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17 Top AI and Machine Learning Conferences for Developers in 2017 - IBM Watson

#artificialintelligence

Whether you're interested in cognitive computing, artificial intelligence or machine learning, you probably know that the fourth industrial revolution is well underway and accelerating rapidly. The speed of change presents a challenge to developers who want to stay abreast of the latest ideas and approaches. Conferences, workshops and other meetings provide opportunities to learn where the jobs and technology is headed and a chance to learn and practice the skills necessary to keep up. Why you should attend: AI engineers, practitioners, researchers and scientists will discuss the latest developments in the field, while tutorials and workshops will give attendees a chance to hone their skills. Speakers will be drawn from a wide variety of sectors, including Microsoft, MIT, the National Science Foundation and NASA Ames Research Center.


17 Top AI and Machine Learning Conferences for Developers in 2017 - Watson

#artificialintelligence

Whether you're interested in cognitive computing, artificial intelligence or machine learning, you probably know that the fourth industrial revolution is well underway and accelerating rapidly. The speed of change presents a challenge to developers who want to stay abreast of the latest ideas and approaches. Conferences, workshops and other meetings provide opportunities to learn where the jobs and technology is headed and a chance to learn and practice the skills necessary to keep up. Why you should attend: AI engineers, practitioners, researchers and scientists will discuss the latest developments in the field, while tutorials and workshops will give attendees a chance to hone their skills. Why you should attend: Part of DeveloperWeek will be a two-day conference on Artificial Intelligence, consisting of both technical presentations and thought leadership talks.


15 AI and Machine Learning Events [Be There Or Be Square] Botunity

#artificialintelligence

Conferences, workshops and other meetings provide opportunities to learn where the jobs and technology is headed and a chance to learn and practice the skills necessary to keep up. Why you should attend: The focus of this Summit is "the rise of intelligent machines to make sense of data." "Deep-dive workshops" will give attendees the opportunity to explore specific topics, from natural language processing to pattern recognition. Speakers include development engineers and scientists from top Bay Area companies such as Flickr, Airbnb and Pandora. Why you should attend: The MLconf, which began as a partnership with Carnegie Mellon University's GraphLab, focuses on solutions to organizing and analyzing large, noisy data sets.


AI vs. Humans: Upending the Division of Labor

#artificialintelligence

Despite transitional growing pains, the promise of artificial intelligence (AI) in innovation and decision-making offers a future with better decisions made at the command of but not by humans. That's what Pradeep Dubey, director of the Parallel Computing Laboratory at Intel, told attendees of a plenary talk at the PEARC18 conference in Pittsburgh, Pa., on July 25. "Humans and machines have had this very nice separation of labor," Dubey said. "Humans make decisions; machines crunch numbers … but humans are terrible decision makers." The annual Practice and Experience in Advanced Research Computing (PEARC) conference--with the theme Seamless Creativity--stresses key objectives for those who manage, develop and use advanced research computing throughout the U.S. and the world.


[N] NIPS 2017 Workshop Call for Papers -- Hierarchical Reinforcement Learning • r/MachineLearning

@machinelearnbot

We invite all researchers to submit their manuscripts for review. Please address questions to: hrlnips2017@gmail.com Reinforcement Learning (RL) has become a powerful tool for tackling complex sequential decision-making problems as demonstrated in high-dimensional robotics or game-playing domains. Nevertheless, modern RL methods have considerable difficulties when facing sparse rewards, long planning horizons, and more generally a scarcity of useful supervision signals. Hierarchical Reinforcement Learning (HRL) is emerging as a key component for finding spatio-temporal abstractions and behavioral patterns that can guide the discovery of useful large-scale control architectures, both for deep-network representations and for analytic and optimal-control methods.