September 26-27, 2016
New York, NY

Schedule: Verticals and applications sessions

3:45pm–4:25pm Monday, 09/26/2016
Location: River Pavilion B Level: Beginner
Urs Muller (NVIDIA)
Urs Muller presents the architecture and training methods used to build an autonomous road-following system. A key aspect of the approach is eliminating the need for hand-programmed rules and procedures such as finding lane markings, guardrails, or other cars, thereby avoiding the creation of a large number of “if, then, else” statements. Read more.
1:30pm–2:10pm Tuesday, 09/27/2016
Location: 3D08 Level: Non-technical
Aman Naimat (Demandbase), MARK PATEL (McKinsey & Company)
Average rating: *****
(5.00, 3 ratings)
Aman Naimat and Mark Patel present an analysis of the current adoption of AI in industry based on a systematic study of the entire business Internet at over 500,000 companies. Drawing on this data, Aman and Mark offer a new economic framework to discover, measure, and motivate future use cases for AI. Read more.
2:20pm–3:00pm Tuesday, 09/27/2016
Location: 3D09 Level: Intermediate
Laura Deming (The Longevity Fund), Sasha Targ (UCSF Institute for Human Genetics)
Average rating: *****
(5.00, 1 rating)
Each human genome is a 3 billion-base-pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. Laura Deming and Sasha Targ describe novel machine-learning search algorithms that allow us to find architectures suited to decode genomics. Read more.
3:45pm–4:25pm Tuesday, 09/27/2016
Location: 3D08 Level: Beginner
Jennifer Rubinovitz (DBRS Innovation Lab), Amelia Winger-Bearskin (Contentful)
Average rating: **...
(2.50, 2 ratings)
Jennifer Rubinovitz and Amelia Winger-Bearskin offer an overview of how artificial intelligence researchers and artists at the DBRS Innovation Lab have collaborated on five different projects (and counting), ranging from composing modern classical music to visualizing deep neural networks in virtual reality. Read more.
3:45pm–4:25pm Tuesday, 09/27/2016
Location: 3D09 Level: Beginner
Ash Damle (Lumiata)
AI in healthcare demands models that can handle the complexity of health data and implementation of automation, precision, speed, and transparency with minimal error. Drawing on Lumiata’s experience with building medical AI, Ash Damle discusses key considerations in dealing with high-dimensional data, deep learning, and how to apply practical AI in healthcare today. Read more.