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Artificial Intelligence Market by Technology, Application, & Geography - Global Forecast to 2020


"Diversified application areas are expected to drive the artificial intelligence market" The artificial intelligence market is estimated to grow from USD 419.7 million in 2014 to USD 5.05 billion by 2020, at a CAGR of 53.65% from 2015 to 2020. This growth can be attributed to the factors such as diversified application areas, improved productivity, and increased customer satisfaction. "Machine learning technology to gain maximum traction during the forecast period" The machine learning technology is expected to account for the largest share of the overall AI market duing the forecast period. In addition, due to the increase in demand for AI from the media & advertising and finance sectors, the artificial intelligence market is expected to gain traction in the next five years. The machine learning technology market for the retail, healthcare, law, and oil & gas sectors is also expected to witness growth during the forecast period. - #AI News: Global #ArtificialIntelligence Market to Reach $16 Million by 2022 - Analysis by Technology, Application & Geography - Research and Markets


Newswire) Research and Markets has announced the addition of the "Global Artificial Intelligence Market (2016 - 2022)" report to their offering. The global artificial intelligence market is estimated to reach USD 16,274.0 The report aims at estimating the size and future growth potential of the market across different segments such as technology, application, and region. With the rise in adoption of AI in the media & advertising, retail, finance, and healthcare sectors, the machine learning and natural language processing technologies are expected to play a key role in propelling the growth of the AI market in the next five years. Artificial intelligence (AI) could double annual economic growth rates within two decades by changing the nature of work and spawning a new relationship between man and machine.

nick lally // art, geography, software » Blog Archive » geographies of software, AAG 2017


A variety of technologies have emerged in the last decade that make it easier and cheaper than ever before to make representations of everyday mobile embodiment. Increasing numbers of people are quantifying and self-tracking their everyday lives recording behavioural, biological and environmental data (Beer, 2016; Neff & Nafus, 2016) using a variety of technologies, for example: • lightweight wearable cameras such as the GoPro allowing users to record footage of their most banal everyday activities; • devices such as the Fitbit and Apple Watch bringing continuous physiological monitoring out of the medical realm and into mainstream culture; • apps like Strava allowing people to quantify their cycling, running and walking activities; • lightweight devices for measuring brain activity (EEG) and stimulation (EDA) becoming sufficiently robust and discreet to be used in non-lab environments. None of the underlying technologies are novel, but as they are made accessible in cheaper and more user-friendly packages, new techniques and sources of data are becoming more readily available for geographical analysis. Engagement with these technologies has created a rapidly expanding area of investigation within geography. The emergence of the quantified-self poses both opportunities and dilemmas for geographical thought. We wish to move past simplistic protests that dismiss such technology as offering another take on Haraway's (1988) 'god trick', presenting partial, and highly situated data as objective truth. Instead, this session will build on the potential identified by Delyser and Sui (2013) to take more inventive approaches toward mobile methods. The focus will be on how these technologies can be engaged with by critical geographers to bring new perspectives to their analysis of everyday embodiment.

LOLA Probabilistic Navigation for Topological Maps

AI Magazine

LOLA's entry in the Office Delivery event of the 1995 Robot Competition and Exhibition, held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence, was the culmination of a three-month design and implementation period for an indoor navigation system for topological maps. This article describes the major components of the robot's navigation architecture. It also summarizes the experiences and lessons learned from the competition.