What AI Can Do: Strengths and Limitations of Artificial Intelligence

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Section I: Nature and Culture of the Algorithm
	1. AI Ethics as a Form of Research Ethics
		1.1 Introduction
		1.2 Technology as a Means
			1.2.1 Values – Metaethical Considerations
			1.2.2 Technology and Values
			1.2.3 The Ambivalence of Technology
		1.3 How to Ask Ethical Questions in AI Research
			1.3.1 Bad Scientific Practice I: Methodological Aspects
			1.3.2 Bad Scientific Practice II: Ethical Aspects
		1.4 Discussion
		1.5 Conclusion and Outlook
		References
	2. Going through the Challenges of Artificial Intelligence: Gray Eminences, Algocracy, Automated Unconsciousness
		2.1 The Intervention of Gray Eminences in the Digital World
		2.2 Algocracy as the Sterilization of Politics
		2.3 Discussion: The Ethics of AI in the Mirror of its Weaknesses
		2.4 Who Must Adapt to Whom? A Matter of Strategic Enveloping
		2.5 Conclusion
		References
	3. AI, Ethics, and Coloniality: A Feminist Critique
		3.1 Introduction
		3.2 Structural Violence and the Decolonial Turn
			3.2.1 Decolonial Feminism
		3.3 Decoloniality as Praxis: A Brief Genealogy
		3.4 Power and Knowledge: The Coloniality of Technology, Gender, Race/Ethnicity, and Nature
		3.5 Decolonizing AI: Geopolitics and Body Politics
		3.6 AI and Ethics: Between Geopolitics and Body Politics
			3.6.1 Geopolitics
			3.6.2 Body Politics
		3.7 Conclusion
		References
	4. A Cultural Vision of Algorithms: Agency, Practices, and Resistance in Latin America
		4.1 The Judges\' Question
		4.2 Studies on Technology and Culture in Latin America
		4.3 Algorithmic Cultures
		4.4 Agency and Play
		4.5 Collectives and Resistance
		4.6 Who Knows How Algorithms Work?
		References
	5. From Deepfake to Deeptruth: Toward a Technological Resignification with Social and Activist Uses
		5.1 Introduction
		5.2 The Use of Synthetic Media for Denunciation and Social Activism
		5.3 A Theoretical Framework to Study Content Created with Synthetic Media
			5.3.1 Techno-Aesthetics
			5.3.2 Post-Truth
			5.3.3 Discourse
			5.3.4 Criticism
		5.4 Case Study: Javier Valdez\'s Synthetic Video
			5.4.1 Technical Procedures
			5.4.2 Ethical and Legal Transparency
			5.4.3 Discourse
			5.4.4 Context
			5.4.5 Critical Sense
		5.5 Conclusions: The Resignification from Deepfake to Deeptruth
		References
	6. The Neurocomputational Becoming of Intelligence: Philosophical Challenges
		6.1 The Shadow of Phenomenology on Contemporary Philosophy
		6.2 The Philosophical Reductionism of Immediacy
		6.3 The Neurocognitive Model of the Mind
		6.4 The Threat of the Transhumanism
		6.5 Posthuman Transcendental Structure
		6.6 AI Frame Problems
		6.7 Non-Propositional Metacognition
		6.8 AI Historical and Social Context
		6.9 Discussion: What Would it Mean to Survive for an AI?
		6.10 Conclusions
		References
Section II: Knowledge Areas Facing Al
	7. A Cluster Analysis of Academic Performance in Higher Education through Self-Organizing Maps
		7.1 Introduction
		7.2 What Are Self-Organizing Maps (SOMs)?
			7.2.1 Structure of SOMs
			7.2.2 Competitive Learning of SOMs
			7.2.3 Designing of SOMs
			7.2.4 SOMs Quality Measures
		7.3 How SOMs Can be Used to Analyze Academic Performance in Higher Education?
			7.3.1 Description of the Participants
			7.3.2 Description of the Variables
			7.3.3 Data Analysis
				7.3.3.1 Preparation of the Data
				7.3.3.2 Designing the SOM Model
			7.3.4 Patterns between Prior Academic Achievement, SES, and Academic Performance in Higher Education
			7.3.5 Grouping Students Based on their Prior Academic Achievement, SES, and Academic Performance
		7.4 What are the Takeaway Messages of this Chapter?
		References
	8. Artificial Intelligence as a Way to Improve Educational Practices
		8.1 Introduction
		8.2 What is a Predictive Algorithm Based on Artificial Intelligence (AI)?
