MIT Computer Science & AI Lab: AI-Machine Learning-Deep Learning-NLP-RPA: Executive Guide:
including Deep Learning, Natural Language Processing, Autonomous Cars, Robotic Process Automation
Malhotra, Y., Integrating Knowledge Management Technologies in Organizational Business Processes: Getting Real Time Enterprises to Deliver Real Business Performance, Journal of Knowledge Management, Vol. 9, Issue 1, April 2005, 7-28. PDF:
Beyond The Master Algorithm: AI & Machine Learning:
Sense Making for a Non-Deterministic World (Latest Update)
- Published on February 13, 2018: LinkedIn: https://www.linkedin.com/pulse/dear-ceo-ai-machine-learning-advice-top-industry-leading-malhotra/ .
- Updates continuously posted since the above publication date on the LinkedIn Feed of the author: https://www.linkedin.com/in/yogeshmalhotra/ .
AUTHOR: Dr. Yogesh Malhotra , Who's Who in America®, Who's Who in the World®, Who's Who in Finance & Industry®, Who's Who in Science & Engineering® | Global Risk Management Network, LLC | New York, USA | | 2018 Princeton Fintech & Quant Conference (See you at Princeton University). 2017-2018 MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL): Management and Leadership: Artificial Intelligence: Implications for Business Strategy | MIT Sloan Executive Education: AI, Machine Learning, Natural Language Processing, and, Robotics (including Robotic Process Automation & Cognitive Automation): AI & Machine Learning Subject Matter Expert, Industry Expert, Head Resource Mentor, and, Learning Facilitator | 2015-2018: Princeton Quant Trading Conference Presentations (Sponsors: Princeton University, Goldman Sachs, Citadel) | 2015-2018: SSRN: AI-Algorithms-Machine Learning-Deep Learning: 63 Top-10 Research Rankings: Top 2% Authors | 2009-2018: Post-Doc R&D: AI-Algorithms-Machine Learning-Deep Learning. | AACSB: Research Impact among Nobel Laureates such as Black-Scholes. | Wall Street Quant: Wall Street Investment Banks & Hedge Funds with $1 Trillion AUM | Big-3 Banking & Finance & Big-3 IT Leader: Global Financial Systems | Digital Transformation Pioneer: Clients & Patrons: Goldman Sachs, Google, IBM, Intel, Microsoft, MIT, Harvard... | New York State IT Administration & Network Administration CISO-Leader | National Association of Insurance Commissioners National Expert Panel | National Science Foundation U.S. Computer Scientists Expert Panels | United Nations Global Expert Economists Expert Panels | Invited Keynotes & Advisor: Silicon Valley VCs-CEOs, US & World Governments,... | Chartered Engineer, Computer Scientist, Quantitative Methods Scientist, Information Scientist, ... | Executive Education Faculty: Carnegie Mellon University, Kellogg School of Management... | Professor of Advanced Analytics, Computer Science, Cybersecurity (Security+), Operations Research, Quantitative Methods, Information Technology, and, Management Information Systems...
The MIT-AI Strategy Executive Guide is inspired by many of the Management & Leadership Industry Executives in the MIT-AI Learning Communities of Practice of which I am the Learning Facilitator. The experience of leading the global industry leaders as their Learning Facilitator in pioneering AI & ML industry practices in respective firms and industries also provides an outstanding opportunity for enhancing the MIT Sloan and MIT C-SAIL Faculty Curriculum in Community of Practice discussions supported by in-depth supplementary resources included in this guide. More than 300 of the in-depth supplementary resources are included in this guide arranged over the weekly Module-by-Module Curriculum followed and related online discussions. That experience is founded upon 20-year applied and industrial R&D in advancing global Knowledge Management, and, AI & Machine Learning practices. The earliest milestone of that journey that I recall was the communication of 1995 with the Genetic Algorithms pioneer Dr. John Holland. At that time, he was at the Santa Fe Institute, the Nobel laureates’ think tank on Complexity Theory and Self-Adaptive Systems, themes on which our Digital ventures were recognized by the Wall Street Journal and New York Times as global benchmarks for industry practices. Dialog with Holland inspired the top-ranked journal paper on AI & Machine Learningin the Expert Systems with Applications journal which stimulated attention of world's smartest intelligence agencies at the time. That paper and the related R&D programguiding worldwide practices on Digital Transformation underpin some of the central issues advancing the design of AI & Machine Learning 'sense-making' systems as evident in latest developments in AI, Machine Learning, and Deep Learning R&D including the latest advances such as in Generative Adversarial Networks. Of course, they were not known by that name at that time, just like John Holland's Genetic Algorithms were not known by that name when he pioneered those AI technologies.
Given strong applied interest of MIT AI-Strategy Industry Executives in software and services based robots for workflow and process automation and process optimization, Robotic Process Automation (RPA) & Cognitive Automation focus developed for and integrated in Module 4 of the MIT AI-Strategy program is also included at the beginning for ease-of-access — from the compilation of supplementary readings and study materials developed and included in the subsequent sections of this article. Also, included ahead of the weekly multimedia supplementary readings for all Modules, is a letter to a CEO participant from the recently concluded cohort responding to his query about how to get to the next technical level in strategic, tactical, and operational terms in ongoing execution and implementation of the Strategic Road Map blueprint developed in course of the MIT AI-Strategy program with many more supplementary resources. AI and Machine Learning Frameworks of 'Information Processing vs. Sense Making', and, Digital Transformation & Business Model Innovation Frameworks of Knowledge Management enterprises drawn from my published research round off the discussion and provide overarching frameworks as well as specific depth to Strategy, Processes, People, and Technology focus for the Strategic Road Map execution and implementation.
