An Exploration of Artificial Intelligence (AI) Marketing Practitioner Perceptions and Practices
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A Comprehensive Workplace Environment Based on a Deep
International Journal on Advances in Software, vol 11 no 3 & 4, year 2018, http://www.iariajournals.org/software/ 358 A Comprehensive Workplace Environment based on a Deep Learning Architecture for Cognitive Systems Using a multi-tier layout comprising various cognitive channels, reflexes and reasoning Thorsten Gressling Veronika Thurner Munich University of Applied Sciences ARS Computer und Consulting GmbH Department of Computer Science and Mathematics Munich, Germany Munich, Germany e-mail: [email protected] e-mail: [email protected] Abstract—Many technical work places, such as laboratories or All these settings share a number of commonalities. For test beds, are the setting for well-defined processes requiring both one thing, within each of these working settings a human being high precision and extensive documentation, to ensure accuracy interacts extensively with technical devices, such as measuring and support accountability that often is required by law, science, instruments or sensors. For another thing, processing follows or both. In this type of scenario, it is desirable to delegate certain a well-defined routine, or even precisely specified interaction routine tasks, such as documentation or preparatory next steps, to protocols. Finally, to ensure that results are reproducible, some sort of automated assistant, in order to increase precision and reduce the required amount of manual labor in one fell the different steps and achieved results usually have to be swoop. At the same time, this automated assistant should be able documented extensively and in a precise way. to interact adequately with the human worker, to ensure that Especially in scenarios that execute a well-defined series of the human worker receives exactly the kind of support that is actions, it is desirable to delegate certain routine tasks, such required in a certain context. -
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: with Great Power Comes Great Responsibility
ARTIFICIAL INTELLIGENCE: WITH GREAT POWER COMES GREAT RESPONSIBILITY JOINT HEARING BEFORE THE SUBCOMMITTEE ON RESEARCH AND TECHNOLOGY & SUBCOMMITTEE ON ENERGY COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY HOUSE OF REPRESENTATIVES ONE HUNDRED FIFTEENTH CONGRESS SECOND SESSION JUNE 26, 2018 Serial No. 115–67 Printed for the use of the Committee on Science, Space, and Technology ( Available via the World Wide Web: http://science.house.gov U.S. GOVERNMENT PUBLISHING OFFICE 30–877PDF WASHINGTON : 2018 COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY HON. LAMAR S. SMITH, Texas, Chair FRANK D. LUCAS, Oklahoma EDDIE BERNICE JOHNSON, Texas DANA ROHRABACHER, California ZOE LOFGREN, California MO BROOKS, Alabama DANIEL LIPINSKI, Illinois RANDY HULTGREN, Illinois SUZANNE BONAMICI, Oregon BILL POSEY, Florida AMI BERA, California THOMAS MASSIE, Kentucky ELIZABETH H. ESTY, Connecticut RANDY K. WEBER, Texas MARC A. VEASEY, Texas STEPHEN KNIGHT, California DONALD S. BEYER, JR., Virginia BRIAN BABIN, Texas JACKY ROSEN, Nevada BARBARA COMSTOCK, Virginia CONOR LAMB, Pennsylvania BARRY LOUDERMILK, Georgia JERRY MCNERNEY, California RALPH LEE ABRAHAM, Louisiana ED PERLMUTTER, Colorado GARY PALMER, Alabama PAUL TONKO, New York DANIEL WEBSTER, Florida BILL FOSTER, Illinois ANDY BIGGS, Arizona MARK TAKANO, California ROGER W. MARSHALL, Kansas COLLEEN HANABUSA, Hawaii NEAL P. DUNN, Florida CHARLIE CRIST, Florida CLAY HIGGINS, Louisiana RALPH NORMAN, South Carolina DEBBIE LESKO, Arizona SUBCOMMITTEE ON RESEARCH AND TECHNOLOGY HON. BARBARA COMSTOCK, Virginia, Chair FRANK D. LUCAS, Oklahoma DANIEL LIPINSKI, Illinois RANDY HULTGREN, Illinois ELIZABETH H. ESTY, Connecticut STEPHEN KNIGHT, California JACKY ROSEN, Nevada BARRY LOUDERMILK, Georgia SUZANNE BONAMICI, Oregon DANIEL WEBSTER, Florida AMI BERA, California ROGER W. MARSHALL, Kansas DONALD S. BEYER, JR., Virginia DEBBIE LESKO, Arizona EDDIE BERNICE JOHNSON, Texas LAMAR S. -
Real Vs Fake Faces: Deepfakes and Face Morphing
Graduate Theses, Dissertations, and Problem Reports 2021 Real vs Fake Faces: DeepFakes and Face Morphing Jacob L. Dameron WVU, [email protected] Follow this and additional works at: https://researchrepository.wvu.edu/etd Part of the Signal Processing Commons Recommended Citation Dameron, Jacob L., "Real vs Fake Faces: DeepFakes and Face Morphing" (2021). Graduate Theses, Dissertations, and Problem Reports. 8059. https://researchrepository.wvu.edu/etd/8059 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. Real vs Fake Faces: DeepFakes and Face Morphing Jacob Dameron Thesis submitted to the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Xin Li, Ph.D., Chair Natalia Schmid, Ph.D. Matthew Valenti, Ph.D. Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2021 Keywords: DeepFakes, Face Morphing, Face Recognition, Facial Action Units, Generative Adversarial Networks, Image Processing, Classification. -
