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Artificial intelligence in action : = real-world applications and innovations /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Artificial intelligence in action :/ Ahmed Banafa.
其他題名:	real-world applications and innovations /
作者:	Banafa, Ahmed,
出版者:	Denmark :River Publishers, : c2025. ,
面頁冊數:	xxx, 331 p. :ill. (chiefly col.) ; : 24 cm.;
附註:	Includes index.
標題:	Artificial intelligence. -
ISBN:	9788770046206(hbk.) :

Artificial intelligence in action : = real-world applications and innovations /
Banafa, Ahmed,

Artificial intelligence in action :real-world applications and innovations /Ahmed Banafa. - Denmark :River Publishers,c2025. - xxx, 331 p. :ill. (chiefly col.) ;24 cm. - River Publishers series in computing and information science and technology. - River Publishers series in computing and information science and technology..

Includes index.

Preface xxiii List of Figures xxv List of Abbreviations xxix Part: I Fundamentals of AI 1 1 Generative AI and Other Types of AI 3 1.1 Another Classification of AI 4 1.2 Technical Types of AI (Figure 1.2) 4 1.3 Generative AI 6 1.4 Risks of Generative AI 7 1.5 Future of Generative AI 8 1.6 ChatGPT 9 2 Challenges in Learning Generative AI 11 2.1 Challenges in Learning Generative AI 12 3 Exploring the Potential Downfalls of AI Technology 15 3.1 Vulnerability to Bias and Manipulation 15 3.2 Automation Anxiety and Social Discontent 16 3.3 Loss of Control and the Rise of Superintelligence 17 3.4 The Technological Singularity: A Point of No Return? 17 3.5 Ethical Considerations and Governance Challenges 17 3.6 Lack of Trust and Public Skepticism 17 3.7 Unforeseen Risks and the Unknown Unknowns 17 3.8 Shaping a Brighter Future for AI 18 4 AI Hallucinations 19 4.1 Opportunities 20 4.2 Concerns 21 4.3 AI Hallucinations and ChatGPT 22 4.4 AI Hallucinations and Generative AI 23 5 AI and Machine Unlearning: Navigating the Forgotten Path 25 5.1 Machine Learning vs. Machine Unlearning 26 5.2 The Importance of Adaptability in AI 27 5.3 Strategies for Implementing Machine Unlearning 27 5.3.1 Regularization techniques 28 5.3.2 Dynamic memory allocation 28 5.3.3 Memory networks and attention mechanisms 28 5.3.4 Incremental learning and lifelong adaptation 28 5.4 Applications of Machine Unlearning 28 5.4.1 Copyright compliance 29 5.4.2 Personalized recommendations and content delivery 29 5.4.3 Healthcare and medical diagnosis 30 5.4.4 Autonomous vehicles and robotics 30 5.4.5 Ethical considerations and bias mitigation 30 5.5 Ethical Implications and Considerations 30 5.5.1 Transparency and accountability 30 5.5.2 Privacy and data retention 30 5.5.3 Unintended consequences 31 5.5.4 Bias amplification 31 5.6 The Road Ahead: Challenges and Future Directions 31 5.6.1 Developing effective algorithms 31 5.6.2 Granularity and context 31 5.6.3 Dynamic and contextual adaptability 31 5.6.4 Ethical frameworks 31 5.7 The Future 32 6 Programming Languages Used in AI Development 33 6.1 Python: The Lingua Franca of AI 34 6.1.1 Natural language processing (NLP) with Python and NLTK 34 6.1.2 Computer vision with OpenCV and Python 35 6.1.3 Machine learning classification with scikit-learn 35 6.1.4 Reinforcement learning with OpenAI Gym and Python 36 6.2 Java: Scalability and Performance 37 