AI’s Role in Cancer Research

AI’s Role in Cancer Research

Guest Post by Anna Suarez

It’s no secret the general public has mixed views about artificial intelligence, largely stemming from a misunderstanding of the topic. In the public’s mind, AI tends to be equated to the creation of nearly lifelike robots and, although sometimes it is, there is much more to the rapidly-advancing technology.

In today’s society AI is playing an everyday role in the lives of most people, ranging from ride-hailing apps like Uber and Lyft to Facebook’s facial recognition, but the technology is constantly advancing. For example, Google’s new AI assistantis revolutionizing the way people go about their daily tasks, and recent advancements made possible by AI in healthcare are changing the way cancer research is approached.

Although the term “AI” dates back to the mid-1950s, it has become so sophisticated in recent years that a cure for cancer could be around the corner. Vice President Joe Biden’s Cancer Moonshot Initiative aims to find a cure for cancer and provide patients with more treatment options using AI technology to process and sort data from cancer researchers. In an attempt to reach its mission of driving 10 years’ worth of research in only five, AI is also being used to detect certain cancers earlier than what’s possible using other currently available diagnostic procedures.

The ability to diagnose certain cancers, including brain cancer, skin cancer and mesothelioma, through the use of this technology is, arguably, one of the most important advancements in healthcare as a result of AI. This is especially groundbreaking for patients battling mesothelioma, a rare cancer that develops in the mesotheliumof the lungs, heart or abdomen. Mesothelioma has a decades-long latency period and symptoms are often mistaken for those associated with more common ailments. Unfortunately, the disease has an average prognosis of 6-12 months and leaves patients with little time to coordinate treatment. Ultimately, the earlier detection and diagnosis of cancers may lead to better prognoses and outcomes.s

Using AI, the cloud and other tools, companies like IBM and Microsoft are attempting to change the way healthcare is approached. Following its creation in 2013, IBM’s supercomputer, Watson, successfully won $1 million in a game of Jeopardy!against two of the show’s most successful contestants, and is making attempts to streamline the process of diagnosing diseases in patients more efficiently.

Although IBM Watson hasn’t made as much progress as anticipated, the technology was proven capable during a 2017 study. The research monitored the amount of time it took Watson to create a treatment plan versus the amount of time it took doctors. The results showed that Watson was able to create a plan of treatment for a brain cancer patient in 10 minutes, while the process took between 160 hours for doctors.

However, the study wasn’t a complete win for Watson. Comparatively, Watson’s suggested plan of action was short sighted due to an inefficiency to consider multiple treatment options. While doctors were able to consider several possibilities at once, Watson could not.

Healthcare NExT, Microsoft’s internal initiative announced in 2017, is also focusing on using technology to find solutions to questions in the healthcare industry, including a cure for cancer.

In a Microsoft blog post about Healthcare NExT, Peter Lee, Corporate Vice President of Microsoft AI + Research says, Microsoft is “expanding [its] commitment to building a healthier future with new initiatives and solutions, making it easier for health industry partners and organizations to use intelligent technology to improve the lives of people around the world.”

Technology has changed dramatically within the past decade. Newly-developed diagnostic methods and an advanced approach to healthcare that we could have only dreamed of in the past are changing the way we see the world today. Although machines are still learning and there is a lot of room to improve, the work that’s been done in such a short period of time is nothing short of incredible.

Are we closer to a cure for cancer than even we know?

Resources:

https://www.forbes.com/sites/zarastone/2017/11/07/everything-you-need-to-know-about-sophia-the-worlds-first-robot-citizen/#74e645b846fa

https://www.theverge.com/2018/5/8/17332070/google-assistant-makes-phone-call-demo-duplex-io-2018

https://www.maacenter.org/mesothelioma/

https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/ibm-watson-makes-treatment-plan-for-brain-cancer-patient-in-10-minutes-doctors-take-160-hours

https://blogs.microsoft.com/blog/2017/02/16/microsoft-partners-combine-cloud-ai-research-industry-expertise-focus-transforming-health-care/

ACM/SIGAI Autonomous Agents Research Award 2018: Craig Boutilier

The selection committee for the ACM/SIGAI Autonomous Agents Research Award is pleased to announce that Dr. Craig Boutilier, Principal Research Scientist at Google, is the recipient of the 2018 award. Over the years, Dr. Boutilier has made seminal contributions to research on decision-making under uncertainty, game theory, and computational social choice. He is a pioneer in applying decision-theoretic concepts in novel ways in a variety of domains including (single- and multi-agent) planning and reinforcement learning, preference elicitation, voting, matching, facility location, and recommender systems. His recent research continues to significantly influence the field of computational social choice through the novel computational and methodological tools he introduced and his focus on modeling realistic preferences. In addition to his reputation for outstanding research, Dr. Boutilier is also recognized as an exceptional teacher and mentor.

