Lorien Pratt, Ph.D. Curriculum Vita
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
AAAI 2008 Workshop Reports
University of Pennsylvania ScholarlyCommons Technical Reports (CIS) Department of Computer & Information Science 5-2009 AAAI 2008 Workshop Reports Mark Dredze University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/cis_reports Recommended Citation Mark Dredze, "AAAI 2008 Workshop Reports", . May 2009. Sarabjot Singh Anand, Razvan Bunescu, Vitor Carvalho, Jan Chomicki, Vincent Conitzer, Michael T Cox, Virginia Dignum, Zachary Dodds, Mark Dredze, David Furcy, Evgeniy Gabrilovich, Mehmet H Göker, Hans Guesgen, Haym Hirsh, Dietmar Jannach, Ulrich Junker. (2009, April). AAAI 2008 Workshop Reports. AI Magazine, 30(1), 108-118. Copyright AAAI 2009. The copies do not imply AAAI endorsement of a product or a service of the employer, and that the copies are not for sale. Publisher URL: http://proquest.umi.com/ pqdlink?did=1680350011&sid=1&Fmt=2&clientId=3748&RQT=309&VName=PQD This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_reports/904 For more information, please contact [email protected]. AAAI 2008 Workshop Reports Abstract AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An vE olving Synergy. -
Decision Intelligence in Public Health – DIONE Stolk, J. and Nyon, S
22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 to 8 December 2017 mssanz.org.au/modsim2017 Decision Intelligence in Public Health – DIONE Jacob Stolk1 and Simone Nyon1 1 Dione Complex Systems, Gold Coast, Queensland, Australia Email: [email protected] Abstract: Public health surveillance is the ongoing systematic collection, analysis, interpretation, and dissemination of health data for the planning, implementation, and evaluation of public health action. To achieve effective public health interventions, it is pivotal to analyse and interpret the vast amounts of data collected by surveillance systems to enable good understanding of all factors having an impact on health. For example, we can consider child protection, which is an important public health issue. Often relatively extensive data exist on families in official statistics, research reports, social services reports, school and medical records, etc. However, these data are dispersed and hard if not impossible to relate and compare. This leads to numerous interventions that are conducted without adequate knowledge of target families that are to benefit from these interventions. Inadequate knowledge also leads to lack of intervention where it is needed, such as undetected cases of child abuse. In many cases the basic data that are needed for intervention decisions exist, but are not available to decision makers due to inadequate communication and lack of data integration, analysis and interpretation. Chronic condition management is another area where extensive disparate data exist from statistics and various health services and intervention agencies. In this area too there are numerous organizations offering services and a great need to better coordinate these services to achieve better outcomes for patients and also to reduce soaring costs of the healthcare system. -
Introduction to Decision Intelligence - Towards Data Science
10/26/2019 Introduction to Decision Intelligence - Towards Data Science Listen to this story --:-- 18:26 Introduction to Decision Intelligence A new discipline for leadership in the AI era Cassie Kozyrkov Follow Aug 3 · 13 min read Curious to know what the psychology of avoiding lions on the savannah has in common with responsible AI leadership and the challenges of designing data warehouses? Welcome to decision intelligence! Source: xijian/Getty Decision intelligence is a new academic discipline concerned with all aspects of selecting between options. It brings together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them. It’s a vital science for the AI era, covering https://towardsdatascience.com/introduction-to-decision-intelligence-5d147ddab767 1/12 10/26/2019 Introduction to Decision Intelligence - Towards Data Science the skills needed to lead AI projects responsibly and design objectives, metrics, and safety-nets for automation at scale. Decision intelligence is the discipline of turning information into better actions at any scale. Let’s take a tour of its basic terminology and concepts. The sections are designed to be friendly to skim-reading (and skip-reading too, that’s where you skip the boring bits… and sometimes skip the act of reading entirely). What’s a decision? Data are beautiful, but it’s decisions that are important. It’s through our decisions — our actions — that we affect the world around us. We define the word “decision” to mean any selection between options by any entity, so the conversation is broader than MBA-style dilemmas (like whether to open a branch of your business in London). -
The Founder of the Creative Destruction Lab Describes Its Moonshot Mission to Create a Canadian AI Ecosystem