- "This course has been a fantastic experience. Thank you especially Dr Yogesh Malhotra, your contribution has been amazing, one of the best things of the course (if not the best)." - 2017-2018 MIT-AI Program Executive Feedback
- "Fantastic course and great learning experience. Even though I came here with a fair amount of knowledge on AI and some of its business applications, I have certainly learnt a lot more from this course. Special thanks to Dr Yogesh Malhotra... the forum discussions and resources shared have been by far my biggest takeaway." - 2017-2018 MIT-AI Program Executive Feedback
MIT AI-Strategy Executive Guide to RPA 2.0 and Cognitive Automation
- Smart Minds Using Smart Tools Beyond AlphaZero, AlphaGoZero, AlphaGo
- MIT: Partner 'Smart Minds' with 'Smart Tools' for AI & Machine Learning Success
- Financial Times: Businesses turn to software robots for office work
- MIT: Automated Feature Engineering from Feature Labs
- How BNY Mellon Became a Pioneer in Software Robots
- Robotic Process Automation: The Automation Of Automation
- Robotic Process Automation: Taking the Robot out of the Human
- How outsourcing companies are using Robotic Process Automation
- RPA and Strategy Examples and Capabilities: Use Cases: Multiple Industries
- McKinsey & Company: RPA (Robotic Process Automation)
- CIO: What is RPA? A revolution in Business Process Automation
- EY: Intelligent Automation Framework: RPA to Cognitive Automation
- E&Y: Get ready for Robots
- Deloitte: Automate this: A guide to Robotic Process Automation
RPA Implementation Partners: Beyond RPA 2.0
- Boston Consulting Group: Robotic Process Automation & Artificial Intelligence
- Boston Consulting Group: Bank Automation: Smart Processing = (RPA + BPM) + AI
- McKinsey: The value of Robotic Process Automation
- PwC 2017 Robotic Process Automation (RPA) Survey and Beyond RPA
- Deloitte: RPA & IA: Business Leader’s Guide: Service Delivery Transformation
- Accenture: Intelligent Automation & RPA: Future technology in financial services
- Genpact: Beyond RPA + AI Automation to "Cognitive Augmentation"
- KPMG: RPA Means the End of Off-Shoring As We Know It
- E&Y: RPA Robots in Action: 3 Automation Levels & 10 Causes of Failures
- IBM: Basic to Advanced Process Automation: Seeking More Practical Challenges
- PegaSystems: More Robotic Process Automation Use Cases: Videos
Top-ranked RPA Providers & Vendors: Beyond RPA 2.0
- Blue Prism - Robotic Process Automation
- Automation Anywhere - Robotic Process Automation Guides
- UiPath among RPA Industry Leaders
- WorkFusion: Beyond RPA to Smart Process Automation (SPA)
RPA & Digital Process Automation Analyst Reports
- Forrester Wave Robotic Process Automation Software, Q1 2017
- Forrester Wave Digital Process Automation Software, Q3 2017
- Gartner Market Guide for Robotic Process Automation Software, Dec. 2017
- Gartner When and How to Use RPA in Finance and Accounting, Dec. 2017
Cognitive Automation, also known as Intelligent Automation, is the next stage of Digital Transformation that combines the power of the RPA with that of AI, Machine Learning & Deep Learning...
AI, Machine Learning & Deep Learning: Realizing Business Value:
VentureBeat: Advice from Rob High, IBM Fellow and VP-CTO of IBM Watson:
"Decision-makers should take time now to consider how AI can help them fundamentally rethink and optimize their business. AI is not a silver bullet and real value comes from a tailored combination of tools designed to address a specific business problem. AI has the power to reinvent the way we do business, but only if company leaders take the time to understand the nuances of these powerful technologies. The stakes are high for companies looking to invest in AI, and incomplete understanding can lead to unsuccessful implementation and lack of ROI."
MIT AI-Strategy Program: Beyond Michael Porter's Frameworks to Digital Transformation Frameworks recognized and ranked as global industry benchmarks in worldwide Business & Technology press including Wall Street Journal, New York Times, Fortune, Inc., Fast Company, Business Week, CIO Enterprise, CIO Insight, Computerworld, Information Week, and, InfoWorld, etc., and, followed by world’s foremost IT visionaries such as Microsoft founder Bill Gates, and, Big-4 CxOs.
Above frameworks build a robust foundation for the Management & Leadership Industry Executives leading AI-Machine Learning industry implementations. Industry Executives engage in the global community-of-practice collaborative applied learning activities led by Subject Matter Experts-Industry Experts serving as Learning Facilitators. Industry Executives build an actionable "Blue Print" called the Strategic Road-Map starting with Digital Transformation Frameworks by integrating specific AI/ML/NLP/Robotics technologies for AI Augmentation in their own and/or client organizations. The Strategic Road-Map is advanced by applied-industrial industry-leading strategic, tactical, operational, and, technical resources to facilitate real world implementation of respective Industry Executive Strategic Road Maps in respective organizations.
So, incomplete understanding can lead to unsuccessful implementation and lack of ROI. How do we ensure successful implementation of AI and Machine Learning so that the Business Value ROI is achieved? This question can be re-framed from AI-Strategy perspective as: "Why?" "Why Do We Need AI & Machine Learning?" The associated question for CxOs is "How?" "How do we keep up with AI-Machine Learning developments that really matter from AI-Strategy perspective?" This article focuses on responding to CxOs on these two critical questions on which their future may very well depend.
In fact, the above question about "Why?" was answered in the following article that pioneered Real Time Enterprise Business Models and Business Processes using Knowledge Management Technologies of Human and Machine Intelligence such as AI and Machine Learning a few years ago.
Malhotra, Y., Integrating Knowledge Management Technologies in Organizational Business Processes: Getting Real Time Enterprises to Deliver Real Business Performance, Journal of Knowledge Management, Vol. 9, Issue 1, April 2005, 7-28.
That response was to the question asked by the Intellectual Capital pioneer Tom Stewart, who served as the editor of Harvard Business Review and also served on the Board of Directors of Fortune. He had asked: "Knowledge management activities are all over the map: building databases, measuring intellectual capital, establishing corporate libraries, building intranets, sharing best practices, installing groupware, leading training programs, leading cultural change, fostering collaboration, creating virtual organizations – all of these are knowledge management, and every functional and staff leader can lay claim to it. But no one claims the big question: Why?"
AI-Machine Learning Advice for CxOs: The AI-Strategy Case
Today, the same question can be framed for AI and Machine Learning as follows:
"AI and Machine Learning activities are all over the map: building databases, measuring intellectual capital, establishing corporate libraries, building intranets, sharing best practices, installing groupware, leading training programs, leading cultural change, fostering collaboration, creating virtual organizations – all of these are AI and Machine Learning, and every functional and staff leader can lay claim to it. But no one claims the big question: Why?" "Why Do We Need AI & Machine Learning?"The associated question is "How to keep up with the AI-Machine Learning developments that really matter from an AI-Strategy perspective?"
