Artificial Intelligence and Data Science
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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Artificial Intelligence
TechnoVision The Impact of AI 20 18 CONTENTS Foreword 3 Introduction 4 TechnoVision 2018 and Artificial Intelligence 5 Overview of TechnoVision 2018 14 Design for Digital 17 Invisible Infostructure 26 Applications Unleashed 33 Thriving on Data 40 Process on the Fly 47 You Experience 54 We Collaborate 61 Applying TechnoVision 68 Conclusion 77 2 TechnoVision 2018 The Impact of AI FOREWORD We introduce TechnoVision 2018, now in its eleventh year, with pride in reaching a second decade of providing a proven and relevant source of technology guidance and thought leadership to help enterprises navigate the compelling and yet complex opportunities for business. The longevity of this publication has been achieved through the insight of our colleagues, clients, and partners in applying TechnoVision as a technology guide and coupling that insight with the expert input of our authors and contributors who are highly engaged in monitoring and synthesizing the shift and emergence of technologies and the impact they have on enterprises globally. In this edition, we continue to build on the We believe that with TechnoVision 2018, you can framework that TechnoVision has established further crystalize your plans and bet on the right for several years with updates on last years’ technology disruptions, to continue to map and content, reflecting new insights, use cases, and traverse a successful digital journey. A journey technology solutions. which is not about a single destination, but rather a single mission to thrive in the digital epoch The featured main theme for TechnoVision 2018 through agile cycles of transformation delivering is AI, due to breakthroughs burgeoning the business outcomes. -
BIOINFORMATICS Doi:10.1093/Bioinformatics/Bti144
Vol. 00 no. 0 2004, pages 1–11 BIOINFORMATICS doi:10.1093/bioinformatics/bti144 Solving and analyzing side-chain positioning problems using linear and integer programming Carleton L. Kingsford, Bernard Chazelle and Mona Singh∗ Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, 35, Olden Street, Princeton, NJ 08544, USA Received on August 1, 2004; revised on October 10, 2004; accepted on November 8, 2004 Advance Access publication … ABSTRACT set of possible rotamer choices (Ponder and Richards, 1987; Motivation: Side-chain positioning is a central component of Dunbrack and Karplus, 1993) for each Cα position on the homology modeling and protein design. In a common for- backbone. The goal is to choose a rotamer for each position mulation of the problem, the backbone is fixed, side-chain so that the total energy of the molecule is minimized. This conformations come from a rotamer library, and a pairwise formulation of SCP has been the basis of some of the more energy function is optimized. It is NP-complete to find even a successful methods for homology modeling (e.g. Petrey et al., reasonable approximate solution to this problem. We seek to 2003; Xiang and Honig, 2001; Jones and Kleywegt, 1999; put this hardness result into practical context. Bower et al., 1997) and protein design (e.g. Dahiyat and Mayo, Results: We present an integer linear programming (ILP) 1997; Malakauskas and Mayo, 1998; Looger et al., 2003). In formulation of side-chain positioning that allows us to tackle homology modeling, the goal is to predict the structure for a large problem sizes. -
The Importance of Generation Order in Language Modeling
The Importance of Generation Order in Language Modeling Nicolas Ford∗ Daniel Duckworth Mohammad Norouzi George E. Dahl Google Brain fnicf,duckworthd,mnorouzi,[email protected] Abstract There has been interest in moving beyond the left-to-right generation order by developing alter- Neural language models are a critical compo- native multi-stage strategies such as syntax-aware nent of state-of-the-art systems for machine neural language models (Bowman et al., 2016) translation, summarization, audio transcrip- and latent variable models of text (Wood et al., tion, and other tasks. These language models are almost universally autoregressive in nature, 2011). Before embarking on a long-term research generating sentences one token at a time from program to find better generation strategies that left to right. This paper studies the influence of improve modern neural networks, one needs ev- token generation order on model quality via a idence that the generation strategy can make a