Programming Computer Vision with Python
by Jan Erik Solem · 26 Jun 2012
Programming Computer Vision with Python Jan Erik Solem Published by O’Reilly Media Beijing ⋅ Cambridge ⋅ Farnham ⋅ Köln ⋅ Sebastopol ⋅ Tokyo Preface Today, images and video are everywhere. Online photo-
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gigabytes of photos and videos on their devices. Programming a computer and designing algorithms for understanding what is in these images is the field of computer vision. Computer vision powers applications like image search, robot navigation, medical image analysis, photo management, and many more. The idea behind this book is to give an easily
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accessible entry point to hands-on computer vision with enough understanding of the underlying theory and algorithms to be a foundation for students, researchers, and enthusiasts. The Python programming language, the language choice
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and open software with a low learning threshold. Python was the obvious choice. Be complete and self-contained. This book does not cover all of computer vision but rather it should be complete in that all code is presented and explained. The reader should be able to reproduce the examples and build
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broad rather than detailed, inspiring and motivational rather than theoretical. In short, it should act as a source of inspiration for those interested in programming computer vision applications. Prerequisites and Overview This book looks at theory and algorithms for a wide range of applications and problems. Here is a short summary of
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derivatives and gradients. Some of the more advanced mathematical examples can be easily skipped. What You Will Learn Hands-on programming with images using Python. Computer vision techniques behind a wide variety of real-world applications. Many of the fundamental algorithms and how to implement and apply them yourself. The code examples
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image into meaningful regions using clustering, user interactions, or image models. Chapter 10 Shows how to use the Python interface for the commonly used OpenCV computer vision library and how to work with video and camera input. There is also a bibliography at the back of the book. Citations of bibliographic entries
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are made by number in square brackets, as in [20]. Introduction to Computer Vision Computer vision is the automated extraction of information from images. Information can mean anything from 3D models, camera position, object detection and recognition to grouping and searching
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image content. In this book, we take a wide definition of computer vision and include things like image warping, de-noising, and augmented reality.[1] Sometimes computer vision tries to mimic human vision, sometimes it uses a data and statistical approach, and sometimes geometry is the key
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to solving problems. We will try to cover all of these angles in this book. Practical computer vision contains a mix of programming, modeling, and mathematics and is sometimes difficult to grasp. I have deliberately tried to present the material with a minimum
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. For beginners to Python, Mark Lutz’ book Learning Python [20] and the online documentation at http://www.python.org/ are good starting points. When programming computer vision, we need representations of vectors and matrices and operations on them. This is handled by Python’s NumPy module, where both vectors and matrices are
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’s documentation does require permission. We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Programming Computer Vision with Python by Jan Erik Solem (O’Reilly). Copyright © 2012 Jan Erik Solem, 978-1-449-31654-9.” If you feel your use of code
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, Points, and Lines Although it is possible to create nice bar plots, pie charts, scatter plots, etc., only a few commands are needed for most computer vision purposes. Most importantly, we want to be able to show things like interest points, correspondences, and detected objects using points and lines. Here is an
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space. A megapixel image has dimensions in the millions. With such high dimensionality, it is no surprise that dimensionality reduction comes in handy in many computer vision applications. The projection matrix resulting from PCA can be seen as a change of coordinates to a coordinate system where the coordinates are in descending
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an affine transformation matrix H on image patches is called warping (or affine warping) and is frequently used in computer graphics but also in several computer vision algorithms. A warp can easily be performed with SciPy using the ndimage package. The command transformed_im = ndimage.affine_transform(im,A,b,size) transforms