To address the above two critical questions, the following discussion is divided into four sections in the given order: Synopsis of Digital Transformation Frameworks beyond Porter's Frameworks; Personal Advice to a CEO participant in the MIT AI-Strategy program on advancing to the next technical level beyond the MIT AI-Strategy program; more than three hundred additional supplementary multimedia readings and study materialsfocused on "Stratical" tactical, operational, and technical execution and implementation to complement the strategic focus of the MIT AI-Strategy program— integrated within the respective weekly Modules focus on themes including Artificial Intelligence, Machine Learning & Deep Learning, Natural Language Processing, and, Robotics and Robotic Process Automation; and, Articles and Books that pioneered advancing Digital Transformation and Business Model Innovation frameworks beyond Porter's Frameworks.
"Knowledge management [including AI and Machine Learning] embodies organizational processes that seek synergistic combination of data and information-processing capacity of information technologies, and the creative and innovative capacity of human beings." Malhotra, Y., Deciphering the Knowledge Management Hype. Journal for Quality & Participation (American Society for Quality: Administrator of the Malcolm Baldrige National Quality Award), July-August, 1998.
Synopsis of Digital Transformation Frameworks beyond Porter
MIT: Partner 'Smart Minds' with 'Smart Tools' for AI & Machine Learning Success
Malhotra, Y., Integrating Knowledge Management Technologies in Organizational Business Processes: Getting Real Time Enterprises to Deliver Real Business Performance, Journal of Knowledge Management, Vol. 9, Issue 1, April 2005, 7-28.
Excerpt from my MIT AI-Strategy Industry Expert presentation note titled:
AI-Strategy Digital Transformation Frameworks for thinking about Your Vision.
1. Always focus on Performance Outcomes that you need to achieve
- All or most Technologies are means to achieve those outcomes.
- All or most Strategies are means to achieve those outcomes.
2. Technologies and what can be done with them are moving targets, hence know the key technologies but more significantly know "what capabilities you can derive" from them to achieve the Performance Outcomes that "you envision to achieve".
3. Technologies and programming languages come and go: what is there will be replaced by new technology or next 'shiny thing' tomorrow. Hence, focus on *broad 'capabilities' and 'trajectories'* of where the above are going and how you can adroitly 'master and manage' them to "make them achieve" "what you envision to do".
4. Regardless of choice of Strategy or Technology, Strategic Execution, i.e., how you translate the '36,000 feet view from the sky' into your business processes and practices 'where rubber meets the road' matters. Superior "Strategic Execution" can often result in superior Performance Outcomes even if one may not have the most superior Strategy or Technology.
(On a related note of evolution of applied IT-Strategy practices beyond Porter's seminal contributions to Strategy, my prior LinkedIn article discusses how IT is clearly no more a competitive advantage in itself, most likely it never was - it was always the smart use of IT, as documented in related refereed research publications such as the Real Time Enterprises article listed below. Making AI & Deep Learning Work Better: Designing 'Smart Minds' Using 'Smart Tools': "In a world characterized by continuous, radical and unpredictable change, there is hardly any competitive advantage or core competence that is sustainable. This applies as well to any competitive advantage based upon IT and information." So what is the solution to the conundrum? Here is one proposed solution in my Inc. magazine interview: "Obsolete what you know before others obsolete it and profit by creating the challenges and opportunities others haven't even thought about." That prescription seems to work quite well for the case where human skills are being substituted by the routine, structured, and, procedural skills embedded and embodied in the modern intelligent machines. So, how about humans pro-actively self-obsoleting their own skills and continuously advancing beyond the ongoing advancements in the prowess of the intelligent machines?)
Designing Real Time Enterprises that Really Deliver Real Time Performance
'Agile' Model of Digital Transformation for Human & Machine Intelligence*
REFERENCES: Listed at the end of the article are 18 articles related to Business Processes, Digital Transformation, and, Business Model Innovation that can be enabled by AI and Machine Learning technologies to help CxOs "fundamentally rethink and optimize their business" and to "reinvent the way they do business" as noted above by IBM Fellow and VP-CTO of IBM Watson Rob High in VentureBeat. Also listed are two Books that pioneered Intelligence-Based Digital Transformation & Virtual Work by the pioneering Global Digital Transformation Virtual Community of Practice (CoP) including 200 Global PhD Industry Experts as Authors-Reviewers; 130,000 'opt-in' CoP Network Members; and, Millions of Worldwide Network Users.
Personal Advice to a CEO participant in the MIT AI-Strategy program
Reproduced below is personal advice shared with a CEO who shared about enjoying my recently concluded presentation of the MIT Sloan & MIT CSAIL Artificial Intelligence Program for Management and Leadership track industry executives and asked for my advice on going to the next level of technical understanding for his company's consulting practices.
2018, February 10, Saturday
Dear <CEO name>,
Thank you for sharing that your strategy is to help improve clients' decision making in key result areas. Related to your focus on <Industry Name>, there is a whole area of Population Health Outcomes wherein Activity-based Costing is being applied to measure and manage patient profitability. The central emphasis is transitioning beyond revenue-driven models to fee-for-value based care. Many of the applications of AI and Machine Learning in these domains making headlines in popular and research press are focused on predictive diagnostics in specific areas and predictive life expectancy based care models in addition to Natural Language Processing driven interactive patient care models. Relevant contents for any such applied domain(s) can be aggregated and applied just along the lines of liquidity risk modeling projects with JP Morgan that I shared about in the span of the course - that aggregated about 60,000 pages of research across about 5,000 documents focused on and driven by the very specifics of quantitative risk analytics needs of the specific client(s). [Interestingly, despite 25-30 years of academic and applied-industrial research compiled from across academia, policy, and practice, there was no clear agreement to be found on even definitions or measurements of constructs such as 'liquidity risk'. Most academic studies were found to have focused on 'convenience samples' of data based upon popular market indices unrelated to the world of alternative asset investments and hedge funds practices of the largest investment banks and hedge funds.]