6.3 R: Statistical Computing for AI Research 37 6.4 TensorFlow (JavaScript): Bringing AI to the Browser 38 7 Unraveling the Challenges: Navigating the Barriers to Generative AI Success 41 7.1 Data Quality and Quantity: The Cornerstone Challenge 42 7.2 Computational Power: The Hunger for Resources 43 7.3 Explainability and Interpretability: Deciphering the Black Box 43 7.4 Ethical Concerns: Navigating the Moral Landscape 44 7.5 Adversarial Attacks: Testing the Robustness 44 7.6 Transferability and Generalization: Beyond Training Data 44 7.7 Legal and Regulatory Challenges: Navigating the Legal Landscape 45 8 Exploring the Challenges and Progress in AI Alignment 47 8.1 Understanding AI Alignment 48 8.1.1 The alignment problem 48 8.1.2 Types of AI alignment 49 8.2 Challenges in AI Alignment 49 8.2.1 Ambiguity in human values 49 8.2.2 Value drift 49 8.2.3 Scalability 49 8.2.4 Adversarial manipulation 50 8.3 Approaches to AI Alignment 50 8.3.1 Value learning 50 8.3.2 Inverse reinforcement learning 50 8.3.3 Cooperative inverse reinforcement learning 50 8.3.4 Formal verification 50 8.4 Progress in AI Alignment 51 8.4.1 Research initiatives 51 8.4.2 Collaborative efforts 51 8.4.3 Ethical guidelines 51 8.4.4 Public awareness and engagement 51 8.5 Future Directions and Considerations 51 8.5.1 Continued research and innovation 52 8.5.2 Ethical governance 52 8.5.3 Human⁰́₃AI collaboration 52 8.5.4 Education and awareness 52 9 Creating AI Models: From Data to Deployment 53 9.1 Building AI Models 53 9.1.1 Step 1: Data collection and preprocessing 53 9.1.2 Step 2: Model selection and architecture design 54 9.1.3 Step 3: Model training 55 9.1.4 Step 4: Model evaluation and tuning 56 9.1.5 Step 5: Model deployment and integration 56 9.1.6 Ethical considerations 57 9.2 Putting It All Together: An End-to-End Example 58 9.2.1 Step 1: Data collection and preprocessing 58 9.2.2 Step 2: Model selection and architecture design 58 9.2.3 Step 3: Model training 58 9.2.4 Step 4: Model evaluation and tuning 58 9.2.5 Step 5: Model deployment and integration 59 10 Large Language Models as Data Compression Engines 61 10.1 Data Compression Fundamentals 62 10.1.1 Information theory principles 62 10.1.2 Traditional data compression techniques 62 10.2 Large Language Models Unveiled (Figure 10.1) 62 10.2.1 Neural networks and transformers 62 10.2.2 Pre-training and fine-tuning 63 10.3 LLMs as Data Compressors (Figure 10.2) 63 10.3.1 Pattern extraction 63 10.3.2 Semantic encoding 64 10.3.3 Contextual analysis 64 10.3.4 Data compression efficiency 64 10.3.5 Efficient parameterization 64 10.3.6 Adaptive compression 65 10.3.7 Contextual optimization 65 10.3.8 Comparative efficiency 65 10.4 The LLM as an Information-theoretic Compressor (Figure 10.3) 66 10.4.1 Entropy and information gain 66 10.4.2 Compression ratios and efficiency 66 10.5 Applications and Implications 66 10.5.1 Real-world applications 66 10.5.2 Ethical considerations 66 10.6 Future Directions and Challenges 67 Part: II AI Applications 69 11 Can We Stop Robots from Replacing Humans 71 11.1 How Humans Can Secure Their Jobs in the Age of Advancing AI 73 11.2 ⁰́₋Self-Replicating Robots⁰́₊ 75 11.3 ⁰́₋Kill Switch⁰́₊ 76 12 Green Artificial Intelligence 81 12.1 Factors Contributing to Carbon Emissions 82 12.2 Mitigation Strategies