Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
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Chapter 5 Names, Bindings, and Scopes
Chapter 5 Names, Bindings, and Scopes 5.1 Introduction 198 5.2 Names 199 5.3 Variables 200 5.4 The Concept of Binding 203 5.5 Scope 211 5.6 Scope and Lifetime 222 5.7 Referencing Environments 223 5.8 Named Constants 224 Summary • Review Questions • Problem Set • Programming Exercises 227 CMPS401 Class Notes (Chap05) Page 1 / 20 Dr. Kuo-pao Yang Chapter 5 Names, Bindings, and Scopes 5.1 Introduction 198 Imperative languages are abstractions of von Neumann architecture – Memory: stores both instructions and data – Processor: provides operations for modifying the contents of memory Variables are characterized by a collection of properties or attributes – The most important of which is type, a fundamental concept in programming languages – To design a type, must consider scope, lifetime, type checking, initialization, and type compatibility 5.2 Names 199 5.2.1 Design issues The following are the primary design issues for names: – Maximum length? – Are names case sensitive? – Are special words reserved words or keywords? 5.2.2 Name Forms A name is a string of characters used to identify some entity in a program. Length – If too short, they cannot be connotative – Language examples: . FORTRAN I: maximum 6 . COBOL: maximum 30 . C99: no limit but only the first 63 are significant; also, external names are limited to a maximum of 31 . C# and Java: no limit, and all characters are significant . C++: no limit, but implementers often impose a length limitation because they do not want the symbol table in which identifiers are stored during compilation to be too large and also to simplify the maintenance of that table. -
Artificial Intelligence
TechnoVision The Impact of AI 20 18 CONTENTS Foreword 3 Introduction 4 TechnoVision 2018 and Artificial Intelligence 5 Overview of TechnoVision 2018 14 Design for Digital 17 Invisible Infostructure 26 Applications Unleashed 33 Thriving on Data 40 Process on the Fly 47 You Experience 54 We Collaborate 61 Applying TechnoVision 68 Conclusion 77 2 TechnoVision 2018 The Impact of AI FOREWORD We introduce TechnoVision 2018, now in its eleventh year, with pride in reaching a second decade of providing a proven and relevant source of technology guidance and thought leadership to help enterprises navigate the compelling and yet complex opportunities for business. The longevity of this publication has been achieved through the insight of our colleagues, clients, and partners in applying TechnoVision as a technology guide and coupling that insight with the expert input of our authors and contributors who are highly engaged in monitoring and synthesizing the shift and emergence of technologies and the impact they have on enterprises globally. In this edition, we continue to build on the We believe that with TechnoVision 2018, you can framework that TechnoVision has established further crystalize your plans and bet on the right for several years with updates on last years’ technology disruptions, to continue to map and content, reflecting new insights, use cases, and traverse a successful digital journey. A journey technology solutions. which is not about a single destination, but rather a single mission to thrive in the digital epoch The featured main theme for TechnoVision 2018 through agile cycles of transformation delivering is AI, due to breakthroughs burgeoning the business outcomes. -
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. -
Digital Health in a Broadband Land: the Role of Digital Health Literacy Within Rural Environments Wuyou Sui1*, Danica Facca2
Health Science Inquiry Volume 11 | 2020 Digital health in a broadband land: The role of digital health literacy within rural environments Wuyou Sui1*, Danica Facca2 1Department of Kinesiology, Western University, London, ON, Canada 2Faculty of Information and Media Sciences, Western University, London, ON, Canada *Author for correspondence ([email protected]) Abstract: The rapid rise and widespread integration of digital technologies (e.g., smartphones, personal computers) into the fabric of our society has birthed a modern means of delivering healthcare, known as digital health. Through leveraging the accessibility and ubiquity of digital technologies, digital health represents an unprecedented level of reach, impact, and scalability for health- care interventions, known as digital behaviour change interventions (DBCIs). The potential benefits associated with employing DBCIs are of particular interest for populations that are disadvantaged to receiving traditional healthcare, such as rural popu- lations. However, several factors should be considered before implementing a DBCI into a rural environment, notably, digital health literacy. Digital health literacy describes the skills necessary to successful navigate and utilize a digital health solution (e.g., DBCI). Given their limited access to high-speed internet, higher cost associated for similar services, and poorer development of information and communication technologies (ICTs), most rural populations likely report lower digital health literacy – specif- ically, computer literacy, the ability to utilize and leverage digital technologies to solve problems. Hence, DBCIs should address this ‘digital divide’ between urban and rural populations before implementation. Practical solutions could include evaluating rural communities’ access to ICTs, needs assessments with rural community members, as well as integrating rural community stakeholders into the design of digital literacy education and interventions. -
The Ingham County News Oh! ~.No ••••• Published THURSDAY AFTERNOONS Rev
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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. -
Cybersecurity and the Digital-Health: the Challenge of This Millennium