From an architectural standpoint, the white paper that I shared from Gartner goes deeper into the macro-level architecture and can provide a great starting point to dig deeper as needed. At the architectural level, the big shift is occurring in terms of traditional computer programming logic driven models to data driven Machine Learning models mostly around selected Python libraries. The three big ones are Scikit Learn, TensforFlow (What is a Tensor?), and Keras which is a user-friendly wrapper for both. Other big shifts that are occurring are the transitions beyond Supervised Machine Learning models driven by Big Data and Big Computing to Unsupervised Machine Learning models driven by Smarter Algorithms - with related shifts beyond Stochastic Gradient Descent models based Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) toward Reinforcement Learning based Neuroevolution and Evolutionary Search Models - within which the shifts are beyond Fitness-driven models toward Novelty-driven models. Within the widely followed and applied overarching frameworks founded in my own scholarly, industrial, and applied R&D, all these transitions make sense from a Risk and Uncertainty Management perspective that is discussed across my recent SSRN papers and presentationsaccessible online. Within that perspective, most transitions are occurring at the intersection of Math/Statistics/Probability, Computing, and, Coding/Development/Programming that I call the DigitalComputationalQuantitativeCyberQuantum paradigms (see related SSRN papers and presentations). While cross-disciplinary intersecting practices in these areas are already emerging such as in Quantitative Finance & Trading, 'books' at these intersections may get published later in the future, and, 'courses' may likely follow.
Over the span of the MIT AI-Strategy course, I had shared post-course implementation notesfor translating the individual Strategic Road-Maps developed over the span of the course into actual implementations in respective enterprises. These notes were shared as write-ups in our action learning community of practice discussions wherein I outlined among implementation tools many of the top Machine Learning-Deep Learning libraries focused books (such as on Scikit Learn, TensforFlow, and Keras) written by Machine Learning engineers and practitioners from an applied-industrial perspective. I had also shared top-ranked books that are significantly referenced by AI and Machine Learning R&D and implementation practitioners including the Deep Learning book, the Reinforcement Learning (RL) book, and, a number of Statistical Machine Learning books from Stanford and UC Berkeley. In addition, I had also shared the code development implementation focus grounded in computer science and AI from the development and implementation focused computer science and AI experts followed by worldwide AI and Machine Learning developers and practitioners. One resource lists about a dozen books for developers and practitioners [all of which I subscribe to and progress through as feasible] primarily that go into specific applied development and implementation of AI and Machine Learning technologies such as Long Short Term Memory Networks, Natural Language Processing, etc. The above applied-industrial books capture the key significant areas for developing greater depth of practice in the mathematical, statistical, probabilistic, coding-developmental aspects of AI and Machine Learning and Deep Learning. These are among some of the sources that I draw upon depending on the specific technical real world implementation projects that I need to deliver in specific applied domains of practice. Specific instances of how I do so, I shared and demonstrated in the application of Long Short Term Memory-Word2Vec and related models for Natural Language Processingapplications and discussion on Sparse Matrices in the context of the Week 4 Module on Robotics (and RPA) while discussing AI, Machine Learning, and Deep Learning technologies powering self-guided autonomous cars, one among many other areas of applied-industrial AI, Machine Learning, and Deep Learning practices pioneered by MIT-CSAIL.
There are too many AI, Machine Learning, and Deep Learning 'courses' on the above technologies and related programming languages and libraries. Most leading-edge applied real-world knowledge however is in the latest technological [computational & mathematical] capabilities that leapfrog traditional performance models. These developments are shared by word-of-mouth typically over diverse social networks and communities of AI and Machine Learning-Deep Learning technical practitioners and developers often before or just when they are published as first drafts of working research papers, if they are published as formal papers at all. For instance, my formal academic training happened to accumulate graduate credits equivalent to multiple PhDs toward building domain specific depths while advancing applications in real practice in areas such as Quantitative Finance and Cybersecurity and related applied practice areas such as Cyber Risk Insurance given my focus on computational quantitative mathematical models of Risk Modeling and Uncertainty Management. Even then, I am subscribed to additional dozens of most highly rated courses by individuals and entities such as Andrew Ng, Geoffrey Hinton, and, Google across Udemy, Udacity, Coursera, edX, etc. that I scan whenever feasible. However, most such courses are relatively academic in nature often influenced by the depth and background of applied-industrial experience and expertise of the instructors. In addition, those courses often also have significant overlaps in terms of coverage. For instance, if one wants to dig deeper into dynamic motion image processing which is the central bread-and-butter for many of the Machine Learning and Deep Learning applications, one needs to be digging deeper into Computer Vision Machine Learning practitioner applications and use cases - which again are disseminated via Machine Learning communities of practice - and skip almost everything else. Even in that specific context, one would focus beyond Convolutional Neural Networks(CNN) to the latest developments in RNN-CNN combinations, Recurrent Neural Networks(RNN)-Long Short Term Memory Networks and Capsule Networks. [As one may consider AI-based computer vision applications, it may help to also consider if the specific applications can be enabled by Fuzzy Logic based Computer Vision for instance: remember that there are multiple types of AI and not all applications need to be based on Neural Networks.] Hence choosing specific parts of specific 'courses' and 'books' is significantly driven by specific contexts of the applied-industrial applications even when most such 'books' and 'courses' often cater to what is generating the most buzz.
In summary, given the specific 'books' and 'courses', what to pick and what to skip, is highly dependent upon applied context application, implementation and development, with most of the real domain-specific and context-specific applications driving the leading-edge practices being defined and shared across communities of practice.
Wishing you all the best in action learning and active practice.
Best, Yogi
p.s. How Good to Great Companies Have Done It
*'Agile' Model of Digital Transformation for Human & Machine Intelligence
MIT AI-Strategy Program Supplementary Multimedia Readings & Study Materials
MIT AI-STRATEGY PROGRAM: Selected Supplementary Readings
MODULE 0: Orientation
- MIT: Partner 'Smart Minds' with 'Smart Tools' for AI & Machine Learning Success
- Michael Porter's Generic Competitive Strategies
- How Above Framework is Relevant Today
- Digital Transformation Info Graph
- Michael Porter's Value Chain Model
- SWOT Analysis: 25+ Ideas: Detail: Spectrum
- Michael Porter's Competitive Forces Model
- Getting Real Time Enterprises to Deliver Real Business Performance
- Beyond Michael Porter to Digital Transformation
- Beyond Michael Porter to "Anticipation of Surprise"
- Beyond Michael Porter to Risk and Uncertainty Management
- APPENDIX: Articles & Books: Digital Transformation & Business Model Innovation
MODULE 1: Artificial Intelligence
- MIT: Partner 'Smart Minds' with 'Smart Tools' for AI & Machine Learning Success
- McKinsey: 5 Fundamental Strategies for how to get the most out of AI
- MIT Technology Review: Artificial Intelligence Issue
- Google's Glossary on AI/ML
- Quartz guide to AI
- Nvidia: AI, Machine Learning, and, Deep Learning
- AI, Machine Learning, and Deep Learning
- Demystifying Artificial Intelligence
- What AI & ML Can Do—And What It Can’t
- Deep Learning Book, MIT Press
- Data Science, Machine Learning, Big Data Analytics
- Data Science, Machine Learning and Data Mining
- Introduction to K-means Clustering
- Agile machine learning: From theory to production
- Andreessen Horowitz: The Promise of AI
- What Changes When AI Is So Accessible That Everyone Can Use It?
