Toward AI Security
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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. -
AI and Education
AI and educationducation Guidance for policymakerspolicy-makers UNESCO Education Sector The Global Education 2030 Agenda Education is UNESCO’s top priority because UNESCO, as the United Nations’ specialized it is a basic human right and the foundation agency for education, is entrusted to lead and on which to build peace and drive sustainable coordinate the Education 2030 Agenda, which is development. UNESCO is the United Nations’ part of a global movement to eradicate poverty specialized agency for education and the through 17 Sustainable Development Goals by Education Sector provides global and 2030. Education, essential to achieve all of these regional leadership in education, strengthens goals, has its own dedicated Goal 4, which aims to national education systems and responds “ensure inclusive and equitable quality education to contemporary global challenges through and promote lifelong learning opportunities for education with a special focus on gender all.” The Education 2030 Framework for Action equality and Africa. provides guidance for the implementation of this ambitious goal and commitments. Published in 2021 by the United Nations Educational, Scientific and Cultural Organization 7, place de Fontenoy, 75352 Paris 07 SP, France © UNESCO 2021 ISBN 978-92-3-100447-6 This publication is available in Open Access under the Attribution-ShareAlike 3.0 IGO (CC-BY-SA 3.0 IGO) license (http://creativecommons.org/licenses/by-sa/3.0/igo/). By using the content of this publication, the users accept to be bound by the terms of use of the UNESCO Open Access Repository (http://www.unesco.org/open-access/terms-use-ccbysa-en). The designations employed and the presentation of material throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
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. -
Artificial Intelligence and Fundamental Physics Research Tommaso Dorigo, INFN – Padova
Artificial Intelligence and Fundamental Physics Research Tommaso Dorigo, INFN – Padova 5th USERN Congress, Workshop 2 November 8, 2020 - Tehran Contents • Artificial Intelligence: Where It Comes From, What It Is, Where It Is Going • Applications in Fundamental Physics Research • Quantum Computing: Physics Research for AI • The Future What is Intelligence ? Before we discuss artificial intelligence, we should agree on what intelligence is – and what isn’t. • The term «intelligence» comes from Latin “intelligo” to comprehend, to perceive …but that does not help much. We can consider the literature for help. We find that notable definitions differ significantly, also in relation to what can be general and what is specific of human beings: “The aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment” [Wechsler 1944] “The unique propensity of human beings to change or modify the structure of their cognitive functioning to adapt to the changing demands of a life situation” [Feuerstein 1990] But also, and more useful to us, are more abstract definitions such as: “Intelligence measures an agent's ability to achieve goals in a wide range of environments” [Legg 2007] “Intelligence is goal-directed adaptive behavior” [Sternberg 1982] What is Artificial Intelligence ? Curiously, a precise definition of Artificial Intelligence is not less challenging than that of Intelligence at large • AI: “Intelligence demonstrated by machines” – devices that have the ability to perceive -
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. -
AI Computer Wraps up 4-1 Victory Against Human Champion Nature Reports from Alphago's Victory in Seoul
The Go Files: AI computer wraps up 4-1 victory against human champion Nature reports from AlphaGo's victory in Seoul. Tanguy Chouard 15 March 2016 SEOUL, SOUTH KOREA Google DeepMind Lee Sedol, who has lost 4-1 to AlphaGo. Tanguy Chouard, an editor with Nature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go. This week, he is watching top professional Lee Sedol take on AlphaGo, in Seoul, for a $1 million prize. It’s all over at the Four Seasons Hotel in Seoul, where this morning AlphaGo wrapped up a 4-1 victory over Lee Sedol — incidentally, earning itself and its creators an honorary '9-dan professional' degree from the Korean Baduk Association. After winning the first three games, Google-DeepMind's computer looked impregnable. But the last two games may have revealed some weaknesses in its makeup. Game four totally changed the Go world’s view on AlphaGo’s dominance because it made it clear that the computer can 'bug' — or at least play very poor moves when on the losing side. It was obvious that Lee felt under much less pressure than in game three. And he adopted a different style, one based on taking large amounts of territory early on rather than immediately going for ‘street fighting’ such as making threats to capture stones. This style – called ‘amashi’ – seems to have paid off, because on move 78, Lee produced a play that somehow slipped under AlphaGo’s radar. David Silver, a scientist at DeepMind who's been leading the development of AlphaGo, said the program estimated its probability as 1 in 10,000. -
Relative Study on Artificial Intelligence Interaction with Human Emotions
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 02 | Feb 2019 www.irjet.net p-ISSN: 2395-0072 Relative Study on Artificial Intelligence Interaction with Human Emotions Chandrika1, K Radha2 1M.Tech, I –Year, CSE, GITAM UNIVERSITY, RUDRARAM HYDERABAD, TELANAGANA, INDIA 2Asst.Professor, CSE, GITAM UNIVERSITY, RUDRARAM, HYDERABAD, TELANAGANA, INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract – Artificial Intelligence is a multidisciplinary field 1950 Alan Turing published “Computing Machinery and whose goal is to making software which thinks intelligently Intelligence” in which he proposes “the imitation game” similar to human activities and makes a lot easier to human to which will later become known as the “Turing Test.”[5] achieve their goal. There are some recent successes in Artificial Intelligence from computerized medical diagnosticians and It may be said that Alan Turing’s ideas for computational systems that automatically customize hardware to particular thinking lay the foundation for AI. user requirements. This Paper presents about, how does 1952 Arthur Samuel develops the first computer checkers- Artificial Intelligence interact with humans, Does Artificial playing program and the first computer program to learn on Intelligence can understand the human emotions, what can be its own. the impact of Artificial Intelligence on humans in upcoming years, and Does Artificial Intelligence can replace humans. August 31, 1955 The term “artificial intelligence” is coined in a proposal for a “2 month, 10 man study of artificial Key Words: Artificial Intelligence, computerized medical intelligence” submitted by John McCarthy (Dartmouth diagnosticians, Human Interaction, Human Emotions. College), Marvin Minsky (Harvard University), Nathaniel 1. What is Artificial Intelligence Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories). -
