Index
A
- AARRR (acquisition, activation, retention, referral, and revenue), Measure Success
- acquihiring, Acquire, Retain, and Train Talent
- acquire phase of GABDO AI Process Model, Acquire-Example continued
- action-readiness, Action ready
- actionable insights, Deep Actionable Insights, Take Action
- activation, defined, Biological Neural Networks Overview
- advanced analytics, Racing to Business Success
- AGI (see artificial general intelligence)
- Agile development methodology, An Agile Analogy
- Agile Manifesto, An Agile Analogy
- AI (generally)
- applying to real-world applications, How Can I Apply AI to Real-World Applications?-How Can I Apply AI to Real-World Applications?
- as science, AI as Science-AI as Science
- cause and effect in, A Note on Cause and Effect
- data science and, What Is Data Science, and What Does a Data Scientist Do?
- definition and concepts, AI Definition and Concepts
- economic impact of, AI Opportunities
- future issues, The Future of AI-Summary
- impact on jobs, The Impact of AI on Jobs-Summary
- in production, Production versus Development Environments-Learning and Solution Maintenance
- learning like humans, Learning Like Humans
- nontechnical overview, AI and Machine Learning: A Nontechnical Overview-Summary
- opportunities presented by, AI Opportunities
- simple definition, An Introduction to the AI for People and Business Framework
- types, AI Types-AI Types
- AI chips, Hardware
- AI effect, Delight and Stickiness, Technology Convergence, Integration, and Speech Dominance, The AI Effect
- AI for People and Business Framework (see AIPB Framework)
- AI hype, AI Hype versus Reality, Managing Customer Expectations
- AI initiatives
- AI key considerations (see key considerations)
- AI maturity, AI Readiness and Maturity, AI Maturity-AI Maturity
- AI Readiness Model, AI Readiness and Maturity, AI Readiness-Data democratization
- AI strategy, Strategy-Strategy
- creating an AIPB prioritized roadmap, Creating an AIPB Prioritized Roadmap-Aligned Goals, Initiatives, Themes, and Features
- creating an AIPB solution, Creating An AIPB Solution Strategy
- design thinking and, Design Thinking
- hypothetical (podcast) example, An AI Strategy Example-Aligned Goals, Initiatives, Themes, and Features
- AI vision
- AI winter, AI Hype versus Reality
- AI-complete (AI-hard) problems, AI Types
- AIPB (AI for People and Business) Framework
- Agile as analogous method, An Agile Analogy
- and Five Ds, Strategy-Strategy
- as term, Harnessing the Power of AI for the Win
- assessment phase, Assess
- basics, An Introduction to the AI for People and Business Framework-Summary
- benefits of, AIPB Benefits-Science Focused
- benefits pseudocomponent, The AIPB Benefits Pseudocomponent
- build phase, Strategy-Strategy
- compared to existing frameworks, Existing Frameworks and the Missing Pieces of the Puzzle
- core components (see core components)
- creating a vision statement, AI-Driven Taste
- creating an AIPB prioritized roadmap, Creating an AIPB Prioritized Roadmap-Aligned Goals, Initiatives, Themes, and Features
- creating an AIPB solution strategy, Creating An AIPB Solution Strategy
- creating an assessment strategy, Assess
- deliver phase, Deliver
- experts component, Experts Component-Experts Component
- explainability focus of, Explainable Focused
- in hypothetical taste-sensing example, AI-Driven Taste
- Methodology Component, Methodology Component-Optimize
- North Star, A General Framework for Innovation, Methodology Component
- optimize phase, Optimize
- people and business focus of, People and Business Focused
- process categories (see AIPB Process Categories)
- science focus of, Science Focused
- strategy phase, Strategy-Strategy, An AI Strategy Example-Aligned Goals, Initiatives, Themes, and Features
- unified and holistic focus of, Unified and Holistic Focused-Unified and Holistic Focused
- vision phase, Vision-Vision
- why focus of, Why Focused
- AIPB Process Categories, Existing Frameworks and the Missing Pieces of the Puzzle-Existing Frameworks and the Missing Pieces of the Puzzle
- AIPB vision statement, AI-Driven Taste
- algorithmic bias, Mitigating Bias and Prioritizing Inclusion
- algorithmic transparency, Mitigate Liabilities
- algorithms, for machine learning, AI and Machine Learning Algorithms-Summary
- alignment, defined, Adoption and alignment
- Altman, Sam, Lean and Agile Product Development
- Amazon Go, Drive differentiation and competitive advantage
- Amazon recommendation engines, Personalization and Recommender Systems, Better UX, convenience, and delight, The AI Effect
- analytics maturity, AI Maturity
- ANI (artificial narrow intelligence), AI Types
- ANNs (see artificial neural networks)
- anomaly detection, Clustering and Anomaly Detection
- Apple (see iPhone)
- applied AI (term), AI Types
- applied AI transformation, Racing to Business Success
- artificial general intelligence (AGI)
- automation/autonomy distinction, AGI, Killer Robots, and the One-Trick Pony