Artificial Intelligence Journal: FUNDING OPPORTUNITIES for PROMOTING AI RESEARCH

Artificial Intelligence Journal:
FUNDING OPPORTUNITIES for PROMOTING AI RESEARCH
Deadline for proposals: extended to January 20th, 2018

The Artificial Intelligence Journal (AIJ) is one of the longest established and most respected journals in AI, and since it was founded in 1970, it has published many of the key papers in the field. The operation of the Editorial Board is supported financially through an arrangement with AIJ’s publisher, Elsevier. Through this arrangement, the AIJ editorial board is able to make available substantial funds (of the order of 230,000 Euros per annum), to support the promotion and dissemination of AI research. Most of these funds are made available through a series of competitive open calls (the remaining part of the budget is reserved for sponsorship of studentships for the annual IJCAI conference).

The current call has a deadline of January 20th, 2018 and a budget of 120,000 Euros.

Proposals should be submitted following the format and content guidelines, as well as submission instructions, that can be found on the AIJ web site:

http://aij.ijcai.org/index.php/funding-opportunities-for-promoting-ai-research

(We posted this call at a time when the above website had not yet been updated but it will soon be, hopefully by the time when you are reading this blog post. In the meantime, you can click here for the details.)

Interview with Ayanna Howard

Welcome!  This column is the fifth in our series profiling senior AI researchers. This month focuses on Dr. Ayanna Howard.  In addition to our interview, Dr. Howard was recently interviewed by NPR and they created an animated video about how “Being Different Helped A NASA Roboticist Achieve Her Dream.”

Ayanna Howard’s Bio

Ayanna Howard

Ayanna Howard, Ph.D. is Professor and Linda J. and Mark C. Smith Endowed Chair in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. As an educator, researcher, and innovator, Dr. Howard’s career focus is on intelligent technologies that must adapt to and function within a human-centered world. Her work, which encompasses advancements in artificial intelligence (AI), assistive technologies, and robotics, has resulted in over 200 peer-reviewed publications in a number of projects – from assistive robots in the home to AI-powered STEM apps for children with diverse learning needs. She has over 20 years of R&D experience covering a number of projects that have been supported by various agencies including: National Science Foundation, NewSchools Venture Fund, Procter and Gamble, NASA, and the Grammy Foundation. Dr. Howard received her B.S. in Engineering from Brown University, her M.S.E.E. from the University of Southern California, her M.B.A. from the Drucker Graduate School of Management, and her Ph.D. in Electrical Engineering from the University of Southern California. To date, her unique accomplishments have been highlighted through a number of awards and articles, including highlights in USA Today, Upscale, and TIME Magazine, as well as being named a MIT Technology Review top young innovator and recognized as one of the 23 most powerful women engineers in the world by Business Insider. In 2013, she also founded Zyrobotics, which is currently licensing technology derived from her research and has released their first suite of STEM educational products to engage children of all abilities. From 1993-2005, Dr. Howard was at NASA’s Jet Propulsion Laboratory. She has also served as the Associate Director of Research for the Georgia Tech Institute for Robotics and Intelligent Machines and as Chair of the multidisciplinary Robotics Ph.D. program at Georgia Tech.

How did you become interested in Computer Science and AI?

I first became interested in robotics as a young, impressionable, middle school girl. My motivation was the television series called The Bionic Women – my goal in life, at that time, was to gain the skills necessary to build the bionic women. I figured that I had to acquire combined skill sets in engineering and computer science in order to accomplish that goal. With respect to AI, I became interested in AI after my junior year in college, when I was required to design my first neural network during my third NASA summer internship in 1992. I quickly saw that, if I could combine the power of AI with Robotics – I could enable the ambitious dreams of my youth.

 

What was your most difficult professional decision and why?

The most difficult professional decision I had to make, in the past, was to leave NASA and pursue robotics research as an academic. The primary place I’d worked at from 1990 until 2005 was at NASA. I’d grown over those 15 years in my technical job positions from summer intern to computer scientist (after college graduation) to information systems engineer, robotics researcher, and then senior robotics researcher. And then, I was faced with the realization that, in order to push my ambitious goals in robotics, I needed more freedom to pursue robotics applications outside of space exploration. The difficulty was, I still enjoyed the space robotics research efforts I was leading at NASA, but I also felt a need to expand beyond my intellectual comfort zone.