for a Greener AI Future (Figure 12.1) 82 13 Artificial Intelligence and Natural Disasters 85 13.1 Understanding Natural Disasters 85 13.2 The Need for Prevention 86 13.3 AI in Disaster Prevention (Figure 13.1) 86 13.3.1 Early warning systems 87 13.3.2 Seismic activity prediction 87 13.3.3 Forest fire prevention 87 13.3.4 Flood prediction and management 87 13.3.5 Landslide detection 87 13.3.6 Climate change mitigation 88 13.3.7 Disaster response coordination 88 13.4 Challenges and Ethical Considerations 88 13.4.1 Data privacy and security 88 13.4.2 Bias in AI 89 13.4.3 Accessibility and equity 89 13.4.4 Accountability and decision-making 90 13.4.5 Overreliance on technology 90 13.4.6 Infrastructure and resource constraints 90 14 AI and Drones 93 14.1 Types of Drones 93 14.2 Key Components 94 14.3 The Convergence of AI and Drones 95 14.3.1 Benefits of combining AI and drones 95 14.3.2 Hardware and software integration 95 14.3.3 Real-time data processing 95 14.4 Applications of AI-Powered Drones 96 14.4.1 Agriculture and precision farming 96 14.4.2 Surveillance and security 96 14.4.3 Logistics and delivery 96 14.4.4 Disaster management and search and rescue 96 14.4.5 Environmental monitoring 96 14.5 Challenges and Ethical Considerations 97 14.5.1 Privacy concerns 97 14.5.2 Regulatory and legal challenges 97 14.5.3 Safety and security 97 14.5.4 Ethical use of AI in drones 97 14.6 Future Prospects 97 14.6.1 Advancements in AI and drone technology 97 14.6.2 Potential industry disruption 98 14.6.3 Ethical and regulatory frameworks 98 14.7 Last Word! 98 15 Nuclear AI: Pioneering the Future of Nuclear Technology 99 15.1 Defining Nuclear AI 99 15.2 Benefits of Nuclear AI (Figure 15.1) 100 15.3 Risks and Challenges (Figure 15.2) 101 15.4 The Future of Nuclear AI 103 15.5 Conclusion 104 16 Artificial Intelligence: A Double-edged Sword for Environment and Climate 105 16.1 Positive Potential: A Green AI Revolution 106 16.1.1 Resource optimization: 106 16.1.2 Sustainable innovations 107 16.1.3 Enhanced environmental monitoring and modeling 107 16.2 The Dark Side: AI⁰́₉s Environmental Footprint 107 16.2.1 Energy-guzzling algorithms 108 16.2.2 E-waste dilemma 108 16.2.3 Bias and unforeseen consequences 108 16.3 Navigating the Green Path: Making AI Sustainable 109 16.3.1 Green AI development 109 16.3.2 Ethical AI governance 109 Part: III Challenges and Opportunities 111 17 How Can Countries Regulate AI 113 17.1 How Can Countries Regulate AI 114 17.2 How to Make a Responsible AI 116 18 The Pinnacle of Artificial Intelligence: Navigating the Technological Odyssey 119 18.1 AI⁰́₉s Evolutionary Epoch 120 18.2 Contemporary AI Landscape 120 18.3 Envisioning the AI Prodigy 121 18.4 Quandaries and Conundrums 123 18.5 Ethical Deliberations 124 18.6 Denouement 125 19 WhatWould Happen If AI Took Over Humanity 127 19.1 The Argument for AI Taking Over Humanity 127 19.2 The Argument Against AI Taking Over Humanity 128 19.2.1 If AI were to take over humanity 129 20 Who Will Really Benefit from AI 133 20.1 Who will really benefit from AI 134 20.1.1 The pioneers and early adopters 134 20.1.2 Small- and medium-sized enterprises (SMEs) 134 20.1.3 Healthcare: Saving lives and transforming medicine 135 20.1.4 Education: Empowering learners 135 20.1.5 Automation and job displacement 135 20.

