The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM Watson cognitive computing services. The series includes an overview of specific IBM Watson services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool.

The AI XPRIZE is a 4-year, $5M competition that challenges teams globally to develop and demonstrate how humans can collaborate with powerful AI technologies to tackle the world's grand challenges. Currently, there are 148 teams in 22 countries competing for the prize, which will be awarded in 2020 on the TED Global stage.

NEW YORK CITY - 15 Jun 2017: LivePerson, Inc. (Nasdaq: LPSN), a leading provider of cloud mobile and online business messaging solutions, and IBM (NYSE: IBM) have announced LiveEngage with Watson, the first global, enterprise-scale, out-of-the-box integration of Watson-powered bots with human agents. The new offering combines IBM's Watson Virtual Agent technology with LivePerson's LiveEngage platform, allowing brands to rapidly and easily deploy conversational bots that get smarter with each interaction, and lets consumers message those brands from their smartphone - via the brand's app, SMS, Facebook Messenger, or even the brand's mobile site - instead of having to call an 800 number. This legacy approach has not kept pace with the consumer move to smartphones and messaging apps, now the dominant way consumers communicate digitally. Forrester's 2017 Customer Service Trends report revealed that "Customers of all ages are moving away from using the phone to using self-service -- web and mobile self-service, communities, virtual agents, automated chat dialogs, or chatbots -- as a first point of contact with a company" and, according to Dimension Data, while there has been a 12 percent decline in phone volume, there has been growth in every digital channel[2]. LiveEngage with Watson helps meet that demand - allowing consumers to message large brands from their smartphones and instantly get answers from AI-powered bots, with human care representatives brought in seamlessly, in real-time, if a bot is not able to resolve an issue satisfactorily.

Equbot, the Fund's sub-advisor, is a technology-based company focused on applying artificial intelligence to investment analyses. It is part of the IBM Global Entrepreneurs start-up roster. IBM already has a Watson effort for financial services more broadly, which includes a Watson analytical tool for wealth advisors and wealth management groups, and Watson applications for financial markets analysis. The filing says Equbot will use IBM's Watson AI to perform a fundamental analysis of U.S.-listed stocks and real estate investment trusts based on up to 10 years of historical data and then apply that analysis to recent economic and news data. "Each day, the Equbot Model ranks each company based on the probability of the company benefiting from current economic conditions, trends and world events and identifies approximately 30 to 70 companies with the greatest potential for appreciation and their corresponding weights, while maintaining volatility comparable to the broader U.S. equity market."

Who will be the first to implement the new suite of Watson services? From the newly formed Watson Financial Services division, IBM has released the first suite of services covering regulatory requirements, financial crime insights, and financial risk modelling. These cognitive tools have been made possible following IBM's 2016 acquisition of global consulting operation, Promontory Financial Group. Promontory was originally working to provide support to banks dealing with the growing and tightening regulation and risk management within the financial services. It was the knowledge and expertise accessed in this acquisition that brought life to the new financial services-focussed Watson services, with regulation and risk accounting for two thirds of the suite, and a financial crime tool completing the set.

Technology is always evolving and the latest innovation, 'Voice Search' is gathering momentum, with the big players being Amazon's Alexa, Microsoft's Cortana, Ok Google and Apple's Siri at the forefront. Ever asked Siri or Alexa what the weather is like? Instead of typing and searching for a keyword or phrase, you can simply ask out loud where the nearest bar or coffee shop is. Utilising natural language processing, a computer science concerned with artificial intelligence (AI) and machine learning, these artificial assistants can listen and respond to search queries almost like a real human. Used by many consumers already, Voice Search is set to be one of the biggest SEO trends for 2017 and thus comes with many opportunities, as well as challenges to overcome. Google CEO Sundar Pichai announced during his Google I/O keynote that 1/5 searches made with Google Android App is a Voice Search.

By eliminating the friction of the traditional translation process, devices like Translate One2One will not only remove one of the biggest challenges for professionals when meeting and collaborating between cultures, but also offers enormous potential for communities around the world,

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing — Query Planning — is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries. We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including dbpedia, yago, and freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.

Listen to The Modern Customer Podcast with Rob High here. Artificial intelligence seems to be popping up everywhere, and it has the potential to change nearly everything we know about data and the customer experience. However, it also brings up new issues regarding ethics and privacy. One of the keys to keeping AI ethical is for it to be transparent, says Rob High, vice president and chief technology officer of IBM Watson. When customers interact with a chatbot, for example, they need to know they are communicating with a machine and not an actual human.