An Interview with Jim Kurose

Interviewed by Amy McGovern and Eric Eaton, co-editors for AI Matters

Abstract

Our second profile for the interview series is Jim Kurose, Assistant Director of the National Science Foundation (NSF) for the Computer and Information Science and Engineering (CISE).  Please note that NSF is hiring and would love to have you apply!

Jim Kurose

kurose-short-bio

Dr. Jim Kurose is an Assistant Director of the National Science Foundation (NSF), where he leads the Directorate for Computer and Information Science and Engineering (CISE) in its mission to support fundamental CISE research, education and transformative advances in cyberinfrastructure across the nation.  He is currently a Distinguished Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he has been a faculty member since receiving his PhD in Computer Science from Columbia University. His research area is computer networking, but he did manage to pass a PhD qualifying exam in AI.  He is proud to have received a number of research, teaching and service awards over the years, and is particularly proud of the many students with whom he’s been so fortunate to work.  With Keith Ross, he is the author of the widely adopted textbook Computer Networking: a Top Down Approach.  Jim is a Fellow of the ACM and IEEE.

How did you become interested in CS?

My undergraduate degree is in Physics (from Wesleyan University), which didn’t have a program in CS at the time.  But I took the only two CS courses offered – and loved them both; I worked in the computing center, and had a student job that involved analyzing the various plays run by Wesleyan’s football opponents (definitely “small data”!).  Probably most importantly, I did some Monte Carlo modeling that complemented the experimental part of my undergrad thesis.  I loved physics, but I also had a sense that I’d love computer science, and so I went to grad school expecting to get a MS degree in CS.  There, I fell in love with CS research when I met a couple of great faculty who became my PhD advisors.

What was your most difficult professional decision and why?

The hardest decisions are always the ones that affect other people.  When there are decisions that run contrary to what a person wants (e.g., passing a PhD qualifying exam), you really need to believe that the decision is in that person’s best interests.  The people we work with are always so talented that the challenge is really one of helping find the environment in which a given individual will thrive, be happy, and grow.

What professional achievement are you most proud of?

Without a doubt – the students I’ve taught and mentored – that includes nearly 30 PhD students, and many, many MS and undergrad students.  It’s really a privilege to have a job that can impact others.  There’s nothing that makes a day (or a week!) like getting a note from a former student and hearing that you’ve helped make a difference in that person’s life.  In second place is the undergraduate textbook (Computer Networking, a Top-Down Approach) that I’ve written with Keith Ross – we wrote that because we both love to write and teach, and have been incredibly pleased (and perhaps a bit shocked!) to see how it has been adopted at so many universities around the world.  I am also very proud and honored to be able to serve the CS community in my current position as Assistant Director at the National Science Foundation, where I lead the Directorate for Computer and Information Science and Engineering.

What do you wish you had known as a Ph.D. student or early researcher?

Hey – great question!  I’ve given a talk on exactly that topic: “Ten pieces of advice I wished my advisor had told me”.  I’ve given this talk at a bunch of student workshops in my research area over the years.  Among my favorites in that list are learning how to communicate (write, speak, and tell the narrative of your work), finding role models, and studying broadly.

What would you have chosen as your career if you hadn’t gone into CS?

Impossible to say!  I think there’s a surprising degree of randomness in where we end up, and how we get there.  As the saying goes “What a long strange trip it’s been!”  As I mentioned, I didn’t go to grad school planning to get a PhD — but my grad school experience turned out to be phenomenal.  Nor did I really choose grad school from a particularly career-oriented point-of-view; I just wanted to be where my girlfriend (and now wife) wanted to be.  Both turned out great, but the lesson, I think, is to be open to opportunities and to follow your passion.  Sounds a bit trite, perhaps, but definitely true.

What is a “typical” day like for you?

No two days are alike in my job at NSF.  I spend lots of time working with the amazing CISE staff (program directors, division directors, and administrative team) on both current and future programs; I spend a lot of time interacting with staff from the other directorates at NSF – a real treat as well; and I also spend a good deal of time working with other Federal agencies.  Last, I really enjoy spending time in the CS community, at meetings and visiting campuses and hearing about the amazing things going on, as well as individual and institutional hopes, aspirations, and concerns.

What is the most interesting project you are currently involved with?

Pretty much all of the aspects of my job at NSF.  Let me add that CISE is always looking for smart, dedicated and talented folks from the research community who might be interested in serving a rotation as an NSF/CISE Program Director.  I’d encourage anyone interested to contact the relevant CISE division director or me –  we’ll be happy to tell you more about the opportunities.

How do you balance being involved in so many different aspects of the CS community?

