Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Reliability and Safety sessions

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1:50pm2:30pm Wednesday, April 17, 2019
Anand Rao (PwC)
Broader AI adoption and gaining trust from customers requires AI systems to be fair, interpretable, robust, and safe. This talk synthesizes the current research in FAT (Fairness, Accountability, Transparency) into a step-by-step methodology to address these issues. Case studies from financial services and healthcare are used to illustrate the approach. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Sanjay Krishnan (University of Chicago)
I use my work over the last few years on building and deploying an RL-based relational query optimizer, a core component of almost every database system, as an exemplary application that highlights some of the under-appreciated challenges in Deep RL practice. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
Pradip Bose (IBM T. J. Watson Research Center), Augusto Vega (IBM T. J. Watson Research Center), Nandhini Chandramoorthy (IBM T. J. Watson Research Center)
We will describe the fundamentals of a next generation AI research project. It is focused on creating future "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field: in particular, cognitive bias and inaccurate decisions that are perceived as being unethical. Software-hardware system architectures are discussed. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Interacting with AI
Location: Regent Parlor
Behrooz Hashemian (Massachusetts General Hospital)
Artificial Intelligence has shown great potentials to revolutionize clinical medicine and health care delivery. However, incorporating these algorithms into clinical workflows faces a big challenge: convincing clinicians and regulators to trust a “black box” solution. In this talk, I present how we are making deep neural networks interpretable to provide evidences for clinical decisions. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Implementing AI
Location: Rendezvous
Ryan Mukherjee (JHU/APL), Neil Fendley (JHU/APL)
While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. To address this, we released an ImageNet for satellite imagery called functional Map of the World (fMoW). We present our work building the dataset, running a public prize challenge, and investigating how one might attack or defend these deep learning models. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
Mohammad Mavadati (Affectiva)
According to the CDC, up to 6,000 fatal crashes caused by drowsy drivers annually. Driver alertness monitoring systems will allow us to develop more reliable vehicles and safer roads. In this talk, Affectiva introduces state-of-the-art vision-based DNN techniques for drowsiness (intensity) annotations and modeling, and reveals some of the AI solutions for in-car drowsiness predictions. Read more.