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them. 4.1 The Pin-Hole Camera Model The pin-hole camera model (or sometimes projective camera model) is a widely used camera model in computer vision. It is simple and accurate enough for most applications. The name comes from the type of camera, like a camera obscura, that collects light through
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map (or, inversely, a disparity map) where the depth (or disparity) for each pixel in the image is estimated. This is a classic problem in computer vision and there are many algorithms for solving it. The Middlebury Stereo Vision Page (http://vision.middlebury.edu/stereo/) contains a constantly updated evaluation of the
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with them. A graph cut is the partitioning of a directed graph into two disjoint sets. Graph cuts can be used for solving many different computer vision problems like stereo depth reconstruction, image stitching, and image segmentation. By creating a graph from image pixels and their neighbors and introducing an energy or
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neighborhoods into account. 9.3 Variational Methods In this book, you have seen a number of examples of minimizing a cost or energy to solve computer vision problems. In the previous sections it was minimizing the cut in a graph, but we also saw examples like the ROF de-noising, k-means
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and 0 outside. Chapter 10. OpenCV This chapter gives a brief overview of how to use the popular computer vision library OpenCV through the Python interface. OpenCV is a C++ library for real-time computer vision initially developed by Intel and now maintained by Willow Garage. OpenCV is open source and released under a
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and look deeper into tracking and video. 10.1 The OpenCV Python Interface OpenCV is a C++ library with modules that cover many areas of computer vision. Besides C++ (and C), there is growing support for Python as a simpler scripting language through a Python interface on top of the C++ code
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camera move between two consecutive images. It is a 2D vector field of within-image translation. It is a classic and well-studied field in computer vision with many successful applications in, for example, video compression, motion estimation, object tracking, and image segmentation. Optical flow relies on three major assumptions: Brightness constancy
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and download, etc. Appendix B. Image Datasets B.1 Flickr The immensely popular photo-sharing site Flickr (http://flickr.com/) is a gold mine for computer vision researchers and hobbyists. With hundreds of millions of images, many of them tagged by users, it is a great resource to get training data or
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adapted for this book. Appendix D. References [1] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. SURF: Speeded up robust features. In European Conference on Computer Vision, 2006. [2] Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23
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/matematiklth/personal/byrod/papers/sudokuocr.pdf, 2007. [5] Antonin Chambolle. Total variation minimization and a class of binary mrf models. In Energy Minimization Methods in Computer Vision and Pattern Recognition, Lecture Notes in Computer Science, pages 136–152. Springer Berlin / Heidelberg, 2005. [6] T. Chan and L. Vese. Active contours without edges
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. A combined corner and edge detector. In Proceedings Alvey Conference, pages 189–192, 1988. [13] R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition, 2004. [14] Richard Hartley. In defense of the eight-point algorithm. IEEE Transactions on Pattern Analysis and Machine
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, 2004. [17] David G. Lowe. Object recognition from local scale-invariant features. In International Conference on Computer Vision, pages 1150–1157, 1999. [18] David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110, 2004. [19] Bruce D. Lucas and Takeo Kanade. An iterative image registration
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Image Processing and its Applications, pages 302–306, 1992. [23] D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 2161–2168, 2006. [24] Travis E. Oliphant. Guide to NumPy. http://www.tramy.us/numpybook.pdf, 2006. [25
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. Pollefeys, L. Van Gool, M. Vergauwen, F. Verbiest, K. Cornelis, J. Tops, and R. Koch. Visual modeling with a hand-held camera. International Journal of Computer Vision, 59(3):207–232, 2004. [26] Marc Pollefeys. Visual 3d modeling from images—tutorial notes. Technical report, University of North Carolina–Chapel Hill. http://www
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D, 60:259–268, 1992. [29] Daniel Scharstein and Richard Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 2001. [30] Daniel Scharstein and Richard Szeliski. High-accuracy stereo depth maps using structured light. In IEEE Computer Society Conference on
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Computer Vision and Pattern Recognition, 2003. [31] Toby Segaran. Programming Collective Intelligence. O’Reilly Media, 2007. [32] Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation.