healthcare Editorial Cybersecurity and the Digital-Health: The Challenge of This Millennium Daniele Giansanti Centre Tisp, Istituto Superiore di Sanità, Via Regina Elena 299, 00161 Roma, Italy; [email protected]; Tel.: +39-06-4990-2701 1. Cybersecurity The problem of computer security is as old as computers themselves and dates back decades. The transition from: (a) a single-user to multi-user assignment to the resource and (b) access to the computer resource of the standalone type to one of the types distributed through a network made it necessary to start talking about computer security. All network architectures, from peer to peer to client-server type, are subject to IT security problems. The term Cybersecurity has recently been introduced to indicate the set of procedures and methodologies used to defend computers, servers, mobile devices, electronic systems, networks and data from malicious attacks. Cybersecurity [1–3] is therefore applied to various contexts, from the economic one to that relating to mobile technologies and includes various actions: • Network security: the procedures for using the network safely; • Application Security: the procedures and solutions for using applications safely; • Information security: the management of information in a secure way and in a privacy- sensitive manner in accordance with pre-established regulations; • Operational security: the security in IT operations, such as, for example, in bank- type transactions; • Disaster recovery and operational continuity: the procedures for restarting after problems Citation: Giansanti, D. Cybersecurity that have affected the regular/routine operation of a system and to ensure operational and the Digital-Health: The Challenge continuity. For example, using informatic solutions such as an efficient disk mirroring of This Millennium. -
NHS England Briefed Against Gps
this week NHS RESERVES page 381 • MESH CLINICS page 382 • LIVERPOOL TESTING page 384 LMCs: NHS England briefed against GPs GP leaders have demanded that NHS chair of the BMA’s General Practitioners The BMA’s Richard Vautrey England and NHS Improvement apologise Committee, writing to the NHS chief said it seemed NHS England and retract any communications that may executive, Simon Stevens, asking for had “intentionally sought have harmed their reputation or incited NHSE/I to “apologise to the profession and to create negative media coverage of primary care” complaints by implying that practices had correct damage that has been done.” not been fully involved in care of patients Vautrey wrote, “Implying within the throughout the covid-19 pandemic. press release that GPs are not providing A motion passed at the annual patients with the appointments they conference of England’s local medical need and ‘reminding’ them that they face committees on 27 November said that ‘enforcement action’ if they do not has much of NHSE/I’s communications with presented NHSE/I as antagonistic and GPs, the press, and the public had been completely out of touch with the profession. “abhorrent and insulting.” The conference It also seems to many GPs that NHSE/I has, called for NHSE/I to recognise general by using this tactic, intentionally sought to LATEST ONLINE practice’s contribution to the management create negative media coverage of primary of the pandemic and the continuation of care services. Test for HIV normal service, “particularly given the “The BMA is now hearing large numbers wherever blood is general practitioners who have died in the of reports from practices receiving taken, recommends course of their duties to the public.” complaints and many staff members being UK independent The motion added that it “deplores verbally abused by the public based on commission the habit” of briefi ng journalists before these unsupported and ill-informed media Declarations of communicating with the profession and its articles. -
AI Superpowers
Review of AI Superpowers: China, Silicon Valley and the New World Order by Kai-Fu Lee by Preston McAfee1 Let me start by concluding that AI Superpowers is a great read and well worth the modest investment in time required to read it. Lee has a lively, breezy style, in spite of discussing challenging technology applied to a wide-ranging, thought-provoking subject matter, and he brings great insight and expertise to one of the more important topics of this century. Unlike most of the policy books that are much discussed but rarely read, this book deserves to be read. I will devote my review to critiquing the book, but nonetheless I strongly recommend it. Lee is one of the world’s top artificial intelligence (AI) researchers and a major force in technology startups in China. He was legendary in Microsoft because of his role in Microsoft’s leadership in speech recognition. He created Google’s research office in China, and then started a major incubator that he still runs. Lee embodies a rare combination of formidable technical prowess and managerial success, so it is not surprising that he has important things to say about AI. The book provides a relatively limited number of arguments, which I’ll summarize here: AI will transform business in the near future, China’s “copycat phase” was a necessary step to develop technical and innovative capacity, Success in the Chinese market requires unique customization, which is why western companies were outcompeted by locals, Local Chinese companies experienced “gladiatorial” competition, with ruthless copying, and often outright fraud, but the companies that emerged were much stronger for it, The Chinese government helped promote internet companies with subsidies and encouragement but not favoritism as widely believed, Chinese companies “went heavy” by vertically integrating, e.g. -
Transfer Learning Between RTS Combat Scenarios Using Component-Action Deep Reinforcement Learning
Transfer Learning Between RTS Combat Scenarios Using Component-Action Deep Reinforcement Learning Richard Kelly and David Churchill Department of Computer Science Memorial University of Newfoundland St. John’s, NL, Canada [email protected], [email protected] Abstract an enormous financial investment in hardware for training, using over 80000 CPU cores to run simultaneous instances Real-time Strategy (RTS) games provide a challenging en- of StarCraft II, 1200 Tensor Processor Units (TPUs) to train vironment for AI research, due to their large state and ac- the networks, as well as a large amount of infrastructure and tion spaces, hidden information, and real-time gameplay. Star- Craft II has become a new test-bed for deep reinforcement electricity to drive this large-scale computation. While Al- learning systems using the StarCraft II Learning Environment phaStar is estimated to be the strongest existing RTS AI agent (SC2LE). Recently the full game of StarCraft II has been ap- and was capable of beating many players at the Grandmas- proached with a complex multi-agent reinforcement learning ter rank on the StarCraft II ladder, it does not yet play at the (RL) system, however this is currently only possible with ex- level of the world’s best human players (e.g. in a tournament tremely large financial investments out of the reach of most setting). The creation of AlphaStar demonstrated that using researchers. In this paper we show progress on using varia- deep learning to tackle RTS AI is a powerful solution, how- tions of easier to use RL techniques, modified to accommo- ever applying it to the entire game as a whole is not an eco- date actions with multiple components used in the SC2LE. -
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.