- About
- Events
- Calendar
- Graduation Information
- Cornell Learning Machines Seminar
- Student Colloquium
- BOOM
- Fall 2024 Colloquium
- Conway-Walker Lecture Series
- Salton 2024 Lecture Series
- Seminars / Lectures
- Big Red Hacks
- Cornell University - High School Programming Contests 2024
- Game Design Initiative
- CSMore: The Rising Sophomore Summer Program in Computer Science
- Explore CS Research
- ACSU Research Night
- Cornell Junior Theorists' Workshop 2024
- People
- Courses
- Research
- Undergraduate
- M Eng
- MS
- PhD
- Admissions
- Current Students
- Computer Science Graduate Office Hours
- Advising Guide for Research Students
- Business Card Policy
- Cornell Tech
- Curricular Practical Training
- A & B Exam Scheduling Guidelines
- Fellowship Opportunities
- Field of Computer Science Ph.D. Student Handbook
- Graduate TA Handbook
- Field A Exam Summary Form
- Graduate School Forms
- Instructor / TA Application
- Ph.D. Requirements
- Ph.D. Student Financial Support
- Special Committee Selection
- Travel Funding Opportunities
- Travel Reimbursement Guide
- The Outside Minor Requirement
- Diversity and Inclusion
- Graduation Information
- CS Graduate Minor
- Outreach Opportunities
- Parental Accommodation Policy
- Special Masters
- Student Spotlights
- Contact PhD Office
Speaker
Monday, November 11, 2024 - 15:45 to 16:45
Baker Lab, Room 200 and streaming (via Zoom). ZOOM Meeting ID: 924 9911 8564 Passcode: 388714
Host:
Center for Data Science for Enterprise and Society
Categories:
Tools of Thought: Building Algorithms that Enhance Human Capacity
Abstract: The biggest proponents for AI lack no confidence. We are close, they argue, to being able to build algorithms that do everything humans can. The problem with this vision is not hubris, quite the opposite. I will argue that it lacks ambition. Computing will have the biggest (positive) impact on society not from algorithms doing what we already do cheaper or in silico; but from doing things that humans cannot even dream of. In effect, we want thought partners, not substitutes. Building such algorithms requires that we integrate our knowledge of people with our knowledge of computing. I will illustrate with several examples.
Bio: Professor Mullainathan is a recipient of the MacArthur “Genius Grant,” has been designated a “Young Global Leader” by the World Economic Forum, was labeled a “Top 100 Thinker” by Foreign Policy Magazine, and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK).experiments—to study social problems such as discrimination and poverty. He recently co-authored the book Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times. Professor Mullainathan helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, has worked in government in various roles, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences.His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data.
The Peter de Florez Professor, In Electrical Engineering and Computer Science, and Economics at MIT will be on campus as a Dean of Faculty Messenger Lecturer and a Data Science Distinguished Lecture for the Center for Data Science for Enterprise and Society. Professor Mullainathan will be giving three talks Monday, November 11 - Wednesday, November 13.
This is the first talk in the Dean of Faculty Messenger series and the Center for Data Science for Enterprise and Society's Data Science Distinguished Lecture series.
ZOOM Meeting ID: 924 9911 8564. Passcode: 388714