Get off the Giant Wheel! `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.

This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same-the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.

This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same-the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.