Simply AI Facts made fast

Book - 2024

Covering a broad range of fields within AI - from computing and mathematics to politics and philosophy - entries demystify what artificial intelligence is and how it works, how it has dramatically changed how we live, and how it might evolve in the future. Everyone is talking about AI, but this book helps to explain each individual aspect of AI more clearly than ever before.

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Location Call Number   Status
2nd Floor New Shelf 006.3/Simply (NEW SHELF) Due Aug 7, 2024
Instructional and educational works
New York, NY : DK Publishing 2024.
Other Authors
Claire Quigley (contributor)
Second edition. American edition
Item Description
Includes index.
Physical Description
160 pages : color illustrations ; 22 cm
  • Introduction
  • History of Artificial Intelligence
  • An Imitation of Life Automata
  • Defining Intelligence Multiple Intelligences
  • Thinking = Computing Computationalism
  • Zeros and Ones Binary code
  • Step by Step Algorithms
  • Algorithms in Action Computation
  • Instructing Computers Programs
  • The First Mechanical Computers Babbage's machines
  • A Theoretical Computer Turing's universal machine
  • An Electric Brain Neurons and computation
  • Artificial Neurons Threshold logic units
  • A Programmable Computer ENIAC
  • A Theoretical Program Turochamp
  • A Computing Blueprint Von Neumann architecture
  • Two Kinds of AI Weak and strong AI
  • AI in Action Intelligent agents
  • Trial and Error Learning to learn
  • Mimicking the Brain Connectionism
  • AI Models Classical vs. statistical AI
  • Computing Power Moore's law
  • Raw Information Types of data
  • Everything, Everywhere, All of the Time Big data
  • Classical Artificial Intelligence
  • Representing Data Symbols in AI
  • Following the Rules Computer Logic
  • What, When, Why, and How? Kinds of knowledge
  • Presenting Knowledge Knowledge representation
  • If This, Then That Rules
  • The Shortest Route Pathfinding
  • Imperfect Solutions Heuristics
  • Performing a Task Planning and AI
  • Dealing with Uncertainty Probability and AI
  • Modeling Changes The Markov chain
  • Modeling Uncertainty Stochastic models
  • Automated Advice Expert systems
  • Handling "Messy" Data Messiness
  • Neats vs. Scruffies Two fields of AI research
  • Statisitcal Artificial Intelligence
  • Teaching AIs to Think Machine learning
  • Gaining Insight from Data Data mining
  • Teaching Materials Training data
  • Giving Data Meaning Features and labels
  • Looking for Patterns Pattern recognition
  • Yes or No? Decision trees
  • Types of Data Classification
  • The Line of Best Fit Regression
  • Grouping Data Clustering
  • The Odd One Out Anomaly detection
  • The Most Likely Outcome? Predictions
  • Machine Learning with "Labeled" Data Supervised learning
  • Machine Learning with "Raw" Data Unsupervised learning
  • Learning from Feedback Reinforcement learning
  • Working Together Ensemble learning
  • The AI Brain Artificial neural networks
  • Network Structure Layers
  • Assigning Importance Weighting
  • Goals and Thresholds Bias
  • Measuring Success Cost function
  • Improving Performance Gradient descent
  • Refining the Model The Delta rule
  • A One-Way Network Feedforward neural networks
  • Fine-Tuning Data Backpropagation
  • Structured Data Recurrent neural networks
  • Building a Brain Deep learning
  • AI vs. AI Generative adversarial networks
  • Processing Visual Data Convolutional neural networks
  • Using Artificial Intelligence
  • Uses of AI Applications of AI
  • Ranking Data hierarchies
  • Recommending Tailored content
  • Detecting Threats Cybersecurity
  • Online Attacks Cyber warfare
  • Detecting Fraud Transaction monitoring
  • AI in Finance Algorithmic trading
  • Unraveling Proteins Medical research
  • Searching for Planet Astronomical research
  • Digital Doctors AI in medical diagnosis
  • Monitoring Health AI and healthcare
  • Internet of Things Connected devices
  • Smart Devices Embedded AI
  • Monitoring Systems AI and infrastructure
  • "Smart" Farming Precision agriculture
  • Sensory AI Machine perception
  • Processing Sound Machine hearing
  • Mimicking Sight Computer vision
  • Facial Recognition Feature mapping
  • Understanding Words Natural language processing
  • AI Interpreters Machine translation
  • Talking with AI Chatbots
  • AI Write Large language models
  • AI Helpers Virtual assistants
  • AI Artists Generative AI
  • Intelligent Robots Embodied AI
  • AI Companions Social robots
  • Movement and Mobility Physical interactions I
  • Manual Dexterity Physical interactions II
  • Driverless Cars Autonomous vehicles
  • AI and Warfare Autonomous weapons
  • Philosophy of Artificial Intelligence
  • Humanlike AI Artificial general intelligence
  • The Point of No Return The technological singularity
  • Where is Consciousness? Leibniz's question
  • Do Submarines Swim? Functionalism
  • The Imitation Game The Turing test
  • Intelligence Metrics Intelligence tests
  • Machines and Understanding The Chinese Room experiment
  • Philosophical Zombies Human vs. machine intelligence
  • A New Kind of Person AI rights and responsibilities
  • Replicating the Mind Multiple readability
  • Transparent Thinking Opening the box
  • Living with Artificial Intelligence
  • Myth or Reality? The truth about AI
  • Powered by Exploitation? Invisible labor
  • Garbage In, Garbage Out Data quality
  • Prejudiced Outcomes Hidden bias
  • Making Assumptions AI profiling
  • Transparent Processing White box AI
  • An AI Workforce Technological unemployment
  • The AI Balance AI and equality
  • An Echo Chamber Filter bubbles
  • The Limits of Control AI autonomy
  • Right vs. Wrong Ethical design
  • Built-In Ethics Asimov's three laws
  • Who is to Blame? AI and liability
  • What Should We Allow? AI and regulation
  • Existential Risks An AI dystopia
  • Unlimited Rewards An AI Utopia