		8.3 AI Applied in Education to Improve the Learning Experience
		8.4 Limitations in the Educational Field for AI Prediction Algorithms
		8.5 AI-Based Adaptive Learning: Case Study
		8.6 Final Lecture
		References
	9. Using AI for Educational Research in Multimodal Learning Analytics
		9.1 Introduction
		9.2 Learning Analytics
		9.3 Multimodal Learning Analytics
		9.4 Applications of Artificial Intelligence in MMLA
			9.4.1 Analyzing Body Postures in Oral Presentations
			9.4.2 Assessing Spoken Interactions in Remote Active Learning Environments
			9.4.3 Analyzing Physical Environment Factors in Distance Learning
		9.5 Limitations and Future of AI in MMLA
		9.6 Closing Remarks
		References
	10. Artificial Intelligence in Biomedical Research and Clinical Practice
		10.1 Introduction to Artificial Intelligence in Healthcare
		10.2 Digital Health, Person-Centered Attention Paradigm, and Artificial Intelligence
		10.3 Regulatory Affairs Implementing Artificial Intelligence in Medical Devices
		10.4 Radiomics and AI, the Most Extended Application
		10.5 New Drugs\' Development and AI
		10.6 Challenges to Developing and Implementing AI in Medical Areas
		10.7 Conclusion
		References
	11. The Dark Side of Smart Cities
		11.1 Introduction
		11.2 What Kinds of Urban Planning Problems is AI Being Used on?
		11.3 What Kinds of Problems and Resistances Have Been Called Out to the Implementation of AI in Urban Planning?
		11.4 What Can We Learn from the AI that is Already Implemented in Urban Planning?
		11.5 Conclusions
		References
	12. The Control of Violence between the Machine and the Human
		12.1 Introduction
		12.2 Human and Algorithmic Implications for the State Decision to Control Violence
		12.3 The Colonizing Private Sector of Violence Control
		12.4 Human Function and Mechanical Function in the Penal Decision and in the Algorithmic Interpretation
		12.5 Conclusions
		References
	13. AI in Music: Implications and Consequences of Technology Supporting Creativity
		13.1 Why Puccini for Artificial Intelligence? An Introduction
		13.2 These Hopeful and Intelligent Machines
			13.2.1 Knowledge Representation and Reasoning
			13.2.2 Machine Learning
		13.3 Music-Making with AI
		13.4 Solving Puccini
			13.4.1 Solving with KRR
			13.4.2 Solving with ML
		13.5 Implications and Consequences for Art Music Endeavors
			13.5.1 Acceptance
			13.5.2 Efficiency
			13.5.3 Trustworthy AI
			13.5.4 Search Space
		13.6 Encore: Another Muse for Music Creation
		Acknowledgments
		References
Section III: Future Scenarios and Implications for the Application of AI
	14. Classification Machine Learning Applications for Energy Management Systems in Distribution Systems to Diminish CO[sub(2)] Emissions
		14.1 Introduction
		14.2 Energy Management Systems
		14.3 What is Machine Learning?
		14.4 Driving Energy Management Systems with Machine Learning
		14.5 Classification and Trends of Machine Learning Applications in Energy Management Systems
			14.5.1 Costumer-Oriented ML Applications
			14.5.2 System-Oriented ML Applications
			14.5.3 Comparison between ML Strategies in the EMS Context
		14.6 Challenges of Machine Learning Driving Energy Management Systems
		14.7 Wrap-Up
		Acknowledgement(s)
		Disclosure Statement
		Funding
		References
	15. Artificial Intelligence for Construction 4.0: Changing the Paradigms of Construction
		15.1 Introduction
		15.2 Construction 4.0
		15.3 Information Management in Construction 4.0
		15.4 On-Site Surveillance
		15.5 Quality Control
		15.6 Design Optimization
		15.7 Other Artificial Intelligence Technologies
			15.7.1 Smart Robotics
			15.7.2 Digital Twins
			15.7.3 Artificial Intelligence of Things (AIoT)
			15.7.4 Blockchain
			15.7.5 Architectural Design
			15.7.6 Document Management
		15.8 Conclusions
		References
	16. A Novel Deep Learning Structure for Detecting Human Activity and Clothing Insulation
