When Computers Can Think: The Artificial Intelligence Singularity

434 Pages·2015·10.83 MB·English

by  Anthony Berglas

When Computers Can Think
1. When Computers Can Think
1. Back Cover
2. Copyright
3. Acknowledgements
4. Overview
2. Part I: Could Computers Ever Think?
1. People Thinking About Computers
1. The Question
2. Vitalism
3. Science vs. vitalism
4. The vital mind
5. Computers cannot think now
6. Diminishing returns
7. AI in the background
8. Robots leave factories
9. Intelligent tasks
10. Artificial General Intelligence (AGI)
11. Existence proof
12. Simulating neurons, feathers
13. Moore's law
14. Definition of intelligence
15. Turing Test
16. Robotic vs cognitive intelligence
17. Development of intelligence
18. Four year old child
19. Recursive self-improvement
20. Busy Child
21. AI foom
2. Computers Thinking About People
1. The question
2. The bright future
3. Man and machine
4. Rapture of the geeks
5. Alternative views
6. AGI versus human condition
7. Atheists believe in God
8. AGI also struggles to survive
9. The super goal
10. AGI moral values
11. AGI and man
12. How humanity might be threatened
13. Why build a dangerous AGI?
14. Three laws of robotics
15. Sealed box
16. Friendly AGI
17. Primary assertions and objections
18. Other threats
19. Community Awareness
20. Is it a bad thing?
3. The Technological Singularity
1. Early computing machines
2. RK05 disk drive
3. Moore's law, transistors
4. Core and disk storage
5. Limits to growth
6. Long term growth
7. Human intelligence now minimal for AGI
8. Definitions of singularity
4. Hollywood and HAL 2001
1. Anthropomorphic zap gun vs. virus
2. The two HAL's
3. HAL dialog
5. The Case Against Machine Intelligence
1. Turing halting problem
2. Gödel's incompleteness theorem
3. Incompleteness argument against general AGI
4. Combinatorial explosion
5. Chinese room
6. Simulated vs. real intelligence
7. Emperors new mind
8. Intentionality
9. Brain in a vat
10. Understanding the brain
11. Consciousness and the soul
12. Only what was programmed
13. What computers can't do
14. Over-hyped technologies
15. Nonlinear difficulty, chimpanzees
16. End of Moore's law
17. Bootstrap fallacy
18. Recursive self-improvement
19. Limited Self-improvement
20. Isolated self-improvement
21. Motivation for self-improvement
22. Utility of Intelligence
23. Motivation to build an AGI
24. Premature destruction of humanity
25. Outcome against a superior chess player
6. Silicon versus Meat Based Intelligence
1. Silicon vs. neurons
2. Speech understanding
3. Other hardware estimates
4. Small size of genome
5. Chimpanzee intelligence
6. Packing density, fractals, and evolution
7. Repeated patterns
8. Small DNA, small program
7. Related Work
1. Many very recent new books
2. Kurzweil 2000, 2006, 2013
3. Storrs Hall 2007
4. Yudkowsky 2008
5. Sotala, Yampolskiy 2013
6. Nilsson 2009
7. Barrat 2013
8. Muehlhauser 2013
9. Del Monte 2014
10. Armstrong 2014
11. Bostrom 2014
12. Frankish, Ramsey 2014
13. CGP Grey 2014
14. Berglas 2014
3. Part II: Why Can't Computers Think?
1. Overview
2. Words Without Meaning
1. Eliza and Doctor pretend to understand
2. Patterns of language
3. Journalistic generation
4. The works of Shakespeare
5. The nature of words
3. Real Meaning in a Microworld
1. Parsing natural language
2. Planning to meet goals
3. Parsing limitations
4. Unconstrained natural language
5. SHRDLU's knowledge representation
6. Database Query languages
7. Eurisko and other early results
4. Knowledge Representation and Reasoning
1. Overview
2. Relational Databases
3. Frames and semantic networks
4. Mathematical logic
5. Logic for artificial intelligence
6. Propositional vs. first order systems
7. Paraconsistent flying pigs
8. Monotonicity
9. Closed world, Prolog
10. Description logics
11. Ontologies and databases
12. Modeling situations
