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You and AI Conversations About AI Technologies and Their Implications for Society
You and AI Conversations about AI technologies and their implications for society SUPPORTED BY CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY DeepMind1 2 CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY You and AI Conversations about AI technologies and their implications for society Artificial Intelligence (AI) is the science of making computer systems smart, and an umbrella term for a range of technologies that carry out functions that typically require intelligence in humans. AI technologies already support many everyday products and services, and the power and reach of these technologies are advancing at pace. The Royal Society is working to support an environment of careful stewardship of AI technologies, so that their benefits can be brought into being safely and rapidly, and shared across society. In support of this aim, the Society’s You and AI series brought together leading AI researchers to contribute to a public conversation about advances in AI and their implications for society. CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY 3 What AI can, and cannot, do The last decade has seen exciting developments in AI – and AI researchers are tackling some fundamental challenges to develop it further AI research seeks to understand what happens or inputs do not follow a standard intelligence is, and then recreate this through pattern, these systems cannot adapt their computer systems that can automatically rules or adjust their approach. perform tasks that require some level of reasoning or intelligence in humans. In the last decade, new methods that use learning algorithms have helped create In the past, AI research has concentrated computer systems that are more flexible on creating detailed rules for how to carry and adaptive, and Demis Hassabis FRS out a task and then developing computer (co-founder, DeepMind) has been at the systems that could carry out these rules; forefront of many of these developments. -
Annual Report of the Faculty 2018-19* *A Small Number of Items That Fall Into 2019-20 Which Are of Current Relevant Interest Are Also Included in This Report
Annual Report of the Faculty 2018-19* *A small number of items that fall into 2019-20 which are of current relevant interest are also included in this report. INTRODUCTION This year was the first full year with Prof. Ann Copestake as Head of Department. The Department’s strategy is to continue to be a home for the world’s best computer science researchers, providing a flexible environment for working collaboratively with each other and partners of all kinds, in an open culture. Our people are part of a wider network of alumni and associates, and these connections sustain the creation of strong and revolutionary fundamental computer science research ideas, many of which will transform practice outside the academy. We continue to deliver exceptional research both individually and in dynamic teams working together across fields, connecting deep fundamentals with the wider context of research and applications in diverse fields, and we translate many of our ideas into practical innovations in whatever form is most appropriate. The University’s Strategic Research Review of the Department in September 2018 provided a valuable opportunity for reflection and strengthening how this strategy is enabled in practice. This year has seen a range of appointments which enhance our links with important fields, including climate change, and development of professional services teams to support the Department’s current size. We continue to improve our support for early career researchers and postgraduate teaching, and to communicate more effectively about our work and opportunities to collaborate with us. In a time of great excitement about data science, almost all our research groups include machine learning activities in diverse forms, and we are exploring ways to unlock potential applications across Schools and build capacity across the research base. -
Recurrent Gaussian Processes and Robust Dynamical Modeling
