Skip to main content

News & Events

CIRC Symposium Series: Elliot Inman

November 17, 2017
11:30 AM - 01:00 PM
Wegmans Hall 1400 (auditorium), River Campus

Elliot Inman
SASĀ® Solutions OnDemand
Manager of Software Development

Every third Friday of the month, the Center for Integrated Research Computing hosts a research symposium (known as the CIRC Symposium), where faculty, staff, and student researchers convene to learn about research projects utilizing the center's resources, meet potential collaborators, and learn about new technologies and trends in research computing. This event is user-driven and features presentations by researchers using CIRC systems. CIRC Symposia are open to all members of the university community and a complimentary lunch is provided.

TITLE: The Human Factor in the Age of Machine Learning

ABSTRACT:  The advent of machine learning algorithms has been described as everything from a "second industrial revolution" to a "national security threat."  Alarming stories in the popular press describe a world in which human beings have become obsolete.  Exceptionally high-speed algorithms that constantly adapt to streaming data will do everything from generating the art that entertains and inspires us to diagnosing cancer early enough to defeat it.  Machine learning will provide an automated control system for a nation with hundreds of millions of self-driving cars and order our groceries based on a diet the machine has determined is best for us to eat.  And all of these machines will do all of that without any human being needed to make it all work.  But, as the poet e.e. cummings wrote, "pity this busy monster, mankind, not."

There will still be a role for human beings.  Machines feed on data.  People will decide what data are collected, who owns those data, and how the data may be used.  People will decide what and when to feed the machine.  Data Scientists and Statisticians will decide the standard for a reliable system and determine which machine is consistent enough to be trusted.  Researchers with expertise in physics, chemistry, biology, and botany from fields like health care, agriculture, and education, and so on will set the standard for a valid system.  They will decide which machine has a true understanding of what science has already uncovered.  They will be the first to know whether the machine is right or wrong.  And finally, human beings will be the ones to make machine learning algorithms better and more useful to us all.

In this talk, Elliot Inman, Ph.D., a manager of software development for SAS, will discuss his perspective on machine learning and the role of data scientists and other researchers in the future of computational science.  This talk is a part of the Goergen Institute for Data Science’s Data Science Industry Speakers Series.

In addition to his talk, Inman will also lead a series of workshops called "Microcontrollers for the Rest of Us” with the River Campus Libraries TinkerSpace program.   

BIO: Elliot Inman, Ph.D., is a Manager of Software Development for SAS® Solutions OnDemand. Over the past 25 years, he has analyzed a wide variety of data in areas as diverse as the effectiveness of print and digital advertising, social service outcomes analysis, healthcare claims analysis, employee safety, educational achievement, clinical trial monitoring, sales forecasting, risk-scoring and fraud analytics, general survey analysis, performance benchmarking, product pricing, text processing, and basic scientific research on human memory and cognitive processes. After completing his undergraduate degree at North Carolina State University, he went on to earn a Ph.D. in Experimental Psychology from the University of Kentucky in 1997. In 2005, he started at SAS as an Analytical Consultant. In 2010, he joined SAS Solutions OnDemand, SAS’ high performance cloud computing center. His current focus is on implementing Visual Analytics to provide non-statisticians deeper insight into the results of data mining and predictive models. In addition to his work at SAS, he has led makerspace workshops using microcontrollers to gather data for the Internet of Things and other applications.

Category: Talks