Sep 9–12, 2019

Schedule: Case Studies sessions

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1:45pm2:25pm Wednesday, September 11, 2019
Location: LL21 E/F
Secondary topics:  Health and Medicine
Dave Ferrell (Dynam.AI)
Average rating: *****
(5.00, 2 ratings)
Dave Ferrell explores three examples of nontraditional techniques pushing the boundaries of computer vision in industries today, including identifying "unseen" objects. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: LL21 E/F
Juby Jose (Intel), Rohit Israni (Intel)
Average rating: *****
(5.00, 1 rating)
Across segments, enterprises are exploring novel ways of providing stellar customer service. Conversational AI is delivering just that—high-quality customer service, available 24-7, and in a geography-agnostic manner. Juby Jose and Rohit Israni explore how enterprise customer service is being reimagined with the power of conversational AI. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: LL21 E/F
Danny Lange (Unity Technologies)
Average rating: *****
(5.00, 4 ratings)
This year, Unity introduced Obstacle Tower, a procedurally generated game environment designed to test the capabilities of AI-trained agents. Then, they invited the public to try to solve the challenge. Danny Lange reveals what Unity learned from the contest and the real-world impact of observing the behaviors of multiple AI agents in a simulated virtual environment. Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: LL21 E/F
Leslie De Jesus (Wovenware)
Average rating: *****
(5.00, 1 rating)
Leslie De Jesus examines a machine learning solution enabling the Puerto Rico Science, Technology & Research Trust to identify and classify mosquitoes that may be carrying diseases such as Zika and dengue fever. She outlines the challenges, strategy, and technologies used, the results achieved to date, and the implications of the AI project in helping to address a global threat. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: 230 A
Sanji Fernando (Optum)
Average rating: *****
(5.00, 1 rating)
Sanji Fernando explores his experience building, deploying, and operating a deep learning model that improves hospital revenue cycle management, including business alignment, data preparation, model development, model selection, deployment, and operations. Sanji also details key knowledge and opportunities for improvement with deep learning models in healthcare. Read more.

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