The Future of Artificial Intelligence: An IBM Chief Science Office Cognitive Perspective

The Future of Artificial Intelligence: An IBM Research, Chief Science Office Cognitive, PerspectiveGuru Banavar, IBM VP, Chief Science Office CognitiveJim Spohrer, Director, Understanding Cognitive SystemsDRAFT v5: September 7, 2016

Note: This presentation has a companion whitepaper, available on request.9/7/16Future of AI1

Request:White House OSTP The White House Office of Science and Technology Policy is particularly interested in responses related to the following topics: (1) the legal and governance implications of AI; (2) the use of AI for public good; (3) the safety and control issues for AI; (4) the social and economic implications of AI; (5) the most pressing, fundamental questions in AI research, common to most or all scientific fields; (6) the most important research gaps in AI that must be addressed to advance this field and benefit the public; (7) the scientific and technical training that will be needed to take advantage of harnessing the potential of AI technology; (8) the specific steps that could be taken by the federal government, research institutes, universities, and philanthropies to encourage multi-disciplinary AI research; and (9) the use of open data sets to close fundamental research gaps;(10) the role of incentives and prizes to accelerate public benefits;(11) any additional information related to AI research or policymaking, not requested above, that you believe OSTP should consider.

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References:

White House OSTP (2016) Request for Information: Preparing for the Future of Artificial Intelligence. Office of Science and Technology Policy.URL: http://www.research.ibm.com/cognitive-computing/ostp/rfi-response.shtml

Request for Information: Preparing for the Future of Artificial IntelligenceOFFICE OF SCIENCE AND TECHNOLOGY POLICYRequest for Information: Preparing for the Future of Artificial IntelligenceSUMMARY: Artificial intelligence (AI) technologies offer great promise for creating new and innovative products, growing the economy, and advancing national priorities in areas such as education, mental and physical health, addressing climate change, and more. Like any transformative technology, however, AI carries risks and presents complex policy challenges along a number of different fronts. The Office of Science and Technology Policy (OSTP) is interested in developing a view of AI across all sectors for the purpose of recommending directions for research and determining challenges and opportunities in this field. The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields. The purpose of this RFI is to solicit feedback on overarching questions in AI, including AI research and the tools, technologies, and training that are needed to answer these questions.DATES: Responses must be received by July 22, 2016 to be considered. This form is now closed.Instructions: Response to this RFI is voluntary. Responses exceeding 2,000 words will not be considered. Respondents need not reply to all questions; however, they should clearly indicate the number of each question to which they are responding (correspondent to the above). Brevity is appreciated. Responses to this RFI may be posted without change online. OSTP therefore requests that no business proprietary information or personally identifiable information be submitted in response to this RFI. Please note that the U.S. Government will not pay for response preparation, or for the use of any information contained in the response.SUPPLEMENTARY INFORMATION: On May 3, 2016, the White House Office of Science and Technology Policy announced a number of new actions related to AI: https://www.whitehouse.gov/blog/2016/05/03/preparing-future-artificial-i... As a part of this initiative, the Federal Government is working to leverage AI for public good and to aid in promoting more effective government. OSTP is in the process of co-hosting four public workshops in 2016 on topics in AI in order to spur public dialogue on these topics and to identify challenges and opportunities related to this emerging technology. These topics include the legal and governance issues for AI, AI for public good, safety and control for AI, and the social and economic implications of AI. A newNational Science and Technology Council (NSTC)Subcommittee on Machine Learning and Artificial Intelligence has also been established. This group will monitor state-of-the-art advances and technology milestones in artificial intelligence and machine learning within the Federal Government, in the private sector, and internationally, as well as help coordinate Federal activity in this space. Ultimately, dialogue from these workshops and the efforts of the NSTC Subcommittee may feed into the development of a public report.The Administration is working to leverage AI as an emergent technology for public good and toward a more effective government. Applications in AI to areas of government that are not traditionally technology-focused are especially significant; there are myriad opportunities to improve government services in areas related to urban systems and smart cities, mental and physical health, social welfare, criminal justice, and the environment. There is also tremendous potential in AI-driven improvements to programs that help disadvantaged and vulnerable populations.OSTP is particularly interested in responses related to the following topics: (1) the legal and governance implications of AI; (2) the use of AI for public good; (3) the safety and control issues for AI; (4) the social and economic implications of AI; (5) the most pressing, fundamental questions in AI research, common to most or all scientific fields; (6) the most important research gaps in AI that must be addressed to advance this field and benefit the public; (7) the scientific and technical training that will be needed to take advantage of harnessing the potential of AI technology; (8) the specific steps that could be taken by the federal government, research institutes, universities, and philanthropies to encourage multi-disciplinary AI research; and (9) any additional information related to AI research or policymaking, not requested above, that you believe OSTP should consider.

