Separate the AI facts from the AI fiction, and discover how you can best put these tools to work in your organization. It’s hard to say what’s bigger: AI’s tremendous promise, or all the hype surrounding it. Is it just another flash in the pan—or is AI going to change the way we all work? In AI Reality and Illusion, AI professor and entrepreneur Dr. Akli Adjaoute shares his 30 years of experience in a vital guide to the whole AI field. It lays out a pragmatic blueprint that every leader needs to drive innovation with artificial intelligence and shape the future of their business. Inside AI Reality and Illusion you’ll learn how to: Distinguish between AI hype and reality Identify the capabilities and limitations of AI systems Gain insight into diverse AI techniques and methodologies Understand successful and failed uses of AI in business Manage AI projects effectively AI Reality and Illusion tours every leading technique of AI and machine learning, showing you how they work, and how you can incorporate them into your business. There’s no hype here—you’ll get the kind of grounded, evidence-based insights that are vital for making strategic decisions about AI. Accessible, non-technical language and real-world use cases help you develop a practical AI literacy so you can start using these tools to their full potential. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book AI Reality and Illusion is a comprehensive—but accessible—overview of AI for leaders and decision makers that sifts the truth from the myths for multiple different AI techniques. Inside, you’ll explore everything from traditional expert systems to modern deep learning including Large Language Models (LLMs) and generative AI. Throughout, Dr. Adjaoute shares his real-world experience applying AI to mission-critical applications, letting you learn from both his successes and failures. You’ll learn how to identify strategic opportunities with AI, manage an AI adoption, and plan your investment strategies with consideration to AI’s future advancements. About the reader For anyone seeking grounded, hype-free insights into AI’s capabilities. No technological expertise required. About the author Dr. Akli Adjaoute is the founder of Exponion, a venture capital firm that provides cutting-edge startup companies with financial resources and expertise. Prior to Exponion, he was the founder and CEO of Brighterion, which was acquired in 2017 by Mastercard. Brighterion provides enterprise AI applications for payment service providers and financial institutions. Additionally, Dr. Adjaoute founded and led Conception Intelligence Artificielle, a company focused on AI technology. Alongside his entrepreneurial pursuits, he has shared his real-world AI experience as an adjunct professor in both France and the USA. Dr. Adjaoute has been awarded 28 patents with over 1800 citations.
Inside AI
Praise for Inside AI
brief contents
contents
foreword
preface
acknowledgments
about the book
Who should read this book?
How this book is organized
liveBook discussion forum
about the author
about the cover illustration
1 The rise of machine intelligence
1.1 What is artificial intelligence?
1.2 The AI revolution
1.3 Error-prone intelligence
1.4 Chatbots
1.5 Looking ahead
Summary
2 AI mastery: Essential techniques, Part 1
2.1 Expert systems
2.2 Business rules management system
2.3 Case-based reasoning
2.4 Fuzzy logic
2.5 Genetic algorithms
Summary
3 AI mastery: Essential techniques, Part 2
3.1 Data mining
3.2 Decision trees for fraud prevention
3.3 Artificial neural networks
3.4 Deep learning
3.4.1 The benefits of deep learning
3.4.2 Limitations of deep learning
3.5 Bayesian networks
3.6 Unsupervised learning
3.7 So, what is artificial intelligence?
Summary
4 Smart agent technology
4.1 Principles of smart agents
4.1.1 Adaptability: The true mark of intelligence
4.1.2 Smart agent language
Summary
5 Generative AI and large language models
5.1 Generative artificial intelligence
5.2 Large language models
5.3 ChatGPT
5.3.1 How ChatGPT creates human-like text
5.3.2 ChatGPT hallucination
5.4 Bard
5.5 Humans vs. LLMs
5.6 AI does not understand
5.7 Benefits of LLMs
5.8 LLM limits
5.9 Generative AI and intellectual property
5.10 Risks of generative AI
5.11 LLMs and the Illusion of Understanding
Summary
6 Human vs. machine
6.1 The human brain
6.1.1 Thoughts
6.1.2 Memory
6.1.3 The subconscious mind
6.1.4 Common sense
6.1.5 Curiosity
6.1.6 Imagination
6.1.7 Creativity
6.1.8 Intuition
6.1.9 Analogy
6.2 Human vision vs. computer vision
6.2.1 AI and COVID
6.2.2 Image reasoning
Summary
7 AI doesn’t turn data into intelligence
7.1 Machines defeating world champions
7.2 Lack of generalization
Summary
8 AI doesn’t threaten our jobs
8.1 Are simple human tasks easy to automate?
Summary
9 Technological singularity is absurd
9.1 The genesis of technological singularity
9.2 The truth about the evolution of robotics
9.3 Merging human with machine?
9.4 Science fiction vs. reality
Summary
10 Learning from successful and failed applications of AI
10.1 AI successes
10.2 AI misuse
10.3 AI failures
10.4 How to set your AI project up for success
10.4.1 Data: The lifeblood of AI
10.4.2 The realistic AI perspective
10.4.3 The importance of planning
10.4.4 Risk mitigation
10.4.5 Collaboration and expertise
10.5 AI model lifecycle management
10.5.1 Data preparation
10.5.2 Behavior analysis
10.5.3 Data transformation
10.5.4 Model creation
10.5.5 Live production
10.5.6 Data storage
10.5.7 Notifications
10.5.8 Back-office review
10.5.9 Adaptive learning
10.5.10 Administration
10.5.11 Remark on AI platforms
10.6 Guiding principles for successful AI projects
Summary
11 Next-generation AI
11.1 Data flexibility
11.2 Sampling
11.3 Elimination of irrelevant attributes
11.4 Data coherence
11.5 Lack of bias in data and algorithms
11.6 Feature engineering
11.7 Technique combination
11.8 Unsupervised learning
11.9 AI factory
11.10 Quality Assurance
11.11 Prediction reliability
11.12 Effective data storage and processing
11.13 Deployability and interoperability
11.14 Scalability
11.15 Resilience and robustness
11.16 Security
11.17 Explicability
11.18 Traceability and monitoring
11.19 Privacy
11.20 Temporal reasoning
11.21 Contextual reasoning
11.22 Causality inference
11.23 Analogical reasoning and transferability
11.24 Personalization
11.25 Sustainable AI
11.26 Adaptability
11.27 Human–machine collaboration
Summary
appendix A—Tracing the roots: From mechanical calculators to digital dreams
A.1 Can machines think?
