Bridging Human Intelligence and Artificial Intelligence

Foreword
Preface
Contents
About the Editors
Part I: Trajectory of AI. From Statistics and Machine Learning to Deep Learning
Understanding Machine Learning Through Data-Oriented and Human Learning Approaches
Introduction
Why Machine Learning?
Machine Learning Problems and Methods
Supervised Learning
Unsupervised Learning
Hierarchical Clustering
Hidden Markov model
Association Rule Learning
Semi-Supervised Learning
Reinforcement Learning
Creating Effective Models
Data Quality
Bias in the ML Models
Fairness in the ML Models
Validation Strategies
Train/Test Split
K-Fold Cross-Validation
Leave-P-Out Cross-Validation
Nested Cross-Validation
Time-Series Cross-Validation
Advancements in the Field
Big Data
Deep Learning
Autoencoders and Embeddings
Transfer Learning
Computer Vision
Natural Language Processing
References
Deep Learning: Why Neural Networks Are State of the Art
Introduction
What Is a Neural Network?
How Do Neural Networks Improve?
What Sets Deep Learning Apart?
Various Types of Deep Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Success of Deep Neural Networks
Limitations of Deep Learning
Conclusions
References
Autoencoders and Embeddings: How Unsupervised Structural Learning Enables Fast and Efficient Goal-Directed Learning
Introduction to Unsupervised Learning
Reducing Dimensionality
Principal Component Analysis (PCA)
Autoencoders
Standard Autoencoder
Stacked Autoencoder
Denoising Autoencoder
Contractive Autoencoder
Sparse Autoencoder
Convolutional Autoencoders
Variational Autoencoders
Applications of Autoencoders
Image Generation
Sequence to Sequence Prediction
Recommendation Systems
Anomaly Detection
Embeddings
Word Embeddings
Location Embeddings
Graph Embeddings
Conclusion
References
Transfer Learning: Leveraging Trained Models on Novel Tasks
What Is Transfer Learning?
History of Transfer Learning
Why Transfer Learning?
Types of Transfer Learning Techniques
Inductive Transfer Learning
Transductive Transfer Learning
Unsupervised Transfer Learning
Pre-trained Models in Transfer Learning
VGG-16
Inception
ULMFiT
BERT
Applications of Transfer Learning
Negative Transfer
Conclusion
References
Progress in Computer Vision: Object Recognition
Brief History
How Computer Vision Works
Features
Feature Extraction
Deep Learning and Computer Vision
Putting All Pieces Together: ConvNet
Improvements of Computer Vision Over Time
Future Development
Bibliography
Progress in Natural Language Processing and Language Understanding
Introduction
Types of Tasks (Syntactic and Semantic)
Evolutionary Stages of NLP
Rule-Based NLP
Statistical NLP
Neural NLP
Benchmarks in Natural Language Processing
BiLingual Evaluation Understudy
Stanford Question Answering Dataset
General Language Understanding Evaluation
The Social Aspect of Natural Language Processing
Conclusion
References
Part II: Enhancing Human Intelligence Through AI
AI-Enhanced Education: Teaching and Learning Reimagined
Why AI in Education
Fundamental Technologies in Educational AI
Learning Analytics
Machine Learning
Agencies of AI in Education
Education Administration
Instruction
Learning
AI in Future Education
Concerns of AI Educational Application
References
Supporting Social and Emotional Well-Being with Artificial Intelligence
Introduction
Mental Health
Depression and Anxiety
Social Interactions
Social and Emotional Well-Being
Robot Personification and Emotional Attachment
Social and Psychological Development
Challenges
Conclusions
References
Will Virtual Reality Connect or Isolate Students?
Introduction: Immersing Students Through Virtual Reality
What Is Virtual Reality?
Levels of Immersion in Learning
Measuring the Effectiveness of Virtual Reality Experiences for Learning
Learning and Behavior Change in Virtual Reality
Virtual Field Trips
Empathy
Social Learning
Experiential VR
Situated Learning and Embodied Cognition
Collaborative Spaces
Solitude or Isolation
Balance Between Solitude and Connection by Design
Glossary
References
Augmented Intelligence: Enhancing Human Decision Making
Introduction
What Is Augmented Intelligence
Augmented Intelligence in Business
Business Intelligence
Manufacturing
Augmented Intelligence for Entertainment
Predictions and Algorithms
Inverse Augmented Reality
Augmented Intelligence for Education
Smart Education
Adaptive Learning Technologies
Mobile and Ubiquitous Technologies
Data Driven Applications
Ethics of Augmented Intelligence in Education
Augmented Intelligence for Healthcare
Healthcare Decision Making Tools
Smart Robots
Augmented Intelligence and COVID-19
Inventory Management Systems
Ethics of Augmented Intelligence in Healthcare
Augmented Intelligence for Travel
Applications in the Automobile Industry
Augmented Intelligence in Air Travel
Advancements and Limitations of Augmented Intelligence
Current Advancements
Limitations
Conclusion
References
Cybernetic Systems: Technology Embedded into the Human Experience
Introduction
Existing Working Systems
How the Technology Was Created and Designed
Future Applications on Humans
Medical Applications
Human Condition Restoration
Human Condition Enhancement
Ultrasonic Sense for the Blind
Deep Brain Stimulation
Reverse Systems: Developing a Human Brain in a Robotic System
Ethical Implications of Cybernetic Systems
Conclusion
References
Part III: How Artificial Intelligence Imitates Human Neuroanatomy
Early Visual Processing: A Computational Approach to Understanding Primary Visual Cortex
Visual Processing
How Neural Codes Are Represented
Gabor Filters
How Gabor Filters Work
Efficient Coding Hypothesis
Natural Vs. Non-natural Images
ICA
Applications of Neural Modeling
Decoding V1 to See What Images Are Being Perceived
Conclusion
References
Visual Object Recognition: The Processing Hierarchy of the Temporal Lobe
Temporal Hierarchy
What Is Visual Object Recognition?