- barriers to replicating human intelligence, AGI, Killer Robots, and the One-Trick Pony-AGI, Killer Robots, and the One-Trick Pony
- defined, AI Types
- future issues, AGI, Superintelligence, and the Technological Singularity
- hype vs. reality, AI Hype versus Reality-AI Hype versus Reality
- artificial narrow intelligence (ANI), AI Types
- artificial neural networks (ANNs), Biological Neural Networks Overview
- artificial super intelligence (ASI), AI Types
- Assessment Component of AIPB, Assessment Component-AI Readiness and Maturity
- assumptions, testing, Testing Risky Assumptions
- attributes, Data Structure and Format For AI Applications
- audio recognition, Pattern Recognition
- augmented intelligence, Augment Human Intelligence
- auto racing, Racing to Business Success
- automated machine learning (AutoML), Increased AI Understanding, Adoption, and Proliferation
- automated science, Augment Human Intelligence
- automation
- autonomous cars, Drive differentiation and competitive advantage
B
- baking (TCPR model analogy), A TCPR Model Analogy
- balanced data, Well-Balanced Data
- batch learning (offline learning), Learning and Solution Maintenance
- benefits pseudocomponent, of AIPB Framework, The AIPB Benefits Pseudocomponent, AIPB Core Components-Summary
- BI (business intelligence), Deep Actionable Insights, Drive differentiation and competitive advantage
- bias
- bias versus variance trade-off, Train, Validate, Test
- big data, Big Data
- binary classifier, Classification
- biometric identification, Pattern Recognition
- black box algorithms, Mitigate Liabilities
- blue ocean strategies, Build Versus Buy
- Bridgestone, Data democratization
- Brown, Tim, Design Thinking
- Brynjolfsson, Erik, Mitigate Liabilities, AI, Job Replacement, and the Skills Gap
- budgeting, Budgeting
- build phase of AIPB process, Strategy-Strategy
- build phase of GABDO AI Process Model, Build-Example continued
- exploratory data analysis, Explore
- model improvement, Improve
- model testing, Train, Validate, Test
- model training, Train, Validate, Test
- model validation, Train, Validate, Test
- selecting data to test hypothesis, Select
- build vs. buy, Scientific innovation and disruption, Build Versus Buy-Build Versus Buy
- business intelligence (BI), Deep Actionable Insights, Drive differentiation and competitive advantage
- business metrics, Measure Success
- business models, creating with AI, Create New and Innovative Business Models, Products, and Services
- business stakeholders, Defining Stakeholders and Introducing Their Goals, Goals by Stakeholder
- business transformation, Transform business and disrupt industries
- buy vs. build, Scientific innovation and disruption, Build Versus Buy-Build Versus Buy
C
- C-3PO, The AI Effect
- CAIS (comprehensive AI services), AGI, Superintelligence, and the Technological Singularity
- California Consumer Privacy Act, Mitigate Liabilities, Societal Impact
- call centers, Augment Human Intelligence
- CAP (credit assignment path), An Introduction to ANNs
- Carnegie Mellon University, Augment Human Intelligence
- causation, correlation vs., AGI, Killer Robots, and the One-Trick Pony, A Note on Cause and Effect
- CDP (customer data platform) tools, Specific Data Sources
- certainty, as human requirement, A Data Dependency Analogy
- Christensen, Clayton M., Importance versus Satisfaction
- classification
- cleanliness, of data, Clean Data
- clustering, Clustering and Anomaly Detection, How Machine Learning Models Are Learned
- cold-start problem, Personalization and Recommender Systems
- collaboration, consensus vs., Leadership and Generating a Shared Vision and Understanding
- competitive advantage, Drive differentiation and competitive advantage-Drive differentiation and competitive advantage
- completeness, of data, Complete Data
- comprehensive AI services (CAIS), AGI, Superintelligence, and the Technological Singularity
- computer vision, Computer Vision
- conceptual design, Design and Usability
- confirmation bias, Mitigating Bias and Prioritizing Inclusion
- confounding variables, AGI, Killer Robots, and the One-Trick Pony
- consensus, collaboration vs., Leadership and Generating a Shared Vision and Understanding
- consumer trust, Mitigate Liabilities
- convenience, as potential AI benefit, Better UX, convenience, and delight-Better UX, convenience, and delight
- convergence, Technology Convergence, Integration, and Speech Dominance
- core components of AIPB Framework, AIPB Core Components-AI Readiness and Maturity
- correlation, causation vs., AGI, Killer Robots, and the One-Trick Pony, A Note on Cause and Effect
- cost, TCPR and, Time and Cost
- credit assignment path (CAP), An Introduction to ANNs
- CRM (customer relationship management) tools, Specific Data Sources
- cultural category of AI Readiness Model, Cultural-Data democratization
- curse of dimensionality, Adequate Data Depth
- customer data platform (CDP) tools, Specific Data Sources