What professional achievement are you most proud of?

The professional achievement I am proudest of is founding of a startup company, Zyrobotics, which has commercialized educational products based on technology licensed from my lab at Georgia Tech. I’m most proud of this achievement because it allowed me to combine all of the hard-knock lessons I’ve learned in designing artificial intelligence algorithms, adaptive user interfaces, and human-robot interaction schemes with a real-world application that has large societal impact – that of engaging children of diverse abilities in STEM education, including coding.

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

As a Ph.D. student, I wish I had known that finding a social support group is just as important to your academic growth as finding an academic/research home.  I consider myself a fairly stubborn person – I consider words of discouragement a challenge to prove others wrong. But psychological death by a thousand cuts (i.e. words of negativism) is a reality for many early researchers.  A social support group helps to balance the negativism that others, sometimes unconsciously, subject others too.

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

If I hadn’t gone into the field of Robotics/AI, I would have chosen a career as a forensic scientist. I’ve always loved puzzles and in forensic science, as a career, I would have focused on solving life puzzles based on the physical evidence. The data doesn’t lie (although, as we know, you can bias the data so it seems to).

What is a “typical” day like for you?

Although I have no “typical” day – I can categorize my activities into five main buckets, in no priority order: 1) human-human interactions, 2) experiments and deployments, 3) writing (including emails), 4) life balance activities, and 5) thinking/research activities. Human-human interactions involve everything from meeting with my students to talking with special education teachers to one-on-one observations in the pediatric clinic. Experiments and deployments involve everything from running a participant study to evaluating the statistics associated with a study hypothesis. Writing involves reviewing my students’ publication drafts, writing proposals, and, of course, addressing email action items. Life-balance activities include achieving my daily exercise goals as well as ensuring I don’t miss any important family events. Finally thinking/research activities covers anything related to coding up a new algorithm, consulting with my company, or jotting down a new research concept on a scrap of paper.

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

The most interesting project that I currently lead involves an investigation in developing robot therapy interventions for young children with motor disabilities. For this project, we have developed an interactive therapy game called SuperPop VR that requires children to play within a virtual environment based on a therapist-designed protocol. A robot playmate interacts with each child during game play and provides both corrective and motivational feedback. An example of corrective feedback is when the robot physically shows the child how to interact with the game at the correct movement speed (as compared to a normative data profile). An example of motivational feedback is when the robot, through social interaction, encourages the child when they have accomplished their therapy exercise goal. We’ve currently deployed the system in pilot studies with children with Cerebral Palsy and have shown positive changes with respect to their kinematic outcome metrics. We’re pushing the state-of-the-art in this space by incorporating additional factors for enhancing the long-term engagement through adaptation of both the therapy protocol as well as the robot behaviors.

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

In order for me to become involved in any new AI initiative and still maintain a healthy work-life balance, I ask myself – Is this initiative something that’s important to me and aligned with my value system; Can I provide a unique perspective to this initiative that would help to make a difference; Is it as important or more important than other initiatives I’m involved in; and Is there a current activity that I can replace so I have time to commit to the initiative now or in the near-future. If the answer is yes to all those questions, then I’m usually able to find an optimal balance of involvement in the different AI initiatives of interest.

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

My favorite AI-related movie is the Matrix. What fascinates me about the Matrix is the symbiotic relationship that exists between humans and intelligent agents (both virtual and physical).  One entity can not seem to exist without the other. And operating in the physical world is much more difficult than operating in the virtual, although most agents don’t realize that difference until they accept the decision to navigate in both types of worlds.