appendix B—Algorithms and programming languages
B.1 Algorithms
B.2 Programming languages
epilogue
references
Chapter 2
Chapter 3
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Appendix A
Appendix B
Praise for Inside AI
brief contents
contents
foreword
preface
acknowledgments
about the book
Who should read this book?
How this book is organized
liveBook discussion forum
about the author
about the cover illustration
1 The rise of machine intelligence
1.1 What is artificial intelligence?
1.2 The AI revolution
1.3 Error-prone intelligence
1.4 Chatbots
1.5 Looking ahead
Summary
2 AI mastery: Essential techniques, Part 1
2.1 Expert systems
2.2 Business rules management system
2.3 Case-based reasoning
2.4 Fuzzy logic
2.5 Genetic algorithms
Summary
3 AI mastery: Essential techniques, Part 2
3.1 Data mining
3.2 Decision trees for fraud prevention
3.3 Artificial neural networks
3.4 Deep learning
3.4.1 The benefits of deep learning
3.4.2 Limitations of deep learning
3.5 Bayesian networks
3.6 Unsupervised learning
3.7 So, what is artificial intelligence?
Summary
4 Smart agent technology
4.1 Principles of smart agents
4.1.1 Adaptability: The true mark of intelligence
4.1.2 Smart agent language
Summary
5 Generative AI and large language models
5.1 Generative artificial intelligence
5.2 Large language models
5.3 ChatGPT
5.3.1 How ChatGPT creates human-like text
5.3.2 ChatGPT hallucination
5.4 Bard
5.5 Humans vs. LLMs
5.6 AI does not understand
5.7 Benefits of LLMs
5.8 LLM limits
5.9 Generative AI and intellectual property
5.10 Risks of generative AI
5.11 LLMs and the Illusion of Understanding
Summary
6 Human vs. machine
6.1 The human brain
6.1.1 Thoughts
6.1.2 Memory
6.1.3 The subconscious mind
6.1.4 Common sense
6.1.5 Curiosity
6.1.6 Imagination
6.1.7 Creativity
6.1.8 Intuition
6.1.9 Analogy
6.2 Human vision vs. computer vision
6.2.1 AI and COVID
6.2.2 Image reasoning
Summary
7 AI doesn’t turn data into intelligence
7.1 Machines defeating world champions
7.2 Lack of generalization
Summary
8 AI doesn’t threaten our jobs
8.1 Are simple human tasks easy to automate?
Summary
9 Technological singularity is absurd
9.1 The genesis of technological singularity
9.2 The truth about the evolution of robotics
9.3 Merging human with machine?
9.4 Science fiction vs. reality
Summary
10 Learning from successful and failed applications of AI
10.1 AI successes
10.2 AI misuse
10.3 AI failures
10.4 How to set your AI project up for success
10.4.1 Data: The lifeblood of AI
10.4.2 The realistic AI perspective
10.4.3 The importance of planning
10.4.4 Risk mitigation
10.4.5 Collaboration and expertise
10.5 AI model lifecycle management
10.5.1 Data preparation
10.5.2 Behavior analysis
10.5.3 Data transformation
10.5.4 Model creation
10.5.5 Live production
10.5.6 Data storage
10.5.7 Notifications
10.5.8 Back-office review
10.5.9 Adaptive learning
10.5.10 Administration
10.5.11 Remark on AI platforms
10.6 Guiding principles for successful AI projects
Summary
11 Next-generation AI
11.1 Data flexibility
11.2 Sampling
11.3 Elimination of irrelevant attributes
11.4 Data coherence
11.5 Lack of bias in data and algorithms
11.6 Feature engineering
11.7 Technique combination
11.8 Unsupervised learning
11.9 AI factory
11.10 Quality Assurance
11.11 Prediction reliability
11.12 Effective data storage and processing
11.13 Deployability and interoperability
11.14 Scalability
11.15 Resilience and robustness
11.16 Security
11.17 Explicability
11.18 Traceability and monitoring
11.19 Privacy
11.20 Temporal reasoning
11.21 Contextual reasoning
11.22 Causality inference
11.23 Analogical reasoning and transferability
11.24 Personalization
11.25 Sustainable AI
11.26 Adaptability
11.27 Human–machine collaboration
Summary
appendix A—Tracing the roots: From mechanical calculators to digital dreams
A.1 Can machines think?
appendix B—Algorithms and programming languages
B.1 Algorithms
B.2 Programming languages
epilogue
references
Chapter 2
Chapter 3
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Appendix A
Appendix B
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جزئیات کتاب
بازدید : 3175 بازدیدتاریخ انتشار : 20 مه 2025
نویسنده : Akli Adjaoute
سال انتشار : 2024
شابک 10 : 1-63343-772-8
شابک 13 : 978-1-63343-772-2
ناشر : Manning Publications Co. LLC
زبان کتاب: انگلیسی
حجم فایل : 31.9 MB
فرمت فایل :
PDF
برچسب ها : https://downloadketab.ir/?p=2609