ISBN: 9788770046206(hbk.) :NT4459

Nat. Bib. No.: GBC553166bnbSubjects--Topical Terms:

122134
Artificial intelligence.

LC Class. No.: Q335 / .B36 2025

Dewey Class. No.: 006.3

Artificial intelligence in action : = real-world applications and innovations /
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505 0 $a Preface xxiii List of Figures xxv List of Abbreviations xxix Part: I Fundamentals of AI 1 1 Generative AI and Other Types of AI 3 1.1 Another Classification of AI 4 1.2 Technical Types of AI (Figure 1.2) 4 1.3 Generative AI 6 1.4 Risks of Generative AI 7 1.5 Future of Generative AI 8 1.6 ChatGPT 9 2 Challenges in Learning Generative AI 11 2.1 Challenges in Learning Generative AI 12 3 Exploring the Potential Downfalls of AI Technology 15 3.1 Vulnerability to Bias and Manipulation 15 3.2 Automation Anxiety and Social Discontent 16 3.3 Loss of Control and the Rise of Superintelligence 17 3.4 The Technological Singularity: A Point of No Return? 17 3.5 Ethical Considerations and Governance Challenges 17 3.6 Lack of Trust and Public Skepticism 17 3.7 Unforeseen Risks and the Unknown Unknowns 17 3.8 Shaping a Brighter Future for AI 18 4 AI Hallucinations 19 4.1 Opportunities 20 4.2 Concerns 21 4.3 AI Hallucinations and ChatGPT 22 4.4 AI Hallucinations and Generative AI 23 5 AI and Machine Unlearning: Navigating the Forgotten Path 25 5.1 Machine Learning vs. Machine Unlearning 26 5.2 The Importance of Adaptability in AI 27 5.3 Strategies for Implementing Machine Unlearning 27 5.3.1 Regularization techniques 28 5.3.2 Dynamic memory allocation 28 5.3.3 Memory networks and attention mechanisms 28 5.3.4 Incremental learning and lifelong adaptation 28 5.4 Applications of Machine Unlearning 28 5.4.1 Copyright compliance 29 5.4.2 Personalized recommendations and content delivery 29 5.4.3 Healthcare and medical diagnosis 30 5.4.4 Autonomous vehicles and robotics 30 5.4.5 Ethical considerations and bias mitigation 30 5.5 Ethical Implications and Considerations 30 5.5.1 Transparency and accountability 30 5.5.2 Privacy and data retention 30 5.5.3 Unintended consequences 31 5.5.4 Bias amplification 31 5.6 The Road Ahead: Challenges and Future Directions 31 5.6.1 Developing effective algorithms 31 5.6.2 Granularity and context 31 5.6.3 Dynamic and contextual adaptability 31 5.6.4 Ethical frameworks 31 5.7 The Future 32 6 Programming Languages Used in AI Development 33 6.1 Python: The Lingua Franca of AI 34 6.1.1 Natural language processing (NLP) with Python and NLTK 34 6.1.2 Computer vision with OpenCV and Python 35 6.1.3 Machine learning classification with scikit-learn 35 6.1.4 Reinforcement learning with OpenAI Gym and Python 36 6.2 Java: Scalability and Performance 37 6.3 R: Statistical Computing for AI Research 37 6.4 TensorFlow (JavaScript): Bringing AI to the Browser 38 7 Unraveling the Challenges: Navigating the Barriers to Generative AI Success 41 7.1 Data Quality and Quantity: The Cornerstone Challenge 42 7.2 Computational Power: The Hunger for Resources 43 7.3 Explainability and Interpretability: Deciphering the Black Box 43 7.4 Ethical Concerns: Navigating the Moral Landscape 44 7.5 Adversarial Attacks: Testing the Robustness 44 7.6 Transferability and Generalization: Beyond Training Data 44 7.7 Legal and Regulatory Challenges: Navigating the Legal Landscape 45 8 Exploring the Challenges and Progress in AI Alignment 47 8.1 Understanding AI Alignment 48 8.1.1 The alignment problem 48 8.1.2 Types of AI alignment 49 8.2 Challenges in AI Alignment 49 8.2.1 Ambiguity in human values 49 8.2.2 Value drift 49 8.2.3 Scalability 49 8.2.4 Adversarial manipulation 50 8.3 Approaches to AI Alignment 50 8.3.1 Value learning 50 8.3.2 Inverse reinforcement learning 50 8.3.3 Cooperative inverse reinforcement learning 50 8.3.4 Formal verification 50 8.4 Progress in AI Alignment 51 8.4.1 Research initiatives 51 8.4.2 Collaborative efforts 51 8.4.3 Ethical guidelines 51 8.4.4 Public awareness and engagement 51 8.5 Future Directions and Considerations 51 8.5.1 Continued research and innovation 52 8.5.2 Ethical governance 52 8.5.3 Human⁰́₃AI collaboration 52 8.5.4 Education and awareness 52 9 Creating AI Models: From Data to Deployment 53 9.1 Building AI Models 53 9.1.1 Step 1: Data collection and preprocessing 53 9.1.2 Step 2: Model selection and architecture design 54 9.1.3 Step 3: Model training 55 9.1.4 Step 4: Model evaluation and tuning 56 9.1.5 Step 5: Model deployment and integration 56 9.1.6 Ethical considerations 57 9.2 Putting It All Together: An End-to-End Example 58 9.2.1 Step 1: Data collection and preprocessing 58 9.2.2 Step 2: Model selection and architecture design 58 9.2.3 Step 3: Model training 58 9.2.4 Step 4: Model evaluation and tuning 58 9.2.5 Step 5: Model