- customer expectation management, Managing Customer Expectations-Managing Customer Expectations
- customer relationship management (CRM) tools, Specific Data Sources
- customer service, augmented intelligence and, Augment Human Intelligence
- customers, as stakeholders, Defining Stakeholders and Introducing Their Goals
- cybersecurity, Clustering and Anomaly Detection
D
- data
- acquire phase of GABDO AI Process Model, Acquire-Example continued
- AI and, The Data Powering AI-Clean Data
- amount of, Adequate Data Amount
- avoiding bias in, Highly Representative and Unbiased Data
- balance of, Well-Balanced Data
- big data, Big Data
- cleanliness, Clean Data
- completeness, Complete Data
- curse of dimensionality, Adequate Data Depth
- depth of, Adequate Data Depth
- evolution of, Drive differentiation and competitive advantage-Drive differentiation and competitive advantage
- readiness and quality, Data Readiness and Quality (the “Right” Data)-Clean Data
- representative, Highly Representative and Unbiased Data
- specific sources of, Specific Data Sources-Specific Data Sources
- storage and sourcing, Data Storage and Sourcing
- structure and format for AI applications, Data Structure and Format For AI Applications
- data cleaning, Clean Data
- data democratization, Data democratization
- data dependency, A Data Dependency Analogy-A Data Dependency Analogy
- data maturity, AI Maturity
- data privacy, Mitigate Liabilities, Societal Impact
- data readiness
- data science, What Is Data Science, and What Does a Data Scientist Do?
- data scientists
- ideal vs. real, Acquire, Retain, and Train Talent
- roles and responsibilities, What Is Data Science, and What Does a Data Scientist Do?
- talent acquisition/retention/training, Acquire, Retain, and Train Talent-Acquire, Retain, and Train Talent
- data security, Clustering and Anomaly Detection, Societal Impact
- data-driven organization, Gut-to-data driven
- databases, Data Storage and Sourcing
- Dean, Jeff, Learning Like Humans
- decision-making
- deep actionable insights, Deep Actionable Insights
- deep AI, AI Types
- deep learning
- deficiency needs, Maslow’s Hierarchy of Needs
- definition stage, of design thinking, Design Thinking
- delight
- deliver phase of AIPB, Deliver
- deliver phase of GABDO AI Process Model, Deliver
- depth, of data, Adequate Data Depth
- descriptive analytics, Deep Actionable Insights
- design thinking, Design Thinking-Design Thinking
- design, of great products, Design and Usability-Design and Usability
- designers, as experts, Experts Component
- detection, defined, Pattern Recognition
- determinate system, The TCPR Model
- development environment, production environment vs., AI in Production, Production versus Development Environments
- development, local vs. remote, Local versus Remote Development
- diagnostic difficulty, Mitigate Liabilities
- differentiation, driving with AI, Drive differentiation and competitive advantage-Drive differentiation and competitive advantage
- digital age, Drive differentiation and competitive advantage
- digital transformation, applied AI transformation vs., Racing to Business Success
- dimensionality, curse of, Adequate Data Depth
- disruption
- Domingos, Pedro, AGI, Killer Robots, and the One-Trick Pony, Adequate Data Depth
- driverless cars, Drive differentiation and competitive advantage
- drones (unmanned aerial vehicles), Computer Vision
- drug discovery, Influence new and optimized processes
E
- economic impact of AI, AI Opportunities
- EDA (exploratory data analysis), Explore
- edge computing, Advancements in Computing Architecture
- efficiency, as potential benefit from AI, Better productivity, efficiency, and enjoyment
- 80/20 Pareto Principle, AI as Science
- Einstein, Albert, A Data Dependency Analogy
- Elliott, Larry, The Future of Automation, Jobs, and the Economy
- empathetic design, Human-centered over business-centered products and features, Design Thinking
- empirical (term), AI as Science
- employee expectations, managing, Managing Employee Expectations
- enjoyment, as potential AI benefit, Better productivity, efficiency, and enjoyment
- entertainment, AI and, Better learning and entertainment
- error-based learning, How Machine Learning Models Are Learned
- errors, as potential liability, Mitigate Liabilities-Mitigate Liabilities
- European Union, data protection in, Mitigate Liabilities, Societal Impact
- executive leadership, AI and, AI and Executive Leadership-AI and Executive Leadership
- expectation management, Science Focused
- experience (term), Experience Defined
- (see also human experiences)
- experience economy, The Experience Economy
- experience interfaces, Experience Interfaces
- experience, human (see human experience)
- experimentation, optimization and, Optimize
- experts component of AIPB Framework, Experts Component-Experts Component
- explainability
- exploratory data analysis (EDA), Explore
- Eyal, Nir, Delight and Stickiness
F
- failure of AI initiatives, Why Do AI Initiatives Fail?-Why Do AI Initiatives Fail?
