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Bringing AI to enterprise integration

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Driving long distances (or using New York City's subway system) used to be a much more complicated affair, generally requiring maps, a sense of direction, some luck and the occasional stop to ask questions of strangers.


Movix uses artificial intelligence to hit you with the best movie suggestions

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It turns out that in addition to making spooky trailers and writing quirky scripts, artificial intelligence is also pretty damn good at making awesome movie recommendations.


How to Talk to Your Data Scientist

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Machine learning is poised to help marketers garner phenomenal new insights and results, and to change many processes and jobs along the way. We discussed this potential in "Machine Learning is About to Turn the Marketing World Upside Down."


The Dawn of Artificial Intelligence

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WASHINGTON - U.S. Sen. Ted Cruz (R-Texas), chairman of the Subcommittee on Space, Science, and Competitiveness, will convene a hearing on Wednesday, November 30, 2016, at 2:30 p.m. on "The Dawn of Artificial Intelligence." The hearing will conduct a broad overview of the state of artificial intelligence, including policy implications and effects on commerce. The report outlined opportunities for artificial intelligence, including recommendations on how the technology can be used to advance social good and improve government operations. Witnesses: - Dr. Eric Horvitz, Interim Co-Chair, Partnership on Artificial Intelligence; Managing Director, Microsoft Research Lab - Dr. Andrew Moore, Dean, School of Computer Science, Carnegie Mellon University - Dr. Andrew Futreal, Professor, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center - Mr. Greg Brockman, Cofounder and Chief Technology Officer, OpenAI - Dr. Steve Chien, Senior Research Scientist, Autonomous Space Systems and Technical Group Supervisor, Artificial Intelligence Group, NASA Jet Propulsion Laboratory, California Institute of Technology * Witness list subject to change Hearing Details: Subcommittee on Space, Science, and Competitiveness Hearing Wednesday, November 30, 2016 2:30 p.m.




Recruiting Gets Smart Thanks to Artificial Intelligence

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Whether analyzing the facial expressions of job candidates in video interviews, sorting through multitudes of online applications or keeping job prospects apprised of their hiring status, artificial intelligence (AI) is moving rapidly from experimentation to mainstream use in the talent acquisition world.


Bringing AI to enterprise integration

#artificialintelligence

Driving long distances (or using New York City's subway system) used to be a much more complicated affair, generally requiring maps, a sense of direction, some luck and the occasional stop to ask questions of strangers.


Cablevision Argentina Chooses ContentWise for Machine Learning Light Reading

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ContentWise, the personalization, discovery, analytics and metadata expert, today announced that CablevisiĆ³n Argentina (CVA) has successfully deployed the ContentWise personalization system in CablevisiĆ³n Flow, its new suite of multiscreen television services as part of its drive to provide new, next-generation services to its customers.


Hybrid content-based and collaborative filtering recommendations with {ordinal} logistic regression (1): Feature engineering

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I will use {ordinal} clm() (and other cool R packages such as {text2vec} as well) here to develop a hybrid content-based, collaborative filtering, and (obivously) model-based approach to solve the recommendation problem on the MovieLens 100K dataset in R. All R code used in this project can be obtained from the respective GitHub repository; the chunks of code present in the body of the post illustrate the essential steps only. The MovieLens 100K dataset can be obtained from the GroupLens research laboratory of the Department of Computer Science and Engineering at the University of Minnesota. The first part of the study introduces the new approach and refers to the feature engineering steps that are performed by the OrdinalRecommenders_1.R script (found on GitHub). The second part, to be published soon, relies on the R code in OrdinalRecommenders_3.R and presents the model training, cross-validation, and analyses steps. The OrdinalRecommenders_2.R script encompasses some tireless for-looping in R (a bad habbit indeed) across the dataset only in order to place the information from the dataset in the format needed for the modeling phase. The study aims at (a) the demonstration of the improvement in predicted ratings for recommending on a well-known dataset, and (b) attempts to shedd light on the importance of various types of information in the work of recommendation engines. Consequently, the code is not suited for use in production; additional optimizations are straightforward, simple, and necessary as well.