		16.1 Classification Algorithms for Computer Vision
			16.1.1 Convolutional Neural Networks
				16.1.1.1 Convolutional Layer
				16.1.1.2 Pooling and Flattening Layer
				16.1.1.3 Activation Functions
				16.1.1.4 Fully Connected Layer and Loss Functions
			16.1.2 CNN Regularization
			16.1.3 Object Recognizers
				16.1.3.1 R-CNN
				16.1.3.2 Fast R-CNN
				16.1.3.3 Faster R-CNN
				16.1.3.4 Mask R-CNN
				16.1.3.5 YOLO
				16.1.3.6 YOLOv2
				16.1.3.7 YOLOv3
				16.1.3.8 YOLOv4
				16.1.3.9 YOLOv5
				16.1.3.10 PP-YOLO
				16.1.3.11 YOLOX
				16.1.3.12 YOLOv6
				16.1.3.13 YOLOv7
			16.1.4 Recurrent Neural Networks (RNNs)
				16.1.4.1 LSTM Networks
		16.2 Clothing Classification
			16.2.1 Methods Used
			16.2.2 Clothing Detection Implementation
		16.3 Activity Recognition
			16.3.1 Deep Learning Model for HAR
				16.3.1.1 Data Acquisition
				16.3.1.2 Pre-Processing
				16.3.1.3 Feature Extraction and Classification
		16.4 Case Study: Thermal Comfort
			16.4.1 Clothing Level and Metabolic Rate Estimations
		16.5 Discussion
		References
	17. Building Predictive Models to Efficiently Generate New Nanomaterials with Antimicrobial Activity
		17.1 Introduction
			17.1.1 The Process to Generate a New Material with Certain Desired Properties
				17.1.1.1 Synthesis Route
				17.1.1.2 Nanomaterial Characterization
				17.1.1.3 Antibacterial Assays
		17.2 Artificial Intelligence: The Tool for Complex Problems
			17.2.1 Machine Learning Algorithms
			17.2.2 Supervised Machine Learning and Nanomaterial Development
				17.2.2.1 Phase 1: Understanding the Problem
				17.2.2.2 Phase 2: Understanding the Data
				17.2.2.3 Phase 3: Prepare the Data
			17.2.3 Combine Data from Different Sources
				17.2.3.1 Phase 4: Building the Model
				17.2.3.2 Phase 5: Error Analysis
			17.2.4 Define Evaluation Criteria
				17.2.4.1 Phase 6: Deployment
		17.3 Our Application
		17.4 The Future
		References
	18. Neural Networks for an Automated Screening System to Detect Anomalies in Retina Images
		18.1 Introduction
		18.2 Artificial Intelligence and Machine Learning
		18.3 Artificial Neural Networks for Retinal Abnormalities Detection
			18.3.1 Data
			18.3.2 Preprocessing and Feature Extraction
			18.3.3 Classification
		18.4 The Future
		References
	19. Artificial Intelligence for Mental Health: A Review of AI Solutions and Their Future
		19.1 Introduction
		19.2 Mental Health Overview
		19.3 Part 1. Cognitive Behavioral Therapy (CBT)
		19.4 Part 2. How Does Artificial Intelligence Provide Mental Health Support?
			19.4.1 Mental Health Support through Natural Language Processing
			19.4.2 Mental Health Support through Computer Vision
			19.4.3 Mental Health Support through AI Robotics
		19.5 Part 3. Current Applications of AI
			19.5.1 Chatbots
				19.5.1.1 Wysa – Mental Health Chatbot
			19.5.2 Virtual AI Therapists
			19.5.3 Robot AI Therapists
				19.5.3.1 Commercial Therapy Robots
				19.5.3.2 Humanoid Therapy Robots
		19.6 Concerns and Ethical Implications of AI in Mental Health
		19.7 Conclusions
		References
	20. What AI Can Do for Neuroscience: Understanding How the Brain Represents Word Meanings
		20.1 Introduction
		20.2 Overview of the Key Ideas
		20.3 Models of Semantic Representation
			20.3.1 Distributional Semantic Models (DSMs)
			20.3.2 Embodied Semantic Model
		20.4 Case Study: The CEREBRA Model
		20.5 Hybrid Approach to Ground NLP Applications
		20.6 Challenges for Building More Brain-Like AIs
		20.7 Conclusion
		References
	21. Adversarial Robustness on Artificial Intelligence
		21.1 Recent Advances in Artificial Intelligence
		21.2 Model Vulnerabilities
			21.2.1 Adversarial Robustness
		21.3 Limitations of Current Models
		21.4 Designing Robust Models for Evasion Attacks
			21.4.1 Adversarial Examples
			21.4.2 Perturbations Sets
			21.4.3 Training Robust Models
		21.5 Recent Advances on Robust Models
		21.6 Discussion
		References
Index