The founder of the Creative Destruction Lab describes its moonshot mission to create a Canadian AI ecosystem. Thought Leader Interview: by Karen Christensen Describe what happens at the Creative Destruction Lab by companies that went through the Lab. When we finished our (CDL). fifth year in June 2017, we had exceeded $1.4 billion in equity The CDL is a seed-stage program for massively scalable science- value created. based companies. Some start-ups come from the University of Toronto community, but we now also receive applications from What exactly does the Lab provide to entrepreneurs? Europe, the U.S. (including Silicon Valley), Israel and Asia. Start-up founders benefit from a structured, objectives-oriented We launched the program in September 2012, and each process that increases their probability of success. The process is autumn since, we’ve admitted a new cohort of start-ups into orchestrated by the CDL team, while CDL Fellows and Associ- the program. Most companies that we admit have developed a ates generate the objectives. Objective-setting is a cornerstone working prototype or proof of concept. The most common type of the process. Every eight weeks the Fellows and Associates set of founder is a recently graduated PhD in Engineering or Com- three objectives for the start-ups to achieve, at the exclusion of puter Science who has spent several years working on a problem everything else. In other words, they define clear goals for an and has invented something at the frontier of their field. eight week ‘sprint’. Objectives can be business, technnology or The program does not guarantee financing, but the majority HR-oriented. -
A Strategic Metagame Player for General Chesslike Games
From: AAAI Technical Report FS-93-02. Compilation copyright © 1993, AAAI (www.aaai.org). All rights reserved. A Strategic Metagame Player for General Chess-Like Games Barney Pell* Computer Laboratory University of Cambridge Cambridge, CB2 3QG, UK b dp((~cl c am. ac Abstract game will transfer also to new games. Such criteria in- clude the use of learning and planning, and the ability This paper reviews the concept of Metagameand to play more than one game. discusses the implelnentation of METAGAMER,a program which plays Metagame in the class of 1.1 Bias when using Existing Games symmetric chess-like games, which includes chess, However,even this generality-oriented research is sub- Chinese-chess, checkers, draughts, and Shogi. The ject to a potential methodological bias. As the human l)rogram takes as input the rules of a specific game researchers know at the time of program-development and analyses those rules to construct for that game which specific game or games the program will be tested an efficient representation and an evaluation func- on, it is possible that they import the results of their tion, both for use with a generic search engine. The own understanding of the game dh’ectly into their pro- strategic analysis performed by the program relates gn’am. In this case, it becomes difficult to determine a set of general knowledgesources to the details of whether the subsequent performance of the program the particular game. Amongother properties, this is due to the general theory it implements, or merely analysis determines the relative value of the differ- to the insightful observations of its developer about the eat pieces in a given game. -
Lukas Biewald
Lukas Biewald 823 Capp St., San Francisco, CA 94110 (650) 804 5470 lukas@crowdflower.com Awards Netexplorateur of the Year http://www.netexplorateur.org/ (2010) TechCrunch 50 http://techcrunch50.com (2009) First Place, Caltech Turing Tournament http://turing.ssel.caltech.edu Work Founder, CEO: CrowdFlower 2008 — Present • Built leading Labor-on-Demand company from the ground up. Designed and coded core technology. Built customer base up from nothing to 100 customers including several Fortune-500 companies. Hired 25 employees. • Raised two rounds of financing totalling $6.2 million in 2009 in tough economic climate from well-known angels and VCs including Trinity, Bessemer, Founders´ Fund, Auren Hoffman, Aydin Senkut, Manu Kumar, Gary Kremen and Travis Kalanick. Senior Scientist: Powerset 2007 — 2008 • As the 20th employee, created and led two core teams: metrics and search ranking. • Developed the Amazon Mechanical Turk interface, identified as a key technology in Microsoft’s $100 million acquisition. Relevance Engineer, Engineering Manager: Yahoo! Inc. 2005 — 2007 • Technical lead for research, development and deployment of new web search ranking functions in five major markets. • Chosen as most productive employee in Applied Science business unit year end review. Research Assistant: Stanford AI Lab (sail.stanford.edu) 2003 — 2004 • Researched word-sense disambiguation, machine translation and Japanese word segmentation. • Published two papers with over 100 citations. Education Stanford University 1998 — 2004 BS in Mathematical and Computational Science with Honors (2003) MS in Computer Science with Concentration in Artificial Intelligence (2004) Publications David Vickrey, Lukas Biewald, Marc Teyssier, and Daphne Koller Word-Sense Disambiguation for Machine Translation. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2005. -