- false negative/false positive (type 1/type 2) errors, Mitigate Liabilities
- feasibility, technical, Assess Technical Feasibility
- feature engineering, Adequate Data Amount
- feature learning, An Introduction to Deep Learning
- feature space, Data Readiness and Quality (the “Right” Data), An Introduction to Deep Learning
- features, Data Structure and Format For AI Applications, Adequate Data Depth
- fields, Data Structure and Format For AI Applications
- financial category of AI Readiness Model, Financial
- financial performance, savings, and insights, Better financial performance, savings, and insights
- financial services industry, Create New and Innovative Business Models, Products, and Services
- Five Ds, Strategy-Strategy
- five whys, Defining Stakeholders and Introducing Their Goals
- flipped classroom, The Flipped Classroom
- fog computing, Advancements in Computing Architecture
- For People and Business (FPB) Framework, A General Framework for Innovation
- Ford, Henry, Managing Customer Expectations
- free will, AGI, Killer Robots, and the One-Trick Pony
- future issues, The Future of AI-Summary
- AGI, superintelligence, and technological singularity, AGI, Superintelligence, and the Technological Singularity
- AI and executive leadership, AI and Executive Leadership-AI and Executive Leadership
- and AI effect, The AI Effect
- computing architecture advances, Advancements in Computing Architecture
- hardware advances, Hardware
- impact of AI on jobs, The Impact of AI on Jobs-Summary
- increases in AI understanding/adoption/proliferation, Increased AI Understanding, Adoption, and Proliferation-Increased AI Understanding, Adoption, and Proliferation
- research, Research
- societal impact, Societal Impact-Societal Impact
- software advances, Software
- technology convergence/integrations/speech dominance, Technology Convergence, Integration, and Speech Dominance
- trends in AI, What to Expect and Watch For-The AI Effect
G
- GABDO AI Process Model, The AIPB Benefits Pseudocomponent, The AI Process-Summary
- GDPR (General Data Protection Regulation), Mitigate Liabilities, Societal Impact
- goals of AI
- AI techniques organized by, How Can I Apply AI to Real-World Applications?-How Can I Apply AI to Real-World Applications?
