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UNIVERSITY of CALIFORNIA RIVERSIDE Unsupervised And
UNIVERSITY OF CALIFORNIA RIVERSIDE Unsupervised and Zero-Shot Learning for Open-Domain Natural Language Processing A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by Muhammad Abu Bakar Siddique June 2021 Dissertation Committee: Dr. Evangelos Christidis, Chairperson Dr. Amr Magdy Ahmed Dr. Samet Oymak Dr. Evangelos Papalexakis Copyright by Muhammad Abu Bakar Siddique 2021 The Dissertation of Muhammad Abu Bakar Siddique is approved: Committee Chairperson University of California, Riverside To my family for their unconditional love and support. i ABSTRACT OF THE DISSERTATION Unsupervised and Zero-Shot Learning for Open-Domain Natural Language Processing by Muhammad Abu Bakar Siddique Doctor of Philosophy, Graduate Program in Computer Science University of California, Riverside, June 2021 Dr. Evangelos Christidis, Chairperson Natural Language Processing (NLP) has yielded results that were unimaginable only a few years ago on a wide range of real-world tasks, thanks to deep neural networks and the availability of large-scale labeled training datasets. However, existing supervised methods assume an unscalable requirement that labeled training data is available for all classes: the acquisition of such data is prohibitively laborious and expensive. Therefore, zero-shot (or unsupervised) models that can seamlessly adapt to new unseen classes are indispensable for NLP methods to work in real-world applications effectively; such models mitigate (or eliminate) the need for collecting and annotating data for each domain. This dissertation ad- dresses three critical NLP problems in contexts where training data is scarce (or unavailable): intent detection, slot filling, and paraphrasing. Having reliable solutions for the mentioned problems in the open-domain setting pushes the frontiers of NLP a step towards practical conversational AI systems. -
ARCHITECTS of INTELLIGENCE for Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS of INTELLIGENCE
MARTIN FORD ARCHITECTS OF INTELLIGENCE For Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS OF INTELLIGENCE THE TRUTH ABOUT AI FROM THE PEOPLE BUILDING IT MARTIN FORD ARCHITECTS OF INTELLIGENCE Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Acquisition Editors: Ben Renow-Clarke Project Editor: Radhika Atitkar Content Development Editor: Alex Sorrentino Proofreader: Safis Editing Presentation Designer: Sandip Tadge Cover Designer: Clare Bowyer Production Editor: Amit Ramadas Marketing Manager: Rajveer Samra Editorial Director: Dominic Shakeshaft First published: November 2018 Production reference: 2201118 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK ISBN 978-1-78913-151-2 www.packt.com Contents Introduction ........................................................................ 1 A Brief Introduction to the Vocabulary of Artificial Intelligence .......10 How AI Systems Learn ........................................................11 Yoshua Bengio .....................................................................17 Stuart J. -
Keeping AI Legal
Vanderbilt Journal of Entertainment & Technology Law Volume 19 Issue 1 Issue 1 - Fall 2016 Article 5 2016 Keeping AI Legal Amitai Etzioni Oren Etzioni Follow this and additional works at: https://scholarship.law.vanderbilt.edu/jetlaw Part of the Computer Law Commons, and the Science and Technology Law Commons Recommended Citation Amitai Etzioni and Oren Etzioni, Keeping AI Legal, 19 Vanderbilt Journal of Entertainment and Technology Law 133 (2020) Available at: https://scholarship.law.vanderbilt.edu/jetlaw/vol19/iss1/5 This Article is brought to you for free and open access by Scholarship@Vanderbilt Law. It has been accepted for inclusion in Vanderbilt Journal of Entertainment & Technology Law by an authorized editor of Scholarship@Vanderbilt Law. For more information, please contact [email protected]. Keeping Al Legal