- Financial Times: MIT moves to probe human and artificial intelligence
- MIT Technology Review: More efficient ML could upend the AI paradigm
- How companies can navigate the age of ML
- MIT Technology Review: Is AI Riding a One-Trick Pony?
- AI and Strategy Examples and Capabilities: Use Cases: Multiple Industries
- Google’s AI Wizard Unveils a New Twist on Neural Networks
- Capsule Networks: Intuition - How Capsules Work - Dynamic Routing
- Dynamic Routing Between Capsules
- Matrix capsules with EM routing
- Machine Learning — Andrew Ng, Stanford University
- Machine intelligence: Technology mimics human cognition to create value
- Artificial intelligence and machine learning in financial services
- Cognitive collaboration: Why humans and computers think better together
- Cognitive technologies: The real opportunities for business
- McKinsey: Artificial intelligence in business: Separating the real from the hype
- MIT Technology Review: This is not Intelligence
- MIT Technology Review: The Importance of Feelings
- AlphaGo Zero: Learning from scratch
- Why AlphaGo Zero is a Quantum Leap Forward in Deep Learning
- AlphaGo Zero Shows Machines Can Become Superhuman Without Any Help
- Why humans and computers think better together: Algorithms can be biased, too
- Nvidia: Snark Bite: Like an AI Could Ever Spot Sarcasm
- There are two kinds of AI, and the difference is important
- The Keras Blog
- TensorFlow
MODULE 2: Machine Learning
- MIT: Partner 'Smart Minds' with 'Smart Tools' for AI & Machine Learning Success
- MIT Technology Review: The Seven Deadly Sins of AI Predictions
- McKinsey: An Executive’s Guide to AI
- McKinsey: 120 Machine Learning business ideas
- Gartner: Preparing & Architecting for Machine Learning
- J.P.Morgan: Machine Learning and Big Data in Finance
- JP Morgan: Machine Learning & Alternative Data Approach to Investing
- The State of AI: Anrew Ng: What AI Can Do For Your Business
- 10 Companies Using Machine Learning in Cool Ways
- What is Artificial Intelligence?
- Interpretable Machine Learning
- Ideas on interpreting machine learning
- ML and Strategy Examples and Capabilities: Use Cases: Multiple Industries
- Interpretability is crucial for trusting AI and ML
- MIT Researchers Offer Algorithm for Picking 'Winning' Startups
- Picking Winners: A Framework For Venture Capital Investment
- Consumer Credit Risk Models via Machine-Learning Algorithms
- Risk and Risk Management in the Credit Card Industry
- Basic Concepts of Feature Selection
- Machine Learning Mastery Method
- Best of arXiv.org for AI, Machine Learning, and Deep Learning
- arxiv.org: Latest Papers in Machine Learning
- Deep Learning Patterns, Methodology and Strategy
- Convolutional Neural Networks For All
- Understanding Convolutional Neural Networks 1
- Understanding Convolutional Neural Networks 2
- Understanding Convolutional Neural Networks 3
- How Deep Neural Networks Work
- How Convolutional Neural Networks Work
- How Recurrent Neural Networks and LSTM Networks Work
- Kaggle: Introduction to Predicting Credit Default
- LSTM Networks Crash Course: Hands-On Implementation
- arxiv.org: More about arXiv: https://en.wikipedia.org/wiki/ArXiv
MODULE 3: Natural Language Processing (NLP)
- Forrester: Messaging Works but Chatbots can Frustrate
- Forrester: The State of Chatbots: Chatbots in App+ Mobile Strategy
- Gartner: Data Science and Machine-Learning: Magic Quadrant
- Gartner: Preparing and Architecting for Machine Learning
- Chatbot Landscape
- Automating the Law: Legal AI Landscape
- What is a Virtual Assistant?
- Virtual Assistants and their Comparative Review
- Chatbot (Artificial Conversational Entity)
- How to Create a Facebook Messenger Chatbot
- Chatbots for Ad Analytics: Slack, Facebook Messenger & Google Sheets
- Facebook Bots Have Amazing Potential (And You Should Still Ignore Them)
- Scale 1:1 Engagement with Facebook Messenger Chatbots
- Brands Using Facebook Messenger Bots for Social Media Strategies
- Bots (Software Robots) with Good Chat
- Human Like Robot Assistants: 'Physical' Chatbots
- NLP, AI, & Machine Learning Use Cases
- NLP and Strategy Examples and Capabilities: Use Cases: Multiple Industries
- Know the risks of Amazon Alexa and Google Home
- How Google Translate Works
- NLP that You Use Every Day without Noticing
- Natural Language Processing in Big Data
- Natural Language Processing: An Introduction
- From Natural Language Processing to Artificial Intelligence
- Big Data throws Big Biases into Machine Learning Data Sets
- Stanford: Natural Language Processing with Deep Learning
- Oxford: Deep Learning for Natural Language Processing
- Stanford NLP Group: Publications: Blog: Software
- Training NLP Bots & Agents: Amazon Turk & Facebook ParlAI
- Non-Expert Annotations for NL Tasks: Amazon Mechanical Turk
- NIPS 2016 Tutorial: Generative Adversarial Networks
- Will the Next Mozart be a Robot?
- Cognitive Capabilities Defined
- Deep Voice 2: Multi-Speaker Neural Text-to-Speech
- Google: DeepBreath: Preventing angry emails with NLP/ML
- Terminology: NLU vs. NLP vs. ASR
- NLP vs. NLU: What's the Difference?
