Potential revival of OTA progress – from the House appropriations subcommittee: “Technology Assessment Study: The Committee has heard testimony on, and received dozens of requests advocating for restoring funding to the Office of Technology Assessment (OTA).
White House 2018 Summit on AI for American Industry
Background from the report:
“Artificial intelligence (AI) has tremendous potential to benefit the American people, and has already demonstrated immense value in enhancing our national security and growing our economy.
AI is quickly transforming American life and American business, improving how we diagnose and treat illnesses, grow our food, manufacture and deliver new products, manage our finances, power our homes, and travel from point A to point B.
On May 10, 2018, the White House hosted the Artificial Intelligence for American Industry summit, to discuss the promise of AI and the policies we will need to realize that promise for the American people and maintain U.S. leadership in the age of artificial intelligence.
‘Artificial intelligence holds tremendous potential as a tool to empower the American worker, drive growth in American industry, and improve the lives of the American people. Our free market approach to scientific discovery harnesses the combined strengths of government, industry, and academia, and uniquely positions us to leverage this technology for the betterment of our great nation.’ – Michael Kratsios, Deputy Assistant to the President for Technology Policy
The summit brought together over 100 senior government officials, technical experts from top academic institutions, heads of industrial research labs, and American business leaders who are adopting AI technologies to benefit their customers, workers, and shareholders.”
Issues addressed at the 2018 summit are as follows:
Support for the national AI R&D ecosystem – “free market approach to scientific discovery that harnesses the combined strengths of government, industry, and academia.”
American workforce that can take full advantage of the benefits of AI – “new types of jobs and demand for new technical skills across industries … efforts to prepare America for the jobs of the future, from a renewed focus on STEM education throughout childhood and beyond, to technical apprenticeships, re-skilling, and lifelong learning programs to better match America’s skills with the needs of industry.”
Barriers to AI innovation in the United States – included “need to promote awareness of AI so that the public can better understand how these technologies work and how they can benefit our daily lives.”
High-impact, sector-specific applications of AI – “novel ways industry leaders are using AI technologies to empower the American workforce, grow their businesses, and better serve their customers.”
As a small agency within the Legislative Branch, the Office of Technology Assessment (OTA) originally provided the United States Congress with expert analyses of new technologies related to public policy, but OTA was defunded and ceased operations in 1995. A non-binding Resolution was introduced in the House of Representatives last week by Reps. Bill Foster (D-IL) and Bob Takano (D-CA) (press release), and Sen. Ron Wyden (D-OR) is expected to introduce a parallel bill in the Senate, expressing the non-binding “sense of Congress” that the agency and its funding should be revived. New coordinated efforts also are now underway among many groups to urge Congress to do exactly that.
Our colleagues at USACM have delivered letters of support for an inquiry into whether restoring OTA or its functions to the Legislative Branch would be advisable to the leaders of the House and Senate Appropriations Committees. The House Subcommittee met recently and voted to advance legislation to fund the Legislative Branch for FY 2019 to the full House Appropriations Committee but without addressing this issue. The full Committee’s meeting, at which an amendment to provide pilot funding for an inquiry into OTA-like services could be offered, is expected later in May. The Senate’s parallel Subcommittee and full Appropriations Committee is expected to act later this spring or early summer on the Legislative Branch’s FY19 funding bill. OTA-related amendments could be offered at either of their related business meetings.
AAAS Forum on Science & Technology Policy, Washington, D.C., June 21 – 22, 2018.
From AAAS: “The annual AAAS Forum on Science and Technology Policy is the conference for people interested in public policy issues facing the science, engineering, and higher education communities. Since 1976, it has been the place where insiders go to learn what is happening and what is likely to happen in the coming year on the federal budget and the growing number of policy issues that affect researchers and their institutions.”
AAAS Forum on Science & Technology Policy
Washington, D.C., June 21 – 22, 2018. https://www.aaas.org/page/forum-science-technology-policy?et_rid=35075781&et_cid=1876236 From AAAS: “The annual AAAS Forum on Science and Technology Policy is the conference for people interested in public policy issues facing the science, engineering, and higher education communities. Since 1976, it has been the place where insiders go to learn what is happening and what is likely to happen in the coming year on the federal budget and the growing number of policy issues that affect researchers and their institutions.”