We all depend on so many other people – as students, we depend on our teachers, staff, mentors and other students; as faculty, we depend on our students, colleagues and collaborators; in academic leadership, we depend on the people with whom we work to help make things happen.  For these many activities to be successful we need to rely on other people, and be reliable to those with whom we work; we really do achieve both more and better things by working together.  At NSF, it’s been great to work with Lynne Parker, NSF/CISE Division Director for Information and Intelligent Systems, and her team, who provide NSF’s technical vision, leadership and management of programs in AI and Information and Intelligent Systems more broadly.

What is your favorite CS or AI-related movie or book and why?

I can still remember being completely blown away as a kid when I saw 2001: A Space Odyssey.  It was visually stunning, had the HAL 9000 computer (of course, I’d never even seen a computer then), and was wildly inscrutable to a twelve-year-old.  For CS/AI-related books, my favorites are anything written by Isaac Asimov, and Snowcrash by Neal Stephenson.  Beyond science fiction, I’ve just finished The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson and Andrew McAfee.  All of these books speak to the relationship between humans and technology – a topic of increasing importance for everyone.

 

AI Matters in the New Administration

At the recent AAAI 2016 Fall Symposium, we heard from two experts on the future of technology and policy in the field of cognitive assistance in government and public sector applications. Mark Maybury, Chief Security Officer at Mitre, spoke about the unprecedented rapid changes in AI technology applications and the prospects for good and bad impacts on society. Edward Felton, Deputy U.S. CTO in the Office of the Chief Technology Officer, reviewed recent and current initiatives including the impact of AI and cognitive assistants. CTO “helps shape Federal policies, initiatives, and investments that support the OSTP mission, while also working to anticipate and guard against the consequences that can accompany new discoveries and technologies.”

The discussion about unprecedented opportunities and challenges for AI public policy were exciting, and the symposium was also in the aftermath of an unprecedented presidential election. While not explicitly raised at the symposium, the near future of AI technology and policy was on people’s minds. In that spirit, your comments and insights about AI matters and policy issues as the next administration is being assembled are most welcome!

As a reminder, our goal is to post AI & Policy information on the 1st and 15th of each month.

Policy Matters Posting for November 15, 2016: AAAS

AAAS Policy Issues and Events

In our survey of organizations related to AI & policy, this post reminds us of the potential opportunities to partner with the American Association for the Advancement of Science, particularly the Center of Science, Policy, and Society. While AAAS policy issues are usually not directly about AI, a regular look at their Policy Alert notifications is useful for larger policy issues and opportunities for SIGAI to be involved.

A recent event directly related to AI policy was the 41st Annual AAAS Forum on Science & Technology Policy. At the panel “Best Friend or Worst Nightmare? Autonomy and AI in the Lab and in Society,” AI professionals discussed the role of policy in integrating new technologies into people’s lives, particularly for autonomous systems.

Also keep in mind the AAAS Science & Technology Policy Fellowships and encourage applications by AI professionals in the spectrum of career stages, from recent PhD graduates to faculty on sabbatical to retired scientists and engineers.

The 2016 Leadership Seminar is a Science & Technology Policy “crash course” being held this week. This and other policy events at AAAS may be useful for future participation by SIGAI members.

We welcome your comments to this blog and ideas sent to lrm@gwu.edu.

As a reminder, our goal is to post AI & Policy information on the 1st and 15th of each month.

Policy Matters at AAAI FSS-16

The AAAI Fall Symposium Series on November 17-19, 2016, comprises six symposia, all of which are relevant to AI public policy:
Accelerating Science: A Grand Challenge for AI
Artificial Intelligence for Human-Robot Interaction
Cognitive Assistance in Government and Public Sector Applications
Cross-Disciplinary Challenges for Autonomous Systems
Privacy and Language Technologies
Shared Autonomy in Research and Practice.

Themes include human-machine relationships and the need for stakeholders to be in dialogue about legal impacts and potential legislative actions. Public policy must address the encouragement or discouragement of short-term technology development goals, the longer-term implications of autonomous systems, and the increasing influence of AI on human activities.

Should the creators of autonomous systems be responsible for the actions of those systems?
Could autonomous systems gain personhood and legal responsibility?

We welcome your comments and perspectives!

If you are able to participate in FSS-16, we look forward to you giving your ideas and questions at the symposia. If you cannot attend, please let us know questions you would like asked by adding your comments and discussions in this blog posting. We also welcome your ideas sent to lrm@gwu.edu so they can be shared at FSS-16.

Public Policy Post

Welcome to our new AI public policy blog section!

News for October 27, 2016:

NSF yesterday released a statement in support of the National Artificial Intelligence Research and Development Strategic Plan.

Check out the National AI R&D Strategic Plan.

NSF released a “Dear Colleague” letter encouraging reproducibility in computing research.

Note that the USEC 2017 call for papers includes AI. The deadline is December 1.

Please contribute information about AI public policy issues to this blog. You may also send email to Larry Medsker at lrm@gwu.edu for posting.