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Response: IBM Chief Science Office Cognitive

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References:

Banavar G (2016) Charting the Future of Artificial Intelligence. IBM Think Blog.URL: https://www.ibm.com/blogs/think/2016/08/03/future-of-ai/

IBM Research (2016) Response to Request for Information: Preparing for the Future of Artificial Intelligence.URL: http://www.research.ibm.com/cognitive-computing/ostp/rfi-response.shtml

August 3, 2016Charting the Future of ArtificialIntelligenceFor decades, the world has been producing digital information at an unprecedented rate. We have digitized the history of the worlds literature and all of its medical journals, enabling wide access to troves of information.We can now understand the movements of planes, trains, automobiles, not to mention everything else from cattle to mobile phones to weather patterns. And we are privy to the real-time public sentiments of billions of people through social media.It is not unreasonable to expect that within this rapidly growing body of digital information lie the much needed clues for professionals to solve the major societal challenges of our time from defeating cancer and reversing climate change to managing the complexity of the global economy. And we at IBM believe that AI, or cognitive, systems are the tools that will help us accomplish these ambitious goals.We are not alone in this belief. Momentum is building rapidly in the development of AI. And its potential is being recognized by businesses and governments alike. To that end, the White Houses Office of Science and Technology Policy (OSTP) is gathering diverse perspectives from myriad organizations to prepare for the future of this transformational technology.IBM has delivered a detailed response outlining our point of view, available in full here. Our position, in brief, is that AI systems are augmenting human intelligence in every field, and will ultimately transform our personal and professional lives. We believe that the benefits of AI can be realized sooner and more broadly through progressive social and economic policies, education and workforce programs, and investment in long-range interdisciplinary research programs.In the response, we lay out many examples where AI systems are already making a difference in domains like healthcare, financial services, and education, etc. And while we have made much progress in building AI systems, fundamental advances in research still are needed. Deeply understanding the domains of human expertise, such as medicine, engineering, law and thousands more, poses particularly difficult issues. And when deployed at scale, AI systems will need computing infrastructure that can handle unprecedented new workloads.We recognize that AI systems are powerful, and like all powerful tools, great care must be taken in their deployment. A key point we make is that in order to reap the societal benefits of artificial intelligence, we will first need to trust it. That trust will be earned through experience, of course, in the same way we learn to trust that an ATM will register a deposit, or that an automobile will stop when the brake is applied. Put simply, we trust things that behave as we expect them to.We believe governments will be important partners in achieving the full potential of AI. And IBM is convening groups of leading thinkers in the field from academia, research, government and the business community to collaboratively address these topics and more.There is much work to do. And we hope that our response to the OSTPs request for information will help lay the foundation for a global conversation that will advance the science and design of AI systems. Because we firmly believe that many of the mysteries that underlie the critical systems that facilitate life on this planet can be solved using AI.______________________________________IBMs full OSTP RFI response can be found here.

Response to - Request for InformationPreparing for the Future of Artificial Intelligence

