Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
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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. -
Data Science Initiative 1
Data Science Initiative 1 3 credits in data and computational science, 1 credit in societal implications and opportunities, Data Science Initiative 1 elective credit to be drawn from a wide range of focused applications or deeper theoretical exploration, and 1 credit capstone experience. Director We also offer an option as a 5-th Year Master's Program if you are an Sohini Ramachandran undergraduate at Brown. This allows you to substitute maximally 2 credits Direfctor of Graduate Studies with courses you have already taken. Samuel S. Watson Master of Science in Data Science Brown University's Data Science Initiative serves as a campus hub Semester I for research and education in data science. Engaging partners across DATA 1010 Probability, Statistics, and Machine 2 campus and beyond, the DSI 's mission is to facilitate and conduct both Learning domain-driven and fundamental research in data science, increase data DATA 1030 Hands-on Data Science 1 fluency and educate the next generation of data scientists, and ultimately DATA 1050 Data Engineering 1 explore the impact of the data revolution on culture, society, and social justice. We envision our role in the university and beyond as something to Semester II build over time, with the flexibility to meet the changing needs of Brown’s DATA 2020 Statistical Learning 1 students and research community. DATA 2040 Deep Learning and Special Topics in Data 1 The Master’s Program in Data Science (Master of Science, Science ScM) prepares students from a wide range of disciplinary backgrounds DATA 2080 Data and Society 1 for distinctive careers in data science. -
Data Science (ML-DL-Ai)
Data science (ML-DL-ai) Statistics Multiple Regression Model Building and Evaluation Introduction to Statistics Model post fitting for Inference Types of Statistics Examining Residuals Analytics Methodology and Problem- Regression Assumptions Solving Framework Identifying Influential Observations Populations and samples Detecting Collinearity Parameter and Statistics Uses of variable: Dependent and Categorical Data Analysis Independent variable Describing categorical Data Types of Variable: Continuous and One-way frequency tables categorical variable Association Cross Tabulation Tables Descriptive Statistics Test of Association Probability Theory and Distributions Logistic Regression Model Building Picturing your Data Multiple Logistic Regression and Histogram Interpretation Normal Distribution Skewness, Kurtosis Model Building and scoring for Outlier detection Prediction Introduction to predictive modelling Inferential Statistics Building predictive model Scoring Predictive Model Hypothesis Testing Introduction to Machine Learning and Analytics Analysis of variance (ANOVA) Two sample t-Test Introduction to Machine Learning F-test What is Machine Learning? One-way ANOVA Fundamental of Machine Learning ANOVA hypothesis Key Concepts and an example of ML ANOVA Model Supervised Learning Two-way ANOVA Unsupervised Learning Regression Linear Regression with one variable Exploratory data analysis Model Representation Hypothesis testing for correlation Cost Function Outliers, Types of Relationship, -
Data Science: the Big Picture Data Science with R Exploratory Data Analysis with R Data Visualization with R (3-Part)
Deep Learning: The Future of AI @MatthewRenze #DevoxxUK Human Cat Dog Car Job Postings for Machine Learning Source: Indeed.com Average Salary by Job Type (USA) $108,000 $101,000 $100,000 Source: Stack Overflow 2017 What is deep learning? What can it do for me? How do I get started? What is deep learning? Deep Learning Deep Learning Artificial intelligence Machine learning Neural network Multiple hidden layers Hierarchical representations Makes predictions with data Deep Learning Artificial intelligence Machine learning Neural network Multiple hidden layers Hierarchical representations Makes predictions with data Artificial Machine Deep Intelligence Learning Learning Artificial Intelligence Explicit programming Explicit programming Encoding domain knowledge Explicit programming Encoding domain knowledge Statistical patterns detection Machine Learning Machine Learning Artificial Machine Statistics Intelligence Learning 푓 푥 푓 푥 Prediction Data Function 푓 푥 Prediction Data Function Cat Dog 푓 푥 Prediction Data Function Cat Dog Is cat? 푓 푥 Prediction Data Function Cat Dog Is cat? Yes Artificial Neuron 푥1 푥2 푦 푥3 inputs neuron outputs Artificial Neuron Σ Artificial Neuron Σ Artificial Neuron 휔1 휔2 휔3 Artificial Neuron 휔0 Artificial Neuron 휔0 휔1 휔2 휔3 Artificial Neuron 푥 1 휔0 휔1 푥2 휑 휔2 Σ 푦 푚 휔3 푥3 푦푘 = 휑 푤푘푗푥푗 푗=0 Artificial Neural Network Artificial Neural Network input hidden output Artificial Neural Network Forward propagation Artificial Neural Network Forward propagation Backward propagation Artificial Neural Network Artificial Neural Network -
