AI Matters: our blog
Interview with Thomas Dietterich
Introduction
Welcome to the eighth interview in our se- ries profiling senior AI researchers. This month we are especially happy to interview our SIGAI advisory board member, Thomas Dietterich, Director of Intelligent Systems at the Institute for Collaborative Robotics and In- telligence Systems (CoRIS) at Oregon State University.
Biography
Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stan- ford University 1984) is Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. Dietterich is one of the pioneers of the field of Machine Learning and has authored more than 200 refereed publications and two books. His research is motivated by challenging real world problems with a special focus on ecological science, ecosystem management, and sustainable development. He is best known for his work on ensemble methods in machine learning including the development of error- correcting output coding. Dietterich has also invented important reinforcement learning algorithms including the MAXQ method for hierarchical reinforcement learning. Dietterich has devoted many years of service to the research community. He served as President of the Association for the Advancement of Artificial Intelligence (2014-2016) and as the founding president of the International Machine Learning Society (2001-2008). Other major roles include Executive Editor of the journal Machine Learning, co-founder of the Journal for Machine Learning Research, and Program Chair of AAAI 1990 and NIPS 2000. Dietterich is a Fellow of the ACM, AAAI, and AAAS.
Getting to Know Tom Dietterich
When and how did you become interested in CS and AI?
I learned to program in Basic in my early teens; I had an uncle who worked for GE on their time-sharing system. I learned Fortran in high school. I tried to build my own adding machine out of TTL chips around that time too. However, despite this interest, I didn’t really know what CS was until I reached graduate school at the University of Illinois. I first engaged with AI when I took a graduate assistant position with Ryszard Michalski on what became machine learning, and I took an AI class from Dave Waltz. I had also studied phi- losophy of science in college, so I had already thought a bit about how we acquire knowledge from data and experiment.
What would you have chosen as your career if you hadn’t gone into CS?
I had considered going into foreign service, and I have always been interested in policy issues. I might also have gone into technical management. Both of my brothers have been successful technical managers.
What do you wish you had known as a Ph.D. student or early researcher?
I wish I had understood the importance of strong math skills for CS research. I was a software engineer before I was a computer science researcher, and it took me a while to understand the difference. I still struggle with the difference between making an incremental advance within an existing paradigm versus asking fundamental questions that lead to new research paradigms.
What professional achievement are you most proud of?
Developing the MAXQ formalism for hierarchical reinforcement learning.
What is the most interesting project you are currently involved with?
I’m fascinated by the question of how machine learning predictors can have models of their own competence. This is important for mak- ing safe and robust AI systems. Today, we have ML methods that give accurate predictions in aggregate, but we struggle to provide point-wise quantification of uncertainty. Related to these questions are algorithms for anomaly detection and open category detection. In general, we need AI systems that can work well even in the presence of “unknown unknowns”.
Recent advances in AI led to many success stories of AI technology undertaking real-world problems. What are the challenges of deploying AI systems?
AI systems are software systems, so the main challenges are the same as with any soft- ware system. First, are we building the right system? Do we correctly understand the users’ needs? Have we correctly expressed user preferences in our reward functions, constraints, and loss functions? Have we done so in a way that respects ethical standards? Second, have we built the system we intended to build? How can we test software com- ponents created using machine learning? If the system is adapting online, how can we achieve continuous testing and quality assurance? Third, when ML is employed, the re- sulting software components (classifiers and similar predictive models) will fail if the input data distribution changes. So we must mon- itor the data distribution and model the pro- cess by which the data are being generated. This is sometimes known as the problem of “model management”. Fourth, how is the deployed system affecting the surrounding social and technical system? Are there unintended side-effects? Is user or institutional behavior changing as a result of the deployment?
One promising approach is combining humans and AI into a collaborative team. How can we design such a system to successfully tackle challenging high-risk applications? Who should be in charge, the human or the AI?
I have addressed this in a recent short paper (Robust Artificial Intelligence and Robust Human Organizations. Frontiers of Computer Science, 13(1): 1-3). To work well in high- risk applications, human teams must function as so-called “High reliability organizations” or HROs. When we add AI technology to such teams, we must ensure that it contributes to their high reliability rather than disrupting and degrading it. According to organizational researchers, HROs share five main practices: (a) continuous attention to anomalous and near-miss events, (b) seeking diverse explanations for such events, (c) maintaining continuous situational awareness, (d) practicing improvisational problem solving, and (e) delegating decision making authority to the team member who has the most expertise about the specific decision regardless of rank. AI systems in HROs must implement these five practices as well. They must be constantly watch- ing for anomalies and near misses. They must seek multiple explanations for such events (e.g., via ensemble methods). They must maintain situational awareness. They must support joint human-machine improvisational problem solving, such as mixed-initiative plan- ning. And they must build models of the expertise of each team member (including them- selves) to know which team member should make the final decision in any situation.