IntroductionIBM has been researching, developing and investing in AI technology for more than 50 years. The public became aware of a major advance in 2011, when IBM Watson won the historic Jeopardy! exhibition on prime time television. Since that time, the company has advanced and scaled the Watson platform, and applied it to various industries, including healthcare, finance, commerce, education, security, and the Internet of Things. We are deeply committed to this technology, and believe strongly in its potential to benefit society, as well as transform our personal and professional lives. To this end, we have engaged thousands of scientists and engineers from IBM Research and Development, and partnered with our clients, academics, external experts, and even our competitors to explore all topics around AI. And we have developed a unique point-of-view, informed by decades of research and commercial application of AI.At IBM, we are guided by the term augmented intelligence rather than artificial intelligence. It is the critical difference between systems that enhance and scale human expertise rather than those that attempt to replicate all of human intelligence. We focus on building practical AI applications that assist people with well-defined tasks, and in the process, expose a range of generalized AI services on a platform to support a wide range of new applications.We call our particular approach to augmented intelligence cognitive computing. Cognitive computing is a comprehensive set of capabilities based on technologies such as machine learning, reasoning and decision technologies; language, speech and vision technologies; human interface technologies; distributed and high-performance computing; and new computing architectures and devices. When purposefully integrated, these capabilities are designed to solve a wide range of practical problems, boost productivity, and foster new discoveries across many industries. This is what we bring to market today in the form of IBM Watson.The following are brief responses to the questions in the RFI (re-ordered and slightly re-factored), with links to more detailed information.

A. The use of AI for public good (RFI question 2)For decades, we have been stockpiling digital information. We have digitized the history of the worlds literature and all of its medical journals. We track and store the movements of automobiles, trains, planes and mobile phones. And we are privy to the real-time sentiments of billions of people through social media. It is not unreasonable to expect that within this rapidly growing body of digital information lies the secrets to defeating cancer, reversing climate change, or managing the complexity of the global economy. We believe that many of the ambiguities and inefficiencies of the critical systems that facilitate life on this planet can be eliminated. And we believe that AI systems are the tools that will help us accomplish these ambitious goals.We are already doing much of this work:For healthcare, AI systems can advance precision medicine by ingesting patients electronic medical history and relevant medical literature, performing cohort analysis, identifying micro-segments of similar patients, evaluating standard-of-care practices and available treatment options, ranking by relevance, risk and preference, and ultimately recommending the most effective treatments for their patients.For social services, AI systems can provide timely and relevant answers to citizens in need, assist citizens with insurance, tax, and social programs, predict the needs of individuals and population groups, and develop plans for efficient deployment of resources.For education, AI systems can assist teachers in developing personalized educational programs for individuals or groups of students, assist students using a range of learning styles and methods, and develop effective early education, primary, secondary, and higher education programs.For financial services, AI systems can expand financial inclusion by qualifying applicants, assist in providing the best insurance coverage at the right cost, ensure compliance with federal, state and local regulations, and reduce fraud and waste in tax and other financial programs.For transportation, AI systems can improve the efficiency of public transportation systems, support public vehicles with driver assistance using semi-automated features, manage incidents, optimize the use of fuel and support maintenance of infrastructure and rolling stock.For public safety, AI systems can support safety personnel with anomaly detection using machine vision, build predictive models for crime, and help investigators find associations in massive amounts of information.For the environment, AI systems can understand complex relationships and help construct environmental models for accurate prediction and management of pollutants and carbon footprints. For infrastructure, AI systems can assist with prediction of demand, supply, and use of infrastructure, planning and execution of projects, and maintenance of built infrastructure.See more here.

B. Social and economic implications of AI (RFI question 4)AI systems are already changing the way work gets done. But history suggests that new technologies like AI result in higher productivity, higher earnings, and overall job growth. In particular, we believe that new companies, new jobs, and entirely new markets will be built on the shoulders of this technology. And we believe that AI systems will improve access to critical services for underserved populations. Overall, we anticipate widespread improvements in quality of life.In order to be fully accepted into society, AI systems need to have significant social capabilities, because their presence in our lives has a profound impact on our emotions and on our decision making capabilities (e.g., elder care). AI systems also need to understand how to learn and comply with specific behavioral principles for aligning with human values.See more here.

C. Education for harnessing AI technologies (RFI question 7)The potential for AI solutions for public and private uses has created a fast growing demand for AI skills. To meet this demand, top universities are crafting new AI curricula. Leading firms offer faculty and students access to cloud platforms with AI-based services, from image recognition to machine learning. However, most courses and platforms require programming skills and advanced mathematics as prerequisites. Government agencies, research institutions, universities, and foundations can work together to make learning to build, understand, and work with AI systems more accessible to a broader range of students and professionals retooling their careers.See more here.

