AI Matters Interview: Getting to Know Maja Mataric

AI Matters Interview with Maja Mataric

Welcome!  This month we interview Maja Mataric, Vice Dean for Research and the Director of the Robotics and Autonomous Systems Center at the University of Southern California.

Maja Mataric’s Bio

Maja Mataric named as one 10 up-and-coming LA innovators to watch

Maja Matarić is professor and Chan Soon-Shiong chair in Computer Science Department, Neuroscience Program, and the Department of Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (RASC), co-director of the USC Robotics Research Lab and Vice Dean for Research in the USC Viterbi School of Engineering. She received her PhD in Computer Science and Artificial Intelligence from MIT in 1994, MS in Computer Science from MIT in 1990, and BS in Computer Science from the University of Kansas in 1987.

How did you become interested in robotics and AI?

When I moved to the US in my teens, my uncle wisely advised me that “computers are the future” and that I should study computer science. But I was always interested in human behavior. So AI was the natural combination of the two, but I really wanted to see behavior in the real world, and that is what robotics is about. Now that is especially interesting as we can study the interaction between people and robots, my area of research focus.

Do you have any suggestions for people interested in doing outreach to K-12 students or the general public?

Getting involved with K-12 students in incredibly rewarding! I do a huge amount of K-12 outreach, including students, teachers, and families. I find the best way to do so is by including my PhD students and undergraduates, who are naturally more relatable to the K-12 students: I always have them say what “grade” they are in and how much more fun “school” is once they get to do research. The other key parts to outreach include letting the audience do more than observe: the audience should get involved, touch, and ask questions. And finally, the audience should get to take something home, such as concrete links to more information and accessible and affordable activities so the outreach experience is not just a one-off. Above all, I think it’s critical to convey that STEM is changing on almost a daily basis, that everyone can do it, and that whoever gets into it can shape its future and with it, the future of society.

How do you think robotics or AI researchers in academia should best connect to industry?

Recently connections to industry have become especially pressing in robotics, which has gone, during my career so far, from being a small area of specialization to being a massive and booming area of employment opportunity and huge technology leaps. This means undergraduate and graduate students need to be trained in latest and most relevant skills and methods, and all students need to be inspired and empowered to pursue skills and careers in these areas, not just those who self-select as their most obvious path; we have to proactively work on diversity and inclusion as these are clearly articulated needs by industry. There are great models of companies that have strong outreach to researchers, such as Microsoft and Google to name two, both holding annual faculty research summits and having grant opportunities for faculty to connect with their research and business units. As in all contexts, it is best to develop personal relationships with contacts at relevant companies, as they tend to lead to most meaningful collaborations.

What was your most difficult professional decision and why?

It’s hard to pick one, but here are, briefly, three that are interesting: 1) I had to actively choose whether to speak up against unfair treatment when I was still pre-tenure and in a very under-repreresented group, or to stay silent and not make waves. I spoke up and never regretted being true to myself. 2) I had to choose whether to take part of my time away from research to get involved and stay involved in academic administration. I chose to do so, but also chose to never let it take more than the official half time, and never stomp on my research. 3) I had to choose whether to leave academia for a startup or industry. These days, that is an increasingly complex choice, but as long as academia allows us to explore and experiment, it will remain the best choice.

What professional achievement are you most proud of?

The successes of my students and of my research field. Seeing my PhD students receive presidential awards while having balanced lives with families and still responding to my emails just makes me beam with pride. Pioneering a field, socially assistive robotics, that focuses on helping users with special needs, from those with autism to those with Alzheimer’s, to reach their potential. Seeing that field become established and grow from the enthusiasm of wonderful students and young researchers is an unparalleled source of professional satisfaction.

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

Nobody, no matter how senior or famous, knows how things are going to work out and how much another person can achieve. So when receiving advice, believe encouragement and utterly ignore discouragement. I am fortunate to be very stubborn by nature, but it was still a hard lesson and I see too many young people taking advice too seriously; it’s good to get advice but take it with a grain of salt: keep pushing for what you enjoy and believe in, even if it makes some waves and raises some eyebrows.

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

I think about that when I talk to K-12 students; I try to tell them that it is fine to have a meandering path. I finally understand that what really fascinates me is people and what makes us tick. I could have studied that from various perspectives, including medicine, psychology, neuroscience, anthropology, economics, history… but since I was advised (by my uncle, see above) to go into computer science, I found a way to connect those paths. It’s almost arbitrary but it turned out to be lucky, as I love what I do.

What is a “typical” day like for you?

I have no typical day, they are all crazy in enjoyable ways. I prefer to spend my time in face-to-face interactions with people, and there are so many to collaborate with, from PhD students and undergraduate students, to research colleagues, to dean’s office colleagues, to neighbors on my floor and around my lab, to K-12 students we host. It’s all about people. And sure, there is a lot of on-line work, too, too much of it given how much less satisfying it is compared to human-human interactions, but we have to read, review, evaluate, recommend, rank, approve, certify, link, purchase, pay, etc.