UNIVERSIDADE FEDERAL DO CEARA´ CENTRO DE TECNOLOGIA DEPARTAMENTO DE ENGENHARIA DE TELEINFORMATICA´ PROGRAMA DE POS-GRADUA¸C´ AO~ EM ENGENHARIA DE TELEINFORMATICA´ DOUTORADO EM ENGENHARIA DE TELEINFORMATICA´ CESAR´ LINCOLN CAVALCANTE MATTOS RECURRENT GAUSSIAN PROCESSES AND ROBUST DYNAMICAL MODELING FORTALEZA 2017 CESAR´ LINCOLN CAVALCANTE MATTOS RECURRENT GAUSSIAN PROCESSES AND ROBUST DYNAMICAL MODELING Tese apresentada ao Curso de Doutorado em Engenharia de Teleinform´atica do Programa de P´os-Gradua¸c~aoem Engenharia de Teleinform´atica do Centro de Tecnologia da Universidade Federal do Cear´a, como requisito parcial `a obten¸c~ao do t´ıtulo de doutor em Engenharia de Teleinform´atica. Area´ de Concentra¸c~ao: Sinais e sistemas Orientador: Prof. Dr. Guilherme de Alencar Barreto FORTALEZA 2017 Dados Internacionais de Catalogação na Publicação Universidade Federal do Ceará Biblioteca Universitária Gerada automaticamente pelo módulo Catalog, mediante os dados fornecidos pelo(a) autor(a) M39r Mattos, César Lincoln. Recurrent Gaussian Processes and Robust Dynamical Modeling / César Lincoln Mattos. – 2017. 189 f. : il. color. Tese (doutorado) – Universidade Federal do Ceará, Centro de Tecnologia, Programa de Pós-Graduação em Engenharia de Teleinformática, Fortaleza, 2017. Orientação: Prof. Dr. Guilherme de Alencar Barreto. 1. Processos Gaussianos. 2. Modelagem dinâmica. 3. Identificação de sistemas não-lineares. 4. Aprendizagem robusta. 5. Aprendizagem estocástica. I. Título. CDD 621.38 CESAR´ LINCOLN CAVALCANTE MATTOS RECURRENT GAUSSIAN PROCESSES AND ROBUST DYNAMICAL MODELING A thesis presented to the PhD course in Teleinformatics Engineering of the Graduate Program on Teleinformatics Engineering of the Center of Technology at Federal University of Cear´ain fulfillment of the the requirement for the degree of Doctor of Philosophy in Teleinformatics Engineering. -
Signature Redacted a Uthor
Social Inductive Biases for Reinforcement Learning by Dhaval D.K. Adjodah B.S., Physics (2011), Massachusetts Institute of Technology M.S., Technology and Policy (2011), Massachusetts Institute of Technology Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Media Arts and Sciences at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2019 @ Massachusetts Institute of Technology 2019. All rights reserved. Signature redacted A uthor .................. Q .gi'gratiFMediaArts and Sciences, Sch of Architecture and Planning, ignature redacted 2, 2019 Certified by.......... KlV 'Sandy P. Pentland Toshiba Pr fessor of Media Arts and Sciences Thesis Supe risor Accepted by......... Signature redacted MASSACUET LNSTITUTE Tod Machover OF TECHNOLOGY (2 Academic Head 0 OCT 0 4 2019 Program in Media Arts and Sciences LIBRARIES ci 77 Massachusetts Avenue Cambridge, MA 02139 MITLibraries http://Iibraries.mit.edu/ask DISCLAIMER NOTICE The pagination in this thesis reflects how it was delivered to the Institute Archives and Special Collections. The Table of Contents does not accurately represent the page numbering. Social Inductive Biases for Reinforcement Learning by Dhaval D.K. Adjodah Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning, on July 2, 2019, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Media Arts and Sciences Abstract How can we build machines -
Beneficial AI 2017
Beneficial AI 2017 Participants & Attendees 1 Anthony Aguirre is a Professor of Physics at the University of California, Santa Cruz. He has worked on a wide variety of topics in theoretical cosmology and fundamental physics, including inflation, black holes, quantum theory, and information theory. He also has strong interest in science outreach, and has appeared in numerous science documentaries. He is a co-founder of the Future of Life Institute, the Foundational Questions Institute, and Metaculus (http://www.metaculus.com/). Sam Altman is president of Y Combinator and was the cofounder of Loopt, a location-based social networking app. He also co-founded OpenAI with Elon Musk. Sam has invested in over 1,000 companies. Dario Amodei is the co-author of the recent paper Concrete Problems in AI Safety, which outlines a pragmatic and empirical approach to making AI systems safe. Dario is currently a research scientist at OpenAI, and prior to that worked at Google and Baidu. Dario also helped to