D. Fundamental questions in AI research, and the most important research gaps (RFI questions 5 and 6)In order for AI systems to enhance quality of life, both personally and professionally, they must acquire broad and deep knowledge from multiple domains, learn continuously from interactions with people and environments, and support reasoned decisions. Broadly, the AI fields long-term progress depend upon many advances: Machine learning and reasoning: Most current AI systems use supervised learning, using massive amounts of labeled data for training. Fundamental research is needed for AI systems that learn as humans do: through instruction, interaction (by discussing, debating, watching other people learn), by doing things (utilizing motor skills), generalizing from very little data, and by transferring skills across many tasks.Decision techniques: For AI-based systems to succeed broadly, new techniques must be developed for modeling systemic risks, analyzing tradeoffs, detecting anomalies in context, analyzing data while preserving privacy, and making decisions under uncertainty.Domain-specific AI systems: Deeply understanding the domains of human expertise, such as medicine, engineering, law and thousands more, poses particularly difficult issues of knowledge acquisition, representation, and reasoning. AI systems must ultimately perform professional-level tasks, such as managing contradictions, designing experiments, and negotiating. Data assurance and trust: Training and test data can be biased, incomplete, or maliciously compromised. Significant effort should be devoted to techniques for measuring entropy of datasets, validating the quality and integrity of data, and for making AI systems more objective, resilient, and accurate. People will trust AI systems when systems know users intents and priorities, explain their reasoning, learn from mistakes, and can be independently certified.Radically efficient computing infrastructure: When deployed at scale, AI systems will need to handle unprecedented workloads that will require the development of high-performance distributed cloud systems, new computing architectures such as neuromorphic and approximate computing, and new devices such as quantum and new types of memory devices.See more here.

E. Data sets that can accelerate AI research (RFI question 9)A major bottleneck in developing and validating AI systems is public access to sufficiently large, openly curated, public training data sets. Machine learning, supervised and unsupervised, requires large, unbiased data sets to train accurate models. Deep learning is advancing speech transcription, language translation, image captioning, and question and answering capabilities. Each new AI advance, e.g., video comprehension, requires the creation of new data sets. Deep domain tasks, such as cancer radiology, or insurance adjustment, requires specialized and often hard-to-get datasets. Incentives must be created for greater sharing of both input datasets and trained models through mechanisms like model zoos.See more here.

F. Multi-disciplinary research (RFI question 8)Most of the research areas in Section D cannot be achieved by AI researchers alone. Collaboration with experts in multiple disciplines -- such as psychology, philosophy, sociology, art, regulation, and law -- will be crucial. In addition, there is an important role for professional associations with industry-specific knowledge to play in informing AI applications. To this end, IBM is in the process of creating a network of several academic centers to jumpstart the scientific ecosystem.See more here.

G. Role of incentives and prizes (RFI question 10)As the fundamental building blocks of AI improve, so too should the incentives that inspire next-generation, people-centered systems design. As an example, IBM established a $5 million AI XPrize for the best use of AI system to empower teams of people to tackle the worlds grand challenges. IBM is developing additional scientific challenges for the AI research community.See more here.

H. Safety and control issues for AI (RFI question 3)To reap the societal benefits of artificial intelligence, we will first need to trust it. That trust will be earned through experience, of course, in the same way we learn to trust that an ATM will register a deposit, or that an automobile will stop when the brake is applied. Put simply, we trust things that behave as we expect them to.But trust will also require a system of best practices that can guide the safe and ethical management of AI; a system that includes alignment with social norms and values; algorithmic accountability; compliance with existing legislation and policy; and protection of privacy and personal information. IBM is in the process of developing this system in collaboration with our partners, university researchers, and competitors.See more here.

I. Legal and governance implications of AI (RFI question 1)Responsibility must be the foundation for AI policymaking. Inclusive dialogues can explore relevant topics, going beyond the headlines and hype, promoting deeper understanding and a new skills focus. Every transformative tool that people have created from the steam engine to the microprocessor augment human capabilities and enable people to dream bigger and do more. People with these tools will solve whole new classes of big data problems. Our responsibility as members of the global community is to ensure, to the best of our ability, that AI is developed the right way and for the right reasons.See more here.