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

Since I got involved with socially assistive robotics, I truly love all my research projects: we are working with children with autism, with reducing pain in hospital patients, and addressing anxiety, loneliness and isolation in the elderly. I share with my students the curiosity to try new things and enjoy the opportunity to do so collaborative and often in a very interdisciplinary way, so there is never a shortage of new things to discover, learn, and overcome, and, hopefully, to do some good.

How do you balance being involved in so many different aspects of the robotics and AI communities?

With daily difficult choices: it’s an hourly struggle to focus on what is most important, set the rest aside, and then get back to enough of it but not all of it and, above all, to know what is in what category. I find that my family provides an anchoring balance that helps greatly with prioritizing.

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

“Wall*E”: it’s a wonderfully human (vulnerable, caring, empathetic, idealistic) portrayal of a robot, one that has all the best of our qualities and none of the worst. After that, “Robot and Frank” and “Big Hero 6”.

AI Matters Interview with Peter Stone

Welcome!  This column is the third in our series profiling senior AI researchers. This month focuses on Peter Stone, a Professor at the University of Texas Austin and the COO and co-founder of Cogitai, Inc.

Peter Stone’s Bio

Peter Stone

Dr. Peter Stone is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Chair of the Robotics Portfolio Program, at the University of Texas at Austin. In 2013 he was awarded the University of Texas System Regents’ Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone’s research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, robotics, and e-commerce. Professor Stone received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs – Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2003, he won an NSF CAREER award for his proposed long term research on learning agents in dynamic, collaborative, and adversarial multiagent environments, in 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award.

How did you become interested in AI?

The first I remember becoming interested in AI was on a field trip to the University of Buffalo when I was in Middle School or early High School (I don’t remember which).  The students rotated through a number of science labs and one of the ones I ended up in was a computer science “lab.”  The thing that stands out in my mind is the professor showing us pictures of various shapes such as triangles and squares, pointing out how easy it was for us to distinguish them, but then asserting that nobody knew how to write a computer program to do so (to date myself, this must have been the mid ’80s).  I had already started programming computers, but this got me interested in the concept of modeling intelligence with computers.

What made you decide the time was right for an AI startup?

Reinforcement learning has been a relatively “niche” area of AI since I became interested in it my first year of graduate school.  But with recent advances, I became convinced that now was the time to move to the next level and work on problems that are only possible to attack in a commercial setting.

How did I become convinced?  For that, I owe the credit to Mark Ring, one of my co-founders at Cogitai.  He and I met at the first NIPS conference I attended back in the mid ’90s.  We’ve stayed in touch intermittently.  But then in the fall of 2014 he visited Austin and got in touch.  He pitched the idea to me of starting a company based on continual learning, and it just made sense.

What professional achievement are you most proud of?

I’m made proud over and over again by the achievements of my students and postdocs.  I’ve been very fortunate to work with a phenomenal group of individuals, both technically and personally.  Nothing makes me happier than seeing each succeed in his or her own way, and to think that I played some small role in it.

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

It’s cliche, but it’s true.  There’s no better time of life than when you’re a Ph.D. student.  You have the freedom to pursue one idea that you’re passionate about to the greatest possible, with very few other responsibilities.  You don’t have the status, appreciation, or salary that you deserve and that you’ll eventually inevitably get.  And yes, there are pressures.  But your job is to learn and to change the world in some small way.  I didn’t appreciate it when I was a student even though my advisor (Manuela Veloso) told me.  And I don’t expect my students to believe me when I tell them now.  But over time I hope they come to appreciate it as I have.  I loved my time as a Ph.D. student. But if I had known how many aspects of that time of life would be fleeting, I may have appreciated it even more.

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

I have no idea.  When I graduated from the University of Chicago as an undergrad, I applied to 4 CS Ph.D. programs, the Peace Corps, and Teach for America.  CMU was the only Ph.D. program that admitted me.  So I probably would have done the Peace Corps or Teach for America.  Who knows where that would have led me?

What is a “typical” day like for you?

I live a very full life.  Every day I spend as much time with my family as they’ll let me (teenagers….) and get some sort of exercise (usually either soccer, swimming, running, or biking).  I also play my violin about 3-4 times per week.  I schedule those things, and other aspects of my social life, and then work in all my “free” time.  That usually means catching up on email in the morning, attending meetings with students and colleagues either in person or by skype, reading articles, and editing students’ papers.  And I work late at night and on weekends when there’s no “fun” scheduled.  But really, there’s no “typical” day.  Some days I’m consumed with reading; others with proposal writing; others with negotiations with prospective employees; others with university politics; others with event organization; others with coming up with new ideas to burning problems.

I do a lot of multitasking, and I’m no better at it than anyone else. But I’m never bored.

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

I don’t know.  I have many interests and I can’t help but pursue them all.  And I multitask.

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

Rather than a book, I’ll choose an author.  As a teenager, I read Isaac Asimov’s books voratiously – both his fiction (of course “I, Robot” made an impression, but the Foundation series was always my favorite), and his non-fiction.  He influenced my thoughts and imagination greatly.

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.

 

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.