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Trans. Pattern Anal. Mach. Intell., 22:888–905, August 2000. [33] Jianbo Shi and Carlo Tomasi. Good features to track. In 1994 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’94), pages 593–600, 1994. [34] Noah Snavely, Steven M. Seitz, and Richard Szeliski. Photo tourism: Exploring photo collections in 3d
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Algorithms: Theory and Practice, ICCV ’99, pages 298–372. Springer-Verlag, 2000. [36] A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008. [37] Deepak Verma and Marina Meila. A comparison of spectral clustering algorithms. Technical report, 2003. [38] Luminita A. Vese
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and Tony F. Chan. A multiphase level set framework for image segmentation using the mumford and shah model. International Journal of Computer Vision, 50:271–293, December 2002. [39] Paul Viola and Michael Jones. Robust real-time object detection. In International Journal of
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Zuliani. Ransac for dummies. Technical report, Vision Research Lab, UCSB, 2011. Appendix E. About the Author Jan Erik Solem is a Python enthusiast and a computer vision researcher and entrepreneur. He is an applied mathematician and has worked as associate professor, startup CTO, and now also book author. He sometimes writes about
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computer vision and Python on his blog www.janeriksolem.net. He has used Python for computer vision in teaching, research, and industrial applications for many years. He currently lives in San Francisco. Index A note
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index, Setting Up the Database X XML, Registering Images xml.dom, Registering Images About the Author Jan Erik Solem is a Python enthusiast and a computer vision researcher and entrepreneur. He is an applied mathematician and has worked as associate professor, startup CTO, and now also book author. He sometimes writes about
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computer vision and Python on his blog www.janeriksolem.net. He has used Python for computer vision in teaching, research and industrial applications for many years. He currently lives in San Francisco. Colophon The animal
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on the cover of Programming Computer Vision with Python is a bullhead. Often referred to as “bullhead catfish,” members of the genus Ameiurus come in three common types: the black bullhead (Ameiurus
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; the heading font is Adobe Myriad Condensed; and the code font is LucasFont’s TheSansMonoCondensed. Programming Computer Vision with Python Jan Erik Solem Editor Mike Hendrickson Editor Andy Oram Copyright © 2012 Jan Erik Solem Programming Computer Vision with Python by Jan Erik Solem All rights reserved. O’Reilly books may be purchased for educational
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?isbn=0636920022923 for release details. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. Programming Computer Vision with Python, the image of a bullhead fish, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by
OpenCV Computer Vision With Python
by Joseph Howse · 22 Apr 2013 · 138pp · 27,404 words
Table of Contents OpenCV Computer Vision with Python Credits About the Author About the Reviewers www.PacktPub.com Support files, eBooks, discount offers and more Why Subscribe? Free Access for Packt
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> Creating <positive_description> Creating <binary_description> by running <opencv_createsamples> Creating <cascade> by running <opencv_traincascade> Testing and improving <cascade> Summary Index OpenCV Computer Vision with Python * * * OpenCV Computer Vision with Python Copyright © 2013 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted
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applications and games. In 2005, he finished his studies in IT from the Universitat Politécnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He had a final project based on this subject and published it on HCI Spanish congress. He participated in Blender source
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. David now has more than 10 years of experience in IT, with more than seven years experience in computer vision, computer graphics, and pattern recognition working on different projects and startups, applying his knowledge of computer vision, optical character recognition, and augmented reality. He is the author of the DamilesBlog (http://blog.damiles.com
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), where he publishes research articles and tutorials about OpenCV, computer vision in general, and Optical Character Recognition algorithms. He is the co-author
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of Mastering OpenCV with Practical Computer Vision Projects , Daniel Lélis Baggio, Shervin Emami, David Millán Escrivá, Khvedchenia Ievgen, Naureen Mahmood, Jasonl Saragih, and
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in his blog articles. He also works as a freelancer during college holidays and even helps school students grow their interest in OpenCV Python and computer vision. Congrats to the author and all those who worked on this book. I think this might be the first book exclusively on OpenCV Python. And