novel two-pass language model that produces large difference. This paper presents one way of partially-filled sentence “templates” and then isolating the generation strategy from the general fills in missing tokens. We compare various neural network design problem. Our key techni- strategies for structuring these two passes and cal contribution involves developing a flexible and observe a surprisingly large variation in model quality. We find the most effective strategy tractable architecture that incorporates different generates function words in the first pass fol- generation orders, while enabling exact computa- lowed by content words in the second. We be- tion of the log-probabilities of a sentence. -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
• La Gestion Efficace De L’Énergie • Le Réseautage Planétaire • Les Processus Géophysiques • L’Économie Globale, La Sécurité Et La Stabilité
SupérieureS atiqueS athéM e M inaire D SéM The planet on which we live and the challenges that we face on this planet become increasingly complex as ecological, economic and social systems are large intertwined networks governed by dynamic processes and feedback loops. Mathematical models are indispensable in understanding and managing such systems since they provide insight into governing processes; they help predict future behavior; and they allow for risk-free evaluation of possible interventions. The goal of this thematic program is to tackle pressing and emerging challenges in population and ecosystem health, including understanding and controlling major transmissible diseases, optimizing and monitoring vaccination, predicting the impacts of climate change on invasive species, protecting biodiversity and managing ecosystems sustainably. This pan-Canadian program will bring together the international community of researchers who work on these topics in a series of workshops to foster exchange and stimulate cross-disciplinary research between all scientific areas involved, to discuss perspectives and directions for future advances in the field, including new models and methods and to foster tighter links between the research community, government agencies and policy makers. Three summer schools will introduce graduate students and postdoctoral fellows to the art of modeling living systems and to the latest tools and techniques to analyze these models. SCIENTIFIC COMMITTEE Jacques Bélair Mark Lewis (Montréal) Models and Methods in Ecology (Alberta) Frithjof Lutscher and Epidemiology Mathematical Modeling James Watmough (Ottawa) February 6-8, 2013 (UNB) of Indigenous Population Jianhong Wu CRM, Montréal (York) Organizers: Jacques Bélair (Montréal), Health Jianhong Wu (York) September 28-29, 2013 AISENSTADT CHAIRS BIRS Bryan Grenfell (Princeton), May 2013 Graphic Design: www.neograf.ca Simon A. -
Ai & the Sustainable Development Goals
1 AI & THE SUSTAINABLE DEVELOPMENT GOALS: THE STATE OF PLAY Global Goals Technology Forum 2 3 INTRODUCTION In 2015, 193 countries agreed to the United Nations (UN) 2030 Agenda for Sustainable Development, which provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries – developed and developing – in a global partnership. Achieving the SDGs is not just a moral imperative, but an economic one. While the world is making progress in some areas, we are falling behind in delivering the SDGs overall. We need all actors – businesses, governments, academia, multilateral institutions, NGOs, and others – to accelerate and scale their efforts to deliver the SDGs, using every tool at their disposal, including artificial intelligence (AI). In December 2017, 2030Vision 2030Vision published its first report, Uniting to “WE ARE AT A PIVOTAL Deliver Technology for the Global ABOUT 2030VISION Goals, which addressed the role MOMENT. AS ARTIFICIAL of digital technology – big data, AI & The Sustainable Development Goals: The State of Play State Goals: The Development Sustainable AI & The Founded in 2017, 2030Vision INTELLIGENCE BECOMES robotics, internet of things, AI, and is a partnership of businesses, MORE WIDELY ADOPTED, other technologies – in achieving NGOs, and academia that aims to the SDGs. transform the use of technology to WE HAVE A TREMENDOUS support the delivery of the SDGs. OPPORTUNITY TO In this paper, we focus on AI for 2030Vision serves as a platform REEVALUATE WHAT WE’VE the SDGs. -
BIOGRAPHICAL SKETCH NAME: Berger