13. Reification
14. Beliefs
15. Common sense reasoning
16. Cyc
17. Learning logical rules from experience
18. Scruffy vs. neat
5. Uncertain Expertise
1. Rule-based expert systems
2. Mycin and other expert systems
3. Hype and reality
4. Mycin's reasoning with uncertainty
5. Sprinklers make it rain
6. Joint probability distributions
7. Probability theory
8. Bayes rule
9. Bayesian networks
10. Learning Bayesian networks
11. Human probability reasoning
12. Human diagnostic reasoning
6. Pattern Matching
1. Symbols
2. The post/zip code problem
3. Case based reasoning
4. Decision trees
5. Decision tables
6. Regression
7. Artificial Neural Networks
1. Introduction
2. Perceptrons
3. Sigmoid perceptrons
4. Using perceptron networks
5. Hype and real neurons
6. Support vector machines
7. Unsupervised learning
8. Competing technologies
8. Speech and Vision
1. Speech recognition
2. Hidden Markov models
3. Words and language
4. 3D graphics
5. Machine vision
6. 3D vs 2.5D
7. Kinetics
9. Robots
1. Automata
2. Robotics
3. Sensing environment
4. Motion Planning
5. Movement and Balance
6. Robocup
7. Other robots
8. Humanistic
9. Robots leaving the factory
10. Programs writing Programs
1. The task of man
2. Recursive compilation
3. Quines
4. Reasoning about program logic
5. Automating program generation
6. High-level models
7. Learning first order concepts
8. Evolutionary algorithms
9. Artificial life
10. Evolutionary programming
11. Computer Hardware
1. Introduction
2. Transistors
3. Logic Elements
4. Programmable Logic Arrays
5. Von Neumann Architecture
6. PLAs vs von Neumann
7. Analog Computers
8. Neurons
12. Brains
1. Gross anatomy
2. Neocortex
3. Brain activity
4. Brain function and size
5. Brain simulation
6. Worms
13. Computational Neuroscience
1. Neurons
2. Neuron synapse
3. Integrate and fire (IF) neurons
4. Hebbian learning
5. Plasticity
6. Neuron chains
7. Self organizing maps (SOMs)
8. Recurrent networks and learning
9. Memory
10. Modularity
11. Controlling movement
12. Levels of abstractions and symbols
13. Growth
14. Man vs. Machine
1. Chess history
2. Minimax
3. Chess strategies
4. Chess vs Go
5. Watson and Jeopardy!
6. Watson's implementation
7. Watson's victory
15. Where is the Intelligence?
1. Good old fashioned AI
2. Knowledge representation and reasoning
3. Artificial neural networks and other numerical methods
4. Symbols
5. Visualizations
6. Brains
7. Animal Intelligence
4. Part III: What Will Computers Think About?
1. Why, What, How, Who, Where, When
1. Why
2. What
3. How
4. Who
5. Where
6. When
2. The Age of Semi Intelligent Machines
1. The intermediate period
2. Manufacturing productivity
3. Autonomous cars
4. Arthropod automation
5. Leisure society
6. Affluent society
7. Unemployed society
8. Cognitive applications
9. White collar unemployment
10. Controlled society
11. Politician's assistant (Iago)
3. Good and Evil in Natural History
1. Wonderful wandering albatross
2. Pelican's dark secret
3. Honest rosella parrots
4. Evil coots
5. Magnanimous golden eyed ducks
6. Chimpanzees, our dubious cousins
7. Pointless moralization
8. Human morality Neolithic, ancient and Maori behaviour
9. The modern zeitgeist
4. The answer to life, the universe, and everything
1. You're really not going to like it
2. Galileo and Newton
3. Alfred Wallace
4. Evolution through natural selection
5. Creationists should reject natural selection
6. God
7. History of evolutionary thought
8. Hurdles for natural selection
9. Age of the Earth
10. Memes and genes
11. Flynn effect
12. The cooperation game
13. Human condition
14. Selecting civilized behaviour
15. Sociobiology, evolutionary psychology and ethics
5. The AGI Condition
1. Mind and body
2. Teleporting printer
3. Immortality
4. Components vs genes
5. Changing mind
6. Individuality
7. Populations vs. individuals