- What is the difference between NLP and NLU
- Scikit-Learn: Text Analysis of Amazon Fine Food Reviews
- Paving the Way for Human-Level Sentence Corrections
- NLP with Python – Analyzing Text with Natural Language Toolkit
- Deep Learning for NLP: Tutorials with Jupyter Notebooks
- How to solve 90% of NLP problems: a Step-by-Step guide
- Keras LSTM tutorial – Building deep learning language model
- Twitter Sentiment Analysis Using Combined LSTM-CNN Models
- Supporting Agile Software Development by Natural Language Processing
- Object Oriented Analysis using Natural Language Processing concepts: A Review
- Leveraging Natural Language Processing in Requirements Analysis
- Vector Semantics
- How to Develop Word Embeddings in Python with Gensim
- Gensim
- Content Creation Tools: NLP Writes Unique Articles
- Narrative Science: Quill
- Automated Insights: Wordsmith
- yseop: Compose
- NLP/AI-ML for Developers
- When to Use Which Algorithm
- Deep Learning for NLP Crash Course: Hands-On Implementation
- MIT Technology Review: AI & ML: When Our Clothes become Part of IoT
- MIT Technology Review: Intelligent Machines: The “Black Mirror” Scenarios
- MIT Technology Review: AI Time Bombs could Sneak Cyberattacks
- Chasing A.I. Breakthroughs: Q&A With Director of New MIT-IBM Lab
- AI Tools you can use — for personal use
- AI tools you can use — for businesses (1)
- AI Tools you can use — for businesses (2)
- AI Tools you can use — industry specific
- TensorFlow Playground
- Learn TensorFlow and DL, without a Ph.D.
MODULE 4(a): Robotic Process Automation (RPA) & Cognitive Automation
MIT AI-Strategy Executive Guide to RPA 2.0 and Cognitive Automation
Beyond RPA 2.0 to RTE Cognitive Automation & Intelligent Automation
- Smart Minds Using Smart Tools Beyond AlphaZero, AlphaGoZero, AlphaGo
- MIT: Partner 'Smart Minds' with 'Smart Tools' for AI & Machine Learning Success
- Financial Times: Businesses turn to software robots for office work
- MIT: Automated Feature Engineering from Feature Labs
- How BNY Mellon Became a Pioneer in Software Robots
- Robotic Process Automation: The Automation Of Automation
- Robotic Process Automation: Taking the Robot out of the Human
- How outsourcing companies are using Robotic Process Automation
- RPA and Strategy Examples and Capabilities: Use Cases: Multiple Industries
- McKinsey & Company: RPA (Robotic Process Automation)
- CIO: What is RPA? A revolution in Business Process Automation
- E&Y: Get ready for Robots
- Deloitte: Automate this: A guide to Robotic Process Automation
RPA Implementation Partners: Beyond RPA 2.0
- Boston Consulting Group: Robotic Process Automation & Artificial Intelligence
- Boston Consulting Group: Bank Automation: Smart Processing = (RPA + BPM) + AI
- McKinsey: The value of Robotic Process Automation
- PwC 2017 Robotic Process Automation (RPA) Survey and Beyond RPA
- Deloitte: RPA & IA: Business Leader’s Guide: Service Delivery Transformation
- Accenture: Intelligent Automation & RPA: Future technology in financial services
- Genpact: Beyond RPA + AI Automation to "Cognitive Augmentation"
- KPMG: RPA Means the End of Off-Shoring As We Know It
- E&Y: RPA Robots in Action: 3 Automation Levels & 10 Causes of Failures
- IBM: Basic to Advanced Process Automation: Seeking More Practical Challenges
- PegaSystems: More Robotic Process Automation Use Cases: Videos
Top-ranked RPA Providers & Vendors: Beyond RPA 2.0
- Blue Prism - Robotic Process Automation
- Automation Anywhere - Robotic Process Automation Guides
- UiPath among RPA Industry Leaders
- WorkFusion: Beyond RPA to Smart Process Automation (SPA)
RPA & Digital Process Automation Analyst Reports
- Forrester Wave Robotic Process Automation Software, Q1 2017
- Forrester Wave Digital Process Automation Software, Q3 2017
- Gartner Market Guide for Robotic Process Automation Software, Dec. 2017
- Gartner When and How to Use RPA in Finance and Accounting, Dec. 2017
MODULE 4(b): Robotics, Physical Robots, and, Self-Driving Cars
- Autonomous Driving: NHTSA's Levels Of Autonomous Driving
- Consumer Reports: The State Of Self-Driving Cars
- Car Autonomy Levels Explained
- 6 levels of self-driving car - and what they mean for motorists
- Path to Autonomy: Self-Driving Car Levels 0 to 5 Explained
- Driverless Cars - The Race to Level 5 Autonomous Vehicles
- When does a car become truly autonomous?
- Self-Navigation of Driverless Cars: Feature Spaces and Feature Vectors
- Sparse Matrices for Machine Learning
- Toyota Exec Explains Why Simulation Key to Autonomous Driving
- Toyota's latest T-HR3 to safely assist humans with mobility challenges
- Physical Robots Examples and Capabilities: Use Cases: Multiple Industries
- Smartest robotics company in the world
- This robot aced an exam without understanding a thing
- Watch Yamaha's Humanoid Robot Ride a Motorcycle Around a Racetrack
- MIT Technology Review - Why Self-Driving Cars Must Be Programmed to Kill
- At the Mercedes museum, your rental car parks itself
- MIT’s Autonomous Golf Carts: Future Driverless City Might Not Even Need Cars
- Self-driving golf carts | MIT News
- Taking a ride in MIT’s self-driving wheelchair
- Meet your newest friend, nurse and carer: a robot who reads your moods and emotions
- This robot acts as your personal nurse
- MIT Technology Review: These Robots Install Solar Panels
- McKinsey: What the future of work will mean for jobs, skills, and wages
- MIT Technology Review: Tech companies should stop pretending AI won’t destroy jobs
- RAAS Robotics As a Service: Numbers and Industry Use Cases
- Why Automation and AI are Cool, Until They're Not
- Boston Dynamics... Google Bought It... Sold to SoftBank
- FarmBot brings robotic farming to your backyard garden
- Anthropomorphism: Opportunities and Challenges in Human-Robot Interaction
- Disney's $1 Billion Bet on a Magical Wrist Band
- Disney Uses Big Data, IoT And Machine Learning To Boost Customer Experience
- Disney Research: Machine Learning and Optimization
MODULE 5: Artificial Intelligence in Business & Society
Where AI Still Remains Reliant Upon Human Intelligence
AI & Human 'Sense Making', 'Common Sense', & 'Reasoning'
- MIT: Partner 'Smart Minds' with 'Smart Tools' for AI & Machine Learning Success
- MIT Technology Review: AI doesn't have "Common Sense"
- McKinsey: 5 Fundamental Strategies for how to get the most out of AI
- MIT: ML 2.0: Automated Feature Engineering from Feature Labs
- AI, Machine Learning, Robotics: How EQ & IQ Work Together
- Human 'Sense Making' & Machine 'Information Processing': AI & ML
- Despite All Fancy AI, Solving Intelligence Remains "the Greatest Problem in Science."