BIOGRAPHICAL SKETCH NAME: Berger, Bonnie eRA COMMONS USER NAME (credential, e.g., agency login): BABERGER POSITION TITLE: Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.) EDUCATION/TRAINING DEGREE Completion (if Date FIELD OF STUDY INSTITUTION AND LOCATION applicable) MM/YYYY Brandeis University, Waltham, MA AB 06/1983 Computer Science Massachusetts Institute of Technology SM 01/1986 Computer Science Massachusetts Institute of Technology Ph.D. 06/1990 Computer Science Massachusetts Institute of Technology Postdoc 06/1992 Applied Mathematics A. Personal Statement Advances in modern biology revolve around automated data collection and sharing of the large resulting datasets. I am considered a pioneer in the area of bringing computer algorithms to the study of biological data, and a founder in this community that I have witnessed grow so profoundly over the last 26 years. I have made major contributions to many areas of computational biology and biomedicine, largely, though not exclusively through algorithmic innovations, as demonstrated by nearly twenty thousand citations to my scientific papers and widely-used software. In recognition of my success, I have just been elected to the National Academy of Sciences and in 2019 received the ISCB Senior Scientist Award, the pinnacle award in computational biology. My research group works on diverse challenges, including Computational Genomics, High-throughput Technology Analysis and Design, Biological Networks, Structural Bioinformatics, Population Genetics and Biomedical Privacy. I spearheaded research on analyzing large and complex biological data sets through topological and machine learning approaches; e.g. -
Fall 2016 Is Available in the Laboratory of Dr
RNA Society Newsletter Aug 2016 From the Desk of the President, Sarah Woodson Greetings to all! I always enjoy attending the annual meetings of the RNA Society, but this year’s meeting in Kyoto was a standout in my opinion. This marked the second time that the RNA meeting has been held in Kyoto as a joint meeting with the RNA Society of Japan. (The first time was in 2011). Particular thanks go to the local organizers Mikiko Siomi and Tom Suzuki who took care of many logistical details, and to all of the organizers, Mikiko, Tom, Utz Fischer, Wendy Gilbert, David Lilley and Erik Sontheimer, for putting together a truly exciting and stimulating scientific program. Of course, the real excitement in the annual RNA meetings comes from all of you who give the talks and present the posters. I always enjoy meeting old friends and colleagues, but the many new participants in this year’s meeting particularly encouraged me. (Continued on p2) In this issue : Desk of the President, Sarah Woodson 1 Highlights of RNA 2016 : Kyoto Japan 4 Annual Society Award Winners 4 Jr Scientist activities 9 Mentor Mentee Lunch 10 New initiatives 12 Desk of our CEO, James McSwiggen 15 New Volunteer Opportunities 16 Chair, Meetings Committee, Benoit Chabot 17 Desk of the Membership Chair, Kristian Baker 18 Thank you Volunteers! 20 Meeting Reports: RNA Sponsored Meetings 22 Upcoming Meetings of Interest 27 Employment 31 1 Although the graceful city of Kyoto and its cultural months. First, in May 2016, the RNA journal treasures beckoned from just beyond the convention instituted a uniform price for manuscript publication hall, the meeting itself held more than enough (see p 12) that simplifies the calculation of author excitement to keep ones attention! Both the quality fees and facilitates the use of color figures to and the “polish” of the scientific presentations were convey scientific information. -
ABSTRACT HISTORICAL GRAPH DATA MANAGEMENT Udayan
ABSTRACT Title of dissertation: HISTORICAL GRAPH DATA MANAGEMENT Udayan Khurana, Doctor of Philosophy, 2015 Dissertation directed by: Professor Amol Deshpande Department of Computer Science Over the last decade, we have witnessed an increasing interest in temporal analysis of information networks such as social networks or citation networks. Finding temporal interaction patterns, visualizing the evolution of graph properties, or even simply com- paring them across time, has proven to add significant value in reasoning over networks. However, because of the lack of underlying data management support, much of the work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. Unfortunately, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, informa- tion diffusion, formation of online communities, and so on. In the absence of appropriate support, an analyst today has to manually navigate the added temporal complexity of large evolving graphs, making the process cumbersome and ineffective. In this dissertation, I