J. Other issues: Business models (RFI question 11)In market-driven economies, progress also crucially depends upon the creation of new business models that rewards more effective outcomes and overall benefits to society.See more here.

Concluding RemarksAI systems are augmenting human intelligence and will ultimately transform our personal and professional lives. Its benefits far outweigh its risks. And with the right policies and support, those benefits can be realized sooner. Policy makers should focus on:Facilitating a fact-based dialogue on the capabilities and limitations of AI technologiesDeveloping progressive social and economic policies to deploy AI systems for broad public goodDeveloping progressive education and workforce programs for future generationsInvesting in a long-range interdisciplinary research program for advancing the science and design of AI systemsSee more here.

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A. The Use of AI for the Public GoodHealthcareSocial ServicesEducationFinancial ServicesTransportationPublic SafetyThe EnvironmentInfrastructure

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Reference

IBM Watson (2016) IBM Watson HealthURL: http://www.ibm.com/smarterplanet/us/en/ibmwatson/health/

A. The use of AI for public good (RFI question 2)For decades, we have been stockpiling digital information. We have digitized the history of the worlds literature and all of its medical journals. We track and store the movements of automobiles, trains, planes and mobile phones. And we are privy to the real-time sentiments of billions of people through social media. It is not unreasonable to expect that within this rapidly growing body of digital information lies the secrets to defeating cancer, reversing climate change, or managing the complexity of the global economy. We believe that many of the ambiguities and inefficiencies of the critical systems that facilitate life on this planet can be eliminated. And we believe that AI systems are the tools that will help us accomplish these ambitious goals.We are already doing much of this work:For healthcare, AI systems can advance precision medicine by ingesting patients electronic medical history and relevant medical literature, performing cohort analysis, identifying micro-segments of similar patients, evaluating standard-of-care practices and available treatment options, ranking by relevance, risk and preference, and ultimately recommending the most effective treatments for their patients.For social services, AI systems can provide timely and relevant answers to citizens in need, assist citizens with insurance, tax, and social programs, predict the needs of individuals and population groups, and develop plans for efficient deployment of resources.For education, AI systems can assist teachers in developing personalized educational programs for individuals or groups of students, assist students using a range of learning styles and methods, and develop effective early education, primary, secondary, and higher education programs.For financial services, AI systems can expand financial inclusion by qualifying applicants, assist in providing the best insurance coverage at the right cost, ensure compliance with federal, state and local regulations, and reduce fraud and waste in tax and other financial programs.For transportation, AI systems can improve the efficiency of public transportation systems, support public vehicles with driver assistance using semi-automated features, manage incidents, optimize the use of fuel and support maintenance of infrastructure and rolling stock.For public safety, AI systems can support safety personnel with anomaly detection using machine vision, build predictive models for crime, and help investigators find associations in massive amounts of information.For the environment, AI systems can understand complex relationships and help construct environmental models for accurate prediction and management of pollutants and carbon footprints. For infrastructure, AI systems can assist with prediction of demand, supply, and use of infrastructure, planning and execution of projects, and maintenance of built infrastructure.See more here.

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B. Social and economic implications of AI History: Technologies with broad potential across industries like AI increase:ProductivityEarningsJob growthAdjustment: Social, learning, and decision making capabilities are key to acceptance of AI in societyUnderserved: Potential to help underserved populations and improve their quality of life

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References

Kelly III J, Hamm S (2013) Smart Machines: IBMs Watson and the Era of Cognitive Computing. Columbia University Press.

B. Social and economic implications of AI (RFI question 4)AI systems are already changing the way work gets done. But history suggests that new technologies like AI result in higher productivity, higher earnings, and overall job growth. In particular, we believe that new companies, new jobs, and entirely new markets will be built on the shoulders of this technology. And we believe that AI systems will improve access to critical services for underserved populations. Overall, we anticipate widespread improvements in quality of life.In order to be fully accepted into society, AI systems need to have significant social capabilities, because their presence in our lives has a profound impact on our emotions and on our decision making capabilities (e.g., elder care). AI systems also need to understand how to learn and comply with specific behavioral principles for aligning with human values.