- DARPA Explainable Artificial Intelligence (XAI) Program
- Explaining the Predictions of Any Classifier: Related Algorithms for Trust
- Explainable Artificial Intelligence: More References
- MIT Technology Review: What Skills Will You Need to Be Employable in 2030?
- AI, Machine Learning, Robotics: How EQ & IQ Work Together
- Leading Leaders: Hire for Energy, Ability to Energize & Passion
- Making AI & Deep Learning Work Better: Designing 'Smart Minds' Using 'Smart Tools'
- Getting Real Time Enterprises to Deliver Real Business Performance
- Navigating the future of work: Deloitte
- Minds & machines: Art of forecasting in the age of AI: Deloitte
- Redesigning Work in an era of Cognitive Technologies: Deloitte
- MIT Mind Machine Project: From Artificial Intelligence to Artificial Consciousness
- The Surgeon Who Wants to Connect You to the Internet with a Brain
- Fired Tech Workers Turn to Chatbots for Counseling
- The Next Big Step for AI? Understanding Video - MIT Technology Review
- Types of AI: From Reactive to Self-Aware
- Can Machines be Conscious?
- Trying to Combat How AI can be used to Lie and Deceive
- Tesla Is Building Its Own Custom AI Chips - MIT Technology Review
- Google Taught an AI That Sorts Cat Photos to Analyze DNA
- Google makes AI tool for Precision Medicine Open Source
- DeepVariant: Highly Accurate Genomes With Deep Neural Networks
- Descartes' Error: Emotion, Reason, and the Human Brain
- MIT Technology Review - The Importance of Feelings
- MIT Technology Review - Peering Inside the Workings of the Brain
- MIT Technology Review - Eliminating the Human
- Man's Search for Meaning: Viktor Frankl
- IEEE: New Quantum Crypto Scheme Looks Ahead to "Quantum Internet"
- MIT: Quantum Cryptographers Set 400K Distance Record
- Cognitive Analytics Using Quantum Computing for Next Generation Encryption
- Unsupervised Learning (Andragogy) versus Supervised Learning (Pedagogy)
- Human Machine Systems: Mechanistic Systems versus Organic Systems
- Derman & Wilmott: Financial Modelers' Manifesto: Common Sense & Models
- Common Sense and Machine Learning Models: Cyber Risk Insurance
- Harvard Business Review: Why Incentive Plans Cannot Work
- Harvard Business Review: Rethinking Rewards
- Stanford University Commencement Address: Steve Jobs
- Speech to Graduating Harvard MBA Students: Deepak Malhotra
- Harvard University: Happier: Harvard's Most Popular Course
- Psychology of Flow and Happiness: Mihaly Csikszentmihalyi
- Flow: The Psychology of Optimal Experience
- Finding Flow: The Psychology Of Engagement With Everyday Life
- Beyond Boredom and Anxiety: Experiencing Flow in Work and Play
- Beyond the Information Given: Studies in the Psychology of Knowing
- Deep Learning Book from MIT Press
- Reinforcement Learning: An Introduction, MIT Press
- Deep Learning with Python
- Beyond Model Risk Management to Model Risk Arbitrage
- Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management
- Mathematics for the Nonmathematician
- Calculus: An Intuitive and Physical Approach
- Knowledge Management in Inquiring Organizations
- Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box
- Germany Takes an Ethics Stance on Driverless Cars: Don’t Kill Humans
- MIT: When an AI finally kills someone, who will be responsible?
- McKinsey: Where machines could replace humans—and where they can’t
- AI Augmentation: Sense Making: What it Means to be Human?
- AI Augmentation: Personal Meaning: What it Means to be Human?
- AI Augmentation: Self-Determination: What it Means to be Human?
- AI Augmentation: Motivation & Commitment: What it Means to be Human?
- AI Augmentation: Internal Motivation: What it Means to be Human?
- AI Augmentation: Volition and Commitment: What it Means to be Human?
- AI Augmentation: Motivation and Commitment: What it Means to be Human?
- AI Augmentation: Social Influence & Behavior: What it Means to be Human?
- Hackers Are the Real Obstacle for Self-Driving Vehicles
- DeepMind’s AI became a superhuman chess player in a few hours, just for fun
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- How to Navigate ‘Uncertainty’...When ‘Models’ Are ‘Wrong’
- Risk Modeling for Managing Uncertainty
- Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management
- Future of Finance Beyond 'Flash Boys'
- Beyond Model Risk Management to Model Risk Arbitrage
- CyberFinance: Why Cybersecurity Risk Analytics Must Evolve
- Stress Testing for Cyber Risks: Cyber Risk Insurance Modeling
- Nature: AI Diagnostics Need Attention
- AI researchers embrace Bitcoin technology to share medical data
MODULE 6: The Future of Artificial Intelligence
- Smart Minds Using Smart Tools Beyond AlphaZero, AlphaGoZero, AlphaGo
- AI: Model Risk Management to counter Spurious ML "Patterns"
- Model Risk Management Paper (Follow up to Sloan Mgmt. Review)
- Model Risk Management Presentation (Princeton)
- Model Risk Arbitrage™ Presentation (Princeton)
- MIT Technology Review: 100% Fatal Mind Uploading Service
- KM Common Body of Knowledge KM-CBK
- Knowledge Management: The Supply Chain Nerve Center
- Enabling Next Generation e-Business Architectures
- Why Knowledge Management Systems Fail?