Follow-up on the April 1 Policy Post: Experiments on FaceBook Data
US organizations and individuals influence voters through posts in social media and analysis (and misanalysis) of publicly-available data. Experimentation has been reported on the use of FaceBook data to show techniques that can be used to change elections (Nature, volume 489, pages 295–298 (13 September 2012)). Particularly, the authors looked at data during the 2010 US Congressional elections and showed how to affect voting. They report “results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users’ friends, and friends of friends.”
Current events involving FaceBook and the use of data they collect and analyze relate to issues addressed by SIGAI and USACM working groups on algorithmic accountability, transparency, and bias. The players in this area of ethics and policy include those who are unaware of the issues and ones who intentionally use methods and systems with bias to achieve organizational goals. The issues around use of customer data in ways that are not transparent, or difficult to discover, not only have negative impacts on individuals and society, but they also are difficult to address because they are integral to business models upon which companies are based.
A Forbesrecent article “Google’s DeepMind Has An Idea For Stopping Biased AI” discusses research that addresses AI systems that spread prejudices that humans have about race and gender – the issue that when artificial intelligence is trained with biased data, biased decisions may be made. An example cited in the article include facial recognition systems shown to have difficulty properly recognizing black women.
Machine-learning software is rapidly becoming widely accessible to developers across the world, many of whom are not aware of the dangers of using data contain biases. The Forbes piece discusses an article “Path-Specific Counterfactual Fairness,” by DeepMind researchers Silvia Chiappa and Thomas Gillam. Counterfactual fairness refers to methods of decision-making for machines and ways that fairness might automatically be determined. DeepMind has a new division, DeepMind Ethics & Society that addresses this and other issues on the ethical and social impacts of AI technology.
The Forbes article quotes Kriti Sharma, a consultant in artificial intelligence with Sage, the British enterprise software company as follows: “Understanding the risk of bias in AI is not a problem that that technologists can solve in a vacuum. We need collaboration between experts in anthropology, law, policy makers, business leaders to address the questions emerging technology will continue to ask of us. It is exciting to see increased academic research activity in AI fairness and accountability over the last 18 months, but in truth we aren’t seeing enough business leaders, companies applying AI, those who will eventually make AI mainstream in every aspect of our lives, take the same level of responsibility to create unbiased AI.”
Next week the USACM Council will be holding its annual in-person meeting in Washington, beginning with a reception Wednesday, March 21st from 5 to 7 at the Georgetown home of Law Committee Chair Andy Grosso. We cordially invite DC-area USACM members to join us. If you plan to attend, please RSVP to Adam Eisgrau <eisgrau@HQ.ACM.ORG>, who will provide further details.
Statement of the European Group on Ethics in Science and New Technologies on “Artificial Intelligence, Robotics and ‘Autonomous’ Systems,” published March 9: http://ec.europa.eu/research/ege/pdf/ege_ai_statement_2018.pdf
The statement calls for the EC to “launch a process that paves the way towards a common, internationally recognized ethical and legal framework for the design, production, use and governance of artificial intelligence, robotics, and ‘autonomous’ systems.”
President Donald Trump today tapped Obama-era deputy U.S. CTO Ed Felten to serve on the Privacy and Civil Liberties Oversight Board (https://www.pclob.gov/).
ACM SIGAI Learning Webinar “Advances in Socio-Behavioral Computing”
This live presentation was given on Thursday, March 15 by Tomek Strzalkowski, Director of the Institute for Informatics, Logics, and Security Studies and Professor at SUNY Albany. Plamen Petrov, Director of Cognitive Technology at KPMG LLP and Industry Liaison Officer of ACM SIGAI, and Rose Paradis, Data Scientist at Leidos Health and Life Sciences and SIGAI Secretary/Treasurer, moderated the questions and answers session.
This talk presented ongoing research on computational modeling and understanding of social, behavioral, and cultural phenomena in multi-party interactions. They discussed how various linguistic cues reveal the social dynamics in group interactions, based on a series of experiments conducted in virtual on-line chat rooms, and then showed that these dynamics generalize to other forms of communication including traditional face-to-face discourse as well as the large scale online interaction via social media. They also showed how language compensates for the reduced cue environment in which online interactions take place.