- and purpose of AI for business, Goals and the Purpose of AI for Business-Better learning and entertainment
- augmented intelligence, Augment Human Intelligence
- business transformation, Transform business and disrupt industries
- capturing new markets, Capture new markets or expand TAMs
- deep actionable insights, Deep Actionable Insights
- defining for people and business, Defining Goals for People and Business-Summary
- defining stakeholders and introducing their goals, Defining Stakeholders and Introducing Their Goals-Defining Stakeholders and Introducing Their Goals
- drive differentiation/competitive advantage, Drive differentiation and competitive advantage-Drive differentiation and competitive advantage
- expanding TAM, Capture new markets or expand TAMs
- industry disruption, Transform business and disrupt industries
- influence new and optimized processes, Influence new and optimized processes
- people goals, Goals and the Purpose of AI for People
- stakeholder-specific, Goals by Stakeholder-Better learning and entertainment
- goals phase, of GABDO AI Process Model, Goals-Example
- Godin, Seth, Leadership and Generating a Shared Vision and Understanding
- Google, Search, Information Extraction, Ranking, and Scoring
- government regulation, Mitigate Liabilities, Societal Impact
- graphics processing units (GPUs), data processing with, Hardware
- great products, What Makes a Product Great-Summary
- ability to meet human needs/wants/likes, Ability to Meet Human Needs, Wants, and Likes-Human-centered over business-centered products and features
- delight and stickiness, Delight and Stickiness
- design and usability, Design and Usability-Design and Usability
- four ingredients of, The Four Ingredients of a Great Product-Delight and Stickiness
- importance vs. satisfaction, Importance versus Satisfaction-Importance versus Satisfaction
- Lean and Agile product development, Lean and Agile Product Development-Lean and Agile Product Development
- Netflix and focus on what matters most, Netflix and the Focus on What Matters Most
- products that just work, Products That Just Work
- Product–Market Fit Pyramid, Lean and Agile Product Development-Lean and Agile Product Development
- gut-driven decision making, Gut-to-data driven
H
- hard skills, The Skills of Tomorrow
- Hawkins, Jeff, Spatial–Temporal Sensing and Perception
- HCML (human-centered machine learning), Human-centered over business-centered products and features
- health (of AI system), Optimize
- health (personal), Better health and health-related outcomes
- Heath, Chip, Summary
- highest-paid persons opinion (HiPPO) problem, Human-centered over business-centered products and features
- Holbrook, Jess, Mitigate Liabilities
- Hook Model, Delight and Stickiness
- Horowitz, Ben, Summary
- human brain
- human experiences
- human intelligence
- human needs
- difference between needs, wants, and likes, The difference between needs, wants, and likes
- great products ability to meet, Ability to Meet Human Needs, Wants, and Likes-Human-centered over business-centered products and features
- human-centered vs. business-centered products/features, Human-centered over business-centered products and features-Human-centered over business-centered products and features
- Maslows hierarchy of, Maslow’s Hierarchy of Needs-Maslow’s Hierarchy of Needs
- human resources (see talent)
- human taste, hypothetical AI simulation, An AI Vision Example-Our AIPB Vision Statement
- human-centered machine learning (HCML), Human-centered over business-centered products and features
- human-centered products, Human-centered over business-centered products and features-Human-centered over business-centered products and features
- hypotheses
I
- ideation stage of design thinking, Design Thinking
- image recognition, An Introduction to Deep Learning
- importance, satisfaction vs., Importance versus Satisfaction-Importance versus Satisfaction
- inclusion, Mitigating Bias and Prioritizing Inclusion
- indeterminate system, The TCPR Model
- information age, Drive differentiation and competitive advantage
- information architecture, Design and Usability
- infrastructure, Infrastructure and technologies
- innovation (see also AIPB Framework)
- Innovation Uncertainty Risk versus Reward Model, AI Maturity-AI Maturity
- Insilico Medicine, Influence new and optimized processes
- Intel Nervana Neural Network Processor (NNP), Hardware
- intelligence, defined, AI Definition and Concepts
- interaction design, Design and Usability
- interfaces, AI, Experience Interfaces
- interpretability, Mitigate Liabilities
- iPhone, Vision, Delight and Stickiness, The AI Effect
J
- Jobs to Be Done Framework, Product Leadership and Perspective, Importance versus Satisfaction-Importance versus Satisfaction
- jobs, impact of AI on, The Impact of AI on Jobs-Summary
- and skills of tomorrow, The Skills of Tomorrow
- future of automation, jobs, and the economy, The Future of Automation, Jobs, and the Economy
- job replacement and skills gap, AI, Job Replacement, and the Skills Gap