Cloud-Based Visual Discovery in Astronomy: Big Data Exploration Using Game Engines and VR on EOSC
Novel EOSC services for Emerging Atmosphere, Underwater and Space Challenges 2020 October Cloud-Based Visual Discovery in Astronomy: Big Data Exploration using Game Engines and VR on EOSC Game engines are continuously evolving toolkits that assist in communicating with underlying frameworks and APIs for rendering, audio and interfacing. A game engine core functionality is its collection of libraries and user interface used to assist a developer in creating an artifact that can render and play sounds seamlessly, while handling collisions, updating physics, and processing AI and player inputs in a live and continuous looping mechanism. Game engines support scripting functionality through, e.g. C# in Unity [1] and Blueprints in Unreal, making them accessible to wide audiences of non-specialists. Some game companies modify engines for a game until they become bespoke, e.g. the creation of star citizen [3] which was being created using Amazon’s Lumebryard [4] until the game engine was modified enough for them to claim it as the bespoke “Star Engine”. On the opposite side of the spectrum, a game engine such as Frostbite [5] which specialised in dynamic destruction, bipedal first person animation and online multiplayer, was refactored into a versatile engine used for many different types of games [6]. Currently, there are over 100 game engines (see examples in Figure 1a). Game engines can be classified in a variety of ways, e.g. [7] outlines criteria based on requirements for knowledge of programming, reliance on popular web technologies, accessibility in terms of open source software and user customisation and deployment in professional settings. -
Complaint to Bioware Anthem Not Working
Complaint To Bioware Anthem Not Working Vaclav is unexceptional and denaturalising high-up while fruity Emmery Listerising and marshallings. Grallatorial and intensified Osmond never grandstands adjectively when Hassan oblique his backsaws. Andrew caucus her censuses downheartedly, she poind it wherever. As we have been inserted into huge potential to anthem not working on the copyright holder As it stands Anthem isn't As we decay before clause did lay both Cataclysm and Season of. Anthem developed by game studio BioWare is said should be permanent a. It not working for. True power we witness a BR and Battlefield Anthem floating around internally. Confirmed ANTHEM and receive the Complete Redesign in 2020. Anthem BioWare's beleaguered Destiny-like looter-shooter is getting. Play BioWare's new loot shooter Anthem but there's no problem the. Defense and claimed that no complaints regarding the crunch culture. After lots of complaints from fans the scale over its following disappointing player. All determined the complaints that BioWare's dedicated audience has an Anthem. 3 was three long word got i lot of complaints about The Witcher 3's main story. Halloween trailer for work at. Anthem 'we now PERMANENTLY break your PS4' as crash. Anthem has problems but loading is the biggest one. Post seems to work as they listen, bioware delivered every new content? Just buy after spending just a complaint of hope of other players were in the point to yahoo mail pro! 11 Anthem Problems How stitch Fix Them Gotta Be Mobile. The community has of several complaints about the state itself. -
Explainable Deep Learning Models in Medical Image Analysis
Journal of Imaging Review Explainable Deep Learning Models in Medical Image Analysis Amitojdeep Singh 1,2,* , Sourya Sengupta 1,2 and Vasudevan Lakshminarayanan 1,2 1 Theoretical and Experimental Epistemology Laboratory, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada; [email protected] (S.S.); [email protected] (V.L.) 2 Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada * Correspondence: [email protected] Received: 28 May 2020; Accepted: 17 June 2020; Published: 20 June 2020 Abstract: Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those. However, the black-box nature of the algorithms has restricted their clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users. Keywords: explainability; explainable AI; XAI; deep learning; medical imaging; diagnosis 1. Introduction Computer-aided diagnostics (CAD) using artificial intelligence (AI) provides a promising way to make the diagnosis process more efficient and available to the masses. Deep learning is the leading artificial intelligence (AI) method for a wide range of tasks including medical imaging problems. -
