Chris Wiggins CC’93, associate professor of applied mathematics and systems biology at Columbia Engineering and an affiliated faculty member of the statistics department in Arts & Sciences, has been named Adviser to the Dean for AI Strategy, Columbia College.
“Chris has all the qualities one might hope for in a partner to help us sort through how we best approach AI,” says Dean Josef Sorett. “He possesses a deep knowledge of both the liberal arts and the technologies that are driving the discussion; the rare ability to communicate on these topics with scholars, undergraduates and broader audiences; and a longstanding commitment to Columbia College.”
The role will support the College in taking an informed approach to AI, one that speaks to the concerns of instructors (particularly those in the Core Curriculum), current and future students, staff and alumni.
“These people are all stakeholders here,” says Wiggins, who began the role this past Spring. “They want to have consensus; they want to be heard in terms of their own opinions on subjects; and they want to be in discourse with others, including academic leaders, about what they see and how to think about this.”
Wiggins is a member of the Columbia University Data Science Institute; a founding member of the University’s Center for Computational Biology and Bioinformatics; the author of two books about data; and the co-founder of hackNY, a New York City-based initiative seeking “to create and empower a community of student-technologists.” After serving as the chief data scientist at The New York Times for more than a decade, in April he assumed a newly created role as head of machine learning and AI science at CNN.
How has your experience prepared you for this advisory role?
I’m in this sort of weird intersection. Being a College alumnus on the faculty in Engineering, and then also having worked in machine learning for a quarter-century through my research and teaching, and my roles at the Times and at CNN. And with hackNY, I spend a lot of time talking to new grads and current computer science majors from across the country. I engage with AI quite a bit; it’s something that’s already on my mind.
I feel like the modern world involves people realizing their career after the fact; how they’ve stitched together a variety of skills and experiences to meet the current moment. I would also say I’ve been informed by being on many sides of the table. I’ve been in the shoes of a College student, and I’ve been in the shoes of somebody who’s trying to evaluate students. I’ve been on the side of an alumnus who cares for the University, and in conversation with many other alumni who are wondering about AI — I’ve actually just joined the board of the Columbia Alumni Association.
Because I am trying to take more seriously the ways I give back to Columbia and try to help make sure that Columbia continues sailing straight in a world that’s so turbulent. The world has been a source of turbulence for a while, particularly for Columbia and particularly around AI.
What’s been your experience of AI use in the classroom? What would you say are its benefits and drawbacks?
As an instructor, particularly as an instructor who teaches people how to code, it’s really been a source of efficiency. If you have a problem that’s been solved and documented many times over — like writing code — it’s a tremendous source of efficiency to use these tools. However, the phenomenon that students quickly learn is that just because it’s good at one thing doesn’t mean it’s good at something else you expect it to be good at.
The other lesson — which is particularly relevant to education — is that learning involves struggling with ideas. In order for your brain to have its own neural network rewired, you have to really think hard, and that is a type of work that is worth doing. The work of thinking creates long-term benefits, but that puts it at odds with many of these technologies, which promise to make exactly that type of task not be work.
And so I think there’s a challenge to students who think that the goal of the work is to produce the written document, when instead the goal is for the writing to change the writer. And if you are not the writer, then you will not be changed by that experience. So there’s also a challenge for instructors to design assignments — because the assignments are also assessments — to design assignments that don’t incentivize people not to be changed by the assignment.
You know, it’s a complicated thing we do in university. We do research and teach, and the teaching also involves assessing how well people have learned, and there are ways that AI sort of fractures relationships among these things. It’s particularly true of seminar classes like in the Core. Part of what you do in a seminar class is this collective act of focus, where you are collectively struggling with difficult ideas; and so AI takes you from that, because it removes you from the struggle, and the fact that the AI is happening on a device robs you of the collective part.
One of the problems that AI presents right now is really just the exacerbation of something that I think has been a problem for 20 years. Which is the way our information ecosystem has been handed over to an extremely well-funded economy designed to interrupt your attention; with tremendous trillions of dollars going into devices that extract and remove us from the very enjoyable and very beneficial collective struggle with ideas.
What’s interesting to you about being involved in this way right now?
I think that Columbia is such a valuable experiment. When I was young, Ric Burns [CC’78] put out this documentary about New York City, and it had lots of interviews with the great historian Ken Jackson. Jackson described New York as an experiment in whether millions of people of every background can find a way to live alongside one another. And to put one of the world’s greatest universities within that experiment is just so valuable.
It’s really just a very turbulent time for academia in general, and I would like to do what I can — to speak from my own experiences as an undergraduate, as a technologist, as a researcher, as an educator — to be helpful to Columbia College as a community; to help it understand itself; and to get consensus about what individual decisions are consistent with the mission.
Because when it’s time to make and defend difficult decisions, it’s useful if the community is at least aligned on the mission. And then we can think about how to make decisions case to case, from year to year, from department to department, from challenge to challenge.