- skills gap and new job rules, The Skills Gap and New Job Roles-The Skills Gap and New Job Roles
- Jobs, Steve, Why Focused, The AI Effect
K
- kaizen, Optimize
- Kamen, Dean, Testing Risky Assumptions
- Kano Model, Delight and Stickiness
- Kano, Noriaki, Delight and Stickiness
- Karpathy, Andrej, NLG
- key considerations, AI Key Considerations-Summary
- AI hype vs. reality, AI Hype versus Reality-AI Hype versus Reality
- AI in production, Production versus Development Environments-Learning and Solution Maintenance
- assessing technical feasibility, Assess Technical Feasibility
- bias mitigation, Mitigating Bias and Prioritizing Inclusion
- build vs. buy decision, Build Versus Buy-Build Versus Buy
- liability mitigation, Mitigate Liabilities-Mitigate Liabilities
- managing customer expectations, Managing Customer Expectations-Managing Customer Expectations
- managing employee expectations, Managing Employee Expectations
- measuring success, Measure Success-Measure Success
- prioritizing inclusion, Mitigating Bias and Prioritizing Inclusion
- quality assurance, Quality Assurance
- staying current, Stay Current
- talent acquisition/retention/training, Acquire, Retain, and Train Talent-Acquire, Retain, and Train Talent
- testing risky assumptions, Testing Risky Assumptions
L
- labeled data, Ways Machines Learn
- language, as sequence data, Data Structure and Format For AI Applications
- leadership
- Lean and Agile methods, Lean and Agile Product Development, Lean and Agile Product Development
- (see also Agile development model)
- learning
- LeCun, Yann, Learning Like Humans
- LEGO Movie, The, Experts Component
- liabilities, mitigating, Mitigate Liabilities-Mitigate Liabilities
- likes, human, The difference between needs, wants, and likes
- line-of-business (LoB) managers, Goals and the Purpose of AI for Business, Adoption and alignment, Data democratization
- linear regression, Parametric versus Nonparametric Machine Learning
- logging, Optimize
- loss function, An Introduction to ANNs
- Lyft, Transform business and disrupt industries
M
- machine learning
- algorithms, AI and Machine Learning Algorithms-Summary
- artificial neural networks and, An Introduction to ANNs-An Introduction to ANNs
- as defined in Google Design blog, Machine Learning Definition and Key Characteristics
- biological neural networks and, Biological Neural Networks Overview-Biological Neural Networks Overview
- cause and effect in, A Note on Cause and Effect
- deep learning applications, Deep Learning Applications
- deep learning basics, An Introduction to Deep Learning-An Introduction to Deep Learning
- definition and key characteristics, Machine Learning Definition and Key Characteristics-Machine Learning Definition and Key Characteristics
- how machine learning models are learned, How Machine Learning Models Are Learned
- human-centered, Human-centered over business-centered products and features
- nontechnical definition, Machine Learning Definition and Key Characteristics
- nontechnical overview, AI and Machine Learning: A Nontechnical Overview-Summary
- parametric vs. nonparametric, Parametric versus Nonparametric Machine Learning-Parametric versus Nonparametric Machine Learning
- ways that machines learn, Ways Machines Learn-Ways Machines Learn
- machine vision, Computer Vision
- maintenance, AI Readiness Model and, Support and maintain
- managers, as experts, Experts Component
- marketing data, Specific Data Sources
- markets
- Maslows hierarchy of needs, Maslow’s Hierarchy of Needs-Maslow’s Hierarchy of Needs
- master builders, Experts Component
- maturity, AI Readiness and Maturity, AI Maturity-AI Maturity
- Mcafee, Andrew, Mitigate Liabilities
- McAllister, Ian, Vision
- McClure, Dave, Measure Success
- medical research, Influence new and optimized processes
- mental health, potential benefits from AI, Mental health
- Methodology Component of AIPB, Methodology Component-Optimize
- minimum lovable product (MLP), Lean and Agile Product Development
- minimum viable product (MVP), Lean and Agile Product Development, Testing Risky Assumptions
- model drift, Deliver, Monitor
- model selection, Select
- monitoring, of AI model, Monitor
- Ms. Pac-Man, Reinforcement Learning
N
- natural language, Natural Language-NLU
- needs, human (see human needs)
- Netflix
- neural networks
- neurons, Biological Neural Networks Overview
- NLG (natural language generation), NLG
- NLP (natural language processing), NLP
- NLU (natural language understanding), NLU
- no free lunch theorem, AI as Science
- nondeterminism, AI as Science
- nonlinear transformations, An Introduction to Deep Learning
- nonparametric machine learning
- North Star, A General Framework for Innovation, Methodology Component
- NoSQL database systems, Data Storage and Sourcing
- nuTomomy, Drive differentiation and competitive advantage
O
- ODI (outcome-driven innovation), Importance versus Satisfaction
- offline learning, Learning and Solution Maintenance
- Olsen, Dan, Design and Usability, Lean and Agile Product Development
- online learning, Learning and Solution Maintenance
