Nov 01

Human AI Collaboration: A Dynamic Frontier

November 1, 2017

Event Description:

Human AI Collaboration: A Dynamic Frontier
Partnerships Between Human and Artificial Intelligence
November 1, 2017 8:30a-5:30p
Stanford University, Mckenzie Room (3rd FL Jen-Hsun Huang Engineering Center)


Paid Registration Required
If you are a mediaX member or are faculty, staff or student of Stanford, please email Addy Dawes for a special registration code.

In a few decades, we’ve gone from machines that can execute a plan to machines that can plan. We've gone from computers as servants to computers as collaborators and team members.

In the best of circumstances, collaboration and teamwork present challenges. Even teams of highly competent people struggle to clarify goals, understand each other in conversations, define roles and responsibilities, and adapt when necessary. Determining what we want from collaboration is sometimes the hardest task.

Establishing confidence and trust in team members can make or break a project, and this is equally true of the relationships we have with our digital assistants and AI collaborators. The expanding capabilities and applications of intelligent machines call for a more sophisticated understanding of the relationships between people and AI.

AI began by understanding actions as humans performed them. Routine tasks with predictable decision points became computer-controlled through programs based on extracting expertise through observation or questioning human experts. Programmers captured the “how” of human behavior in rules that machines could follow. Automated machines could do it faster, with fewer errors, without fatigue. Humans can explain this type of AI.

Enter machine learning – the capacity of computers to leverage massive amounts of data to act without specific human instruction. By looking at examples, extracting the patterns, turning them into rules, and applying those rules, machine learning now captures the “what” of human behavior to provide artificially intelligent answers for complex tasks - such as visual perception, speech recognition, translation and even decision-making,. AI now does things that humans find difficult to explain.

Join us on November 1, 2017 as we go farther into the creation and harnessing of artificial intelligence and ask three important questions:
1. On which tasks will machines with AI be able to out-perform humans?
2. What do we know about people and technology that will help us establish confidence, certainty and collaboration in the new partnerships between human and artificial intelligence?
And, most importantly:
3. How can intelligent machines truly enhance the human experience?


Paid Registration Required
If you are a mediaX member or are faculty, staff or student of Stanford, please email Addy Dawes for a special registration code.

Presenters:

Neil Jacobstein Co-chairs the Artificial Intelligence and Robotics Track at Singularity University on the NASA Research Park campus in Mountain View, California. He served as President of Singularity University from October 2010 to October 2011. As a mediaX Distinguished Visiting Scholar, his research focuses on augmented decision systems. He Chaired AAAI’s 17th Innovative Applications of Artificial Intelligence Conference, and continues to review technical papers on the IAAI Technical Program Committee.

Peter Norvig is a Director of Research at Google Inc; previously he directed Google's core search algorithms group. He is co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and co-teacher of an Artificial Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.

David Bailey is a leading figure in the field of high-performance scientific computing, with over 100 published paper in this area. His paper "The NAS parallel benchmarks" (co-authored with several colleagues at NASA Ames Research Center) is widely cited in performance studies of scientific computer systems. With colleagues he recently developed a novel ranking methodology to rank the market forecaster. In particular, this method distinguishes forecasts by their specificity, rather than considering all predictions and forecasts equally important, and also analyzes the impact of the number of forecasts made by a particular forecaster.

John Willinsky is Khosla Family Professor of Education, at the Stanford Graduate School of Education and Professor (Part-Time) Publishing Studies, SFU and Distinguished Scholar in Residence, SFU Library. John started the Public Knowledge Project (PKP) in 1998 at the University of British Columbia in an effort to create greater public and global access to research and scholarship through the use of new publishing. technologies. Some of his current research interest are Scholarly Communication, Sociology of Knowledge and Technology and Literacy.