You ask “Who is in charge?” I’m not sure that is the right question. Our goal is to create human-machine teams that are highly reliable as a team. In an important sense, this means every member of the team has responsibil- ity for robust team performance. However, from an ethical standpoint, I think the human team leader should have ultimate responsibil- ity. That task of taking action in a specific situation could be delegated to the AI system, but the team leader has the moral responsibility for that action.
Moving towards transforming AI systems into high-reliable organizations, how can diversity help to achieve this goal?
Diversity is important for generating multiple hypotheses to explain anomalies and near misses. Experience in hospital operating rooms is that often it is the nurses who first detect a problem or have the right solution. The same has been noted in nuclear power plant operations. Conversely, teams often fail when the engage in “group think” and fixate on an incorrect explanation for a problem.
How do you balance being involved in so many different aspects of the AI community?
I try to stay very organized and manage my time carefully. I use a machine learning system called TAPE (Tagging Assistant for Productive Email) developed by my collaborator and student Michael Slater to automatically tag and organize my email. I also take copi- ous notes in OneNote. Oh, and I work long hours…
What was your most difficult professional decision and why?
The most difficult decision is to tell a PhD student that they are not going to succeed in completing their degree. All teachers and mentors are optimistic people. When we meet a new student, we hope they will be very successful. But when it is clear that a student isn’t going to succeed, that is a deep disappointment for the student (of course) but also for the professor.
What is your favorite AI-related movie or book and why?
I really don’t know much of the science fiction literature (in books or films). My favorite is 2001: A Space Odyssey because I think it depicts most accurately how AI could lead to bad outcomes. Unlike in many other stories, HAL doesn’t “go rogue”. Instead, HAL creatively achieves the objective programmed by its creators, unfortunately as a side effect, it kills the crew.
Call for Nominations
Editor-In-Chief ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
The term of the current Editor-in-Chief (EiC) of the ACM Trans. on Asian and Low-Resource Language Information Processing (TALLIP) is coming to an end, and the ACM Publications Board has set up a nominating committee to assist the Board in selecting the next EiC. TALLIP was established in 2002 and has been experiencing steady growth, with 178 submissions received in 2017.
Nominations, including self nominations, are invited for a three-year term as TALLIP EiC, beginning on June 1, 2019. The EiC appointment may be renewed at most one time. This is an entirely voluntary position, but ACM will provide appropriate administrative support.
Appointed by the ACM Publications Board, Editors-in-Chief (EiCs) of ACM journals are delegated full responsibility for the editorial management of the journal consistent with the journal’s charter and general ACM policies. The Board relies on EiCs to ensure that the content of the journal is of high quality and that the editorial review process is both timely and fair. He/she has final say on acceptance of papers, size of the Editorial Board, and appointment of Associate Editors. A complete list of responsibilities is found in the ACM Volunteer Editors Position Descriptions. Additional information can be found in the following documents:
Nominations should include a vita along with a brief statement of why the nominee should be considered. Self-nominations are encouraged, and should include a statement of the candidate’s vision for the future development of TALLIP. The deadline for submitting nominations is April 15, 2019, although nominations will continue to be accepted until the position is filled.
Please send all nominations to the nominating committee chair, Monojit Choudhury (monojitc@microsoft.com).
The search committee members are:
- Monojit Choudhury (Microsoft Research, India), Chair
- Kareem M. Darwish (Qatar Computing Research Institute, HBKU)
- Tei-wei Kuo (National Taiwan University & Academia Sinica) EiC of ACM Transactions on Cyber-Physical Systems; Vice Chair, ACM SIGAPP
- Helen Meng, (Chinese University of Hong Kong)
- Taro Watanabe (Google Inc., Tokyo)
- Holly Rushmeier (Yale University), ACM Publications Board Liaison
AI Hype Not
A recent item in Science|Business “Artificial intelligence nowhere near the real thing, says German AI chief”, by Éanna Kelly, gives policy-worthy warnings and ideas. “In his 20 years as head of Germany’s biggest AI research lab Wolfgang Wahlster has seen the tech hype machine splutter three times. As he hands over to a new CEO, he warns colleagues: ‘Don’t over-promise’. … the computer scientist who has just ended a 20 year stint as CEO of the German Research Centre for Artificial Intelligence says that [warning] greatly underestimates the distance between AI and its human counterpart: ‘We’re years away from a game changer in the field. I always warn people, one should be a bit careful with what they claim. Every day you work on AI, you see the big gap between human intelligence and AI’, Wahlster told Science|Business.”