Object Classification
Object Localization
Object Detection
Object Segmentation
New Replications of Temporal Lobe Functionality
Conclusions
References
Visual-Spatial Processing: The Parietal Lobe in Engaging a 3D World
Visual-Spatial Processing
Mapping to AI
Our Focus
Anatomy and Physiology of the Dorsal Steam
Introduction
Occipital Lobe Processing
Parietal Lobe Processing
Effects of Damage
Similarities with Artificial Intelligence
Introduction
Graph Neural Network and its Variants
Spatial Concepts through Building Relational Networks
A Practice in Application and Examination of Spatial Reasoning
Action Recognition and Prediction through GCN Model
Applications and Potential Benefits
Conclusion
References
Memory: Beyond the Hippocampus: Computer Systems and Their Resemblance to the Human Hippocampus
Introduction
Human Memory
Place Cells and Grid Cells
Types of Memory
Introduction to Neural Networks
Memory Augmented Neural Network
Autoassociative Networks
Convolutional Networks
Artificial Hippocampus
Prosthetics
Machine Applications
Ethical Implications of Integrated Memory Systems
Unemployment and Inequality
Unintended Consequences
Conclusions
References
Reinforcement Learning: Beyond the Basal Ganglia
Introduction to Reinforcement Learning
Basal Ganglia and Reinforcement Learning
Future of Reinforcement Learning
Conclusion
References
Part IV: Understanding the Effects of Artificial Intelligence
Human Intelligence and Artificial Intelligence: Divergent or Complementary Intelligences?
Introduction
Dimensions of Intelligence
A Global Dimension
Problem-Solving and Learning Dimensions
Underlying Foundations
Pattern Matching
Neural Networks
Weak and Strong AI
Measuring Intelligence
Conclusion
References
AI-Complete: What it Means to Be Human in an Increasingly Computerized World
Introduction
AI and the Economy
The New Social Era
Data, Algorithms, and the Industry
Propagation of Misinformation
The Nature of Personhood
Asimov’s Legacy: Redefining the Ethical Framework
The Value of Human Life
Normative Ethics and Machine Design
Following Orders: The AI Control Problem and Existential Threat
Conclusion
References
Bias in AI-Based Decision-Making
Introduction
Defining Bias
The Formation of Bias
Real-World Example of Bias
Trust in Artificial Intelligence
Efforts to Prevent AI Bias
Sources of Bias
The Issue of Gauging Bias
AI Bias and the Law
Conclusions
References
The Paradox of Learning in the Intelligence Age: Creating a New Learning Ecosystem to Meet the Challenge
Introduction
The Changing Technical and Social Context
New Human Capacities
The Changing Configuration of Jobs
The Threat of Growing Inequality
Challenges Posed for the Learning Sector
The Destabilizing of Curriculum
The Teacher Role Under Strain
The Ebbing of the School
What Should Be the New Learning Sector?
The Purpose of Learning
Self-Directed Learning
Life-Long Learning
New Infrastructure
Engaging Volunteers
Envisioning the New Teacher Role
Cultivate an Appetite for Prototyping
Accelerate Digitization
Manage the Decentering of the School
Needs and Opportunities for Research and Development
Monitoring for Educational Equity
Studying Prototypes and Test Beds
Tracking Learning Materials and Resources
Modernizing Educational Research
References
Integrating an Emphasis on Creativity
Creativity Is an Important Human Capability
Creativity Can Be Measured, Evaluated, and Developed in Learners
Development Occurs Through Practice and Divergent Habits
Creativity Is Both General and Domain Specific
Beyond the Individual
Potential Means for Developing Creativity Through Digital Technology
References
Smart Learning in Support of Critical Thinking: Lessons Learned and a Theoretically and Research-Based Framework
Introduction
A Review of Critical Thinking Teaching
Socrates
Dewey
Recent Development
Previous Relevant Efforts: Intelligent Tutoring Systems
A Framework for a Smart Learning Environment
Conclusion
References
A Corpus of Biology Analogy Questions as a Challenge for Explainable AI
Introduction
A Corpus of Biology Analogy Questions
Approach
Analogy between Concept Names that Are Single Words
Model Development
Experimental Results
Analysis of Results
Analogy between Concept Names that Are Multiword Phrases
Model Development
Experimental Results
Analysis
Conclusion
References
Uses of Artificial Intelligence in Healthcare: A Structured Literature Review
Introduction
Methods
Discussion
Timeline of AI in Healthcare
Healthcare Education and AI
Current Applications of AI in Medical Schools
Medical Student Attitudes Toward AI
Instructor Attitudes Toward AI in Medical Curriculum
Promising AI Applications in Medicine
Chatbots
Medical Imaging
Ethics with AI in Medicine
Ethical Concerns
Patient Privacy
Cyber Security
Limitations in AI
Conclusion
References
Index