Call for Nominations: ACM SIGAI Autonomous Agents Research Award 2018

Nominations are solicited for the 2018 ACM SIGAI Autonomous Agents Research Award. This award is made for excellence in research in the area of autonomous agents. It is intended to recognize researchers in autonomous agents whose current work is an important influence on the field. The award is an official ACM award, funded by an endowment created by ACM SIGAI from the proceeds of previous Autonomous Agents conferences. The recipient of the award will receive a monetary prize and a certificate, and will be invited to present a plenary talk at the AAMAS 2018 conference in Stockholm, Sweden.
Previous winners of the ACM SIGAI Autonomous Agents Research Award are: David Parkes (2017), Peter Stone (2016), Catherine Pelachaud (2015), Michael Wellman (2014), Jeff Rosenschein (2013), Moshe Tennenholtz (2012), Joe Halpern (2011), Jonathan Gratch and Stacy Marsella (2010), Manuela Veloso (2009), Yoav Shoham (2008), Sarit Kraus (2007), Michael Wooldridge (2006), Milind Tambe (2005), Makoto Yokoo (2004), Nicholas R. Jennings (2003), Katia Sycara (2002), and Tuomas Sandholm (2001). For more information on the award, see the Autonomous Agents Research Award page.
How to nominate
Anyone can make a nomination. Nominations should be made by email to the chair of the award committee, Jeff Rosenschein (jeff@cs.huji.ac.il), and should consist of a short (< 1 page) statement that emphasizes not only the research contributions that the individual has made that merit the award but also how the individual’s current work is an important influence on the field.
NOTE: a candidate can only be considered for the award if they are explicitly nominated. If you believe that someone deserves the award, then NOMINATE THEM — don’t assume that somebody else will!
Important dates
  • 17 January 2018 — Deadline for nominations
  • 7 February 2018 — Announcement of 2017 winner
  • 10-15 July 2018 — AAMAS-2018 conference in Stockholm

New Conference: AAAI/ACM Conference on AI, Ethics, and Society

ACM SIGAI is pleased to announce the launch of the AAAI/ACM Conference on AI, Ethics, and Society, to be co-located with AAAI-18, February 2-3, 2018 in New Orleans. The Call for Papers is included below and is also available at  http://www.aies-conference.com/. Please note the October 31 deadline for submissions.

We hope to see you at the new conference in New Orleans next February!
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AAAI/ACM Conference on AI, Ethics, and Society
February 2-3, 2018
New Orleans, USA

http://www.aies-conference.com/

As AI is becoming more pervasive in our life, its impact on society is more significant and concerns and issues are raised regarding aspects such as value alignment, data bias and data policy, regulations, and workforce displacement. Only a multi-disciplinary and multi-stakeholder effort can find the best ways to address these concerns, including experts of various disciplines, such as AI, computer science, ethics, philosophy, economics, sociology, psychology, law, history, and politics. In order to address these issues in a scientific context, AAAI and ACM have joined forces to start a new conference, the AAAI/ACM Conference on AI, Ethics, and Society.

The first edition of this conference will be co-located with AAAI-18 on February 2-3, 2018 in New Orleans, USA. The program of the conference will include peer-reviewed paper presentations, invited talks, panels, and working sessions.

The conference welcomes contributions on a broad set of topics, included the following ones:

  • Building ethical AI systems
  • Value alignment
  • Moral machine decision making
  • Trust and explanations in AI systems
  • Fairness and Transparency in AI systems
  • Ethical design and development of AI systems
  • AI for social good
  • Human-level AI
  • Controlling AI
  • Impact of AI on workforce
  • Societal impact of AI
  • AI and law

Submitted papers should adopt a scientific approach to address any questions related to the above topics. Moreover, they should clearly establish the research contribution, its relevance, and its relation to prior research. All submissions must be made in the appropriate format, and within the specified length limit; details and a LaTeX template can be found at the conference web site.

We solicit papers (pdf file) of up to 6 pages + 1 page for references (AAAI format), submitted through the Easychair system.

We expect papers submitted by researchers of several disciplines (AI, computer science, philosophy, economics, law, and others). The program committee includes members that are experts in all the relevant areas, to ensure appropriate review of papers.

IMPORTANT NOTICE: To accommodate the publishing traditions of different fields, authors of accepted papers can ask that only a one-page abstract of the paper appear in the proceedings, along with a URL pointing to the full paper. Authors should guarantee the link to be reliable for at least two years. This option is available to accommodate subsequent publication in journals that would not consider results that have been published in preliminary form in a conference proceedings. Such papers must be submitted electronically and formatted just like papers submitted for full-text publication.

Results previously published or presented at another archival conference prior to this one, or published (or accepted for publication) at a journal prior to the submission deadline, can be submitted only if the author intends to publish the paper as a one-page abstract.

The proceedings of the conference will be published in the ACM Digital Library.

Among all papers, a best paper will be selected by the program committee and will be awarded the AI, People, and Society best paper award, sponsored by the Partnership on AI. The award is $1,000. Also, the winner will be able to participate in a global competition among several conferences, for a grand prize of $7,500.

