Winning the national security AI competition : a practical guide for government and industry leaders /

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, bui...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Whitlock, Chris (Autor), Strickland, Frank (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [New York, NY] : Apress, [2023]
Temas:
Artificial intelligence > Industrial applications > Security measures.
Computer networks > Security measures.
National security > Data processing.
Artificial intelligence > Political aspects.
Data protection > Government policy.
Intelligence artificielle > Applications industrielles > Sécurité > Mesures.
Réseaux d'ordinateurs > Sécurité > Mesures.
Intelligence artificielle > Aspect politique.
Protection de l'information (Informatique) > Politique gouvernementale.
National security > Data processing
Data protection > Government policy
Computer networks > Security measures
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • 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

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