Amitai Etzioni* and Oren Etzioni** ABSTRACT AI programs make numerous decisions on their own, lack transparency, and may change frequently. Hence, unassisted human agents, such as auditors, accountants, inspectors, and police, cannot ensure that Al-guided instruments will abide by the law. This Article suggests that human agents need the assistance of AI oversight programs that analyze and oversee operational AI programs. This Article asks whether operationalAIprograms should be programmed to enable human users to override them; without that, such a move would undermine the legal order. This Article also points out that Al operational programs provide high surveillance capacities and, therefore, are essential for protecting individual rights in the cyber age. This Article closes by discussing the argument that Al-guided instruments, like robots, lead to endangering much more than the legal order-that they may turn on their makers, or even destroy humanity. -
AI Ignition Ignite Your AI Curiosity with Oren Etzioni AI for the Common Good
AI Ignition Ignite your AI curiosity with Oren Etzioni AI for the common good From Deloitte’s AI Institute, this is AI Ignition—a monthly chat about the human side of artificial intelligence with your host, Beena Ammanath. We will take a deep dive into the past, present, and future of AI, machine learning, neural networks, and other cutting-edge technologies. Here is your host, Beena. Beena Ammanath (Beena): Hello, my name is Beena Ammanath. I am the executive director of the Deloitte AI Institute, and today on AI Ignition we have Oren Etzioni, a professor, an entrepreneur, and the CEO of Allen Institute for AI. Oren has helped pioneer meta search, online comparison shopping, machine reading, and open information extraction. He was also named Seattle’s Geek of the Year in 2013. Welcome to the show, Oren. I am so excited to have you on our AI Ignition show today. How are you doing? Oren Etzioni (Oren): I am doing as well as anyone can be doing in these times of COVID and counting down the days till I get a vaccine. Beena: Yeah, and how have you been keeping yourself busy during the pandemic? Oren: Well, we are fortunate that AI is basically software with a little sideline into robotics, and so despite working from home, we have been able to stay productive and engaged. It’s just a little bit less fun because one of the things I like to say about AI is that it’s still 99% human intelligence, and so obviously the human interactions are stilted. -
Oren Etzioni, Phd: CEO of Allen Institute for AI
Behind the Tech Kevin Scott Podcast EP-26: Oren Etzioni, PhD: CEO of Allen Institute for AI [MUSIC] OREN ETZIONI: Whose responsibility is it? The responsibility and liability has to ultimately rest with a person. You can’t say, “Hey, you know, look, my car ran you over, it’s an AI car, I don’t know what it did, it’s not my fault, right?” You as the driver or maybe it’s the manufacturer if there’s some malfunction, but people have to be responsible for the behavior of the machines. [MUSIC] KEVIN SCOTT: Hi, everyone. Welcome to Behind the Tech. I'm your host, Kevin Scott, Chief Technology Officer for Microsoft. In this podcast, we're going to get behind the tech. We'll talk with some of the people who have made our modern tech world possible and understand what motivated them to create what they did. So, join me to maybe learn a little bit about the history of computing and get a few behind- the-scenes insights into what's happening today. Stick around. [MUSIC] CHRISTINA WARREN: Hello, and welcome to Behind the Tech. I’m Christina Warren, senior cloud advocate at Microsoft. KEVIN SCOTT: And I’m Kevin Scott. CHRISTINA WARREN: Today on the show, our guest is Oren Etzioni. Oren is a professor, entrepreneur, and is the chief executive officer for the Allen Institute for AI. So, Kevin, I’m guessing that you guys have already crossed paths in your professional pursuits. KEVIN SCOTT: Yeah, I’ve been lucky enough to know Oren for the past several years. -
