James Kobielus

SiliconANGLE

James Kobielus is SiliconANGLE Wikibon Lead Analyst for Data Science, Deep Learning, and Application Development. Previously he was an IBM Big Data Evangelist.

7 Ways to Get High-Quality Labeled Training Data at Low Cost

By James Kobielus, SiliconANGLE on June 13, 2017 in Crowdsourcing, Data Preparation, Gamification, Machine Learning, Training Data
Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.
Putting Together A Full-Blooded AI Maturity Model

By James Kobielus, SiliconANGLE on April 5, 2017 in AI, Bernard Marr, Maturity Model, Methodology, Mike Gualtieri
Here is a proposed “7A” model that is useful enough to capture of the core of what AI offers without falsely implying there is a static body of best practices in this area.
Homebrewed Deep Learning and Do-It-Yourself Robotics

By James Kobielus, SiliconANGLE on March 14, 2017 in Cognitive Computing, Deep Learning, IBM, Robots
Learn how to experiment with embodied robotic cognition with IBM Project Intu, a platform that extends Deep Learning and other cognitive services to new devices with minimum coding.
Doing the Data Science That Drives Predictive Personalization

By James Kobielus, SiliconANGLE on September 9, 2016 in Clustering, Customer Analytics, IBM, New York City, NY
Agile collaboration within data science teams is essential to the vision of customer analytics and personalization. Attend IBM DataFirst Launch Event on Sep 27 in New York City to engage with open-source community leaders and practitioners.

James Kobielus

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