'Strategy' and 'innovation' are terms that have increasingly lost their meaning in today's cut-throat business environment. This book gives these words a fresh meaning to advocate new pathways for change, showing us how to turn grave adversities into lifetime opportunities. Knowledge Ocean Strategy shows us how companies like Aquachill, AirTight Networks, Serum Institutes, Mapro, Ketan Food Exports, PARI, Tata Group, Chitale Dairies and Aditya Auto Test could find simple, refreshing solutions to complex problems to create their own uncontested knowledge space. In this seminal book, innovation strategist and knowledge innovation expert, Parag Kulkarni challenges competition-based strategies and those based on a mere 'more for less' paradigm using classic examples to unfold effective strategies based on associative knowledge building.
In the midst of fierce competition and a turbulent market, Knowledge Ocean Strategy presents an important breakthrough in innovation and strategic business thinking and will be a great motivator for organisations that aim to expand knowledge boundaries beyond competitive landscape.
It will also help making the transition from competition- to knowledge- centric; analysis- to synthesis-centric and isolation- to association-centric organization building; a systematic approach for a big leap and knowledge advantage.
There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines.
The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making.
Chapters include:Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.