lead the project that developed Deep Speech 2, which was named one of 10 “Breakthrough Technologies of 2016” by MIT Technology Review. Dario holds a PhD in physics from Princeton University, where he was awarded the Hertz Foundation doctoral thesis prize. Amara Angelica is Research Director for Ray Kurzweil, responsible for books, charts, and special projects. Amara’s background is in aerospace engineering, in electronic warfare, electronic intelligence, human factors, and computer systems analysis areas. A co-founder and initial Academic Model/Curriculum Lead for Singularity University, she was formerly on the board of directors of the National Space Society, is a member of the Space Development Steering Committee, and is a professional member of the Institute of Electrical and Electronics Engineers (IEEE). -
OPINION Sysadmin NETWORKING Security Columns
October 2010 VOLUME 35 NUMBER 5 OPINION Musings 2 RikR FaR ow SYSADMin Teaching System Administration in the Cloud 6 Jan Schaumann THE USENIX MAGAZINE A System Administration Parable: The Waitress and the Water Glass 12 ThomaS a. LimonceLLi Migrating from Hosted Exchange Service to In-House Exchange Solution 18 T Roy mckee NETWORKING IPv6 Transit: What You Need to Know 23 mT aT Ryanczak SU EC RITY Secure Email for Mobile Devices 29 B Rian kiRouac Co LUMNS Practical Perl Tools: Perhaps Size Really Does Matter 36 Davi D n. BLank-eDeLman Pete’s All Things Sun: The “Problem” with NAS 42 Pe TeR BaeR GaLvin iVoyeur: Pockets-o-Packets, Part 3 48 Dave JoSePhSen /dev/random: Airport Security and Other Myths 53 RoT BeR G. FeRReLL B OOK reVIEWS Book Reviews 56 El izaBeTh zwicky, wiTh BRanDon chinG anD Sam SToveR US eniX NOTES USA Wins World High School Programming Championships—Snaps Extended China Winning Streak 60 RoB koLSTaD Con FERENCES 2010 USENIX Federated Conferences Week: 2010 USENIX Annual Technical Conference Reports 62 USENIX Conference on Web Application Development (WebApps ’10) Reports 77 3rd Workshop on Online Social Networks (WOSN 2010) Reports 84 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud ’10) Reports 91 2nd Workshop on Hot Topics in Storage and File Systems (HotStorage ’10) Reports 100 Configuration Management Summit Reports 104 2nd USENIX Workshop on Hot Topics in Parallelism (HotPar ’10) Reports 106 The Advanced Computing Systems Association oct10covers.indd 1 9.7.10 1:54 PM Upcoming Events 24th Large InstallatIon system admInIstratIon ConferenCe (LISA ’10) Sponsored by USENIX in cooperation with LOPSA and SNIA november 7–12, 2010, San joSe, Ca, USa http://www.usenix.org/lisa10 aCm/IfIP/USENIX 11th InternatIonaL mIddLeware ConferenCe (mIddLeware 2010) nov. -
Project Final Report
PROJECT FINAL REPORT Grant Agreement number: 305626 Project acronym: RADIANT Project title: Rapid development and distribution of statistical tools for high-throughput sequencing data Funding Scheme: Collaborative Project (FP7-HEALTH-2012-2.1.1-3: Statistical methods for collection and analysis of -omics data) Period covered: from 1.12.2013 to 30.11.2015 Name of the scientific representative of the project's co-ordinator1, Title and Organisation: Prof. Magnus Rattray. The University of Manchester Tel: 0161 275 5094 Fax: E-mail: Magnus. [email protected] Project website address: http://radiant-project.eu 1 Usually the contact person of the coordinator as specified in Art. 8.1. of the Grant Agreement. 4.1 Final publishable summary report 4.1.1 Executive summary The objectives of the RADIANT project were to develop improved computational tools for the analysis of high-throughput sequencing (HTS) data, to rapidly disseminate these tools to the wider scientific community and to support practitioners in the application of these and other advanced HTS data analysis tools in their scientific applications, lowering the bar for the widespread adoption of cutting-edge statistical tools. The project has been successful in achieving these objectives. The RADIANT project supported the development of several superb data analysis tools which have been widely adopted by practitioners. The new DESeq2 tool for differential expression analysis was developed entirely through RADIANT funding and provides substantial improvements over the earlier DESeq tool; it is one of the most widely-used tools in genomics (Love et al., 2014 cited 228 times in Scopus, 34500 software downloads in the last year). -