Demystifying AI in Cybersecurity
WHITE PAPER | Demystifying AI in Cybersecurity Demystifying AI in Cybersecurity A look at the technology, myth vs. reality, and how it benefits the cybersecurity industry Table of Contents Introduction ...................................................................................................................3 AI: What it is and What it isn’t .......................................................................................3 Machine Learning: the Next Step ...................................................................................3 Deep Learning: Putting a Finer Point on it ......................................................................3 Plenty of Promise, for Better or Worse ..........................................................................3 The Difference AI Makes in Cybersecurity .....................................................................4 Malware Detection ....................................................................................................4 Classification and Scoring .........................................................................................4 Phishing Detection ....................................................................................................4 Key Requirements for AI/ML-based Security ..................................................................4 Data ..........................................................................................................................4 Speed ........................................................................................................................5 -
Autonomous Horizons: the Way Forward Is a Product of the Office Air University Press 600 Chennault Circle, Bldg 1405 of the US Air Force Chief Scientist (AF/ST)
Autonomous Horizons The Way Forward A vision for Air Force senior leaders of the potential for autonomous systems, and a general framework for the science and technology community to advance the state of the art Dr. Greg L. Zacharias Chief Scientist of the United States Air Force 2015–2018 The second volume in a series introduced by: Autonomous Horizons: Autonomy in the Air Force – A Path to the Future, Volume 1: Human Autonomy Teaming (AF/ST TR 15-01) March 2019 Air University Press Curtis E. LeMay Center for Doctrine Development and Education Maxwell AFB, Alabama Chief of Staff, US Air Force Library of Congress Cataloging-in-Publication Data Gen David L. Goldfein Names: Zacharias, Greg, author. | Air University (U.S.). Press, publisher. Commander, Air Education and Training | United States. Department of Defense. United States Air Force. Command Title: Autonomous horizons : the way forward / by Dr. Greg L. Zacha- Lt Gen Steven L. Kwast rias. Description: First edition. | Maxwell Air Force Base, AL : AU Press, 2019. “Chief Scientist for the United States Air Force.” | Commander and President, Air University Lt Gen Anthony J. Cotton “January 2019.” |Includes bibliographical references. Identifiers: LCCN 2018061682 | ISBN 9781585662876 Commander, Curtis E. LeMay Center for Subjects: LCSH: Aeronautics, Military—Research—United States. | Doctrine Development and Education United States. Air Force—Automation. | Artificial intelligence— Maj Gen Michael D. Rothstein Military applications—United States. | Intelligent control systems. | Autonomic -
The Semantic
The Fate of the Semantic Web Technology experts and stakeholders who participated in a recent survey believe online information will continue to be organized and made accessible in smarter and more useful ways in coming years, but there is stark dispute about whether the improvements will match the visionary ideals of those who are working to build the semantic web. Janna Quitney Anderson, Elon University Lee Rainie, Pew Research Center’s Internet & American Life Project May 4, 2010 Pew Research Center’s Internet & American Life Project An initiative of the Pew Research Center 1615 L St., NW – Suite 700 Washington, D.C. 20036 202‐419‐4500 | pewInternet.org This publication is part of a Pew Research Center series that captures people’s expectations for the future of the Internet, in the process presenting a snapshot of current attitudes. Find out more at: http://www.pewInternet.org/topics/Future‐of‐the‐Internet.aspx and http://www.imaginingtheinternet.org. 1 Overview Sir Tim Berners‐Lee, the inventor of the World Wide Web, has worked along with many others in the Internet community for more than a decade to achieve his next big dream: the semantic web. His vision is a web that allows software agents to carry out sophisticated tasks for users, making meaningful connections between bits of information so “computers can perform more of the tedious work involved in finding, combining, and acting upon information on the web.”1 The concept of the semantic web has been fluid and evolving and never quite found a concrete expression and easily‐understood application that could be grasped readily by ordinary Internet users. -