University of California Santa Cruz Sample
UNIVERSITY OF CALIFORNIA SANTA CRUZ SAMPLE-SPECIFIC CANCER PATHWAY ANALYSIS USING PARADIGM A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in BIOMOLECULAR ENGINEERING AND BIOINFORMATICS by Stephen C. Benz June 2012 The Dissertation of Stephen C. Benz is approved: Professor David Haussler, Chair Professor Joshua Stuart Professor Nader Pourmand Dean Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by Stephen C. Benz 2012 Table of Contents List of Figures v List of Tables xi Abstract xii Dedication xiv Acknowledgments xv 1 Introduction 1 1.1 Identifying Genomic Alterations . 2 1.2 Pathway Analysis . 5 2 Methods to Integrate Cancer Genomics Data 10 2.1 UCSC Cancer Genomics Browser . 11 2.2 BioIntegrator . 16 3 Pathway Analysis Using PARADIGM 20 3.1 Method . 21 3.2 Comparisons . 26 3.2.1 Distinguishing True Networks From Decoys . 27 3.2.2 Tumor versus Normal - Pathways associated with Ovarian Cancer 29 3.2.3 Differentially Regulated Pathways in ER+ve vs ER-ve breast can- cers . 36 3.2.4 Therapy response prediction using pathways (Platinum Free In- terval in Ovarian Cancer) . 38 3.3 Unsupervised Stratification of Cancer Patients by Pathway Activities . 42 4 SuperPathway - A Global Pathway Model for Cancer 51 4.1 SuperPathway in Ovarian Cancer . 55 4.2 SuperPathway in Breast Cancer . 61 iii 4.2.1 Chin-Naderi Cohort . 61 4.2.2 TCGA Breast Cancer . 63 4.3 Cross-Cancer SuperPathway . 67 5 Pathway Analysis of Drug Effects 74 5.1 SuperPathway on Breast Cell Lines . -
Introduction
Introduction IJCAI-01 Conference Committee IJCAI-01 Program Committee: Contents: CONFERENCE CHAIR: Elisabeth André, DFKI GmbH (Germany) Introduction 2 Hector J. Levesque, University of Toronto (Canada) Minoru Asada, Osaka University (Japan) Sponsors & Committees 2-3 Franz Baader, RWTH Aachen (Germany) PROGRAM CHAIR: IJCAI-01 Awards 4 Craig Boutilier, University of Toronto (Canada) Bernhard Nebel,Albert-Ludwigs-Universität, Freiburg Didier Dubois, IRIT-CNRS (France) Conference at a Glance 5 (Germany) Maria Fox, University of Durham (United Kingdom) Workshop Program 6-7 LOCAL ARRANGEMENTS CHAIR: Hector Geffner, Universidad Simón Bolívar Doctoral Consortium 8 James Hoard, The Boeing Company, Seattle (USA) (Venezuela) Tutorial Program 8 SECRETARY-TREASURER: Georg Gottlob,Vienna University of Technology (Austria) Conference Program Highlights 9 Ronald J. Brachman,AT&T Labs – Research (USA) Invited Speakers 10 Haym Hirsh, Rutgers University (USA) IAAI-01 Conference 11 Eduard Hovy, Information Sciences Institute (USA) Advisory Committee: Joxan Jaffar, National University of Singapore Technical Program 12-19 Bruce Buchanan, University of Pittsburgh (USA) (Singapore) Exhibit Program 20-23 Silvia Coradeschi, Örebro University (Sweden) Daphne Koller, Stanford University (USA) RoboCup 2001 24 Olivier Faugeras, INRIA (France) Fangzhen Lin, Hong Kong University of Science and Registration Information 25 Cheng Hu, Chinese Academy of Sciences (China) Technolog y (Hong Kong) General Information 25-27 Nicholas Jennings, University of London (England) Heikki Mannila, Nokia Research Center (Finland) Conference Maps 28-30 Henry Kautz, University of Washington (USA) Robert Milne, Intelligent Applications (United Kingdom) IJCAI-03 Conference 31 Robert Mercer, University of Western Ontario (Canada) Daniele Nardi, Università di Roma “La Sapienza” Special Meetings 31 Silvia Miksch,Vienna University of Technology (Italy) (Austria) Dana Nau, University of Maryland (USA) Devika Subramanian, Rice University (USA) Patrick Prosser, University of Glasgow (UK) Welcome to IJCAI-01 L. -
The Elephant in the Room: Getting Value from Big Data Serge Abiteboul, Luna Dong, Oren Etzioni, Divesh Srivastava, Gerhard Weikum, Julia Stoyanovich, Fabian Suchanek
