Appendix A Tracing the roots From mechanical calculators to digital dreams
Appendix B Algorithms and programming languages
Appendix B. Programming languages
Chapter 1. Chatbots
Chapter 1. Error-prone intelligence
Chapter 1. Looking ahead
Chapter 1. Summary
Chapter 1. The AI revolution
Chapter 1. The rise of machine intelligence
Chapter 2. AI mastery Essential techniques, Part 1
Chapter 2. Case-based reasoning
Chapter 2. Expert systems
Chapter 2. Fuzzy logic
Chapter 2. Genetic algorithms
Chapter 2. Summary
Chapter 3. AI mastery Essential techniques, Part 2
Chapter 3. Artificial neural networks
Chapter 3. Bayesian networks
Chapter 3. Decision trees for fraud prevention
Chapter 3. Deep learning
Chapter 3. So, what is artificial intelligence
Chapter 3. Summary
Chapter 3. Unsupervised learning
Chapter 4. Smart agent technology
Chapter 4. Summary
Chapter 5. AI does not understand
Chapter 5. Bard
Chapter 5. Benefits of LLMs
Chapter 5. ChatGPT
Chapter 5. Generative AI and intellectual property
Chapter 5. Humans vs. LLMs
Chapter 5. LLMs and the Illusion of Understanding
Chapter 5. LLM limits
Chapter 5. Large language models
Chapter 5. Risks of generative AI
Chapter 5. Summary
Chapter 5. Generative AI and large language models
Chapter 6. Human vs. machine
Chapter 6. Summary
Chapter 6. Human vision vs. computer vision
Chapter 7. Lack of generalization
Chapter 7. Summary
Chapter 7. AI doesn t turn data into intelligence
Chapter 8. AI doesn t threaten our jobs
Chapter 8. Summary
Chapter 9. Merging human with machine
Chapter 9. Science fiction vs. reality
Chapter 9. Summary
Chapter 9. Technological singularity is absurd
Chapter 9. The truth about the evolution of robotics
Chapter 10. AI failures
Chapter 10. AI misuse
Chapter 10. AI model lifecycle management
Chapter 10. Guiding principles for successful AI projects
Chapter 10. How to set your AI project up for success
Chapter 10. Learning from successful and failed applications of AI
Chapter 10. Summary
Chapter 11. AI factory
Chapter 11. Adaptability
Chapter 11. Analogical reasoning and transferability
Chapter 11. Causality inference
Chapter 11. Contextual reasoning
Chapter 11. Data coherence
Chapter 11. Deployability and interoperability
Chapter 11. Effective data storage and processing
Chapter 11. Elimination of irrelevant attributes
Chapter 11. Explicability
Chapter 11. Feature engineering
Chapter 11. Human machine collaboration
Chapter 11. Lack of bias in data and algorithms
Chapter 11. Next-generation AI
Chapter 11. Personalization
Chapter 11. Prediction reliability
Chapter 11. Privacy
Chapter 11. Quality Assurance
Chapter 11. Resilience and robustness
Chapter 11. Sampling
Chapter 11. Scalability
Chapter 11. Security
Chapter 11. Summary
Chapter 11. Sustainable AI
Chapter 11. Technique combination
Chapter 11. Temporal reasoning
Chapter 11. Traceability and monitoring
Chapter 11. Unsupervised learning
epilogue
foreword
preface