AUGUST 2005 43 Your Defense Is Offensive STEVE MANZUIK EDITOR ;Login: Is the Official WORKPLACE Rik Farrow Magazine of the [email protected] USENIX Association
A UGUST 2005 VOLUME 30 NUMBER 4 THE USENIX MAGAZINE OPINION Musings RIK FARROWS Conference Password Sniffing ABE SINGER SYSADMIN The Inevitability of Xen JON CROWCROFT, KEIR FRASER, STEVEN HAND, IAN PRATT, AND ANDREW WARFIELD Secure Automated File Transfer MARK MCCULLOUGH SAN vs. NAS for Oracle: A Tale of Two Protocols ADAM LEVIN Practical Perl ADAM TUROFF ISPadmin ROBERT HASKINS L AW Primer on Cybercrime Laws DANI EL L. APPELMAN SECURITY Forensics for System Administrators SEAN PEISERT Your Defense Is Offensive STEVE MANZUIK WORKPLACE Marketing after the Bubble EMILY W. SALUS AND PETER H. SALUS BOOK REVIEWS Book Reviews RIK FAROWS USENIX NOTES SAGE Update DAVID PARTER ...and much more CONFERENCES 2005 USENIX Annual Technical Conference 2nd Symposium on Networked Systems Design and Implementation (NSDI ’05) 6th IEEE Workshop on Mobile Computing Systems and Applications (WMCSA 2004) The Advanced Computing Systems Association Upcoming Events INTERNET MEASUREMENT CONFERENCE 2005 4TH USENIX CONFERENCE ON FILE AND (IMC ’05) STORAGE TECHNOLOGIES (FAST ’05) Sponsored by ACM SIGCOMM in cooperation with USENIX Sponsored by USENIX in cooperation with ACM SIGOPS, IEEE Mass Storage Systems Technical Committee (MSSTC), OCTOBER 19–21, 2005, NEW ORLEANS, LA, USA and IEEE TCOS http://www.usenix.org/imc05 DECEMBER 14–16, 2005, SAN FRANCISCO, CA, USA http://www.usenix.org/fast05 ACM/IFIP/USENIX 6TH INTERNATIONAL MIDDLEWARE CONFERENCE 3RD SYMPOSIUM ON NETWORKED SYSTEMS NOVEMBER 28–DECEMBER 2, 2005, GRENOBLE, FRANCE DESIGN AND IMPLEMENTATION (NSDI ’06) http://middleware05.objectweb.org -
Gaussian Process Modelling for Audio Signals
School of Electronic Engineering and Computer Science Queen Mary University of London Gaussian Process Modelling for Audio Signals William J. Wilkinson PhD thesis Submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy in Computer Science July 2019 Statement of Originality I, William J. Wilkinson, confirm that the research included within this thesis is my own work or that where it has been carried out in collaboration with, or supported by others, that this is duly acknowledged and my contribution indicated. Previously published material is also acknowledged herein. I attest that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge break any UK law, infringe any third party's copyright or other Intellectual Property Right, or contain any confidential material. I accept that the College has the right to use plagiarism detection software to check the electronic version of the thesis. I confirm that this thesis has not been previously submitted for the award of a degree by this or any other university. The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author. Signature: Date: 2 Abstract Audio signals are characterised and perceived based on how their spectral make-up changes with time. Uncovering the behaviour of latent spectral components is at the heart of many real-world applications involving sound, but is a highly ill-posed task given the infinite number of ways any signal can be decomposed. -
The Ring, No LIII, 2020-01
The RingRingThe Hall of Fame news 1 News from the Department 2 Company spotlight: lowRISC 4 Using deep learning to improve Parkinson’s diagnoses 5 Neil Lawrence appointed DeepMind Professor 6 Cambridge Zero 7 AI for Environmental Risks 8 CHERI-ARM CPU 9 Fact-checking project wins ERC Grant 10 www.cst.cam.ac.uk/ring 2 Hall of Fame news Hall of Fame Awards The winners of last year’s awards Ring Dinner and Hall of were: We are now accepting nominations Fame Awards Ceremony for the 16th Hall of Fame awards. 