Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Schedule: Vision sessions

Advances in classification and object recognition have made vision an area of killer applications for AI. These sessions showcase the ramifications of AI on visual-heavy industries like medical imaging, fashion, self-driving cars, emotional intelligence, and more.

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2:35pm3:15pm Wednesday, June 28, 2017
Verticals and applications
Location: Grand Ballroom West Level: Non-technical
Rana el Kaliouby (Affectiva)
Average rating: *****
(5.00, 2 ratings)
Emotion AI is a branch of artificial intelligence that brings emotional intelligence to AI systems. Rana el Kaliouby reviews the state of emotion AI, its commercial applications, its underlying deep learning methods, and the research roadmap, which includes multimodal emotion recognition and the idea of an emotion chip. Read more.
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11:05am11:45am Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Beginner
Matt Shobe (Mighty AI)
Average rating: ****.
(4.00, 1 rating)
Autonomous vehicles must recognize objects in context, no matter the weather, time of day, or season. What does a cat in the road look like on a sunny summer day? How about on a snow-covered road at night? Matt Shobe shares lessons Mighty AI has learned while creating a training dataset for autonomous driving, including workflow tips and guidance for engineers building computer vision models. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Beginner
Timothy Hazen (Microsoft)
Dramatic progress has been made in computer vision: deep neural networks (DNNs) trained on tens of millions of images can now recognize thousands of different object types. These DNNs can also be easily customized to new use cases. Timothy Hazen shares simple methods and tools that enable you to adapt Microsoft's state-of-the-art DNNs for use in your own computer vision solutions. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Intermediate
Reza Zadeh (Stanford | Matroid)
Providing customized computer vision solutions to a large number of users is a challenge. Matroid allows the creation and serving of computer vision models and algorithms, model sharing between users, and serving infrastructure at scale. Reza Zadeh offers an overview of Matroid's pipeline, which uses TensorFlow, Kubernetes, and Amazon Web Services. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Matt Zeiler (Clarifai)
Average rating: *....
(1.33, 3 ratings)
AI-powered machine learning technologies bring a higher and more complex level of technical debt to applications. Matt Zeiler shares best practices for companies hoping to build AI into their businesses and explores how machine learning increases technical debt, the key contributors, and how to avoid or reduce technical debt related to machine learning. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Implementing AI
Location: Murray Hill E/W Level: Intermediate
Pau Carré (Gilt)
Pau Carré explains how Gilt is reshaping the fashion industry by leveraging the power of deep learning and GPUs to automatically detect similar products and identify facets in dresses. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Yonghua Lin (IBM Research)
Yonghua Lin leads a deep dive into AI Vision, a deep learning system from IBM for image and video analysis in both edge and cloud environments, exploring its system design, performance optimization, and large-scale capability for training and inference. Read more.