In this book there is the authentic author`s text in Russian simultaneously with English STUDY GUIDE on the course by Grigori Grabovoi. "Technology of preventive forecasting and safe development"Этот курс рассчитан на познание. Информация данного курса, проходящая от общих областей к конкретным и частным системам, развивает духовный, логический и иррациональный уровень созидательного управления. Так как акт спасения должен достигаться при любых условиях, следует сочетать и развивать различные практики управления в направлении адекватной цели спасения.Цель параллельного перевода - предоставить читателю возможность идентифицировать текст на английском языке с оригинальным текстом на русском языке. Это позволит читателю более точно воспринимать текст книги на родном языке автора.

Concern about psychology’s fragmentation is not new, but there has for the past decade been increasing calls for psychologists to acknowledge to the costs associated with fragmentation and to search for ways to unify the discipline. A New Unified Theory of Psychology introduces a new system that addresses psychology’s current theoretical and philosophical difficulties. The new theory consists of four interlocking pieces that together provide—for the first time—a macro-level view that clarifies the nature of psychology’s problems and offers a clear way to unify the various elements of the field. The unified theory provides the field of psychology with a well-defined subject matter, allowing both academic and professional psychologists will be able to develop a shared language and conceptual foundation.

“Unified Reality Theory demonstrates that the source of reality is a universal consciousness, and that we are in no way separable from that source, and so in no way truly separable from each other or any other aspect of reality. I recommend this book to anyone interested in understanding the nature of reality and their place in it.” —Deepak Chopra Unified Reality Theory describes how all reality evolves from an absolute existence. It also demonstrates that this absolute existence must have consciousness as an attribute that’s intrinsic to its being. Thus, Unified Reality Theory shows that consciousness, rather than being a product of the evolution of physical reality, is itself the source of what we experience as physical reality, and that physical reality is itself but one aspect of an evolving universal consciousness. Unified Reality Theory shows that, most fundamentally, this absolute consciousness-existence evolves into reality by means of a single process: self-relation. That is, consciousness-existence becomes reality by forming relationships with itself, analogous in a very limited way to what happens to a rubber band that’s twisted upon itself, i.e., it remains whole while differentiating into other forms. Thus, Unified Reality Theory demonstrates that reality is a state of existential self-relation. The idea that the universe consists of existence which has formed relationships with itself isn’t new; Taoists have understood this idea for at least a couple of thousand years. What’s new here is the presentation of this idea in the form of a detailed and defined structural model that correlates with the behavior of physical reality as described by science in general and physics in particular. Ultimately, Unified Reality Theory uses science and logic to make the case that God exists, as a pervasive and absolute consciousness that transcends the realities of space and time, and that we, as well as everything else, are that!

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

NATIONAL BESTSELLER • "A dazzling journey across the sciences and humanities in search of deep laws to unite them." —The Wall Street Journal One of our greatest scientists—and the winner of two Pulitzer Prizes for On Human Nature and The Ants—gives us a work of visionary importance that may be the crowning achievement of his career. In Consilience (a word that originally meant "jumping together"), Edward O. Wilson renews the Enlightenment's search for a unified theory of knowledge in disciplines that range from physics to biology, the social sciences and the humanities. Using the natural sciences as his model, Wilson forges dramatic links between fields. He explores the chemistry of the mind and the genetic bases of culture. He postulates the biological principles underlying works of art from cave-drawings to Lolita. Presenting the latest findings in prose of wonderful clarity and oratorical eloquence, and synthesizing it into a dazzling whole, Consilience is science in the path-clearing traditions of Newton, Einstein, and Richard Feynman.

This is a book about how humans learn. Our focus is on classroom learning although the principles are, as the name of this book indicates, universal. We are concerned with learning from pre-school to post-graduate. We are concerned with most bu- ness, industrial and military training. We do not address how infants learn how to speak or walk, or how grown-ups improve their tennis swing. We do address all learning described by the word “thought”, as well as anything we might try to teach, or instruct in formal educational settings. In education, the words theory and model imply conjecture. In science, these same words imply something that is a testable explanation of phenomena able to predict outcomes of experiments. This book presents a model of learning that the authors offer in the sense of scientists rather than educators. Conjecture implies that information is incomplete, and so it surely is with human learning. On the other hand, we assert that more than enough is known to sustain a “scienti?c” model of learning. This book is not a review of the literature. Instead, it is a synthesis. Scholars and many teachers likely have heard much if not most or even all of the information we use to develop the uni?ed learning model. What you have not read before is a model putting the information together in just this way; this is the ?rst one.

Newell introduces Soar, an architecture for general cognition. A pioneer system in AI, Soar is the first problem-solver to create its own subgoals and learn continuously from its own experience. Its ability to operate within the real-time constraints of intelligent behavior illustrates important characteristics of human cognition.