IBM's POWER9 platform.

The Center for Argument Technology (ARG-tech) located at the University of Dundee now provides tools based on in-house artificial intelligence designed for arguments. While that may sound completely useless given humans do extremely well at arguing each other, this AI is meant to make those arguments more productive, so everyone involved can reach an agreement. According to ARG-tech director Chris Reed, his group first turned to the BBC's Moral Maze 10 years ago. They created large "maps" based on every debate that took place on the show, and turned those maps into infographics using an algorithm to "determine the most central themes." From that data, the team pulled important issues, where participants stood, the highest points in conflict, and more.

Russian President Vladimir Putin speaks at a meeting with students in Yaroslavl, Russia, Friday, Sept. 1, 2017. Putin said that whoever reaches a breakthrough in developing artificial intelligence will come to dominate the world. Putin, speaking Friday at a meeting with students, said the development of AI raises "colossal opportunities and threats that are difficult to predict now." Russian President Vladimir Putin speaks at a meeting with students in Yaroslavl, Russia, Friday, Sept. 1, 2017.

Artificial intelligence (AI) technologies are being deployed for an increasing variety of use cases across consumer, enterprise, and government markets around the world. AI, which is defined as an information system that is inspired by a biological system, is an umbrella term that includes multiple technologies, such as machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, interest in implementing AI systems is surging among companies and institutions around the world. The market intelligence firm forecasts that the revenue generated from the direct and indirect application of AI software will grow from $1.4 billion in 2016 to $59.8 billion by 2025. This forecast represents an upgrade of Tractica's previous projection for AI market growth, which was published in 3Q16, owing to a greater than anticipated pace of change and development in the AI sector.

The Artificial Intelligence market is expected to reach $16 billion by the year 2022 and will continue to grow at a projected rate of 63% over the next five years. The proliferation of Artificial Intelligence across industries is ushering in an age of rapid digital transformation and disruption. Old paradigms are being tossed aside and what was once thought impossible is becoming the everyday norm. "According to a 2016 Markets and Markets Report, the artificial intelligence (AI) market is expected to be worth $16.06 Billion by 2022, growing at a CAGR of 62.9% from 2016 to 2022," reported hiring agency Paysa. Amazon leads the way by far investing almost $100 billion more than that of runner-up, Google.

In heterogeneous multi-robot teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. Yet platform-independence is desirable when planning actions for the various robots. We propose a platform-independent model of robot capabilities which we use as a planning domain. We extend existing planning techniques to support two requirements: generating new objects during planning; and, required concurrency of actions due to data flow which can be cyclic. The first requires online action instantiation, the second a small extension of the Planning Domain Definition Language (PDDL): allowing predicates in continuous effects. We evaluate the planner on benchmark domains and present results on an example object transportation task in simulation.

In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities---tasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independent decisions. We show how the two planners could be interfaced, and how their planning and backtracking could be interleaved. We also provide insights for a methodology for using the combined system, and experimental results to use as a benchmark with future extensions to both the combined system, as well as to the geometric task planner.