- operational data, Specific Data Sources
- opportunities
- optimize phase of AIPB, Optimize
- optimize phase of GABDO AI Process Model, Optimize-Example continued
- organizational category of AI readiness, Organizational-Sponsorship and support
- outcome-driven innovation (ODI), Importance versus Satisfaction
- overfitting, An Introduction to ANNs, Train, Validate, Test
P
- Paradox of Automation, AI Hype versus Reality
- parameter space, Adequate Data Depth
- parametric machine learning
- Pareto Principle, AI as Science
- pattern recognition, Pattern Recognition
- people, People and Business Focused
- people stakeholders, Goals by Stakeholder
- performance
- personal assistants, Experience Interfaces, Managing Customer Expectations, Technology Convergence, Integration, and Speech Dominance
- personal safety/security, Better personal safety and security
- personalization, recommender systems and, Personalization and Recommender Systems
- physical health, AI and, Physical health
- planning, AI and, Better and easier planning and decisions
- precision versus recall error trade-off, Mitigate Liabilities
- predictive analytics, Deep Actionable Insights
- prescriptive analytics, Deep Actionable Insights
- prioritized AIPB roadmap, Creating an AIPB Prioritized Roadmap-Aligned Goals, Initiatives, Themes, and Features
- process categories, AIPB Process Categories and Recommended Methods
- process optimization, Influence new and optimized processes
- product leadership, Product Leadership and Perspective
- production environment, AI in Production, Production versus Development Environments
- production, AI in, Production versus Development Environments-Learning and Solution Maintenance
- productivity, as AI benefit, Better productivity, efficiency, and enjoyment
- products
- Product–Market Fit Pyramid, Lean and Agile Product Development-Lean and Agile Product Development
- project management triangle, The TCPR Model
- prototyping, Design Thinking
R
- RDBMS (relational database management systems), Data Storage and Sourcing
- readiness, AI Readiness and Maturity, AI Readiness and Maturity
- (see also AI Readiness Model)
- real-world applications
- applying AI to, How Can I Apply AI to Real-World Applications?-How Can I Apply AI to Real-World Applications?
- automation, Hybrid, Automation, and Miscellaneous
- clustering and anomaly detection, Clustering and Anomaly Detection
- computer vision, Computer Vision
- examples, Real-World Applications and Examples-Hybrid, Automation, and Miscellaneous
- hybrid applications, Hybrid, Automation, and Miscellaneous
- information searching, extraction, ranking, and scoring, Search, Information Extraction, Ranking, and Scoring
- natural language, Natural Language-NLU
- pattern recognition, Pattern Recognition
- personalization and recommender systems, Personalization and Recommender Systems
- predictive analytics, Predictive Analytics-Classification
- reinforcement learning, Reinforcement Learning-Reinforcement Learning
- sequential data, Time-Series and Sequence-Based Data
- time-series data, Time-Series and Sequence-Based Data
- recall, precision versus, Mitigate Liabilities
- recognition, defined, Pattern Recognition
- recommender systems (recommendation engines), Personalization and Recommender Systems, Better UX, convenience, and delight, The AI Effect
- red ocean strategies, Build Versus Buy
- regression
- regulation, Mitigate Liabilities, Societal Impact
- reinforcement learning (RL), Reinforcement Learning-Reinforcement Learning
- relational database management systems (RDBMS), Data Storage and Sourcing
- representation learning, An Introduction to Deep Learning
- representative data, Highly Representative and Unbiased Data
- requirements, in TCPR, Requirements
- research, Research
- return on investment (ROI), Creating an AIPB Prioritized Roadmap
- ride sharing platforms, Transform business and disrupt industries
- risk
- RL (reinforcement learning), Reinforcement Learning-Reinforcement Learning
- roadmap, AIPB, Creating an AIPB Prioritized Roadmap-Aligned Goals, Initiatives, Themes, and Features
- robo-advisors, Create New and Innovative Business Models, Products, and Services, Better financial performance, savings, and insights
- Rogers, Everett, AGI, Superintelligence, and the Technological Singularity
- ROI (return on investment), Creating an AIPB Prioritized Roadmap
- Rotman, David, Summary
S
- sales data, Specific Data Sources
- sample selection bias (see selection bias)
- sampling bias, Highly Representative and Unbiased Data
- satisfaction, importance vs., Importance versus Satisfaction-Importance versus Satisfaction
- scalability, Production Scalability
- scaling out, Production Scalability
- scaling up, Production Scalability
- science
- scientific innovation
- scientific method, AI as Science
- scientist, as master builder, Experts Component
- scrum, An Agile Analogy
- security
- Segway, Testing Risky Assumptions
- selection bias, Highly Representative and Unbiased Data, Mitigating Bias and Prioritizing Inclusion