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C. Education for harnessing AI technologiesDemand: High demand for AI, machine learning, data science and related courses; many MOOCsIndustry Platforms: Growing set of companies provide learners access to cognitive service capabilities in their cloudsGap: Need curriculum for learners with no programming or advanced mathematics background

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ReferencesIBM Watson (2016) Watson Developer CloudURL: https://www.ibm.com/watson/developercloud/

C. Education for harnessing AI technologies (RFI question 7)The potential for AI solutions for public and private uses has created a fast growing demand for AI skills. To meet this demand, top universities are crafting new AI curricula. Leading firms offer faculty and students access to cloud platforms with AI-based services, from image recognition to machine learning. However, most courses and platforms require programming skills and advanced mathematics as prerequisites. Government agencies, research institutions, universities, and foundations can work together to make learning to build, understand, and work with AI systems more accessible to a broader range of students and professionals retooling their careers.

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D. Fundamental questions in AI research, and the most important research gapsMachine learning and reasoningDecision techniquesDomain-specific AI systemsData assurance and trustRadically efficient computing infrastructure

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ReferencesMerolla PA, Arthur JV, Alvarez-Icaza R, Cassidy AS, Sawada J, Akopyan F, Jackson BL, Imam N, Guo C, Nakamura Y, Brezzo B (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science. Aug 8;345(6197):668-73.

D. Fundamental questions in AI research, and the most important research gaps (RFI questions 5 and 6)In order for AI systems to enhance quality of life, both personally and professionally, they must acquire broad and deep knowledge from multiple domains, learn continuously from interactions with people and environments, and support reasoned decisions. Broadly, the AI fields long-term progress depend upon many advances: Machine learning and reasoning: Most current AI systems use supervised learning, using massive amounts of labeled data for training. Fundamental research is needed for AI systems that learn as humans do: through instruction, interaction (by discussing, debating, watching other people learn), by doing things (utilizing motor skills), generalizing from very little data, and by transferring skills across many tasks.Decision techniques: For AI-based systems to succeed broadly, new techniques must be developed for modeling systemic risks, analyzing tradeoffs, detecting anomalies in context, analyzing data while preserving privacy, and making decisions under uncertainty.Domain-specific AI systems: Deeply understanding the domains of human expertise, such as medicine, engineering, law and thousands more, poses particularly difficult issues of knowledge acquisition, representation, and reasoning. AI systems must ultimately perform professional-level tasks, such as managing contradictions, designing experiments, and negotiating. Data assurance and trust: Training and test data can be biased, incomplete, or maliciously compromised. Significant effort should be devoted to techniques for measuring entropy of datasets, validating the quality and integrity of data, and for making AI systems more objective, resilient, and accurate. People will trust AI systems when systems know users intents and priorities, explain their reasoning, learn from mistakes, and can be independently certified.Radically efficient computing infrastructure: When deployed at scale, AI systems will need to handle unprecedented workloads that will require the development of high-performance distributed cloud systems, new computing architectures such as neuromorphic and approximate computing, and new devices such as quantum and new types of memory devices.

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E. Data sets that can accelerate AI researchBottleneck: Develop and validate data sets that are:Large and unbiasedOpenly curatedPublically accessibleDomains: Novice to expert task performance is hard to get for all occupations and industriesModels: Incentives to share trained models that require lots of data and compute time to create

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ReferencesJia Y, Shelhamer E (2016) Caffe Model Zoo. Berkeley Vision.URL: http://caffe.berkeleyvision.org/model_zoo.html

Wang L, Lee CY, Tu Z, Lazebnik S (2015) Training deeper convolutional networks with deep supervision. arXiv preprint arXiv:1505.02496. May 11.

E. Data sets that can accelerate AI research (RFI question 9)A major bottleneck in developing and validating AI systems is public access to sufficiently large, openly curated, public training data sets. Machine learning, supervised and unsupervised, requires large, unbiased data sets to train accurate models. Deep learning is advancing speech transcription, language translation, image captioning, and question and answering capabilities. Each new AI advance, e.g., video comprehension, requires the creation of new data sets. Deep domain tasks, such as cancer radiology, or insurance adjustment, requires specialized and often hard-to-get datasets. Incentives must be created for greater sharing of both input datasets and trained models through mechanisms like model zoos.