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. -
The Future of AI
Seeing the forest for the trees, and the forests beyond The future of AI A report by the Deloitte AI Institute The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. —Edsger W. Dijkstra, computer science pioneer 2 Contents 01 Everything and 03 The machines 06 Guardrails nothing at all (or, a technological 25 4 history of the future) 8 07 Conclusion 02 Something old, 29 somehow new 04 Us and the machines 6 (or, an anthropological history of the future) 14 05 Us as the machines (or, a biological history of the future) 20 3 Everything and nothing at all 01 As futurists, my team and I secretly And so, when tech headlines begin to Like any gold rush, there’s hope beneath the spend the lion’s share of our time increasingly read as breathless brochures hype. To be sure, there is plenty of actual 02 studying the past. I like to say that for Artificial Intelligence, we grey-hairs can’t gold afoot insofar as we’re seeing a genuine, we’re closet historians. Specifically, help but be reminded of a certain upstart evidence-based phase shift from AI as “cherry- 03 we research the history of various technology category some 20 years ago called on-top” curiosity to “key ingredient” at leading technologies and how they’ve impacted, “the world wide web.” organizations. 61 percent of respondents to or failed to impact, the way the world a recent Deloitte Insights report say AI will 04 works and lives. -
In-Datacenter Performance Analysis of a Tensor Processing Unit
In-Datacenter Performance Analysis of a Tensor Processing Unit Presented by Josh Fried Background: Machine Learning Neural Networks: ● Multi Layer Perceptrons ● Recurrent Neural Networks (mostly LSTMs) ● Convolutional Neural Networks Synapse - each edge, has a weight Neuron - each node, sums weights and uses non-linear activation function over sum Propagating inputs through a layer of the NN is a matrix multiplication followed by an activation Background: Machine Learning Two phases: ● Training (offline) ○ relaxed deadlines ○ large batches to amortize costs of loading weights from DRAM ○ well suited to GPUs ○ Usually uses floating points ● Inference (online) ○ strict deadlines: 7-10ms at Google for some workloads ■ limited possibility for batching because of deadlines ○ Facebook uses CPUs for inference (last class) ○ Can use lower precision integers (faster/smaller/more efficient) ML Workloads @ Google 90% of ML workload time at Google spent on MLPs and LSTMs, despite broader focus on CNNs RankBrain (search) Inception (image classification), Google Translate AlphaGo (and others) Background: Hardware Trends End of Moore’s Law & Dennard Scaling ● Moore - transistor density is doubling every two years ● Dennard - power stays proportional to chip area as transistors shrink Machine Learning causing a huge growth in demand for compute ● 2006: Excess CPU capacity in datacenters is enough ● 2013: Projected 3 minutes per-day per-user of speech recognition ○ will require doubling datacenter compute capacity! Google’s Answer: Custom ASIC Goal: Build a chip that improves cost-performance for NN inference What are the main costs? Capital Costs Operational Costs (power bill!) TPU (V1) Design Goals Short design-deployment cycle: ~15 months! Plugs in to PCIe slot on existing servers Accelerates matrix multiplication operations Uses 8-bit integer operations instead of floating point How does the TPU work? CISC instructions, issued by host. -
AI Testing - Testing AI-Based Systems (AIT – TAI)
Certified Tester AI Testing - Testing AI-Based Systems (AIT – TAI) Foundation Level Syllabus Version 2019 Korean Software Testing Qualifications Board Chinese Software Testing Qualifications Board Certified Tester – Foundation Level INTRODUCTION The testing of traditional systems is well-understood, but AI-Based systems, which are becoming more prevalent and critical to our daily lives, introduce new challenges. This syllabus covers the key concepts of Artificial Intelligence (AI), how we decide acceptance criteria and how we test AI-Based systems. These systems are typically complex (e.g. deep neural nets), based on big data, poorly specified and non- deterministic, which creates many new challenges and opportunities for testing them. This syllabus describes the requirements for a 2-day course to be followed by a 1-hour, closed book, multiple choice exam. In this document an AI-Based system is a system that includes at least one AI component. Throughout this document the acronym AI is used to represent the term ‘Artificial Intelligence’. BUSINESS OUTCOMES At the end of this course candidates will be able to: Understand the current state of AI and expected advances in the near future; Interpret and provide guidance on the specification of acceptance criteria for AI-Based systems; Contribute to the development process for machine learning systems and suggest opportunities for influencing their quality; Understand the new challenges of testing AI-Based systems, such as their complexity and non- determinism; Contribute to the test strategy for an AI-Based system; Apply black box and white box test design techniques to generate test suites for AI-Based systems; Recognize the need for virtual test environments to support the release of complex AI-Based systems; Understand the current state of testing supported by AI.