Winning the National Security AI Competition: A Practical Guide for Government and Industry Leaders
- Length: 252 pages
- Edition: 1
- Language: English
- Publisher: Apress
- Publication Date: 2022-12-13
- ISBN-10: 1484288130
- ISBN-13: 9781484288139
- Sales Rank: #260754 (See Top 100 Books)
In introducing the National Security Commission on AI’s final report, Eric Schmidt, former Google CEO, and Robert Work, former Deputy Secretary of Defense, wrote: “The human talent deficit is the government’s most conspicuous AI deficit and the single greatest inhibitor to buying, building, and fielding AI-enabled technologies for national security purposes.” Drawing upon three decades of leading hundreds of advanced analytics and AI programs and projects in government and industry, Chris Whitlock and Frank Strickland address in this book the primary variable in the talent deficit, i.e., large numbers of qualified AI leaders.
The book quickly moves from a case for action to leadership principles and practices for effectively integrating AI into programs and driving results in AI projects. The chapters convey 37 axioms – enduring truths for developing and deploying AI – and over 100 leader practices set among 50 cases and examples, 40 of which focus on AI in national security. Emphasizing its impact and practical nature, LTG (ret.) Ken Tovo, former commander of U.S. Army special forces, characterized the book as “the Ranger Handbook for AI implementation!”
Whether you are a senior or mid-level leader who lacks hands-on experience with AI, or an AI practitioner who lacks leadership experience, this book will equip you to lead AI programs, projects, people, and technology. As the Honorable Robert Work wrote in the foreword: “This book is not the last word on leading AI in the national security enterprise, but I believe it is an essential starting point.”
You will:
- Review axioms or enduring truths at work in six dimensions of AI: program, budget, project, data science, people, and technology
- Apply best practices―such as decision frameworks, processes, checklists―for leading work in each of the six dimensions.
- See how the axioms and best practices are contextualized to national security missions.
Contents About the Authors Advance Praise Acknowledgments Foreword Introduction Chapter 1: The Three Imperatives to Develop AI Leaders Summary Imperative #1: Mission Needs Imperative #2: AI and Autonomous Systems Imperative #3: AI Leaders Outline of the Book Leaders Are Essential Chapter 2: How Leaders Should Think and Talk About AI Summary AI Value Varies Case Example: Exploring the Limits of AI’s Value Four Types of Projects Understanding Performance A Look at Quantitative Performance Assessments in the Wild An Exemplar Performance Assessment: High Value Individual (HVI) Study The Mission Problem Quantitative Assessment of Mission Value Impact A Look at Dashboards in the Wild An Exemplar Dashboard: Naval Fuel Analytics and Characterization Tool (NFACT) The Mission Problem Standardizing Data Cleaning and Visualization Impact Driving Improvement A Look at Experiments in the Wild An Exemplar AI Experiment Releasing Official Government Records to the Public The Mission Problem Experimenting with Machine Review of Documents Impact A Look at Deployed Models in the Wild An Exemplar Deployed Model Project Maven The Mission Problem Deployed AI Solution Impact Projects Have Same Technical DNA Completely Autonomous is an Oxymoron An Exemplar Fully Autonomous System in the Making: Self-Driving Vehicles Chapter 3: Leading the Program Summary Program Axioms Axioms for Leading AI Programs Illustration of AI Program Contexts Case Example: Centralized DoD Program to Counter IED Threat Case Example: Lean Six Sigma: Centralized and Decentralized Program Elements Case Example: Smartphone Face ID and Implications to Program Design Key Leadership Issues and Best Practice for Program Design, Operation, Adaptation Chapter 4: Government Programming and Budgeting for AI Leaders Summary Programming and Budgeting Demystified Executing: The Underleveraged Starting Point for Programming and Budgeting Formulating: Defending Starts in the Executive Branch Not in Congress Defending: Authorization, Appropriations, and Congressional Oversight Chapter 5: Data Science for AI Leaders Summary Programming Skill Required Thinking About Common Software Statistical Understanding Required Labeled Data is One Path Foundation Methods Are Key Potential Leader Questions Advanced Approaches Can Generate Lift Text Is An Asset Summary Regarding Data Science Chapter 6: Leading the Project Summary Revisiting AI Project Types Project Axioms Six Major Project Activities Activity #1: Selling the Project Case Example: Setting Realistic Expectations Activity #2: Initiating the Project Activity #3: Data Acquisition and Exploration Case Example: Misinterpreting Data—A $10B Error Activity #4: Model Development/Advanced Analytics Case Example: Cost of Documentation Failures Activity #5: Solution Deployment/Conveying Results Activity #6: Monitor, Adjust AI Model Performance Leader Emphasis Points and Engagement Areas by Major Process Activity Chapter 7: Leading the People Summary Leading Data Scientists: Five Axioms Leadership: Setting the Foundation Leading Data Scientists and the AI Team Through the Talent Lifecycle Leading in the Government—Industry Context Chapter 8: Leading the Technology Summary Technology Axioms Technology Leadership Issues by Project Type Technology Choices for Performance Assessment Studies Technology Choices for Performance Dashboards Technology Choices for Driving Improvement Technology Leadership Issues by Leader Role About aiLeaders Index
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