Synthetic Video Generation
Synthetic Video Generation Why seeing should not always be believing! Alex Adam Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image Tampering Historically, manipulated Off the shelf software (e.g imagery has deceived Photoshop) exists to do people this now Has become standard in Public have become tabloids/social media somewhat numb to it - it’s no longer as impactful/shocking How does machine learning fit in? Advent of machine learning Video manipulation is now has made image also tractable with enough manipulation even easier data and compute Can make good synthetic Public are largely unaware of videos using a gaming this and the danger it poses! computer in a bedroom Part I: Faceswap ● In 2017, reddit (/u/deepfakes) posted Python code that uses machine learning to swap faces in images/video ● ‘Deepfake’ content flooded reddit, YouTube and adult websites ● Reddit since banned this content (but variants of the code are open source https://github.com/deepfakes/faceswap) ● Autoencoder concepts underlie most ‘Deepfake’ methods Faceswap Algorithm Image source https://medium.com/@jonathan_hui/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9 Inference Image source https://medium.com/@jonathan_hui/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9 ● Faceswap model is an autoencoder. -
Non-Independent Investment Research Disclaimer
CONTENT 03 2020 theme – Engines of Disruption 04 Chips go cold in AI winter 05 Stagflation rewards value over growth 06 ECB folds and hikes rates 07 In energy, green is not the new black 08 South Africa gets electrocuted by ESKOM debt 09 Trump announces America First Tax 10 Sweden breaks bad 11 Dems win clean sweep in 2020 election 12 Hungary leaves the EU 13 Asia launches digital reserve currency 2020 rent at twice the price of owning and maybe even slightly normalise viewed as plain fun, the principles Outrageous Predictions 2020 – a tax on the poor if ever there rates, while governments step into behind the Outrageous Predictions was one, and a driver of inequality. the breach with infrastructure and resonate very strongly with our This risks leaving an entire climate policy-linked spending. clients and the Saxo Group. Not - Engines of Disruption generation without the savings just in terms of what could happen STEEN JAKOBSEN, CHIEF INVESTMENT OFFICER needed to own their own house, The list of Outrageous Predictions to the portfolios and wealth typically the only major asset that this year all play to the theme of allocation, but also as an input to many medium- and lower-income disruption, because our current our respective areas of business, households will ever obtain. Thus paradigm is simply at the end of careers and life in general. Looking into the future is something of a fool’s game, but it remains a Already, there are a number of we are denying the very economic the road. Not because we want it to This is the spirit of Outrageous useful exercise — helping prepare for what lies ahead by considering cracks in the system that show mechanism that made the older end, but simply because extending Predictions: to create the very the full possible range of economic and political outcomes. -
Build Your Trust Advantage, Leadership in the Era of Data
Global C-suite Study 20th Edition Build Your Trust Advantage Leadership in the era of data and AI everywhere This report is IBM’s fourth Global C-suite Study and the 20th Edition in the ongoing IBM CxO Study series developed by the IBM Institute for Business Value (IBV). We have now collected data and insights from more than 50,000 interviews dating back to 2003. This report was authored in collaboration with leading academics, futurists, and technology visionaries. In this report, we present our key findings of CxO insights, experiences, and sentiments based on analysis as described in the research methodology on page 44. Build Your Trust Advantage | 1 Build Your Trust Advantage Leadership in the era of data and AI everywhere Global C-suite Study 20th Edition Our latest study draws on input from 13,484 respondents across 6 C-suite roles, 20 industries, and 98 countries. 