- MIT AI-Machine Learning Executive Guide including RPA & Cognitive Automation
- Personal Advice to a CEO participant on Next Steps in AI-ML Learning
- Interactive Scikit Learn ML Algorithm Cheat Sheet
- Learn Python the Hard Way
- Data Science from Scratch
- Adventures in Machine Learning
- HBR: How Tech Companies Can Help Upskill the U.S. Workforce
- Advancing Information Strategy to Internet Time
- Cybersecurity Pros Fear AI Attacks by Hackers
- AI & Machine Learning in Cybersecurity: U.S. vs. Japan
- A Strategist’s Guide to Artificial Intelligence
- PwC & Microsoft: Harnessing Data & Analytics using Cloud & AI
- PwC: Real Value of AI for your Business
- Five Dimensions of the So-Called Data Scientist
- Is Knowledge the Ultimate Competitive Advantage?
- Deep Learning, MIT Press
- Reinforcement Learning: An Introduction, MIT Press
- Deep Learning with Python
- The Elements of Statistical Learning
- Computer Age Statistical Inference
- UC Berkeley: Using Machine Learning to Detect Electric Grid Cyber Threats
APPENDIX
Articles & Books: Digital Transformation & Business Model Innovation
REFERENCES
Listed below are 18 articles related to Business Processes, Digital Transformation, and, Business Model Innovation that can be enabled by AI and Machine Learning technologies to help CxOs "fundamentally rethink and optimize their business" and to "reinvent the way they do business" as noted above by IBM Fellow and VP-CTO of IBM Watson Rob High in VentureBeat.
Malhotra, Y., Integrating Knowledge Management Technologies in Organizational Business Processes: Getting Real Time Enterprises to Deliver Real Business Performance, Journal of Knowledge Management, Vol. 9, Issue 1, April 2005, 7-28.
Malhotra, Y., Enabling Knowledge Exchanges for E-Business Communities, Information Strategy: The Executive's Journal, 18(3), Spring 2002, 26-31.
Malhotra, Y., Knowledge Management for E-Business Performance: Advancing Information Strategy to 'Internet Time’. Information Strategy: The Executive's Journal, 16(4), Summer 2000, 5-16.
Malhotra, Y., Expert Systems for Knowledge Management: Crossing the Chasm between Information Processing and Sense Making, Expert Systems with Applications: An International Journal, 20(1), 7-16, 2001.
Malhotra, Y., Business Process Redesign: An Overview. IEEE Engineering Management Review, 26(3), Fall, 1998, 27-31.
Malhotra, Y., When Best Practices Becomes Worst Practices [On Managing Model Risk], Momentum: The Quality Magazine of Australasia [Quality Society of Australasia], NSW, Australia, September 2002, 29-30.
King, W.R., and Malhotra, Y., Developing a Framework for Analyzing IS Sourcing, Information and Management, 37(6), 2000, 323-334.
Malhotra, Y., Knowledge Management and New Organization Forms: A Framework for Business Model Innovation. Information Resources Management Journal, 13(1), January-March, 2000, 5-14.
Malhotra, Y., Knowledge Management for Organizational White Waters: An Ecological Framework. Knowledge Management, 2(6), March, 1999, 18-21.
Malhotra, Y., Knowledge Management: The Supply Chain Nerve Center. Inside Supply Management, Institute for Supply Management, July 2002, pp. 34-43
Malhotra, Y., Enabling Next Generation e-Business Architectures: Balancing Integration and Flexibility for Managing Business Transformation. Intel Corporation, Portland, Oregon. Summer 2001. (Expert Paper invited by the Intel Corporation).
Malhotra, Y., From Information Management to Knowledge Management: Beyond the 'Hi-Tech Hidebound' Systems. In K. Srikantaiah & M.E.D. Koenig (Eds.), Knowledge Management for the Information Professional. Medford, N.J.: Information Today Inc. 37-61, 2000.
Malhotra, Y., Information Ecology and Knowledge Management: Toward Knowledge Ecology for Hyperturbulent Organizational Environments, Encyclopedia of Life Support Systems (EOLSS), UNESCO Invited Expert Paper, UNESCO/Eolss Publishers, Oxford, UK (10,607 words), 2002 [Electronic re-publication in EOLSS, 2008]
Malhotra, Yogesh, Cognitive Computing for Anticipatory Risk Analytics in Intelligence, Surveillance, & Reconnaissance (ISR): Model Risk Management in Artificial Intelligence & Machine Learning (Presentation Slides) (January 28, 2018). Available at SSRN: https://ssrn.com/abstract=3111837 or https://dx.doi.org/10.2139/ssrn.3111837.
Malhotra, Yogesh, Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: Enterprise Risk Management to Model Risk Management: Understanding Vulnerabilities, Threats, & Risk Mitigation (Presentation Slides) (September 15, 2015). Available at SSRN: https://ssrn.com/abstract=2693886.
Malhotra, Y. Making AI & Deep Learning Work Better: Designing 'Smart Minds' Using 'Smart Tools', LinkedIn. 2017.
Malhotra, Y. How Deep Learning Models compare with Human Minds: 'Information Processing vs. Sense Making' Revisited, LinkedIn. 2017.
Malhotra, Y. On Advancing Machine Learning & Deep Learning Systems, LinkedIn. 2017.
Malhotra, Y. Cognitive Analytics of Quantum Computing and Quantum Biology, LinkedIn. 2017.
Malhotra, Y. Beyond Prediction to ‘Anticipation of Risk’, LinkedIn. 2017.
- AI and Machine Learning for Process Automation as seen earlier
- Today's AI and Machine Learning R&D Developments as seen earlier
Books that Pioneered Intelligence-Based Digital Transformation & Virtual Work by the Pioneering Global Digital Transformation Virtual Community of Practice (CoP) including 200 Global PhD Industry Experts as Authors-Reviewers; 130,000 'opt-in' CoP Network Members; and, Millions of Worldwide Network Users.
Malhotra, Y. (ed.), Knowledge Management and Virtual Organizations, Idea Group Publishing, Hershey: PA, April 2000, 408 pages.
- "In his latest book, Knowledge Management and Virtual Organisations, KM luminary, Dr. Yogesh Malhotra, offers some cautionary advice. He exposes three myths often associated with KM solutions." - Microsoft
Malhotra, Y. (ed.), Knowledge Management and Business Model Innovation, Idea Group Publishing, Hershey: PA, April 2001, 470 pages.
- "Knowledge Management and Business Model Innovation is an important addition to the IS researcher's bookshelf. It brings together the latest thinking on issues at the forefront of teaching innovation and professional imagination." - M. Lynne Markus, Professor and Department Chair of Electronic Business, City University of Hong Kong.