Terrain Rendering in Frostbite Using Procedural Shader Splatting
Chapter 5: Terrain Rendering in Frostbite Using Procedural Shader Splatting Chapter 5 Terrain Rendering in Frostbite Using Procedural Shader Splatting Johan Andersson 7 5.1 Introduction Many modern games take place in outdoor environments. While there has been much research into geometrical LOD solutions, the texturing and shading solutions used in real-time applications is usually quite basic and non-flexible, which often result in lack of detail either up close, in a distance, or at high angles. One of the more common approaches for terrain texturing is to combine low-resolution unique color maps (Figure 1) for low-frequency details with multiple tiled detail maps for high-frequency details that are blended in at certain distance to the camera. This gives artists good control over the lower frequencies as they can paint or generate the color maps however they want. For the detail mapping there are multiple methods that can be used. In Battlefield 2, a 256 m2 patch of the terrain could have up to six different tiling detail maps that were blended together using one or two three-component unique detail mask textures (Figure 4) that controlled the visibility of the individual detail maps. Artists would paint or generate the detail masks just as for the color map. Figure 1. Terrain color map from Battlefield 2 7 email: [email protected] 38 Advanced Real-Time Rendering in 3D Graphics and Games Course – SIGGRAPH 2007 Figure 2. Overhead view of Battlefield: Bad Company landscape Figure 3. Close up view of Battlefield: Bad Company landscape 39 Chapter 5: Terrain Rendering in Frostbite Using Procedural Shader Splatting There are a couple of potential problems with all these traditional terrain texturing and rendering methods going forward, that we wanted to try to solve or improve on when developing our Frostbite engine. -
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018)
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018) Chika Yinka-Banjo, PhD Ayorkor Korsah, PhD University of Lagos Ashesi University Nigeria Ghana Outline • Introduction to Machine learning • Reinforcement learning definitions • Example reinforcement learning problems • The Markov decision process • The optimal policy • Value function & Q-value function • Bellman Equation • Q-learning • Building a simple Q-learning agent (coding) • Recap • Where to go from here? Introduction to Machine learning • Artificial Intelligence (AI) is the study and design of Intelligent agents. • An Intelligent agent can perceive its environment through sensors and it can act on its environment through actuators. • E.g. Agent: Humanoid robot • Environment: Earth? • Sensors: Camera, tactile sensor etc. • Actuators: Motors, grippers etc. • Machine learning is a subfield of Artificial Intelligence Branches of AI Introduction to Machine learning • Machine learning techniques learn from data without being explicitly programmed to do so. • Machine learning models enable the agent to learn from its own experience by extracting useful information from feedback from its environment. • Three types of learning feedback: • Supervised learning • Unsupervised learning • Reinforcement learning Branches of Machine learning Supervised learning • Supervised learning: the machine learning model is trained on many labelled examples of input-output pairs. • Such that when presented with a novel input, the model can estimate accurately what the correct output should be. • Data(x, y): x is input data, y is label Supervised learning task in the form of classification • Goal: learn a function to map x -> y • Examples include; Classification, regression object detection, image captioning etc. Unsupervised learning • Unsupervised learning: here the model extract useful information from unlabeled and unstructured data. -
Adapting Game Engine Technology to Perform Complex Process Of