address the key challenges in storing, retrieving, and analyzing large historical graphs. In the first part, I present DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compact recording of the historical information, and that supports efficient retrieval of historical graph snapshots. I present analytical models for estimating required storage space and snapshot retrieval times which aid in choosing the right parameters for a specific scenario. I also present optimizations such as partial materialization and columnar storage to speed up snapshot retrieval. In the second part, I present Temporal Graph Index that builds upon DeltaGraph to support version-centric retrieval such as a node’s 1-hop neighborhood history, along with snapshot reconstruction. -
Olga G. Troyanskaya - Sciencewatch.Com
Olga G. Troyanskaya - ScienceWatch.com Home About Scientific Press Room Contact Us ● ScienceWatch Home ● Interviews Featured Interviews Author Commentaries Institutional Interviews 2008 : September 2008 - Author Commentaries : Olga G. Troyanskaya Journal Interviews Podcasts AUTHOR COMMENTARIES - 2008 ● Analyses September 2008 Featured Analyses Olga G. Troyanskaya What's Hot In... Featured Scientist from Essential Science IndicatorsSM Special Topics Dr. Olga Troyanskaya has been named a Rising Star in the field of Computer ● Data & Rankings Science, according to an analysis published by ScienceWatch.com in May. Her citation record in this field in Essential Science Indicators from Thomson Sci-Bytes Reuters includes 31 papers cited a total of 1,533 times between January 1, 1998 and April 30, 2008. She also has Highly Cited Papers in the field of Fast Breaking Papers Clinical Medicine. Dr. Troyanskaya is an Assistant Professor in the New Hot Papers +enlarge Department of Computer Science and the Lewis-Sigler Institute for Integrative Emerging Research Fronts Genomics at Princeton University. Fast Moving Fronts Research Front Maps In the interview below, she talks with us about her highly cited work. Current Classics Top Topics Please tell us a little about your research and educational background. Rising Stars My background is interdisciplinary—I have a Ph.D. in Biomedical Informatics and undergraduate New Entrants degrees in both Computer Science and Biology. My research has always reflected this—I have been involved in bioinformatics research since undergraduate days, first working with Steven Salzberg, then at Country Profiles Johns Hopkins University and The Institute for Genomic Research, and then with Gad Landau and Alex Bolshoy at Haifa University in Israel. -
John Anthony Capra
John Anthony Capra Contact Vanderbilt University e-mail: tony.capra-at-vanderbilt.edu Information Dept. of Biological Sciences www: http://www.capralab.org/ VU Station B, Box 35-1634 office: U5221 BSB/MRB III Nashville, TN 37235-1634 phone: (615) 343-3671 Research • Applying computational methods to problems in genetics, evolution, and biomedicine. Interests • Integrating genome-scale data to understand the functional effects of genetic differences between individuals and species. • Modeling evolutionary processes that drive the creation of lineage-specific traits and diseases. Academic Vanderbilt University, Nashville, Tennessee USA Employment Assistant Professor, Department of Biological Sciences August 2014 { Present Assistant Professor, Department of Biomedical Informatics February 2013 { Present Investigator, Center for Human Genetics Research Education And Gladstone Institutes, University of California, San Francisco, CA USA Training Postdoctoral Fellow, October 2009 { December 2012 • Advisor: Katherine Pollard Princeton University, Princeton, New Jersey USA Ph.D., Computer Science, June 2009 • Advisor: Mona Singh • Thesis: Algorithms for the Identification of Functional Sites in Proteins M.A., Computer Science, October 2006 Columbia College, Columbia University, New York, New York USA B.A., Computer Science, May 2004 B.A., Mathematics, May 2004 Pembroke College, Oxford University, Oxford, UK Columbia University Oxford Scholar, October 2002 { June 2003 • Subject: Mathematics Honors and Gladstone Institutes Award for Excellence in Scientific Leadership 2012 Awards Society for Molecular Biology and Evolution (SMBE) Travel Award 2012 PhRMA Foundation Postdoctoral Fellowship in Informatics 2011 { 2013 Princeton University Wu Graduate Fellowship 2004 { 2008 Columbia University Oxford Scholar 2002 { 2003 Publications Capra JA* and Kostka D*. Modeling DNA methylation dynamics with approaches from phyloge- netics.