The elephant in the room: getting value from Big Data Serge Abiteboul, Luna Dong, Oren Etzioni, Divesh Srivastava, Gerhard Weikum, Julia Stoyanovich, Fabian Suchanek To cite this version: Serge Abiteboul, Luna Dong, Oren Etzioni, Divesh Srivastava, Gerhard Weikum, et al.. The elephant in the room: getting value from Big Data. Workshop on Web and Databases (WebDB), May 2015, Melbourne, France. 10.1145/2767109.2770014. hal-01699868 HAL Id: hal-01699868 https://hal-imt.archives-ouvertes.fr/hal-01699868 Submitted on 2 Feb 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The elephant in the room: getting value from Big Data Serge Abiteboul (INRIA Saclay & ENS Cachan, France), Luna Dong (Google Inc., USA), Oren Etzioni (Allen Institute for Artificial Intelligence, USA), Divesh Srivastava (AT&T Labs-Research, USA), Gerhard Weikum (Max Planck Institute for Informatics, Germany), Julia Stoyanovich (Drexel University, USA) and Fabian M. Suchanek (Télécom ParisTech, France) 1. INTRODUCTION SIGMOD Innovation Award in 1998, the EADS Award from Big Data, and its 4 Vs { volume, velocity, variety, and the French Academy of sciences in 2007; the Milner Award veracity { have been at the forefront of societal, scientific from the Royal Society in 2013; and a European Research and engineering discourse. -
Toward Automatic Bootstrapping of Online Communities Using
Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization Shih-Wen Huang* Jonathan Bragg* Isaac Cowhey University of Washington University of Washington Allen Institute for AI Seattle, WA, USA Seattle, WA, USA Seattle, WA, USA [email protected] [email protected] [email protected] Oren Etzioni Daniel S. Weld Allen Institute for AI University of Washington Seattle, WA, USA Seattle, WA, USA [email protected] [email protected] ABSTRACT INTRODUCTION Successful online communities (e.g., Wikipedia, Yelp, and The Internet has spawned communities that create extraordi- StackOverflow) can produce valuable content. However, nary resources. For example, Wikipedia’s 23 million users many communities fail in their initial stages. Starting an on- have created over 4 million English articles, a resource over line community is challenging because there is not enough 100 times larger than any other encyclopedia. Similarly, content to attract a critical mass of active members. This StackOverflow has become a top resource for programmers paper examines methods for addressing this cold-start prob- with 14 million answers to 8.5 million questions, while Yelp lem in datamining-bootstrappable communities by attract- users generated more than 67 million reviews.1 ing non-members to contribute to the community. We make four contributions: 1) we characterize a set of communi- In reality, however, most online communities fail. For exam- ple, thousands of open source projects have been created on ties that are “datamining-bootstrappable” and define the boot- SourceForge, but only 10% have three or more members [24]. strapping problem in terms of decision-theoretic optimiza- Furthermore, more than 50% of email-based groups received tion, 2) we estimate the model parameters in a case study involving the Open AI Resources website, 3) we demonstrate no messages during a four-month study period [6]. -
UCLA UCLA Electronic Theses and Dissertations
UCLA UCLA Electronic Theses and Dissertations Title Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods Permalink https://escholarship.org/uc/item/0tr9j01r Author Sun, Yidan Publication Date 2021 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics by Yidan Sun 2021 © Copyright by Yidan Sun 2021 ABSTRACT OF THE DISSERTATION Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods by Yidan Sun Doctor of Philosophy in Statistics University of California, Los Angeles, 2021 Professor Jingyi Li, Chair In a bipartite network, nodes are divided into two types, and edges are only allowed to connect nodes of different types. Bipartite network clustering problems aim to identify node groups with more edges between themselves and fewer edges to the rest of the network. The approaches for community detection in the bipartite network can roughly be classified into algorithmic and model-based methods. The algorithmic methods solve the problem either by greedy searches in a heuristic way or optimizing based on some criteria over all possible partitions. The model-based methods fit a generative model to the observed data and study the model in a statistically principled way. In this dissertation, we mainly focus on bipartite clustering under two scenarios: incorporation of node covariates and detection of mixed membership communities. -
