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Summary of Kenneth O. Stanley & Joel Lehman's Why Greatness Cannot Be Planned
Please note: This is a companion version & not the original book. Sample Book Insights: #1 The purpose of objectives is to measure our progress towards specific goals set by society or by ourselves. We rarely consider how deeply our culture has come to revere objectives, and how much effort and resources are spent measuring progress towards them. #2 The weight of objectives on our thinking is so great that it has even impacted the way we talk about animals in nature. We view animals through the lens of survival and reproduction, evolution’s assumed objective. But this can-do philosophy is so optimistic about objectives that it limits our freedom and robs us of the chance for playful discovery. #3 The problem with objectives is that they take away your freedom to explore creatively and block you from serendipitous discovery. They ignore the value of following a path for its own uniqueness rather than where it may lead. #4 The pursuit of an objective is not always clear, and it is often accompanied by the need for progress towards the objective to be measured. This is where all the measurements and metrics of our culture come into play.
The Cost of Unity
Like other Protestant organizations in the US, the Christian Church was involved in the establishment of schools for African Americans in the South in the years following the end of the Civil War. This book examines the agency of African Americans in the founding of educational institutions for blacks associated with the Christian Church.
My Story
Lola Taubman was born in 1925 in the Carpathian Mountains (then Czechoslovakia). Life was rich in her extended Jewish family, part of a community with citizens from many backgrounds, where multiple languages were common currency, and education mingled with the joys and games of youth. By the late 1930s, anti-Semitism grew, and communities were disrupted. In May 1944, Lola and her family, and the remaining Jews from her town, were sent to Auschwitz. Lola was chosen to work; her immediate family perished. In January 1945, as the allies approached, the Nazis moved her, with many others from Auschwitz, on a series of death marches. Life as a DP followed, with a 4-year struggle to emigrate to the U.S. Arriving in New York in 1949, she later relocated to the Detroit area, where she married Sam Taubman and raised a family. Since the mid-1990s, she has been an inspiring speaker about her Holocaust experiences. Now, she shares her amazing story with us in this moving narrative of her life's journey.
Why Greatness Cannot Be Planned
Why does modern life revolve around objectives? From how science is funded, to improving how children are educated -- and nearly everything in-between -- our society has become obsessed with a seductive illusion: that greatness results from doggedly measuring improvement in the relentless pursuit of an ambitious goal. In Why Greatness Cannot Be Planned, Stanley and Lehman begin with a surprising scientific discovery in artificial intelligence that leads ultimately to the conclusion that the objective obsession has gone too far. They make the case that great achievement can't be bottled up into mechanical metrics; that innovation is not driven by narrowly focused heroic effort; and that we would be wiser (and the outcomes better) if instead we whole-heartedly embraced serendipitous discovery and playful creativity. Controversial at its heart, yet refreshingly provocative, this book challenges readers to consider life without a destination and discovery without a compass.
Theory and Practice of Natural Computing
This book constitutes the refereed proceedings of the Second International Conference, TPNC 2013, held in Cáceres, Spain, in December 2013. The 19 revised full papers presented together with one invited talk were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on nature-inspired models of computation; synthesizing nature by means of computation; nature-inspired materials and information processing in nature.
Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key...
Deep Reinforcement Learning
Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how sub...
Handbook of Evolutionary Machine Learning
This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evoluti...
Applications of Evolutionary Computation
This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions.
Genetic Programming Theory and Practice IX
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics include: modularity and scalability; evolvability; human-competitive results; the need for important high-impact GP-solvable problems;; the risks of search stagnation and of cutting off paths to solutions; the need for novelty; empowering GP search with expert knowledge; In addition, GP symbolic regression is thoroughly discussed, addressing such topics as guaranteed reproducibility of SR; validating SR results, measuring and controlling genotypic complexity; controlling phenotypic complexity; identifying, monitoring, and avoiding over-fitting; finding a comprehensive collection of SR benchmarks, comparing SR to machine learning. This text is for all GP explorers. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