The Dynamics Duo: Microsoft Dynamics GP and AP Automation
Summer 2015 Self-service Business Intelligence for GP: What does that mean for me? The Dynamics Duo: Microsoft Dynamics GP and AP Automation Managing Long-Term Investment Assets in Dynamics GP GP OPTIMIZER I On the Cover GP Optimizer Magazine 5 Published by Rockton Software Self-service Business PO Box 921 Lafayette, CO 80026 Intelligence for GP: Editor: Mark Rockwell Welcome to the Summer 2015 Edition of the GP Optimizer Magazine. Our original goal [email protected] What does that mean for me? of The GP Optimizer Magazine was to reach 15,000 Microsoft Dynamics GP Users. Last Design: Lori Hartmann, Feline Graphics publication, we exceeded this goal by reaching over 50,000 users. [email protected] Advertising Inquiries: Steven Solomon The GP Optimizer Magazine contains articles written by Microsoft Dynamics GP Add-On [email protected] Partners; these articles are focused on making your investment in Microsoft Dynamics Editorial Inquiries: Steven Solomon GP more worthwhile by solving an issue that you might be experiencing. [email protected] Rockton Software has been in the Microsoft Dynamics GP Channel for over 15 years. You may have seen us at Convergence dressed up as bartenders, pirates, Vikings, or The GP Optimizer Magazine is published by cavemen. Regardless of our crazy costumes, we’ve established ourselves as fervent Rockton Software, with principal offices in Lafayette, CO. If you wish to receive this publication, please go to supporters of the greater GP Channel, and we want you to excel in business by www.rocktonsoftware.com and click on the GP leveraging other tools and knowledge from our friends in the community. -
Heuristic Evaluation Functions for General Game Playing
University of California Los Angeles Heuristic Evaluation Functions for General Game Playing A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science by James Edmond Clune III 2008 c Copyright by James Edmond Clune III 2008 The dissertation of James Edmond Clune III is approved. Adnan Y. Darwiche Thomas S. Ferguson Alan C. Kay Todd D. Millstein Richard E. Korf, Committee Chair University of California, Los Angeles 2008 ii Table of Contents 1 Introduction ................................ 1 1.1 General Game Playing: The Problem . 1 1.2 The Importance of General Game Playing . 2 1.2.1 Competitive Performance Metric . 2 1.2.2 Flexible Software Capabilities . 3 1.2.3 Game-Oriented Programming . 3 1.2.4 Generality as a Key Aspect of Intelligence . 4 1.2.5 A Timely Problem . 4 1.3 Overview of the Project . 5 1.4 Overview of the Dissertation . 6 2 Philosophy and Vision ......................... 8 2.1 The Ubiquity of Game Models . 8 2.2 Game-Oriented Programming . 10 3 Literature Review ............................ 13 3.1 General Game Playing . 13 3.1.1 Barney Pell’s Metagamer . 13 3.1.2 AAAI General Game Playing Competition . 14 3.1.3 Other General Game Playing Systems . 15 3.2 Automated Planning . 16 3.2.1 Classical Planning . 16 iii 3.2.2 International Planning Competition . 17 3.2.3 Planning Techniques . 18 3.3 Discovery Systems . 20 3.3.1 Feature Discovery . 20 3.3.2 AM and Eurisko . 21 3.3.3 Learning Heuristic Evaluation Functions . 23 4 General Game Playing Framework ................. -
Will Impact Higher Education WEB
Foroughi Web 3.0: How “the Internet of Everything” will impact Higher Education WEB 3.0: HOW THE “INTERNET OF EVERYTHING” WILL IMPACT HIGHER EDUCATION Abbas Foroughi College of Business, University of Southern Indiana [email protected] 812-465-1667 ABSTRACT Universities are finding that adopting Web 2.0 is necessary to remain competitive, while, at the same time, the evolution to Web 3.0 is on the horizon. This paper discusses Web 3.0, how it builds on Web 2.0, examples of current applications of Web 3.0, the potential benefits for higher education and lifelong learning which experts predict from Web 3.0 and concerns that have also been voiced. INTRODUCTION Over the past two decades, many institutions of higher learning and their students have reaped benefits from using Web 2.0 tools such as Wikis, Facebook, blogs, tagging, LinkedIn, virtual reality, social bookmarking, mashing, rss, podcasts, folksonomies, ePortfolios, chatrooms, and similar technologies, which facilitate communication, give participants a feeling of group membership, and are user-friendly. Specific benefits include: learning-related benefits: (Anderson, 2007; Alexander, 2006; Wesch, 2009; Reuben, 2008; McDonald, 2009; Brainard, 2007; Thompson, 2008; Minocha, 2009), such as facilitation of collaborative learning, development of independent learning skills, problem solving, team work, reflective learning, quick/early feedback from instructors, overcoming isolation of geographical distances, peer-to- peer support/feedback, visibility of students’ work, integration of multimedia assets, and the creation of informal relations between educators and students. Universities are finding that adopting Web 2.0 is necessary to remain competitive, while, at the same time, the evolution to Web 3.0 is on the horizon.