2,131 2,105 2,118 2,924 2,107 2,099 Chief Chief Chief Chief Chief Chief Executive Financial Human Information Marketing Operations Officers Officers Resources Officers Officers Officers Officers 3,363 Europe 1,910 Greater China 3,755 North America 858 Japan 915 Middle East and Africa 1,750 Asia Pacific 933 Latin America 2 | Global C-suite Study Table of contents Executive summary 3 Introduction 4 Chapter 1 Customers: How to win in the trust economy 8 Action guide 19 Chapter 2 Enterprises: How to build the human-tech partnership 20 Action guide 31 Chapter 3 Ecosystems: How to share data in the platform era 32 Action guide 41 Conclusion: Return on trust 42 Acknowledgments 43 Related IBV studies 43 Research methodology 44 Notes and sources 45 Build Your Trust Advantage | 3 Executive summary More than 13,000 C-suite executives worldwide their data scientists, to uncover insights from data. -
Final Exam Review History of Science 150
Final Exam Review History of Science 150 1. Format of the Exam 90 minutes, on canvas 12:25pm December 18. You are welcome to bring notes to the exam, so you could start by filling out this sheet with notes from lectures and the readings! Like the mid-term, the final exam will have two kinds of questions. 1) Multiple choice questions examining your knowledge of key concepts, terms, historical developments, and contexts 2) Short answer questions in which ask you to draw on things you’ve learned in the course (from lecture, readings, videos) to craft a short argument in a brief essay expressing your informed issue on a historical question 2. Sample Questions Multiple Choice: Mina Rees was involved in (and wrote about) which of the following computing projects? A) Silicon Valley start-ups in the dot-com period B) Charles Babbage’s Difference Engine C) Works Projects Administration Tables Project D) Federal funding for computing research after WWII Short Answer: (Your answers should be between 100-200 words, and keep to specifics (events, machines, developments, people) that demonstrate your knowledge of materials covered from the course) A) What are two historical factors important to the development of Silicon Valley’s technology industry after World War II? B) In what ways did the field of programming change (in terms of its status and workers) between World War II and the late 1960s? 3. Topics to Review: Below, is a list of ideas to review for the final exam, which covers material through the entire course. You should review in particular, lecture notes, O’Mara’s The Code and other course readings provided on Canvas. -
Exposing Deepfake Videos by Detecting Face Warping Artifacts
Exposing DeepFake Videos By Detecting Face Warping Artifacts Yuezun Li, Siwei Lyu Computer Science Department University at Albany, State University of New York, USA Abstract sibility to large-volume training data and high-throughput computing power, but more to the growth of machine learn- In this work, we describe a new deep learning based ing and computer vision techniques that eliminate the need method that can effectively distinguish AI-generated fake for manual editing steps. videos (referred to as DeepFake videos hereafter) from real In particular, a new vein of AI-based fake video gen- videos. Our method is based on the observations that cur- eration methods known as DeepFake has attracted a lot rent DeepFake algorithm can only generate images of lim- of attention recently. It takes as input a video of a spe- ited resolutions, which need to be further warped to match cific individual (’target’), and outputs another video with the original faces in the source video. Such transforms leave the target’s faces replaced with those of another individ- distinctive artifacts in the resulting DeepFake videos, and ual (’source’). The backbone of DeepFake are deep neu- we show that they can be effectively captured by convo- ral networks trained on face images to automatically map lutional neural networks (CNNs). Compared to previous the facial expressions of the source to the target. With methods which use a large amount of real and DeepFake proper post-processing, the resulting videos can achieve a generated images to train CNN classifier, our method does high level of realism. not need DeepFake generated images as negative training In this paper, we describe a new deep learning based examples since we target the artifacts in affine face warp- method that can effectively distinguish DeepFake videos ing as the distinctive feature to distinguish real and fake from the real ones. -
JCS Deepfake