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F. Multi-disciplinary researchDisciplines: Breadth of disciplines to tackle issues:Psychology and cognitive science, philosophy, design and art, public policy and management, law and regulationsSystems: Professional associations to tackle industry and system issues, including novice to expert progression on tasksSocio-technical system design loop and smart service systems

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ReferencesKline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press.

NSF (2016) Partnerships for Innovation: Building Innovation Capacity (PFI:BIC). Smart Sevice Systems.URL: http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504708

T Summit (2016) What is the T?URL: http://tsummit.org/t

F. Multi-disciplinary research (RFI question 8)Most of the research areas in Section D cannot be achieved by AI researchers alone. Collaboration with experts in multiple disciplines -- such as psychology, philosophy, sociology, art, regulation, and law -- will be crucial. In addition, there is an important role for professional associations with industry-specific knowledge to play in informing AI applications. To this end, IBM is in the process of creating a network of several academic centers to jumpstart the scientific ecosystem.

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G. Role of incentives and prizesExample:IBM Watson AI XPrize ($5M)Best AI system to empower teams of people to tackle the worlds grand challengesTED 2020, finalists presentI-athlonMore objective scoringRational processesMultiple dimensions of intelligence

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ReferencesAdams SS, Banavar G, Campbell M (2016) I-athlon: Toward a Multidimensional Turing Test. AI Magazine. Mar 1;37(1).

XPrize (2015) IBM Watson AI XPrize: A Cognitive Computing Competition. URL: http://ai.xprize.org/

G. Role of incentives and prizes (RFI question 10)As the fundamental building blocks of AI improve, so too should the incentives that inspire next-generation, people-centered systems design. As an example, IBM established a $5 million AI XPrize for the best use of AI system to empower teams of people to tackle the worlds grand challenges (XPrize 2015). IBM is developing additional scientific challenges for the AI research community.

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H. Safety and control issues for AI Trust & TrustworthinessEthical & Social NormsAlgorithmic TransparencyUnexpected InteractionsSafeguards9/7/16Future of AI11

ReferencesRussell, S, Dewey, D, Tegmark, M (2015) Research Priorities for Robust and Beneficial Artificial Intelligence. AI Magazine. Winter. 36(4):105-114.

H. Safety and control issues for AI (RFI question 3)To reap the societal benefits of AI, we will first need to trust AI. That trust will be earned through experience, of course, in the same way we learn to trust that an ATM will register a deposit, or that an automobile will stop when the brake is applied. Put simply, we trust things that behave as we expect them to.But trust will also require a system of best practices that can guide the safe and ethical management of AI; a system that includes alignment with social norms and values; algorithmic accountability; compliance with existing legislation and policy; and protection of privacy and personal information. IBM is in the process of developing this system in collaboration with our partners, university researchers, and competitors.

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I. Legal and governance implications of AI Responsible & inclusive dialogueElevate the dialogueAlgorithmic responsibilityIndividual privacyJobs and workforce transformationSafetyLearn beyond the headlinesKey: Focus on skills

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References

Padilla, CA (2016) THINKPolicy #10: The Future and Benefits of Cognitive Computing: Human + Machine Collaboration for the Advancement of Humankind. IBM Government and Regulatory Affairs.URL: http://www.ibm.com/ibm/ibmgra/thinkpolicy_cognitive_06232016.html

I. Legal and governance implications of AI (RFI question 1)Responsibility must be the foundation for AI policymaking. Inclusive dialogues can explore relevant topics, going beyond the headlines and hype, promoting deeper understanding and a new skills focus. Every transformative tool that people have created from the steam engine to the microprocessor augment human capabilities and enable people to dream bigger and do more. People with these tools will solve whole new classes of big data problems. Our responsibility as members of the global community is to ensure, to the best of our ability, that AI is developed the right way and for the right reasons.

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J. Other issues: Business models Trusted platformTraining Unbiased dataAlgorithms - TransparencyServices - Open API EconomyTransactions BlockchainApplications Ethically boostingcreativity and productivityGovernance Laws and regulationsTrust takes timeSteam engines boiler explosionsCognitive engines headline hype9/7/16Future of AI13

ReferencesBerman SJ, Korsten PJ, Marchall A (2016) Digital reinvention in action: What to do and how to make it happen. IBM Institution for Business Value.https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=GBE03752USEN

IBM Cognitive (2016) Welcome to the era of cognitive business.URL: https://www.ibm.com/cognitive/

J. Other issues: Business models (RFI question 11)In market-driven economies, progress also crucially depends upon the creation of new business models that reward more effective outcomes and overall benefits to society.

