For CenturyLink, customer surveys play an important role in understanding the customer experience. But, in the past, without the ability to analyze the unstructured data in these surveys, customer service teams couldn't drill down to identify the nature and characteristics of issues. "Using unstructured data is really important for us because that's where the real detail -- the meat -- comes in," said Beth Ard, vice president of customer experience at the Louisiana-based network services provider. "When you do scoring, for example, you don't have enough actionable insight to make the changes that you need without it." Most business analytics processes require clean and well-structured data, but enterprises are increasingly managing unstructured data formats -- emails, chat transcripts, audio and video, and social media posts.

Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: (a) unsupervised learning of vector representations and (b) learning of both vector and matrix representations.

A Honeywell smart thermostat is seen above. If you compare the inside of a modern home to one from about 25 years ago, you're going to notice some stark differences -- not just the phone book on the kitchen counter. Rapid advancements in tech over the past two decades have had an impact on everything from the way we communicate to the conveniences of home life. While you expect some household staples to change from generation to generation, things that were part of an average home for decades are now unnecessary. That's because just about any common household gadget can be replaced with a smarter device.

--In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the gap in terms of a classical architecture for multimedia processing and discuss a structured approach to bridge it. This approach combines machine learning (for mapping signals to objects) and symbolic AI (for linking objects to meanings). Our main goal is to raise awareness and discuss the challenges involved in this structured approach to multimedia understanding, especially in the view of the latest developments in machine learning and symbolic AI. A classic problem in multimedia representation and understanding is the semantic gap problem [1].

New York City, NY: October 25, 2019 – Published via (WiredRelease) – Global (United States, European Union and China) Cognitive Computing Market Research Report 2019-2028. The Cognitive Computing Market report covers all the minute details related to the industry like Technological Developments, Growth Opportunities, Threats to Market Growth, Innovative Strategies and Futuristic Market Trends. Cognitive Computing market report provides a comprehensive overview of current trends and new product development in the global Cognitive Computing market. Featuring global and regional data and over top key players profiles, this report provides the ultimate guide to exploring opportunities in the keyword industry internationally. Some of the key players in the market are, Statistical Analysis System (SAS) Software Ltd, Saffron Technology Inc, Vicarious FPC Inc, IBM corporation, Enterra Solutions LLC, Oracle corporation, SAP Inc, Google LLC, Palantir Technologies Inc and Microsoft corporation.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Though empirically successful, theoretical foundations for MARL are relatively lacking in the literature. In this chapter, we provide a selective overview of MARL, with focus on algorithms backed by theoretical analysis. More specifically, we review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov/stochastic games and extensive-form games, in accordance with the types of tasks they address, i.e., fully cooperative, fully competitive, and a mix of the two. We also introduce several significant but challenging applications of these algorithms. Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc. Some of the new angles extrapolate from our own research endeavors and interests. Our overall goal with this chapter is, beyond providing an assessment of the current state of the field on the mark, to identify fruitful future research directions on theoretical studies of MARL. We expect this chapter to serve as continuing stimulus for researchers interested in working on this exciting while challenging topic.

The "Global Artificial Intelligence in Supply Chain Market Analysis to 2027" is a specialized and in-depth study of the technology, media and telecommunication industry with a special focus on the global market trend analysis. The report aims to provide an overview of the Artificial Intelligence in Supply Chain market with detailed market segmentation by components, technology, application, and industry vertical, and geography. The global artificial intelligence in supply chain market is expected to witness high growth during the forecast period. The report provides key statistics on the market status of the leading artificial intelligence in supply chain market players and offers key trends and opportunities in the market. The reports cover key developments in the artificial intelligence in supply chain market as organic and inorganic growth strategies.

Man-made brainpower (AI) will soon be at the core of each major technological framework on the planet to manage and get to your strategic information. Only a couple of uses are cyber and homeland security, anti-money laundering, payments, financial markets, biotech, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). Artificial Intelligence is turning into a significant staple of innovation, scarcely any individuals comprehend the advantages and weaknesses of AI and Machine Learning innovations. While machine intelligence is sure to assume a key role in the making of cutting edge frameworks in a wide assortment of industry areas sooner rather than later, it is especially applicable in quickly developing businesses, for example, ICT, manufacturing and transportation. Over the globe, mobile operators are preparing to deploy the fifth era of 3GPP mobile wireless networks (5G).