1. Company of the Year: PolyAI The Cambridge Ring dinner and The awards take place annually to 2. Product of the Year: Pur3 Ltd for Hall of Fame Awards ceremony celebrate the success of companies Pixl.js will be held at Queens’ College on Thursday 2nd April 2020. founded by Computer Laboratory 3. Better Future Award: Gemma graduates and staff. Gordon for her work on bridging The winners will be announced by Category 1 – Company of the Year: virtual reality with climate change Professor Ann Copestake, and the the company must be a member of education in the LABScI Imagine awards will be presented by guest the Ring Hall of Fame. If a company project. speaker Steve Pope, Engineering Fellow at Xilinx Inc. is eligible but not currently listed, If you wish to submit a nomination please contact us to ensure that it for any of the categories, please There will be a pre-dinner lecture gets added. send nominations (explaining why from 5.15pm, given by Dr Category 2 – Product of the Year: you have nominated the company Marwa Mahmoud at the William the product concerned must have or product) to ring-organiser@cst. -
Statistical Learning Approaches for the Control of Stormwater Systems
Statistical Learning Approaches for the Control of Stormwater Systems by Abhiram Mullapudi A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Civil Engineering) in The University of Michigan 2020 Doctoral Committee: Associate Professor Branko Kerkez, Chair Associate Professor Andrew D. Gronewold Assistant Professor Neda Masoud Assistant Professor Ramanarayan Vasudevan Abhiram Mullapudi [email protected] ORCID iD: 0000-0001-8141-3621 © Abhiram Mullapudi 2020 DEDICATION This thesis is dedicated to my family. ii ACKNOWLEDGEMENTS I want to thank my advisor, Dr. Branko Kerkez, for his guidance and mentor- ship through my Ph.D. process. I could not have asked for a better mentor. I would also like to thank my dissertation committee: Dr. Neda Masoud, Dr. An- drew D. Gronewold, and Dr. Ramanarayan Vasudevan. Their invaluable insights helped shape my dissertation. This work has been made possible by the Na- tional Science Foundation (grant number #1737432) and Great Lakes Protection Fund (grant number #1035). I want to acknowledge all the people who helped guide my research. Dr. Matthew J. Lewis and Dr. Cyndee L. Gruden, for their help, getting me started with the Reinforcement Learning and Stormwater systems. Dr. Sara P. Rimer, for all the fascinating discussions. Dr. Jonathan L. Goodall, Dr. Jon M. Hathaway, and all the Smart and Connected Communities team, for all the fantastic conver- sations over the years. They have been instrumental in framing the questions I pursued in my dissertation. I would also like to acknowledge the graduate stu- dents in the CEE and, in particular, the members of the Real-time Water Systems Lab for the amazing five years. -
Variational Algorithms for Approximate Bayesian Inference
VARIATIONAL ALGORITHMS FOR APPROXIMATE BAYESIAN INFERENCE by Matthew J. Beal M.A., M.Sci., Physics, University of Cambridge, UK (1998) The Gatsby Computational Neuroscience Unit University College London 17 Queen Square London WC1N 3AR A Thesis submitted for the degree of Doctor of Philosophy of the University of London May 2003 Abstract The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents a unified variational Bayesian (VB) framework which approximates these computations in models with latent variables using a lower bound on the marginal likelihood. Chapter 1 presents background material on Bayesian inference, graphical models, and propaga- tion algorithms. Chapter 2 forms the theoretical core of the thesis, generalising the expectation- maximisation (EM) algorithm for learning maximum likelihood parameters to the VB EM al- gorithm which integrates over model parameters. The algorithm is then specialised to the large family of conjugate-exponential (CE) graphical models, and several theorems are presented to pave the road for automated VB derivation procedures in both directed and undirected graphs (Bayesian and Markov networks, respectively). Chapters 3-5 derive and apply the VB EM algorithm to three commonly-used and important models: mixtures of factor analysers, linear dynamical systems, and hidden Markov models. It is shown how model selection tasks such as determining the dimensionality, cardinality, or number of variables are possible using VB approximations. Also explored are methods for combining sampling procedures with variational approximations, to estimate the tightness of VB bounds and to obtain more effective sampling algorithms.