Sep 9–12, 2019

Schedule: Impact of AI on Business and Society sessions

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9:00am5:00pm Tuesday, September 10, 2019
Location: 230 C
Kristian Hammond (Northwestern Computer Science)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: 230 B
Chris Butler (IPsoft)
Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment. Read more.
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11:05am11:45am Wednesday, September 11, 2019
Location: 230 B
Secondary topics:  Ethics, Security, and Privacy
Nicole Eagan (Darktrace)
While nearly every firm is impacted by a wide variety of external factors, the most robust businesses recognize the need to first learn about themselves. Nicole Eagan explores how a self-learning AI is able to learn how a company functions from the inside, and evolve with changes; this AI enables businesses to detect vulnerabilities, improve processes, and continue to grow. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: 230 B
Haixun Wang (WeWork)
The AI advancements in the cyberworld far surpass those in the physical world. Haixun Wang outlines how WeWork aims to change this by examining the approaches the company is taking to bring AI to the real world, ranging from modeling a neighborhood to creating digital twins of a building, and how AI can make businesses more efficient and improve people’s quality of life. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 B
Dexter Hadley (University of California, San Francisco)
Typically, large healthcare institutions have large-scale quantities of clinical data to facilitate precision medicine through an AI paradigm. However, this hardly translates into improved care. Dexter Hadley details how UCSF uses NLP to curate clinical data for over 1M mammograms and how deep learning, blockchain, and other approaches translates this into precision oncology. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: LL21 A/B
Mazin Gilbert (AT&T Research)
5G promises to change our lives in a big way. Mazin Gilbert provides a technical- and market-landscape overview of how AI creates the 5G world, highlighting how recent developments in AI help accelerate widespread adoption of 5G-based applications for consumers and enterprises. He explores the roles of open source and open platforms as key ingredients of this 5G AI transformation. Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: 230 B
Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)
Enhao Gong and Greg Zaharchuk detail AI solutions, cleared by the FDA and powered by industry framework, that deliver 4x–10x faster MRI scans, 4x faster PET scans, and up to 10x dosage reduction. Clinical evaluation at hospitals such as Hoag Hospital, UCSF, and Stanford demonstrates the significant and immediate values of AI to improve the productivity of healthcare workflow. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: LL21 A/B
Secondary topics:  Ethics, Security, and Privacy
David Castillo (Capital One)
David Castillo outlines Capital One's approach to explainable AI, with a particular focus on fairness in automated decisioning. He details key best practices in implementing fair and responsible AI systems, as well as the challenges Capital One has faced along the way and the research efforts it has initiated to overcome them. Read more.
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4:00pm4:40pm Thursday, September 12, 2019
Location: LL21 A/B
Secondary topics:  Computer Vision, Machine Learning
Shelley Zhuang (11.2 Capital)
According to the 2015 Tufts Center for the Study of Drug Development, it now costs $2.6B to get a new drug to market. This figure represents the average cost of total research and development dollars spent divided by the total number of drugs approved. Shelley Zhuang explores how artificial intelligence is transforming drug discovery and development. Read more.

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