Inside AI

Inside AI

by Akli Adjaoute
Released April 2024
Publisher(s): Manning Publications
ISBN: 9781633437722

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Book description

Separate AI truth from AI hype, and learn how to put this powerful technology to work.

In Inside AI AI professor and entrepreneur Dr. Akli Adjaoute puts AI in perspective, with informed insights from 30 years spent in the field. His book lays out a pragmatic blueprint that every leader can utilize to drive innovation with artificial intelligence.

In Inside AI you’ll learn how to:

  • Gain insight into diverse AI techniques and methodologies
  • Learn from both successful and failed AI applications
  • Identify the capabilities and limitations of AI systems
  • Understand successful and failed uses of AI in business
  • See where human cognition still exceeds AI
  • Bust common myths like AI’s threat to jobs and civilization
  • Manage AI projects effectively

Inside AI takes you on a journey through artificial intelligence, from AI’s origins in traditional expert systems all the way to deep learning and Large Language Models. There’s no hype here—you’ll get the grounded, evidence-based insights that are vital for making strategic decisions and preparing your business for the future.

About the Technology
Artificial Intelligence enthusiasts promise everything from human-like collaboration on everyday tasks to the end of work as we know it. Is AI just a flash in the pan, or can it really transform how you do business? This intriguing book sifts through the hype and separates the truth from the myths, with clear advice on what AI can—and can’t—achieve.

About the Book
Inside AI provides a clear-headed overview of modern artificial intelligence, including the recent advances of Generative AI and Large Language Models. Its accessible and jargon-free explanations of leading AI techniques showcase how AI delivers tangible advantages to businesses. Both inspiring and practical, this book provides a proven framework for developing successful AI applications.

What's Inside
  • Insights from successful and failed AI applications
  • A survey of AI techniques and methodologies
  • Bust common AI myths
  • Manage AI projects effectively


About the Reader
For anyone seeking grounded insights into AI’s capabilities, including business leaders and decision makers.

About the Author
Akli Adjaoute is the founder of multiple AI-related companies. He served as an adjunct professor at the University of San Francisco and as Scientific Committee Chair and Head of the AI department at EPITA.

The technical editor on this book was Richard Vaughan.

Quotes
Provides valuable insights gained from applying AI in high-stakes, mission-critical applications.
- Raymond Kendall, Honorary Secretary General of INTERPOL

A labor of love. Readers will love it.
- Ajay Bhalla, President of Cyber & Intelligence Solutions, Mastercard

With clarity and generosity, Akli Adjaoute makes you understand what AI is and what it is not. Delightful, and also invaluable.
- Patrick Pérez, Kyutai

A framework for how to think about AI, and its transformational impact on every person in the world. It is powerful in its simplicity.
- Karen Webster, PYMNTS

