Choice Computing: Machine Learning and Systemic Economics for Choosing

This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.

Uncovers machine learning, intelligent modeling, and choice learning aspects of choice architecture Discusses choice paradigms and choice computing while unveiling ML aspects of choice innovations Presents examples of AI and ML giving new pathways for building human-centric and choice-centric products

Auteur

Parag is a marathon runner, Tedx speaker, husband of a bright doctor and above all a dreamer. He loves to write poetry and articulate his creative and innovative thoughts and deliver them through his passionate talks. He is also an entrepreneur, Machine Learning researcher and author of best-selling Innovation strategy, ML and Data science books. An avid reader, Parag holds Bachelors from Walchand College of Engineering Sangli (1990), PhD from IIT Kharagpur (2001), management education from IIM Kolkata and was conferred higher doctorate DSc by UGSM monarch, Switzerland (2010). He is the first higher doctorate in the area of knowledge innovation.

Parag s machine learning ideas resulted in pioneering products and have become commercially successful and produced unprecedented impact. He delivered over one thousand keynote addresses and 200+ tutorials across the globe. Over 1000 institutes and 10,00,000+ professionals benefitted from Parag s talks, research and systemic consultations. Parag helped underperforming professionals, start-ups and students to transform into happy and passionate warriors. Fellow of the IET, IETE, and senior member IEEE, Parag is recipient of Oriental Foundation Scholarship, distinguished alumnus award WCE - Sangli and was nominated for prestigious Bhatnagar award in 2013 and 2014. He was also awarded IETE-KR Phadke award for innovative entrepreneurship and research in 2019.

Parag has published over 300+ research papers and articles in peer reviewed journals and renown conferences. He invented over a dozen patents and authored 14 books (with world s best technical and business publishers like Bloomsbury, IEEE, Wiley, Prentice Hall, Springer, Oxford University Press, etc.). His book YD YearDown portrays interesting perspective on education and was adapted for a TV serial by Sony TV by well-known movie director Sameer Patil. His poem collection was specially appreciated by one of the greatest romantic poets of all times late Mangesh Padgaonkar. Parag s book Knowledge Innovation Strategy was listed as a game changing business book by Hindustan Times. Niigata Times Japan mentioned that `it is enlightening experience for readers It has foreword and endorsement with special acclamation by Dr. FC Kohli and Ratan Tata. Maharashtra Times, Hindustan Times, Times of India, Sakal, Pudhari from India and Niigata Times and Mainichi Newspaper from Japan published special articles highlighting Parag s contributions in democratization of AI and ML.

Parag was the first PhD guide in Computer Engineering at COEP Pune University (The second oldest technical institute in the country) and has guided 20 PhD candidates. He has over 30 years of experience in technologies, product building and applications of AI and ML to different verticals. In the past, he headed research divisions of many companies including Siemens (India & Germany), IDeaS (US and India) , ReasonEdge (Singapore) , Capsilon, etc. As an AI consultant he helped to build game changing products for companies like Envestnet, Tech Mahindra, UST Global, Agrisk, Tata Consumers etc. He founded start-ups iKnowlation India, Kvinna Limited New Zealand and created social value through innovation and research. Parag is a prolific speaker and is associated with many technical and B-schools of repute like IITs, IIMs, Tokyo Int. University Japan and Masaryk University, Brno Czech Republic etc. He has been taking special efforts and working closely with remote technical institutes in Maharashtra to inculcate research, thinking and entrepreneurship skills among students, faculties and researchers. He is a pioneer of concepts of Systemic Machine Learning, Reverse Hypothesis Machine Learning, Context Vector Machines and Choice computing. He has helped as AI and ML consultant and innovation strategist to over two dozen organizations in Singapore, US, Japan and India and contributed to their success stories. He worked on social good and developed over a dozen products in health care and education domains with focus on creating value at Bottom of Pyramid.

Contenu
Introduction.- Decoding Choosing.- ML of Choosing: Architecting Intelligent Choice Framework.- Machine Learning of Choice Economics.- Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way.- Choice Architecture Machine Learning Framework.- Artificial Consciousness and Choosing (Towards Conscious Choice Machines).- Choice Computing and Creativity.- Experimental Choice Computing and Choice Learning Through Real-Life Stories.- Beyond Choice Computing. <p