A selected subset of the accepted papers will have the opportunity to be considered for journal publication in the JAIR special track on AI and Society (http://www.jair.org/specialtrack-aisoc-call.html).

Important dates:

Submission: October 31st, 2017
Notification: December 15th, 2017
Final version: March 1st, 2017

(Note: the final version due date is after the conference dates, to include feedback from the conference discussions).

Conference program co-chairs:

AI: Francesca Rossi, IBM Research and University of Padova
AI and workforce: Jason Furman, Harvard University
AI and philosophy: Huw Price, Cambridge University
AI and law: TBD

More information will be available soon on the conference web site.

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”.

Winners of the ACM SIGAI Student Essay Contest on the Responsible Use of AI Technologies

All the submissions have been reviewed, and we are happy to announce the winners of the ACM SIGAI Student Essay Contest on the Responsible Use of AI Technologies. The winning essays argue, convincingly, why the proposed issues are pressing (that is, of current concern), why the issues concern AI technology, and what position or steps governments, industries or organizations (including ACM SIGAI) can take to address the issues or shape the discussion on them. These essays have been selected based on depth of insight, creativity, technical merit and novelty of argument.

The winners (in alphabetical order) are:

  • Jack Bandy, Automation Moderation: Finding symbiosis with anti-human technology
  • Joseph Blass. You, Me, or Us: Balancing Individuals’ and Societies’ Moral Needs and Desires in Autonomous Systems
  • Lukas Prediger, On Monitoring and Directing Progress in AI
  • Matthew Rahtz, Truth in the ‘Killer Robots’ Angle
  • Grace Su, Unemployment in the AI Age
  • Ilse Verdiesen, How do we ensure that we remain in control of our Autonomous Weapons?
  • Christian Wagner, Sexbots: The Ethical Ramifications of Social Robotics’ Dark Side
  • Dennis Wilson, The Ethics of Big Data and Psychographics

All winning essays will be published in the ACM SIGAI newsletter “AI Matters.” ACM SIGAI provides five monetary awards of USD 500 each as well as 45-minute skype sessions with the following AI researchers:

  • Murray Campbell, Senior Manager, IBM Thomas J. Watson Research Center
  • Eric Horvitz, Managing Director, Microsoft Research
  • Peter Norvig, Director of Research, Google
  • Stuart Russell, Professor, University of California at Berkeley
  • Michael Wooldridge, Head of the Department of Computer Science, University of Oxford

Special thanks are in order to our panel of expert reviewers. Each essay was read and scored by three or more of the following AI experts:

  • Sanmay Das, Washington University in St. Louis
  • Judy Goldsmith, University of Kentucky
  • H. V. Jagadish, University of Michigan
  • Albert Jiang, Trinity University
  • Sven Koenig, University of Southern California
  • Benjamin Kuipers, University of Michigan
  • Nicholas Mattei, IBM Research
  • Alexandra Olteanu, IBM Research
  • Rosemary Paradis, Lockheed Martin
  • Francesca Rossi, IBM Research

We hope to run this contest again with a new topic in the future!

— Nicholas Mattei, IBM Research

News from ACM SIGAI

We welcome ACM SIGAI China and its members to ACM SIGAI! ACM SIGAI China held its first event, the ACM SIGAI China Symposium on New Challenges and Opportunities in the Post-Turing AI Era, as part of the ACM Turing 50th Celebration Conference on May 12-14, 2017 in Shanghai. We will report details in an upcoming edition of AI Matters.

The winner of the ACM Prize in Computing is Alexei Efros from the University of California at Berkeley for his work on machine learning in computer vision and computer graphics. The award will be presented at the annual ACM Awards Banquet on June 24, 2017 in San Francisco.

We hope that you enjoyed the ACM Learning Webinar with Tom Mitchell on June 15, 2017 on “Using Machine Learning to Study Neural Representations of Language Meaning”. If you missed it, it is now available on “On Demand.”

The “50 Years of the ACM Turing Award” Celebration will be held on June 23 and 24, 2017 in San Francisco. The ACM SIGAI recipients of the ACM Turing Scholarship to attend this high-profile meeting are Tim Lee from Carnegie Mellon University and Justin Svegliato from the University of Massachusetts at Amherst.

ACM SIGAI now has a 3-month membership requirement before students who join ACM SIGAI can apply for financial benefits from ACM SIGAI, such as fellowships and travel support. Please help us with letting all students know about this new requirement to avoid any disappointments.

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.