Table of contents

  1. copyright
  2. contents
  3. Praise for Inside AI
  4. Inside AI
  5. dedication
  6. foreword
  7. preface
  8. acknowledgments
  9. about the book
  10. about the author
  11. about the cover illustration
  12. 1 The rise of machine intelligence
    1. 1.1 What is artificial intelligence?
    2. 1.2 The AI revolution
    3. 1.3 Error-prone intelligence
    4. 1.4 Chatbots
    5. 1.5 Looking ahead
  13. 2 AI mastery: Essential techniques, Part 1
    1. 2.1 Expert systems
    2. 2.2 Business rules management system
    3. 2.3 Case-based reasoning
    4. 2.4 Fuzzy logic
    5. 2.5 Genetic algorithms
  14. 3 AI mastery: Essential techniques, Part 2
    1. 3.1 Data mining
    2. 3.2 Decision trees for fraud prevention
    3. 3.3 Artificial neural networks
    4. 3.4 Deep learning
      1. 3.4.1 The benefits of deep learning
      2. 3.4.2 Limitations of deep learning
    5. 3.5 Bayesian networks
    6. 3.6 Unsupervised learning
    7. 3.7 So, what is artificial intelligence?
  15. 4 Smart agent technology
    1. 4.1 Principles of smart agents
      1. 4.1.1 Adaptability: The true mark of intelligence
      2. 4.1.2 Smart agent language
  16. 5 Generative AI and large language models
    1. 5.1 Generative artificial intelligence
    2. 5.2 Large language models
    3. 5.3 ChatGPT
      1. 5.3.1 How ChatGPT creates human-like text
      2. 5.3.2 ChatGPT hallucination
    4. 5.4 Bard
    5. 5.5 Humans vs. LLMs
    6. 5.6 AI does not understand
    7. 5.7 Benefits of LLMs
    8. 5.8 LLM limits
    9. 5.9 Generative AI and intellectual property
    10. 5.10 Risks of generative AI
    11. 5.11 LLMs and the Illusion of Understanding
  17. 6 Human vs. machine
    1. 6.1 The human brain
      1. 6.1.1 Thoughts
      2. 6.1.2 Memory
      3. 6.1.3 The subconscious mind
      4. 6.1.4 Common sense
      5. 6.1.5 Curiosity
      6. 6.1.6 Imagination
      7. 6.1.7 Creativity
      8. 6.1.8 Intuition
      9. 6.1.9 Analogy
    2. 6.2 Human vision vs. computer vision
      1. 6.2.1 AI and COVID
      2. 6.2.2 Image reasoning
  18. 7 AI doesn’t turn data into intelligence
    1. 7.1 Machines defeating world champions
    2. 7.2 Lack of generalization
  19. 8 AI doesn’t threaten our jobs
    1. 8.1 Are simple human tasks easy to automate?
  20. 9 Technological singularity is absurd
    1. 9.1 The genesis of technological singularity
    2. 9.2 The truth about the evolution of robotics
    3. 9.3 Merging human with machine?
    4. 9.4 Science fiction vs. reality
  21. 10 Learning from successful and failed applications of AI
    1. 10.1 AI successes
    2. 10.2 AI misuse
    3. 10.3 AI failures
    4. 10.4 How to set your AI project up for success
      1. 10.4.1 Data: The lifeblood of AI
      2. 10.4.2 The realistic AI perspective
      3. 10.4.3 The importance of planning
      4. 10.4.4 Risk mitigation
      5. 10.4.5 Collaboration and expertise
    5. 10.5 AI model lifecycle management
      1. 10.5.1 Data preparation
      2. 10.5.2 Behavior analysis
      3. 10.5.3 Data transformation
      4. 10.5.4 Model creation
      5. 10.5.5 Live production
      6. 10.5.6 Data storage
      7. 10.5.7 Notifications
      8. 10.5.8 Back-office review
      9. 10.5.9 Adaptive learning
      10. 10.5.10 Administration
      11. 10.5.11 Remark on AI platforms
    6. 10.6 Guiding principles for successful AI projects
  22. 11 Next-generation AI
    1. 11.1 Data flexibility
    2. 11.2 Sampling
    3. 11.3 Elimination of irrelevant attributes
    4. 11.4 Data coherence
    5. 11.5 Lack of bias in data and algorithms
    6. 11.6 Feature engineering
    7. 11.7 Technique combination
    8. 11.8 Unsupervised learning
    9. 11.9 AI factory
    10. 11.10 Quality Assurance
    11. 11.11 Prediction reliability
    12. 11.12 Effective data storage and processing
    13. 11.13 Deployability and interoperability
    14. 11.14 Scalability
    15. 11.15 Resilience and robustness
    16. 11.16 Security
    17. 11.17 Explicability
    18. 11.18 Traceability and monitoring
    19. 11.19 Privacy
    20. 11.20 Temporal reasoning
    21. 11.21 Contextual reasoning
    22. 11.22 Causality inference
    23. 11.23 Analogical reasoning and transferability
    24. 11.24 Personalization
    25. 11.25 Sustainable AI
    26. 11.26 Adaptability
    27. 11.27 Human–machine collaboration
  23. appendix A Tracing the roots: From mechanical calculators to digital dreams
    1. A.1 Can machines think?
  24. appendix B Algorithms and programming languages
    1. B.1 Algorithms
    2. B.2 Programming languages
  25. epilogue
  26. references

Product information

  • Title: Inside AI
  • Author(s): Akli Adjaoute
  • Release date: April 2024
  • Publisher(s): Manning Publications
  • ISBN: 9781633437722