- self-actualization needs, Maslow’s Hierarchy of Needs
- semi-structured data, Data Structure and Format For AI Applications
- semi-supervised learning, Ways Machines Learn
- sequence (sequential) data, Data Structure and Format For AI Applications, Time-Series and Sequence-Based Data
- services, creating with AI, Create New and Innovative Business Models, Products, and Services
- Shah, Dharmesh, Delight and Stickiness
- shallow networks, AI Types, An Introduction to Deep Learning
- shared understanding, Leadership and Generating a Shared Vision and Understanding
- shared vision, Leadership and Generating a Shared Vision and Understanding
- similarity-based learning, How Machine Learning Models Are Learned
- Simonite, Tom, Learning Like Humans
- simple linear regression, Parametric versus Nonparametric Machine Learning
- Sinek, Simon, Start with Why, Our AIPB Vision Statement
- skills gap
- social media
- societal impact of AI, Societal Impact-Societal Impact
- soft skills, The Skills of Tomorrow
- spam classifiers, Well-Balanced Data, Classification
- spatial–temporal perception, Spatial–Temporal Sensing and Perception
- speech, as means of interaction with technology, Technology Convergence, Integration, and Speech Dominance
- (see also personal assistants)
- sponsorship, Sponsorship and support
- stakeholders
- stale models, Deliver
- Star Wars (movie), The AI Effect
- Start With Why (Sinek), Start with Why
- statistical learning, How Machine Learning Models Are Learned
- stickiness
- storage, of data, Data Storage and Sourcing
- strategy phase of AIPB, Strategy-Strategy, AIPB Strategy Phase Recap
- structural unemployment, AI, Job Replacement, and the Skills Gap
- structured data, Data Structure and Format For AI Applications
- success
- superintelligence, AGI, Superintelligence, and the Technological Singularity
- (see also artificial general intelligence (AGI))
- supervised learning, Ways Machines Learn, How Machine Learning Models Are Learned
- support, Support and maintain
T
- talent
- acquiring, retaining, training, Acquire, Retain, and Train Talent-Acquire, Retain, and Train Talent
- addressing shortage of, Increased AI Understanding, Adoption, and Proliferation
- TAM (total addressable market), Capture new markets or expand TAMs
- target space, Adequate Data Depth
- taste, sensation of, An AI Vision Example-Our AIPB Vision Statement
- TCPR (Time–Cost–Performance–Requirements) model
- Tech Product Hub-and-Spoke Model, AI and Executive Leadership
- technical feasibility, assessing, Assess Technical Feasibility
- technical maturity, AI Maturity
- Technical Maturity Mixture Model, AI Maturity
- technical unemployment, AI, Job Replacement, and the Skills Gap
- technological category of AI Readiness Model, Technological-Data readiness and quality (the “right” data)
- testers, Experts Component
- testing, Strategy
- text recognition, Pattern Recognition
- tiered roadmap, Creating an AIPB Prioritized Roadmap-Aligned Goals, Initiatives, Themes, and Features
- time-series data, Time-Series and Sequence-Based Data
- Time–Cost–Performance–Requirements model (see TCPR model)
- Toronto Declaration, Societal Impact
- total addressable market (TAM), Capture new markets or expand TAMs
- Toyoda, Sakichi, Defining Stakeholders and Introducing Their Goals
- tractability, AI as Science
- training data, An Introduction to Deep Learning, Train, Validate, Test
- transactional data, Specific Data Sources
- transfer learning, An Introduction to Deep Learning
- transformed businesses, AI and, Transform business and disrupt industries
- transparency, Mitigate Liabilities
- trust, Mitigate Liabilities
- Twitter, Defining Stakeholders and Introducing Their Goals-Defining Stakeholders and Introducing Their Goals
- type 1/type 2 errors, Mitigate Liabilities
U
- UAVs (unmanned aerial vehicles), Computer Vision
- Uber, Transform business and disrupt industries
- uncertainty risk, AI Maturity-AI Maturity
- underfitting, Train, Validate, Test
- understanding, shared, Leadership and Generating a Shared Vision and Understanding
- unemployment, AI, Job Replacement, and the Skills Gap
- unlabeled data, Ways Machines Learn
- unmanned aerial vehicles (UAVs), Computer Vision
- unstructured data, Data Structure and Format For AI Applications, Specific Data Sources
- unsupervised learning, Ways Machines Learn, How Machine Learning Models Are Learned
- usability, great products and, Design and Usability
- user experience (UX)
- delight and stickiness, Delight and Stickiness
- design and usability, Design and Usability-Design and Usability
- designers as scientists, Experts Component
- high quality as business goal, Defining Stakeholders and Introducing Their Goals
- potential benefits from AI, Better UX, convenience, and delight-Better UX, convenience, and delight
- Product–Market Fit Pyramid and, Lean and Agile Product Development
- UX Design Iceberg, Design and Usability
- users, as stakeholders, Defining Stakeholders and Introducing Their Goals
- UX Design Iceberg, Design and Usability
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