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Some reactionsaitrendsArtificial BrillianceFuturismInformationWeekCalburn: IBM: AI Should Stand For Augmented IntelligencePYMNTSTechCrunchColdewey: The White House requested input on artificial intelligence, and IBMs response is a great AI 101 9/7/16Future of AI14

ReferencesClaburn T (2016) IBM: AI Should Stand For 'Augmented Intelligence. InformationWeek. Aug 4.URL: http://www.informationweek.com/government/leadership/ibm-ai-should-stand-for-augmented-intelligence/d/d-id/1326496

Coldewey D (2016) The White House requested input on artificial intelligence, and IBMs response is a great AI 101. Aug 3.URL: https://techcrunch.com/2016/08/03/the-white-house-requested-input-on-artificial-intelligence-and-ibms-response-is-a-great-ai-101/

Futurism (2016) IBM Made a Crash Course For The White House, And Itll Teach You All The AI BasicsGetty Images.URL: http://futurism.com/ibm-made-a-crash-course-for-the-white-house-and-itll-teach-you-all-the-ai-basics/

PYMNTS (2016) IBM schools White House on AI. Aug 4.URL: http://www.pymnts.com/news/2016/ibm-schools-white-house-on-ai/

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White House OSTPFollow UpThe White House OSTP received 161 responses and created a 349 downloadable PDF with URL links The White House Frontiers Conference is being planned as a follow up meetingDate: October 13, 2016Place: Pittsburgh, PA USA9/7/16Future of AI15

References

Felten E, Lyons T (2016) Public Input and Next Steps on the Future of Artificial IntelligenceURL: https://www.whitehouse.gov/blog/2016/09/06/public-input-and-next-steps-future-artificial-intelligence

White House OSTP (2016b) Public Responses. Request for Information on the Future of Artificial Intelligence. Sept 1.URL: https://www.whitehouse.gov/sites/default/files/microsites/ostp/OSTP-AI-RFI-Responses.pdf

Holden JP, Smith M (2016) President Obama to Host White House Frontiers Conference in Pittsburgh, PA. Aug 30.URL: https://www.whitehouse.gov/blog/2016/08/30/president-obama-host-white-house-frontiers-conference-pittsburgh-pa

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AcknowledgementsSam AdamsGuru BanavarRob HighMark ORileyFrancesca RossiVijay SaraswatAnna SekaranJohn R SmithJim SpohrerPeter Williams

9/7/16Future of AI16

===Assigned sections:

RFI Respone: Preparing for the Future of Artificial Intelligence - Guruduth S Banavar/Watson/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/rfi-response.shtml

Section A. The use of AI for public good - Rob High/Austin/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document1.shtml

Section B. Social and economic implications of AI - Anna M Sekaran/Watson/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document2.shtml

Section C. Education for harnessing AI technologies - Jim Spohrer/Almaden/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document3.shtml

Section D. Fundamental questions in AI research, and the most important research gaps - Francesca Rossi/Italy/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document4.shtml(potentially include link to IBM Research Cognitive Computing strategy as well - Guru sent Michael K. and Peter S. a note alerting them to that opportunity)

Section E. Data sets that can accelerate AI research - John R Smith/Watson/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document5.shtml

Section F. Multi-disciplinary research - Jim Spohrer/Almaden/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document6.shtml

Section G. Role of incentives and prizes - Sam Adams/Raleigh/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document7.shtml

Section H. Safety and control issues for AI - Vijay Saraswat/Watson/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document8.shtml

Section I. Legal and governance implications of AI - Mark C O'Riley/Atlanta/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document9.shtml

Section J. Other issues: Business models - Jim Spohrer/Almaden/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document10.shtml

Section Final: Jim Spohrer/Almaden/IBMhttp://www.research.ibm.com/cognitive-computing/ostp/document11.shtml===16