For AI policy, we should remember to look out for over promising, but we also need to be mindful of the time frame for making effective policy and be fully engaged now. Our effort importantly informs policymakers about the real opportunities to make AI successful. A recent article in The Conversation by Ben Shneiderman “What alchemy and astrology can teach artificial intelligence researchers,” gives insightful information and advice on how to avoid being distracted away “… from where the real progress is already happening: in systems that enhance – rather than replace – human capabilities.” Shneiderman recommends that technology designers shift “from trying to replace or simulate human behavior in machines to building wildly successful applications that people love to use.”
AAII and EAAI
President Trump issued an Executive Order on February 11, 2019, entitled “Maintaining American Leadership In Artificial Intelligence”. The full text is at https://www.whitehouse.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/The American AI Initiative of course needs analysis and implementation details. Two sections of the Executive Order give hope for opportunities to provide public input:
Sec (5)(a)(1)(i): Within 90 days of the date of this order, the OMB Director shall publish a notice in the Federal Register inviting the public to identify additional requests for access or quality improvements for Federal data and models that would improve AI R&D and testing. …[T]hese actions by OMB will help to identify datasets that will facilitate non-Federal AI R&D and testing.
and
Sec (6)(b)
(b) To help ensure public trust in the development and implementation of AI applications, OMB shall issue a draft version of the memorandum for public comment before it is finalized.
Please stay tuned for ways that our ACM US Technology Policy Committee (USTPC) can help us provide our feedback on the implementation of the Executive Order.
A summary and analysis report is available from the Center for Data Innovation: Executive Order Will Help Ensure U.S. Leadership in AI. They comment that the administration “needs to do more than reprogram existing funds for AI research, skill development, and infrastructure development” and “should ask Congress for significant funding increases to (a) expand these research efforts;
(b) implement light-touch regulation for AI;
(c) resist calls to implement roadblocks or speed bumps for this technology, including export restrictions;
(d) rapidly expand adoption of AI within government,
implement comprehensive reforms to the nation’s workforce training and adjustment policies.”
The latter point was a topic in my invited talk at EAAI-19. Opportunities and innovation in education and training for the workforce of the future rely crucially on public policymaking about workers in the era of increasing use of AI and other automation technologies. An important issue is who will provide training that is timely (by 2030), practical, and affordable for workers who are impacted by job disruptions and transitioning to the new predicted post-automation jobs. The stakeholders along with workers are the schools, employers, unions, community groups, and others. Even if more jobs are created than lost, work in the AI future is disproportionately available in the range of people in the current and near-future workforce.
Section 1 of the Executive Order “Maintaining American Leadership In Artificial Intelligence” follows:
Section 1. Policy and Principles.Artificial Intelligence (AI) promises to drive growth of the United States economy, enhance our economic and national security, and improve our quality of life. The United States is the world leader in AI research and development (R&D) and deployment. Continued American leadership in AI is of paramount importance to maintaining the economic and national security of the United States and to shaping the global evolution of AI in a manner consistent with our Nation’s values, policies, and priorities. The Federal Government plays an important role in facilitating AI R&D, promoting the trust of the American people in the development and deployment of AI-related technologies, training a workforce capable of using AI in their occupations, and protecting the American AI technology base from attempted acquisition by strategic competitors and adversarial nations. Maintaining American leadership in AI requires a concerted effort to promote advancements in technology and innovation, while protecting American technology, economic and national security, civil liberties, privacy, and American values and enhancing international and industry collaboration with foreign partners and allies. It is the policy of the United States Government to sustain and enhance the scientific, technological, and economic leadership position of the United States in AI R&D and deployment through a coordinated Federal Government strategy, the American AI Initiative (Initiative), guided by five principles:
(a) The United States must drive technological breakthroughs in AI across the Federal Government, industry, and academia in order to promote scientific discovery, economic competitiveness, and national security.
(b) The United States must drive development of appropriate technical standards and reduce barriers to the safe testing and deployment of AI technologies in order to enable the creation of new AI-related industries and the adoption of AI by today’s industries.