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normal webcam or a specialized depth sensor, such as the Microsoft Kinect. OpenCV is an open source, cross-platform library that provides building blocks for computer vision experiments and applications. It provides high-level interfaces for capturing, processing, and presenting image data. For example, it abstracts details about camera hardware and array
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allocation. OpenCV is widely used in both academia and industry. Today, computer vision can reach consumers in many contexts via webcams, camera phones, and gaming sensors such as the Kinect. For better or worse, people love to be
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a live camera feed. Behind this application, you will have a small library of reusable functions and classes that you can apply in your future computer vision projects. Let's look at the book's progression in more detail. What this book covers Chapter 1, Setting up OpenCV, lets us examine the
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camera, such as Microsoft Kinect or Asus Xtion PRO. Who this book is for This book is great for Python developers who are new to computer vision and who like to learn through application development. It is assumed that you have some previous experience in Python and the command line. A basic
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make a sandwich: from the outside in. Bread slices and spread or endpoints and glue, come before fillings or algorithms. We choose this approach because computer vision is extroverted—it contemplates the real world outside our computer—and we want to apply all our subsequent, algorithmic work to the real world through
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,226),(255,255)], rPoints = [(0,0),(56,22),(211,255),(255,255)], dtype = dtype) Highlighting edges Edges play a major role in both human and computer vision. We, as humans, can easily recognize many object types and their pose just by seeing a backlit silhouette or a rough sketch. Indeed, when art
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libraries. We have also practiced wrapping this functionality in a high-level, reusable, and object-oriented design. Congratulations! You now have the skill to develop computer vision applications in Python using OpenCV. Still, there is always more to learn and do! If you liked working with NumPy and OpenCV, please check out
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these other titles from Packt Publishing: NumPy Cookbook, Ivan Idris OpenCV 2 Computer Vision Application Programming Cookbook, Robert Laganière, which uses OpenCV's C++ API for desktops Mastering OpenCV with Practical Computer Vision Projects, (by multiple authors), which uses OpenCV's C++ API for multiple platforms The upcoming book
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of OpenCV's Python bindings. I hope you are able to use this book and its codebase as a starting point for rewarding work in computer vision. Let me know what you are studying or developing next! Appendix A. Integrating with Pygame This appendix shows how to set up the Pygame library
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joysticks/gamepads Creating custom events Playback and synthesis of sounds and music For example, Pygame might be a suitable backend for a game that uses computer vision, whereas HighGUI would not be. Summary By now, we should have an application that uses OpenCV for capturing (and possibly manipulating) images, while using Pygame
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. Green. Microeconomic Theory. Oxford University Press, 1995. Moosavi-Dezfooli, S.-M., A. Fawzi, O. Fawzi, and P. Frossard. “Universal Adversarial Perturbations.” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 1765–73. Paul, K., and D. Milmo. “Facebook Putting Profit Before Public Good, Says Whistleblower Frances Haugen.” Guardian, October 4
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Python Data Analytics: With Pandas, NumPy, and Matplotlib
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Our Final Invention: Artificial Intelligence and the End of the Human Era
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Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
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The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism
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The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats
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The Metaverse: And How It Will Revolutionize Everything
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Future Politics: Living Together in a World Transformed by Tech
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Silicon City: San Francisco in the Long Shadow of the Valley
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Visual Thinking: The Hidden Gifts of People Who Think in Pictures, Patterns, and Abstractions
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The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future
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The Long History of the Future: Why Tomorrow's Technology Still Isn't Here
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Data Mining in Time Series Databases
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Hackers: Heroes of the Computer Revolution - 25th Anniversary Edition
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Why Machines Learn: The Elegant Math Behind Modern AI