deployment and integration 59 10 Large Language Models as Data Compression Engines 61 10.1 Data Compression Fundamentals 62 10.1.1 Information theory principles 62 10.1.2 Traditional data compression techniques 62 10.2 Large Language Models Unveiled (Figure 10.1) 62 10.2.1 Neural networks and transformers 62 10.2.2 Pre-training and fine-tuning 63 10.3 LLMs as Data Compressors (Figure 10.2) 63 10.3.1 Pattern extraction 63 10.3.2 Semantic encoding 64 10.3.3 Contextual analysis 64 10.3.4 Data compression efficiency 64 10.3.5 Efficient parameterization 64 10.3.6 Adaptive compression 65 10.3.7 Contextual optimization 65 10.3.8 Comparative efficiency 65 10.4 The LLM as an Information-theoretic Compressor (Figure 10.3) 66 10.4.1 Entropy and information gain 66 10.4.2 Compression ratios and efficiency 66 10.5 Applications and Implications 66 10.5.1 Real-world applications 66 10.5.2 Ethical considerations 66 10.6 Future Directions and Challenges 67 Part: II AI Applications 69 11 Can We Stop Robots from Replacing Humans 71 11.1 How Humans Can Secure Their Jobs in the Age of Advancing AI 73 11.2 ⁰́₋Self-Replicating Robots⁰́₊ 75 11.3 ⁰́₋Kill Switch⁰́₊ 76 12 Green Artificial Intelligence 81 12.1 Factors Contributing to Carbon Emissions 82 12.2 Mitigation Strategies for a Greener AI Future (Figure 12.1) 82 13 Artificial Intelligence and Natural Disasters 85 13.1 Understanding Natural Disasters 85 13.2 The Need for Prevention 86 13.3 AI in Disaster Prevention (Figure 13.1) 86 13.3.1 Early warning systems 87 13.3.2 Seismic activity prediction 87 13.3.3 Forest fire prevention 87 13.3.4 Flood prediction and management 87 13.3.5 Landslide detection 87 13.3.6 Climate change mitigation 88 13.3.7 Disaster response coordination 88 13.4 Challenges and Ethical Considerations 88 13.4.1 Data privacy and security 88 13.4.2 Bias in AI 89 13.4.3 Accessibility and equity 89 13.4.4 Accountability and decision-making 90 13.4.5 Overreliance on technology 90 13.4.6 Infrastructure and resource constraints 90 14 AI and Drones 93 14.1 Types of Drones 93 14.2 Key Components 94 14.3 The Convergence of AI and Drones 95 14.3.1 Benefits of combining AI and drones 95 14.3.2 Hardware and software integration 95 14.3.3 Real-time data processing 95 14.4 Applications of AI-Powered Drones 96 14.4.1 Agriculture and precision farming 96 14.4.2 Surveillance and security 96 14.4.3 Logistics and delivery 96 14.4.4 Disaster management and search and rescue 96 14.4.5 Environmental monitoring 96 14.5 Challenges and Ethical Considerations 97 14.5.1 Privacy concerns 97 14.5.2 Regulatory and legal challenges 97 14.5.3 Safety and security 97 14.5.4 Ethical use of AI in drones 97 14.6 Future Prospects 97 14.6.1 Advancements in AI and drone technology 97 14.6.2 Potential industry disruption 98 14.6.3 Ethical and regulatory frameworks 98 14.7 Last Word! 98 15 Nuclear AI: Pioneering the Future of Nuclear Technology 99 15.1 Defining Nuclear AI 99 15.2 Benefits of Nuclear AI (Figure 15.1) 100 15.3 Risks and Challenges (Figure 15.2) 101 15.4 The Future of Nuclear AI 103 15.5 Conclusion 104 16 Artificial Intelligence: A Double-edged Sword for Environment and Climate 105 16.1 Positive Potential: A Green AI Revolution 106 16.1.1 Resource optimization: 106 16.1.2 Sustainable innovations 107 16.1.3 Enhanced environmental monitoring and modeling 107 16.2 The Dark Side: AI⁰́₉s Environmental Footprint 107 16.2.1 Energy-guzzling algorithms 108 16.2.2 E-waste dilemma 108 16.2.3 Bias and unforeseen consequences 108 16.3 Navigating the Green Path: Making AI Sustainable 109 16.3.1 Green AI development 109 16.3.2 Ethical AI governance 109 Part: III Challenges and Opportunities 111 17 How Can Countries Regulate AI 113 17.1 How Can Countries Regulate AI 114 17.2 How to Make a Responsible AI 116 18 The Pinnacle of Artificial Intelligence: Navigating the Technological Odyssey 119 18.1 AI⁰́₉s Evolutionary Epoch 120 18.2 Contemporary AI Landscape 120 18.3 Envisioning the AI Prodigy 121 18.4 Quandaries and Conundrums 123 18.5 Ethical Deliberations 124 18.6 Denouement 125 19 WhatWould Happen If AI Took Over Humanity 127 19.1 The Argument for AI Taking Over Humanity 127 19.2 The Argument Against AI Taking Over Humanity 128 19.2.1 If AI were to take over humanity 129 20 Who Will Really Benefit from AI 133 20.1 Who will really benefit from AI 134 20.1.1 The pioneers and early adopters 134 20.1.2 Small- and medium-sized enterprises (SMEs) 134 20.1.3 Healthcare: Saving lives and transforming medicine 135 20.1.4 Education: Empowering learners 135 20.1.5 Automation and job displacement 135 20.
520
588 0 $a Title details screen.
650 0 $a Artificial intelligence. $3 122134
830 0 $a River Publishers series in computing and information science and technology. $3 423133