(c) The United States must train current and future generations of American workers with the skills to develop and apply AI technologies to prepare them for today’s economy and jobs of the future.
(d) The United States must foster public trust and confidence in AI technologies and protect civil liberties, privacy, and American values in their application in order to fully realize the potential of AI technologies for the American people.
(e) The United States must promote an international environment that supports American AI research and innovation and opens markets for American AI industries, while protecting our technological advantage in AI and protecting our critical AI technologies from acquisition by strategic competitors and adversarial nations.
Interview with Iolanda Leite
Introduction
This column is the seventh in our series pro- filing senior AI researchers. This month we are happy to interview Iolanda Leite, Assistant Professor at the School of Computer Science and Electrical Engineering at the KTH Royal Institute of Technology in Sweden. This is a great opportunity to get to know Iolanda, the new AI Matters co-editor in chief. Welcome on board!
Biography
Iolanda Leite is an Assistant Professor at the School of Computer Science and Electri- cal Engineering at the KTH Royal Institute of Technology in Sweden. She holds a PhD in Information Systems and Computer Engineer- ing from IST, University of Lisbon. Prior to join- ing KTH, she was a Research Assistant at the Intelligent Agents and Synthetic Characters Group at INESC-ID Lisbon, a Postdoctoral As- sociate at the Yale Social Robotics Lab and an Associate Research Scientist at Disney Re- search Pittsburgh. Iolanda’s research inter- ests are in the areas of Human-Robot Inter- action and Artificial Intelligence. She aims to develop autonomous socially intelligent robots that can assist people over long periods of time.
Getting to Know Iolanda Leite
When and how did you become interested in CS and AI?
I became interested in CS at the age of 4 when the first computer arrived at our home. It is more difficult to establish a time to define my interest in AI. I was born in the 80s and have always been fascinated by toys that had some level of “intelligence” or “life-likeness” like the Tamagotchi or the Furby robots. During my Master’s degree, I chose the Intelligent Sys- tems specialization. That time was probably when I seriously considered a research career in this area.
What professional achievement are you most proud of?
Seeing my students accomplish great things on their own.
What would you have chosen as your career if you hadn’t gone into CS?
I always loved to work with children so maybe something related to child education.
What do you wish you had known as a Ph.D. student or early researcher?
As an early researcher I often had a hard time dealing with the rejection of papers, applica- tions, etc. What I wish the “past me” could know is that if one keeps working hard, things will eventually work out well in the end. In other words, keeping faith in the system.
What is the most interesting project you are currently involved with?
All of them! If I have to highlight one, we are working with elementary schools that have classes of newly arrived children in a project where we are using social robots to promote inclusion between newly arrived and local chil- dren. This is part of an early career fellowship awarded by the Jacobs Foundation.
We currently observe many promising and exciting advances in using AI in education, going beyond automating Piazza answering, how should we make use of AI to teach AI?
I believe that AI can be used to complement teachers and provide personalized instruction to students of all ages and in a variety of top- ics. Robotic tutors can play an important role in education because the mere physical pres- ence of a robot has shown to have a positive impact on how much information students can recall, for example when compared to a virtual agent displayed in a computer screen deliver- ing the exact same content.
How can we make AI more diverse? Do you have a concrete idea on what we as (PhD) students, researchers, and educators in AI can do to increase diversity our field?
Something we can all do is to participate in outreach initiatives targeting groups underrep- resented in AI to show them that there is space for them in the community. If we start bottom-up, in the long-term I am positive that our community will be more diverse at all lev- els and the bias in opportunities, recruiting, etc. will go away.
What was your most difficult professional decision and why?
Leaving my home country (Portugal) after fin- ishing my PhD to continue my research career because I miss my family and friends, and also the good weather!
How do you balance being involved in so many different aspects of the AI community?
I love what I do and I currently don’t have any hobbies 🙂
AI is grown up – it’s time to make use of it for good. Which real-world problem would you like to see solved by AI in the future?
If AI could fully address any of the Sustainable Development Goals established by the United Nations, it would be (more than) great. Al- though there are excellent research and fund- ing initiatives in that direction, we are still not there yet.
What is your favorite AI-related movie or book and why?
One of my favorite ones recently was the Westworld TV Series because of the power relationships between the human and the robotic characters. I find it hard to believe that humans will treat robots the way they are treated in the series, but it makes me reflect on how our future interactions with technol- ogy that is becoming more personalized and “human-like” might look like.