They described a two-tier analytic approach for detecting and classifying certain sociolinguistic behaviors exhibited by discourse participants, including topic control, task control, disagreement, and involvement, that serve as intermediate models from which presence the higher level social roles and states such as leadership and group cohesion may be inferred. The results of an initial phase of the work used a system of sociolinguistic tools called DSARMD (Detecting Social Actions and Roles in Multiparty Dialogue).
Several extensions of the basic DSARMD model move beyond recognition and understanding of social dynamics and attempt to quantify and measure the effects that sociolinguistic behaviors by individuals and groups have on other discourse participants. Potentially, autonomous artificial agents could be constructed capable of exerting influence and manipulating human behavior in certain situations. Such extended capabilities could possibly be deployed to increase accuracy of predicting online information cascades, persuasion campaigns, and even defend against certain forms of social engineering attacks.
The model and tools presented in the Webinar are interesting to consider in the detection and assessment of algorithmic bias.
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.
The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) was on February 2–7, 2018, at the Hilton New Orleans Riverside. The AAAI/ACM Conference on AI, Ethics, and Society (AIES) was held at the beginning of AAAI-18. Developers and participants included members of SIGAI and USACM.
The AIES conference description follows: “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 handling and bias, 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
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 this new conference.”
The full schedule for the AIES 2018 Conference is available at www.aies-conference.com. A panel relevant to our policy blog discussions “Prioritizing Ethical Considerations in Intelligent and Autonomous Systems – Who Sets the Standards?” was designed by our IEEE/ACM committee and will be covered in a future post.
In the next few blog posts, we will present information and generate discussion on policy issues at the intersection of AI, the future of the workforce, and educational systems. Because AI technology and applications are developing at such a rapid pace, current policies will likely not be able to impact sufficiently the workforce needs even in 2024 — the time frame for middle school students to prepare for low skill jobs and for beginning college students to prepare for higher skilled work. Transparency in educational policies requires goal setting based on better data and insights into emerging technologies, likely changes in the labor market, and corresponding challenges to our educational systems. The topics and resources below will be the focus of future AI Policy posts.
IBM’s Jim Spohrer has an outstanding set of slides “A Look Toward the Future”, incorporating his rich experience and current work on anticipated impacts of new technology with milestones every ten years through 2045. Radical developments in technology would challenge public policy in ways that are difficult to imagine, but current policymakers and the AI community need to try. Currently, AI systems are superior to human capabilities in calculating and game playing, and near human level performance for data-driven speech and image recognition and for driverless vehicles. By 2024, large advances are likely in video understanding, episodic memory, and reasoning.
The roles of future workers will involve increasing collaboration with AI systems in the government and public sector, particularly with autonomous systems but also in traditional areas of healthcare and education. Advances in human-technology collaboration also lead to issues relevant to public policy, including privacy and algorithmic transparency. The increasing mix of AI with humans in ubiquitous public and private systems puts a new emphasis on education for understanding and anticipating challenges in communication and collaboration.
Patterns for the future workforce in the age of autonomous systems and cognitive assistance are emerging. Please take a look at Andrew McAfee’s presentation at the recent Computing Research Summit. Also, see the latest McKinsey Report — Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. Among other things, this quote from page 20 catches attention: “Automation represents both hope and challenge. The global economy needs the boost to productivity and growth that it will bring, especially at a time when aging populations are acting as a drag on GDP growth. Machines can take on work that is routine, dangerous, or dirty, and may allow us all to use our intrinsically human talents more fully. But to capture these benefits, societies will need to prepare for complex workforce transitions ahead. For policy makers, business leaders, and individual workers the world over, the task at hand is to prepare for a more automated future by emphasizing new skills, scaling up training, especially for midcareer workers, and ensuring robust economic growth.”
Education for the Future
An article in Education Week “The Future of Work Is Uncertain, Schools Should Worry Now” addresses the issue of automation and artificial intelligence disrupting the labor market and what K-12 educators and policymakers need to know. A recent report by the Bureau of Labor Statistics “STEM Occupations: Past, Present, And Future” is consistent with the idea that even in STEM professions workforce needs will be less at programming levels and more in ways to collaborate with cognitive assistance systems and in human-computer teams. Demands for STEM professionals will be for verifying, interpreting, and acting on machine outputs; designing and assembling larger systems with robotic and cognitive components; and dealing with ethics issues such as bias in systems and algorithmic transparency.
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:
(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.)
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, 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.