Autonomous Cars
Prof. Roberto V. Zicari Frankfurt Big Data Lab www.bigdata.uni-frankfurt.de Johannes Gutenberg University Mainz January 28, 2019 1 Data as an Economic and Semantic Asset “I think we’re just beginning to grapple with implications of data as an economic asset” (*) –Steve Lohr (The New York Times) Data has become a new economic asset. The companies with big data pools can have great economic power They greatly influence what the philosopher Floridi call our semantic capital (**) (*) Source: Big Data and The Great A.I. Awakening. Interview with Steve Lohr, ODBMS Industry Watch, December 19, 2016 (**) Source: Semantic Capital: Its Nature, Value, and Curation, Luciano Floridi, December 2018, Volume 31, Issue 4, pp 481–497| 2 What is more important, vast data pools, sophisticated algorithms or deep pockets? “No one can replicate your data. It’s the defensible barrier, not algorithms.” (*) -- Andrew Ng, Stanford professor (*) Source: Big Data and The Great A.I. Awakening. Interview with Steve Lohr, ODBMS Industry Watch, December 19, 2016 3 Algorithms and Data “AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. The rocket engine is the learning algorithms but the fuel is the huge amounts of data we can feed to these algorithms.” (*) -- Andrew Ng It is important to note that Big Data is of NO use unless it is analysed. (*) Source: Big Data and The Great A.I. Awakening. Interview with Steve Lohr, ODBMS Industry Watch, December 19, 2016 4 Interplay and implications of Big Data and Artificial Intelligence The Big Data revolution and the new developments in Hardware have made the recent AI advances possible. -
Director's Update
Director’s Update Francis S. Collins, M.D., Ph.D. Director, National Institutes of Health Council of Councils Meeting September 6, 2019 Changes in Leadership . Retirements – Paul A. Sieving, M.D., Ph.D., Director of the National Eye Institute Paul Sieving (7/29/19) Linda Birnbaum – Linda S. Birnbaum, Ph.D., D.A.B.T., A.T.S., Director of the National Institute of Environmental Health Sciences (10/3/19) . New Hires – Noni Byrnes, Ph.D., Director, Center for Scientific Review (2/27/19) Noni Byrnes – Debara L. Tucci, M.D., M.S., M.B.A., Director, National Institute on Deafness and Other Communication Disorders (9/3/19) Debara Tucci . New Positions – Tara A. Schwetz, Ph.D., Associate Deputy Director, NIH (1/7/19) Tara Schwetz 2019 Inaugural Inductees Topics for Today . NIH HEAL (Helping to End Addiction Long-termSM) Initiative – HEALing Communities Study . Artificial Intelligence: ACD WG Update . Human Genome Editing – Exciting Promise for Cures, Need for Moratorium on Germline . Addressing Foreign Influence on Research … and Harassment in the Research Workplace NIH HEAL InitiativeSM . Trans-NIH research initiative to: – Improve prevention and treatment strategies for opioid misuse and addiction – Enhance pain management . Goals are scientific solutions to the opioid crisis . Coordinating with the HHS Secretary, Surgeon General, federal partners, local government officials and communities www.nih.gov/heal-initiative NIH HEAL Initiative: At a Glance . $500M/year – Will spend $930M in FY2019 . 12 NIH Institute and Centers leading 26 HEAL research projects – Over 20 collaborating Institutes, Centers, and Offices – From prevention research, basic and translational research, clinical trials, to implementation science – Multiple projects integrating research into new settings .