AI for People and Business 1st edition by Alex Castrounis – Ebook PDF Instant Download/Delivery:1492036528 , 9781492036524
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ISBN 10: 1492036528
ISBN 13: 9781492036524
Author: Alex Castrounis
If you’re an executive, manager, or anyone interested in leveraging AI within your organization, this is your guide. You’ll understand exactly what AI is, learn how to identify AI opportunities, and develop and execute a successful AI vision and strategy. Alex Castrounis,founder and CEO of Why of AI, Northwestern University Adjunct, advisor, and former IndyCar engineer and data scientist, examines the value of AI and shows you how to develop an AI vision and strategy that benefits both people and business. AI is exciting, powerful, and game changing–but too many AI initiatives end in failure. With this book, you’ll explore the risks, considerations, trade-offs, and constraints for pursuing an AI initiative. You’ll learn how to create better human experiences and greater business success through winning AI solutions and human-centered products. Use the book’s AIPB Framework to conduct end-to-end, goal-driven innovation and value creation with AI Define a goal-aligned AI vision and strategy for stakeholders, including businesses, customers, and users Leverage AI successfully by focusing on concepts such as scientific innovation and AI readiness and maturity Understand the importance of executive leadership for pursuing AI initiatives “A must read for business executives and managers interested in learning about AI and unlocking its benefits. Alex Castrounis has simplified complex topics so that anyone can begin to leverage AI within their organization.” – Dan Park, GM & Director, Uber “Alex Castrounis has been at the forefront of helping organizations understand the promise of AI and leverage its benefits, while avoiding the many pitfalls that can derail success. In this essential book, he shares his expertise with the rest of us.” – Dean Wampler, Ph.D., VP, Fast Data Engineering at Lightbend
AI for People and Business 1st Table of contents:
I. The AI for People and Business Framework
1. Success with AI
Racing to Business Success
Why Do AI Initiatives Fail?
Why Do AI Initiatives Succeed?
Harnessing the Power of AI for the Win
2. An Introduction to the AI for People and Business Framework
A General Framework for Innovation
The AIPB Benefits Pseudocomponent
Existing Frameworks and the Missing Pieces of the Puzzle
AIPB Benefits
Why Focused
People and Business Focused
Unified and Holistic Focused
Explainable Focused
Science Focused
Summary
3. AIPB Core Components
An Agile Analogy
Experts Component
AIPB Process Categories and Recommended Methods
Assessment Component
AI Readiness and Maturity
Methodology Component
Assess
Vision
Strategy
Deliver
Optimize
The Flipped Classroom
Summary
4. AI and Machine Learning: A Nontechnical Overview
What Is Data Science, and What Does a Data Scientist Do?
Machine Learning Definition and Key Characteristics
Ways Machines Learn
AI Definition and Concepts
AI Types
Learning Like Humans
AGI, Killer Robots, and the One-Trick Pony
The Data Powering AI
Big Data
Data Structure and Format For AI Applications
Data Storage and Sourcing
Specific Data Sources
Data Readiness and Quality (the “Right” Data)
A Note on Cause and Effect
Summary
5. Real-World Applications and Opportunities
AI Opportunities
How Can I Apply AI to Real-World Applications?
Real-World Applications and Examples
Predictive Analytics
Personalization and Recommender Systems
Computer Vision
Pattern Recognition
Clustering and Anomaly Detection
Natural Language
Time-Series and Sequence-Based Data
Search, Information Extraction, Ranking, and Scoring
Reinforcement Learning
Hybrid, Automation, and Miscellaneous
Summary
II. Developing an AI Vision
6. The Importance of Why
Start with Why
Product Leadership and Perspective
Leadership and Generating a Shared Vision and Understanding
Summary
7. Defining Goals for People and Business
Defining Stakeholders and Introducing Their Goals
Goals by Stakeholder
Goals and the Purpose of AI for Business
Goals and the Purpose of AI for People
Summary
8. What Makes a Product Great
Importance versus Satisfaction
The Four Ingredients of a Great Product
Products That Just Work
Ability to Meet Human Needs, Wants, and Likes
Design and Usability
Delight and Stickiness
Netflix and the Focus on What Matters Most
Lean and Agile Product Development
Summary
9. AI for Better Human Experiences
Experience Defined
The Impact of AI on Human Experiences
Experience Interfaces
The Experience Economy
Design Thinking
Summary
10. An AI Vision Example
Spatial–Temporal Sensing and Perception
AI-Driven Taste
Our AIPB Vision Statement
III. Developing an AI Strategy
11. Scientific Innovation for AI Success
AI as Science
The TCPR Model
A TCPR Model Analogy
Time and Cost
Performance
Requirements
A Data Dependency Analogy
Summary
12. AI Readiness and Maturity
AI Readiness
Organizational
Technological
Financial
Cultural
AI Maturity
Summary
13. AI Key Considerations
AI Hype versus Reality
Testing Risky Assumptions
Assess Technical Feasibility
Acquire, Retain, and Train Talent
Build Versus Buy
Mitigate Liabilities
Mitigating Bias and Prioritizing Inclusion
Managing Employee Expectations
Managing Customer Expectations
Quality Assurance
Measure Success
Stay Current
AI in Production
Summary
14. An AI Strategy Example
Podcast Example Introduction
AIPB Strategy Phase Recap
Creating An AIPB Solution Strategy
Creating an AIPB Prioritized Roadmap
Aligned Goals, Initiatives, Themes, and Features
IV. Final Thoughts
15. The Impact of AI on Jobs
AI, Job Replacement, and the Skills Gap
The Skills Gap and New Job Roles
The Skills of Tomorrow
The Future of Automation, Jobs, and the Economy
Summary
16. The Future of AI
AI and Executive Leadership
What to Expect and Watch For
Increased AI Understanding, Adoption, and Proliferation
Advancements in Research, Software, and Hardware
Advancements in Computing Architecture
Technology Convergence, Integration, and Speech Dominance
Societal Impact
AGI, Superintelligence, and the Technological Singularity
The AI Effect
Summary
A. AI and Machine Learning Algorithms
Parametric versus Nonparametric Machine Learning
How Machine Learning Models Are Learned
Biological Neural Networks Overview
An Introduction to ANNs
An Introduction to Deep Learning
Deep Learning Applications
Summary
B. The AI Process
The GABDO Model
Goals
Identify Goals
Identify Opportunities
Create Hypothesis
Acquire
Identify Data
Acquire Data
Prepare Data
Build
Explore
Select
Train, Validate, Test
Improve
Deliver
Present Insights
Take Action
Make Decisions
Deploy Solutions
Optimize
Monitor
Analyze
Improve
Summary
C. AI in Production
Production versus Development Environments
Local versus Remote Development
Production Scalability
Learning and Solution Maintenance
Bibliography
Index
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