After the Machines. Episode Two: Transition

After the Machines. Episode Two: Transition
Author: Robert Stanek
Publisher: Big Blue Sky Press
Total Pages: 74
Release: 2014-11-28
Genre: Fiction
ISBN: 1627163913

"This one's memorable and fascinating heroine is someone you're going to love as much as Katniss Everdeen." - Sandra Brown, author "A gripping tale. Perfectly paced and brilliantly plotted." - Cathy Thompson, author "Stanek's written many good, even great, books. This one's exceptional. Read it!" - Shannon Hale, author "Builds and builds to a crescendo. Part Stephen King, part Suzanne Collins, part Max Brooks, 100% phenomenal!" - David Eastman, author "Wonderful action writing. Fast, fun, and smart." - Margaret Brown, author "I can see why Rothfuss doesn't want people to read Stanek. Stanek's a much more capable writer." - Emily Asimov, author "What an amazing book! Unique and innovative, captivating to the end." - Mary Osborne, author "Anyone who enjoyed The Hunger Games, World War Z, or The Maze Runner is going to enjoy this book." - Lisa Gardner, author Episode #2. Where were you when the machine apocalypse began? In the ruins of our world, a new order arose, an order controlled by the very machines humankind created. The end for us came not from a massive global war but from something unthinkable, incomprehensible. The machines simply replaced us and we let them, and so, in the end, humanity went out not with a bang, but with a whimper. No shots fired. No bombs dropped. No cities destroyed. We ended and the machines began—or at least that is what the few human survivors of the machine apocalypse believe. After the Machines Episode One: Awakening Episode Two: Transition Episode Three: Descent Episode Four: Precipice ### To the machines, we became nothing—except maybe outsiders, if they considered us at all. Outsiders looking in on their reality, for the machines weren’t bothered by our existence, or at least, if they were, they weren’t bothered enough to bother us. They certainly didn’t seem to require anything of us or have any need of us at all—if they had needed us, they probably would have enslaved us. But they hadn’t. Enslaved us that is. The machines hadn’t done anything to us really. Except take over the world—and it was their world now. It certainly wasn’t ours. We were outsiders, strangers really. We looked in on their world. They didn’t acknowledge us. They probably didn’t even consider us a part of their world. Just as we didn’t consider the small things that crawled beneath our feet as part of our world. Matthew told us it wasn’t the machines who killed us. Matthew being the only one here now who remembered when we drove the automobiles, flew on the airplanes, and rode on cars behind the locomotives. He said most of us just died. Us being the human race. I didn’t believe that. I believed we died of neglect. The neglect of the machines. The machines who cared not enough to kill or enslave us. Luke would have called it benign neglect. Luke being the one who taught me to read and write my letters and words. He knew all the fancy words. He taught me everything really. He remembered—I didn’t. Don’t, really. These words—his really as much as my own. But Luke was gone. Is gone really, if you don’t mind me slipping into the present. Luke said it’s wrong to slip from past to present or present to past, but I do. The present is—and Luke isn’t. The past was—and sometimes I can see it. ### After the Machines is a story unlike any other you’ve ever read. It’s the story of us, the humans who struggle to survive in a world we no longer control.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
Author: Walter Daelemans
Publisher: Springer
Total Pages: 721
Release: 2008-08-17
Genre: Computers
ISBN: 354087481X

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Advances in Mechanism and Machine Science

Advances in Mechanism and Machine Science
Author: Tadeusz Uhl
Publisher: Springer
Total Pages: 4203
Release: 2019-06-13
Genre: Technology & Engineering
ISBN: 3030201317

This book gathers the proceedings of the 15th IFToMM World Congress, which was held in Krakow, Poland, from June 30 to July 4, 2019. Having been organized every four years since 1965, the Congress represents the world’s largest scientific event on mechanism and machine science (MMS). The contributions cover an extremely diverse range of topics, including biomechanical engineering, computational kinematics, design methodologies, dynamics of machinery, multibody dynamics, gearing and transmissions, history of MMS, linkage and mechanical controls, robotics and mechatronics, micro-mechanisms, reliability of machines and mechanisms, rotor dynamics, standardization of terminology, sustainable energy systems, transportation machinery, tribology and vibration. Selected by means of a rigorous international peer-review process, they highlight numerous exciting advances and ideas that will spur novel research directions and foster new multidisciplinary collaborations.

Probabilistic Machine Learning

Probabilistic Machine Learning
Author: Kevin P. Murphy
Publisher: MIT Press
Total Pages: 1352
Release: 2023-08-15
Genre: Computers
ISBN: 0262376008

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment

Man-Machine Interactions

Man-Machine Interactions
Author: Krzysztof A. Cyran
Publisher: Springer Science & Business Media
Total Pages: 667
Release: 2009-10-01
Genre: Computers
ISBN: 3642005632

This volume reflects a number of research streams on the development of computer systems and software that makes it possible to employ them in a variety of human activities ranging from logic studies and artificial intelligence, rule-based control of technological processes, image analysis, expert systems and decision support, to assistance in creative works. In particular, the volume points to a number of new advances in man-machine communication, interaction between visualization and modeling, rough granular computing in human-centric information processing and the discovery of affinities between perceptual granules. The topical subdivisions of this volume include human-computer interactions, decision support, rough fuzzy investigations, advances in classification methodology, pattern analysis and signal processing, computer vision and image analysis, advances in algorithmics, databases and data warehousing, and embedded system applications.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Author: John D. Kelleher
Publisher: MIT Press
Total Pages: 853
Release: 2020-10-20
Genre: Computers
ISBN: 0262361108

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

An Introduction to Machine Learning

An Introduction to Machine Learning
Author: Gopinath Rebala
Publisher: Springer
Total Pages: 275
Release: 2019-05-07
Genre: Technology & Engineering
ISBN: 3030157296

Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.