Augmented reality provides the potential to deliver actionable information at the point of need, whether it be on the factory floor or in the field. Numerous examples of proof-of-concept and early deployment systems demonstrate how procedures, directional guidance, and both static and dynamic data can be delivered to end-users on wearable and hand-held displays. These solutions use a combination of computer vision, location-awareness, inertial navigation, and user-based information access constraints to determine the experience that will be delivered to the end-user. While valuable in itself, this information nevertheless represents a static view of the world. However, with the use of continuous feedback, predictive analytics, and model-based evolution, the relevance of the experience delivered to the end-user can be continuously updated and enhanced. This presentation will explore the methods and techniques by which machine learning can be used to adapt and evolve the AR experience to reflect changing enterprise, equipment, or individual operational conditions and objectives in order to continuously optimize human performance. Augmented World Expo (AWE) is back for its seventh year in our largest conference and expo featuring technologies giving us superpowers: augmented reality (AR), virtual reality (VR) and wearable tech. Join over 4,000 attendees from all over the world including a mix of CEOs, CTOs, designers, developers, creative agencies, futurists, analysts, investors, and top press in a fantastic opportunity to learn, inspire, partner, and experience first hand the most exciting industry of our times. See more at http://AugmentedWorldExpo.com

1. Smart Context: Using Machine
Learning to Enhance Augmented
Reality Experiences
Carl Byers
Chief Strategy Officer
Contextere

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contextere is an 'Internet of
Things’ software company empowering the 21st
century industrial workforce.
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Today's employee is overwhelmed by data yet
lacks the relevant information to execute, as a
result spending over 60% of their time on non-
productive activities.
Using intelligent context and human-
centric machine learning contextere answers the
simple question: Now What?
An intelligent personal
agent delivering curated
guidance and actionable
insights to the industrial
worker.

3. Industrial Machine Learning
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4. 4Enterprise AR at AWE 2016June 1 or 2, 2016
The Humans at the Edge

5. Why Machine Learning is
Critical
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6. Machine Learning for the
Human at the Edge
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7. Machine Learning Strategies
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Mark Reynolds
Southwest Energy
• Unsupervised Learning
• Data is unlabeled
• Supervised Learning
• Teach and train with data that is
well labeled with a defined output
• Semi-Supervised Learning
• Some of the data is labeled, some
is unlabeled
• Reinforcement Learning
• Validity of data alignment is served
as feedback Mark Reynolds
Southwest Energy

8. Actionable Data at the Point
of Service
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carl@contextere.com
@contextere
+1 613.851.1712
80 Aberdeen Street, Suite 100
Ottawa, Ontario, Canada K1S 5R5
https://ca.linkedin.com/in/cdwbyers
www.contextere.com
@cdwbyers