Don’t Believe Your Eyes (Or Ears): The Weaponization of Artificial Intelligence, Machine Learning, and Deepfakes Joe Littell 1 Agenda ØIntroduction ØWhat is A.I.? ØWhat is a DeepFake? ØHow is a DeepFake created? ØVisual Manipulation ØAudio Manipulation ØForgery ØData Poisoning ØConclusion ØQuestions 2 Introduction Deniss Metsavas, an Estonian soldier convicted of spying for Russia’s military intelligence service after being framed for a rape in Russia. (Picture from Daniel Lombroso / The Atlantic) 3 What is A.I.? …and what is it not? General Artificial Intelligence (AI) • Machine (or Statistical) Learning (ML) is a subset of AI • ML works through the probability of a new event happening based on previously gained knowledge (Scalable pattern recognition) • ML can be supervised, leaning requiring human input into the data, or unsupervised, requiring no input to the raw data. 4 What is a Deepfake? • Deepfake is a mash up of the words for deep learning, meaning machine learning using a neural network, and fake images/video/audio. § Taken from a Reddit user name who utilized faceswap app for his own ‘productions.’ • Created by the use of two machine learning algorithms, Generative Adversarial Networks, and Auto-Encoders. • Became known for the use in underground pornography using celebrity faces in highly explicit videos. 5 How is a Deepfake created? • Deepfakes are generated using Generative Adversarial Networks, and Auto-Encoders. • These algorithms work through the uses of competing systems, where one creates a fake piece of data and the other is trained to determine if that datatype is fake or not • Think of it like a counterfeiter and a police officer. -
A.I. Technologies Applied to Naval CAD/CAM/CAE
A.I. Technologies Applied to Naval CAD/CAM/CAE Jesus A. Muñoz Herrero, SENER, Madrid/Spain, [email protected] Rodrigo Perez Fernandez, SENER, Madrid/Spain, [email protected] Abstract Artificial Intelligence is one of the most enabling technologies of digital transformation in the industry, but it is also one of the technologies that most rapidly spreads in our daily activity. Increasingly, elements and devices that integrate artificial intelligence features, appear in our everyday lives. These characteristics are different, depending on the devices that integrate them, or the aim they pursue. The methods and processes that are carried out in Marine Engineering cannot be left out of this technology, but the peculiarities of the profession and the people that take part in it must be taken into account. There are many aspects in which artificial intelligence can be applied in the field of our profession. The management and access to all the information necessary for the correct and efficient execution of a naval project is one of the aspects where this technology can have a very positive impact. Access all the rules, rules, design guides, good practices, lessons learned, etc., in a fast and intelligent way, understanding the natural language of the people, identifying the most appropriate to the process that is being carried out and above all. Learning as we go through the design, is one of the characteristics that will increase the application of this technology in the professional field. This article will describe the evolution of this technology and the current situation of it in the different areas of application. -
Global-Bibliography.Pdf
Bibliography The following abbreviations are used for frequently cited conferences and journals: AAAI Proceedings of the AAAI Conference on Artificial Intelligence AAMAS Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems ACL Proceedings of the Annual Meeting of the Association for Computational Linguistics AIJ Artificial Intelligence (Journal) AIMag AI Magazine AIPS Proceedings of the International Conference on AI Planning Systems AISTATS Proceedings of the International Conference on Artificial Intelligence and Statistics BBS Behavioral and Brain Sciences CACM Communications of the Association for Computing Machinery COGSCI Proceedings of the Annual Conference of the Cognitive Science Society COLING Proceedings of the International Conference on Computational Linguistics COLT Proceedings of the Annual ACM Workshop on Computational Learning Theory CP Proceedings of the International Conference on Principles and Practice of Constraint Programming CVPR Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition EC Proceedings of the ACM Conference on Electronic Commerce ECAI Proceedings of the European Conference on Artificial Intelligence ECCV Proceedings of the European Conference on Computer Vision ECML Proceedings of the The European Conference on Machine Learning ECP Proceedings of the European Conference on Planning EMNLP Proceedings of the Conference on Empirical Methods in Natural Language Processing FGCS Proceedings of the International Conference on Fifth Generation Computer