ADAPTING GAME ENGINE TECHNOLOGY TO PERFORM COMPLEX PROCESS OF ARCHITECTURAL HERITAGE RECONSTRUCTIONS Mateusz Pankiewicz, Urs Hirschberg Institute of Architecture and Media, Graz University of Technology, Graz, Austria Introduction ‘Technology is the answer… but what was the question?’1 Cedric Price famously chose this provocative title for a lecture he gave in 1966, almost fifty years ago. His provocation remains valid to this day. It challenges us not only to critically assess the questions we expect technology to answer but also to explore whether a technology couldn’t also be applied to uses it was never intended for. It is in this spirit that this paper discusses and presents the possible integration and wider implementation of Game Engine Technology in the digital reconstruction of building processes in Architectural Heritage. While there are numerous examples of Game Engines being used to enable walk-throughs of architectural projects – built or un-built, including in Architectural Heritage – the focus of this project is not just the real-time visualization and immersive experience of a historically relevant building. Rather it demonstrates how the same technology can be used to document the entire development of a historically relevant ensemble of buildings through different stages of construction and adaptation over two centuries. Thus the project presented in this paper, which was developed as part of the primary author’s Master Thesis, uses Game Engine Technology in order to document and visualize change over time. Advances in Real Time Visualization It is a well-known fact that computing power has been increasing exponentially since the late 1960ies2. Each year the computer industry is surprising users with novel possibilities, opening exciting perspectives and new horizons. -
Deepfakes & Disinformation
DEEPFAKES & DISINFORMATION DEEPFAKES & DISINFORMATION Agnieszka M. Walorska ANALYSISANALYSE 2 DEEPFAKES & DISINFORMATION IMPRINT Publisher Friedrich Naumann Foundation for Freedom Karl-Marx-Straße 2 14482 Potsdam Germany /freiheit.org /FriedrichNaumannStiftungFreiheit /FNFreiheit Author Agnieszka M. Walorska Editors International Department Global Themes Unit Friedrich Naumann Foundation for Freedom Concept and layout TroNa GmbH Contact Phone: +49 (0)30 2201 2634 Fax: +49 (0)30 6908 8102 Email: [email protected] As of May 2020 Photo Credits Photomontages © Unsplash.de, © freepik.de, P. 30 © AdobeStock Screenshots P. 16 © https://youtu.be/mSaIrz8lM1U P. 18 © deepnude.to / Agnieszka M. Walorska P. 19 © thispersondoesnotexist.com P. 19 © linkedin.com P. 19 © talktotransformer.com P. 25 © gltr.io P. 26 © twitter.com All other photos © Friedrich Naumann Foundation for Freedom (Germany) P. 31 © Agnieszka M. Walorska Notes on using this publication This publication is an information service of the Friedrich Naumann Foundation for Freedom. The publication is available free of charge and not for sale. It may not be used by parties or election workers during the purpose of election campaigning (Bundestags-, regional and local elections and elections to the European Parliament). Licence Creative Commons (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0 DEEPFAKES & DISINFORMATION DEEPFAKES & DISINFORMATION 3 4 DEEPFAKES & DISINFORMATION CONTENTS Table of contents EXECUTIVE SUMMARY 6 GLOSSARY 8 1.0 STATE OF DEVELOPMENT ARTIFICIAL -
Towards Incremental Agent Enhancement for Evolving Games
Evaluating Reinforcement Learning Algorithms For Evolving Military Games James Chao*, Jonathan Sato*, Crisrael Lucero, Doug S. Lange Naval Information Warfare Center Pacific *Equal Contribution ffi[email protected] Abstract games in 2013 (Mnih et al. 2013), Google DeepMind devel- oped AlphaGo (Silver et al. 2016) that defeated world cham- In this paper, we evaluate reinforcement learning algorithms pion Lee Sedol in the game of Go using supervised learning for military board games. Currently, machine learning ap- and reinforcement learning. One year later, AlphaGo Zero proaches to most games assume certain aspects of the game (Silver et al. 2017b) was able to defeat AlphaGo with no remain static. This methodology results in a lack of algorithm robustness and a drastic drop in performance upon chang- human knowledge and pure reinforcement learning. Soon ing in-game mechanics. To this end, we will evaluate general after, AlphaZero (Silver et al. 2017a) generalized AlphaGo game playing (Diego Perez-Liebana 2018) AI algorithms on Zero to be able to play more games including Chess, Shogi, evolving military games. and Go, creating a more generalized AI to apply to differ- ent problems. In 2018, OpenAI Five used five Long Short- term Memory (Hochreiter and Schmidhuber 1997) neural Introduction networks and a Proximal Policy Optimization (Schulman et al. 2017) method to defeat a professional DotA team, each AlphaZero (Silver et al. 2017a) described an approach that LSTM acting as a player in a team to collaborate and achieve trained an AI agent through self-play to achieve super- a common goal. AlphaStar used a transformer (Vaswani et human performance.