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9 technologies to watch in 2016

#artificialintelligence

Technology advances not so much when it exhibits innovation, but when it becomes truly practical for everyday people. In 2016, we'll see an acceleration of that shift of technologies from the drawing board and geek-only curiosities to consumer devices that change our lives in ways small and big. Here are a handful of technologies that are on the cusp of major action in the coming year. For decades, artificial intelligence was a thing best understood by sci-fi fanatics and screenwriters. That started to change n 2011 with Apple's Siri voice assistant, but 2015 turned out to be a watershed year for computer algorithms that could ape human thought and interaction.



What Marketers Need to Know About Artificial Intelligence and Augmented Reality

#artificialintelligence

In the past 5 years, technology has revolutionized how marketers interact with their customers. Now, you've got access to advanced software and customer data for more relevant, targeted and engaging marketing. No wonder that 50 to 65% of executives expect to spend more on marketing technology in the coming year. But, what if I told you that we're only at the beginning? In the next 5 years, marketing is going to make a huge pivot- probably almost unrecognizable from its current form. You probably won't perform search engine optimization. Instead, you'll create an exotic virtual experience for your customers. A great example is artificial intelligence-powered voice assistants on your smartphones, for performing simple tasks. Sure, they lack context and aren't functional for complex tasks. But, we'll get there soon. To give you an overview, here are the 5 emerging technologies that Forrester expects to change the world, in the next 5 years. We already have some info on how these technologies are going to affect the customer-business relationship.


Building A User-Centric and Content-Driven Socialbot

arXiv.org Artificial Intelligence

To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.


A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.