Larry

An Interview with Peter Norvig

Interviewed by Amy McGovern and Eric Eaton, co-editors for AI Matters

Abstract

This column is the first in a new series profiling senior AI researchers. This month focuses on Peter Norvig.

Introduction

With this issue, AI Matters is introducing a new column profiling senior researchers in AI. We begin with Peter Norvig, who is the Director of Research at Google, Inc. We interviewed him virtually. The interview has been edited for clarity and length. We thank Peter for his time!

Peter Norvig

Peter Norvig
Peter Norvig

Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was 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. His publications include the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world’s longest palindromic sentence. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.

How did you become interested in AI?

I was lucky enough to go to a High School that had access to a computer and a programming class, which was a rarity in 1974, and a Linguistics class; this got me interested in creating models of language. 42 years later, I still haven’t figured it out, but I’ve had fun trying.

What was your most difficult professional decision and why?

In 1998, I was offered the position of leading the Computational Sciences Division at NASA’s Ames Research Center. This would mean changing my role to be a manager of a 200 person team, rather than contributing as an individual researcher/programmer. In the past I remember there had been many times when I had thought to myself “I could ask a co-worker to program this task, but it would be easier to just do it myself.” But at NASA, and later at Google, the quality of the people was so high, that it was worth it to forego the “do it yourself” approach, and concentrate on getting everyone working together well. This required a different skill set, but in the end greatly amplified the overall impact, and therefore was worth it.

What professional achievement are you most proud of?

First, as a mostly personal effort, Stuart Russell and I (with help from others) were able to put together the textbook that has been the primary resource in AI for 20 years. It was gratifying to see our vision of the field embraced and to hear from so many students who enjoyed using it. Later I was able to team with Sebastian Thrun to bring the core ideas to a large group of online students.

Second, as a team effort, I was the manager for the core Google search team during a period of great growth from 2002 to 2006. I was proud that we were able to help billions of people with trillions of questions, through the combined brilliance of so many great team members.

What do you wish you had known as a Ph.D. student or early researcher?

When I finished grad school, there was an expectation that the “right” path was to stay in academia. In my second year of grad school, two of my most respected fellow students, Bill Joy and Eric Schmidt, left to start a company selling workstations. I remember thinking “Why would they do that? They could have been assistant professors at good schools!” It took me a while to realize that there are multiple paths: in academia, industry research, startups, government, and non-profits, and any one of them, or any combination of them, could be the right choice for you.

What would you want for your career if you couldn’t do AI?

If I couldn’t do AI, I suppose I would want to do AI all the more. But I probably would end up in a field that looks at the same problems from a different point of view, such as Linguistics or Statistics.

What is a “typical” day like for you?

I answered a similar question on Quora, and it still holds true. At Google there’s always something new to work on; I can’t really fall into a set routine. Within a project there are always changes of strategy as we learn more and the world changes. And from one year to the next my role has changed. I’ve varied from having two to two hundred people reporting to me, which means that sometimes I have very clear technical insight for every one of the projects I’m involved with, and sometimes I have a higher-level view, and I have to trust my teams to do the right thing. In those cases, my role is more one of communication and matchmaker: to try to explain which direction the company is going in and how a particular project fits in, and to introduce the project team to the right collaborators, producers, and consumers, but to let the team work out the details of how to reach their goals. I don’t write code that ends up on Google, but if I have an idea, I can write code to experiment with the internal tools to see if the idea is worth looking at more carefully. And I do code reviews, both so that I can see more of the code that the teams are producing, and because somebody has to do it.

There is always a backlog of meetings, emails, and documents to get through. Google is less bureaucratic than anyplace else I’ve worked, but some of this is inevitable. I also spend some time going to conferences, talking with Universities and customers, and answering questions like these.

What is your favorite AI-related joke?

I don’t have a good AI joke, but I did invent my own math joke:

“I saw a pair of mathematicians get into a terrible argument about a
Mobius strip. It was one-sided.”

What is your favorite AI-related movie and why?

I liked the movie Her, because the technology is both central to the plot, but mostly receded into the background of the society that is portrayed. When asked what movie Her reminded me of most, I said Monty Python’s Life of Brian, because both movies are about the human capacity for faith — wanting to believe in something.

How do you spend your free time?

My hobbies are photography and bicycling. Photography is a good art form for me because it doesn’t require that much hand-eye coordination. It is all about simplification and subtraction rather than addition and it allows me to think about gadgets and technical equations (as in Marc Levoy’s Lectures on Digital Photography). Bicycling is right for me because it is just the right speed to see the scenery: with walking you don’t get far enough to see much, and in touring by car you go too fast to see much.

What is a skill you would like to learn and why?

I’ve tried a couple of times to play music, but so far I’m better as an avid listener.