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Demystifying Smart Cities
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The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
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Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words
Tools and Weapons: The Promise and the Peril of the Digital Age
by Brad Smith and Carol Ann Browne · 9 Sep 2019 · 482pp · 121,173 words
Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder · 16 Jul 2018 · 332pp · 93,672 words
The Precipice: Existential Risk and the Future of Humanity
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The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--And How We Must Adapt
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The Alignment Problem: Machine Learning and Human Values
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Amazon Unbound: Jeff Bezos and the Invention of a Global Empire
by Brad Stone · 10 May 2021 · 569pp · 156,139 words
The Job: The Future of Work in the Modern Era
by Ellen Ruppel Shell · 22 Oct 2018 · 402pp · 126,835 words
Robot Futures
by Illah Reza Nourbakhsh · 1 Mar 2013
AI Superpowers: China, Silicon Valley, and the New World Order
by Kai-Fu Lee · 14 Sep 2018 · 307pp · 88,180 words
Surfaces and Essences
by Douglas Hofstadter and Emmanuel Sander · 10 Sep 2012 · 1,079pp · 321,718 words
From Bacteria to Bach and Back: The Evolution of Minds
by Daniel C. Dennett · 7 Feb 2017 · 573pp · 157,767 words
Army of None: Autonomous Weapons and the Future of War
by Paul Scharre · 23 Apr 2018 · 590pp · 152,595 words
Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006
by Ben Goertzel and Pei Wang · 1 Jan 2007 · 303pp · 67,891 words
Steve Jobs
by Walter Isaacson · 23 Oct 2011 · 915pp · 232,883 words
Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence
by Richard Yonck · 7 Mar 2017 · 360pp · 100,991 words
Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It
by Marc Goodman · 24 Feb 2015 · 677pp · 206,548 words
Your Computer Is on Fire
by Thomas S. Mullaney, Benjamin Peters, Mar Hicks and Kavita Philip · 9 Mar 2021 · 661pp · 156,009 words
WTF?: What's the Future and Why It's Up to Us
by Tim O'Reilly · 9 Oct 2017 · 561pp · 157,589 words
Autonomous Driving: How the Driverless Revolution Will Change the World
by Andreas Herrmann, Walter Brenner and Rupert Stadler · 25 Mar 2018
The Creativity Code: How AI Is Learning to Write, Paint and Think
by Marcus Du Sautoy · 7 Mar 2019 · 337pp · 103,522 words
SAM: One Robot, a Dozen Engineers, and the Race to Revolutionize the Way We Build
by Jonathan Waldman · 7 Jan 2020 · 277pp · 91,698 words
Whole Earth: The Many Lives of Stewart Brand
by John Markoff · 22 Mar 2022 · 573pp · 142,376 words
The Third Pillar: How Markets and the State Leave the Community Behind
by Raghuram Rajan · 26 Feb 2019 · 596pp · 163,682 words
Four Battlegrounds
by Paul Scharre · 18 Jan 2023
Elon Musk
by Walter Isaacson · 11 Sep 2023 · 562pp · 201,502 words
I, Warbot: The Dawn of Artificially Intelligent Conflict
by Kenneth Payne · 16 Jun 2021 · 339pp · 92,785 words
Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce
by Natalie Berg and Miya Knights · 28 Jan 2019 · 404pp · 95,163 words
The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman · 4 Sep 2023 · 444pp · 117,770 words
Inventors at Work: The Minds and Motivation Behind Modern Inventions
by Brett Stern · 14 Oct 2012 · 486pp · 132,784 words
System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich, Mehran Sahami and Jeremy M. Weinstein · 6 Sep 2021
Ghost Road: Beyond the Driverless Car
by Anthony M. Townsend · 15 Jun 2020 · 362pp · 97,288 words
Makers at Work: Folks Reinventing the World One Object or Idea at a Time
by Steven Osborn · 17 Sep 2013 · 310pp · 34,482 words
When Computers Can Think: The Artificial Intelligence Singularity
by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann and Michelle Estes · 28 Feb 2015
The Age of Surveillance Capitalism
by Shoshana Zuboff · 15 Jan 2019 · 918pp · 257,605 words
Human + Machine: Reimagining Work in the Age of AI
by Paul R. Daugherty and H. James Wilson · 15 Jan 2018 · 523pp · 61,179 words
Architects of Intelligence
by Martin Ford · 16 Nov 2018 · 586pp · 186,548 words
Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words
The History of the Future: Oculus, Facebook, and the Revolution That Swept Virtual Reality
by Blake J. Harris · 19 Feb 2019 · 561pp · 163,916 words
The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity
by Amy Webb · 5 Mar 2019 · 340pp · 97,723 words
21 Lessons for the 21st Century
by Yuval Noah Harari · 29 Aug 2018 · 389pp · 119,487 words
Driverless: Intelligent Cars and the Road Ahead
by Hod Lipson and Melba Kurman · 22 Sep 2016
From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry
by Martin Campbell-Kelly · 15 Jan 2003
Radical Technologies: The Design of Everyday Life
by Adam Greenfield · 29 May 2017 · 410pp · 119,823 words
The Future of Technology
by Tom Standage · 31 Aug 2005
The Future of the Brain: Essays by the World's Leading Neuroscientists
by Gary Marcus and Jeremy Freeman · 1 Nov 2014 · 336pp · 93,672 words
Experience on Demand: What Virtual Reality Is, How It Works, and What It Can Do
by Jeremy Bailenson · 30 Jan 2018 · 302pp · 90,215 words
Fire in the Valley: The Birth and Death of the Personal Computer
by Michael Swaine and Paul Freiberger · 19 Oct 2014 · 459pp · 140,010 words
Facebook: The Inside Story
by Steven Levy · 25 Feb 2020 · 706pp · 202,591 words
Know Thyself
by Stephen M Fleming · 27 Apr 2021
Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone
by Satya Nadella, Greg Shaw and Jill Tracie Nichols · 25 Sep 2017 · 391pp · 71,600 words
The Mind Is Flat: The Illusion of Mental Depth and the Improvised Mind
by Nick Chater · 28 Mar 2018 · 263pp · 81,527 words
Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global
by Rebecca Fannin · 2 Sep 2019 · 269pp · 70,543 words
The Age of Spiritual Machines: When Computers Exceed Human Intelligence
by Ray Kurzweil · 31 Dec 1998 · 696pp · 143,736 words
Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside
by Xiaowei Wang · 12 Oct 2020 · 196pp · 61,981 words
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
by Valliappa Lakshmanan, Sara Robinson and Michael Munn · 31 Oct 2020
Life as a Passenger: How Driverless Cars Will Change the World
by David Kerrigan · 18 Jun 2017 · 472pp · 80,835 words
Robot, Take the Wheel: The Road to Autonomous Cars and the Lost Art of Driving
by Jason Torchinsky · 6 May 2019 · 175pp · 54,755 words
The Future Is Asian
by Parag Khanna · 5 Feb 2019 · 496pp · 131,938 words
Billion Dollar Brand Club: How Dollar Shave Club, Warby Parker, and Other Disruptors Are Remaking What We Buy
by Lawrence Ingrassia · 28 Jan 2020 · 290pp · 90,057 words
Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It
by Kashmir Hill · 19 Sep 2023 · 487pp · 124,008 words
Hope Dies Last: Visionary People Across the World, Fighting to Find Us a Future
by Alan Weisman · 21 Apr 2025 · 599pp · 149,014 words
The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future
by Keach Hagey · 19 May 2025 · 439pp · 125,379 words
Supremacy: AI, ChatGPT, and the Race That Will Change the World
by Parmy Olson · 284pp · 96,087 words
Unit X: How the Pentagon and Silicon Valley Are Transforming the Future of War
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The Power Law: Venture Capital and the Making of the New Future
by Sebastian Mallaby · 1 Feb 2022 · 935pp · 197,338 words
AI 2041: Ten Visions for Our Future
by Kai-Fu Lee and Qiufan Chen · 13 Sep 2021
Inside the Robot Kingdom: Japan, Mechatronics and the Coming Robotopia
by Frederik L. Schodt · 31 Mar 1988 · 361pp · 83,886 words
Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots
by John Markoff · 24 Aug 2015 · 413pp · 119,587 words
What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry
by John Markoff · 1 Jan 2005 · 394pp · 108,215 words
The Second Intelligent Species: How Humans Will Become as Irrelevant as Cockroaches
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Practical Python and OpenCV
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The Deep Learning Revolution (The MIT Press)
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CTOs at Work
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How the Mind Works
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Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
by Eric Topol · 1 Jan 2019 · 424pp · 114,905 words
Fall; Or, Dodge in Hell
by Neal Stephenson · 3 Jun 2019 · 993pp · 318,161 words
How to Turn Down a Billion Dollars: The Snapchat Story
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The New Rules of War: Victory in the Age of Durable Disorder
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The Science and Technology of Growing Young: An Insider's Guide to the Breakthroughs That Will Dramatically Extend Our Lifespan . . . And What You Can Do Right Now
by Sergey Young · 23 Aug 2021 · 326pp · 88,968 words
Artificial Whiteness
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Being You: A New Science of Consciousness
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Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World
by Cade Metz · 15 Mar 2021 · 414pp · 109,622 words
Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation
by Paris Marx · 4 Jul 2022 · 295pp · 81,861 words
Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell · 14 Oct 2019 · 350pp · 98,077 words
The Smart Wife: Why Siri, Alexa, and Other Smart Home Devices Need a Feminist Reboot
by Yolande Strengers and Jenny Kennedy · 14 Apr 2020
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
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AI in Museums: Reflections, Perspectives and Applications
by Sonja Thiel and Johannes C. Bernhardt · 31 Dec 2023 · 321pp · 113,564 words
Ludicrous: The Unvarnished Story of Tesla Motors
by Edward Niedermeyer · 14 Sep 2019 · 328pp · 90,677 words
The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism
by Nick Couldry and Ulises A. Mejias · 19 Aug 2019 · 458pp · 116,832 words
New Laws of Robotics: Defending Human Expertise in the Age of AI
by Frank Pasquale · 14 May 2020 · 1,172pp · 114,305 words
Supertall: How the World's Tallest Buildings Are Reshaping Our Cities and Our Lives
by Stefan Al · 11 Apr 2022 · 300pp · 81,293 words
Attention Factory: The Story of TikTok and China's ByteDance
by Matthew Brennan · 9 Oct 2020 · 282pp · 63,385 words
Do More Faster: TechStars Lessons to Accelerate Your Startup
by Brad Feld and David Cohen · 18 Oct 2010 · 326pp · 74,433 words
Traffic: Why We Drive the Way We Do (And What It Says About Us)
by Tom Vanderbilt · 28 Jul 2008 · 512pp · 165,704 words
Blood in the Machine: The Origins of the Rebellion Against Big Tech
by Brian Merchant · 25 Sep 2023 · 524pp · 154,652 words
The Dawn of Eurasia: On the Trail of the New World Order
by Bruno Macaes · 25 Jan 2018 · 287pp · 95,152 words
The Raging 2020s: Companies, Countries, People - and the Fight for Our Future
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