Intelligence Science: Leading the Age of Intelligence

Intelligence Science
Copyright
Contents
About the author
Preface
Acknowledgment
1 Introduction
	1.1 The Intelligence Revolution
	1.2 The rise of intelligence science
		1.2.1 Brain science
		1.2.2 Cognitive science
		1.2.3 Artificial intelligence
			1.2.3.1 The formation period of artificial intelligence (1956–1976)
			1.2.3.2 Symbolic intelligence period (1976–2006)
			1.2.3.3 Data intelligence period (2006-present)
	1.3 Ten big issues of intelligence science
		1.3.1 Working mechanism of brain neural network
		1.3.2 Mind modeling
		1.3.3 Perceptual representation and intelligence
		1.3.4 Linguistic cognition
		1.3.5 Learning ability
		1.3.6 Encoding and retrieval of memory
		1.3.7 Thought
		1.3.8 Intelligence development
		1.3.9 Emotion
		1.3.10 Nature of consciousness
	1.4 Research contents
		1.4.1 Computational neural theory
		1.4.2 Cognitive mechanism
		1.4.3 Knowledge engineering
		1.4.4 Natural language processing
		1.4.5 Intelligent robot
	1.5 Research methods
		1.5.1 Behavioral experiments
		1.5.2 Brain imaging
		1.5.3 Computational modeling
		1.5.4 Neurobiological methods
		1.5.5 Simulation
	1.6 Prospects
	References
2 Foundation of neurophysiology
	2.1 The human brain
	2.2 Nervous tissues
		2.2.1 Basal composition of neuron
			2.2.1.1 Soma or cell body
			2.2.1.2 Cell membrane
			2.2.1.3 Nucleus
			2.2.1.4 Cytoplasm
			2.2.1.5 Process
			2.2.1.6 Dendrite
			2.2.1.7 Axon
		2.2.2 Classification of neurons
		2.2.3 Neuroglial cells
	2.3 Synaptic transmission
		2.3.1 Chemical synapse
			2.3.1.1 Presynaptic element
			2.3.1.2 Postsynaptic element
			2.3.1.3 Synaptic cleft
		2.3.2 Electrical synapse
		2.3.3 Mechanism of synaptic transmission
	2.4 Neurotransmitter
		2.4.1 Acetylcholine
		2.4.2 Catecholamines
			2.4.2.1 Biological synthesis of catecholamines
			2.4.2.2 Norepinephrine
			2.4.2.3 Dopamine
		2.4.3 5-hydroxytryptamine
		2.4.4 Amine acid and oligopeptide
		2.4.5 Nitric oxide
		2.4.6 Receptor
	2.5 Transmembrane signal transduction
		2.5.1 Transducin
		2.5.2 The second messenger
	2.6 Resting membrane potential
	2.7 Action potential
	2.8 Ion channels
	2.9 The nervous system
		2.9.1 Central nervous system
		2.9.2 Peripheral nervous system
	2.10 Cerebral cortex
	References
3 Neural computing
	3.1 Introduction
	3.2 Back-propagation learning algorithm
	3.3 Adaptive resonance theory model
	3.4 Bayesian linking field model
		3.4.1 Elkhorn model
		3.4.2 Noisy neuron firing strategy
		3.4.3 Bayesian coupling of inputs
		3.4.4 Competition among neurons
	3.5 Recurrent neural networks
	3.6 Long short-term memory
	3.7 Neural field model
	3.8 Neural column model
	References
4 Mind model
	4.1 Mind
		4.1.1 Philosophy issues of mind
			4.1.1.1 Mind–body problem
			4.1.1.2 Consciousness
			4.1.1.3 Sensitibility
			4.1.1.4 Supervenience
			4.1.1.5 The language of thought
			4.1.1.6 Intentionality and content theory
			4.1.1.7 Mental representation
			4.1.1.8 Machine mind
		4.1.2 Mind modeling
			4.1.2.1 To behave flexibly
			4.1.2.2 Adaptive behavior
			4.1.2.3 Real time
			4.1.2.4 Large-scale knowledge base
			4.1.2.5 Dynamic behavior
			4.1.2.6 Knowledge integration
			4.1.2.7 Use language
			4.1.2.8 Consciousness
			4.1.2.9 Learning
			4.1.2.10 Development
			4.1.2.11 Evolution
			4.1.2.12 Brain
	4.2 Turing machine
	4.3 Physical symbol system
	4.4 SOAR
		4.4.1 Basic State, Operator And Result architecture
		4.4.2 Extended version of SOAR
			4.4.2.1 Working memory activation
			4.4.2.2 Reinforcement learning
			4.4.2.3 Semantic memory
			4.4.2.4 Episodic memory
			4.4.2.5 Visual imagery
	4.5 ACT-R model
	4.6 CAM model
		4.6.1 Vision
		4.6.2 Hearing
		4.6.3 Perception buffer
		4.6.4 Working memory
		4.6.5 Short-term memory
		4.6.6 Long-term memory
		4.6.7 Consciousness
		4.6.8 High-level cognition function
		4.6.9 Action selection
		4.6.10 Response output
	4.7 Cognitive cycle
		4.7.1 Perception phase
		4.7.2 Motivation phase
		4.7.3 Action planning phase
	4.8 Perception, memory, and judgment model
		4.8.1 Fast processing path
		4.8.2 Fine processing pathway
		4.8.3 Feedback processing pathway
	References
5 Perceptual intelligence
	5.1 Introduction
	5.2 Perception
	5.3 Representation
		5.3.1 Intuitivity
		5.3.2 Generality
		5.3.3 Representation happens on paths of many kinds of feelings
		5.3.4 Role of representation in thinking
			5.3.4.1 Remembering representation
			5.3.4.2 Imagining representation
	5.4 Perceptual theory
		5.4.1 Constructing theory
		5.4.2 Gestalt theory
		5.4.3 Gibson’s ecology theory
		5.4.4 Topological vision theory
	5.5 Vision
		5.5.1 Visual pathway
		5.5.2 Marr’s visual computing
			5.5.2.1 The primal sketch
			5.5.2.2 2.5-D sketch
			5.5.2.3 Three-dimensional model
		5.5.3 Image understanding
		5.5.4 Face recognition
			5.5.4.1 Face image acquisition and detection
			5.5.4.2 Face image preprocessing
			5.5.4.3 Face image feature extraction
			5.5.4.4 Face image matching and recognition
	5.6 Audition
		5.6.1 Auditory pathway
		5.6.2 Speech coding
			5.6.2.1 Waveform coding
			5.6.2.2 Source coding
			5.6.2.3 Hybrid coding
	5.7 Speech recognition and synthesis
		5.7.1 Speech recognition
		5.7.2 Speech synthesis
			5.7.2.1 Formant synthesis
			5.7.2.2 Linear prediction coding parameter syntheses
			5.7.2.3 LMA vocal tract model
		5.7.3 Concept to speech system
			5.7.3.1 Text analysis module
			5.7.3.2 Prosody prediction module
			5.7.3.3 Acoustic model module
	5.8 Attention
		5.8.1 Attention network
		5.8.2 Attention function
			5.8.2.1 Orientation control
			5.8.2.2 Guiding search
			5.8.2.3 Keeps vigilance
		5.8.3 Selective attention
			5.8.3.1 Filter model
			5.8.3.2 Decay model
			5.8.3.3 Response selection model
			5.8.3.4 Energy distribution model
		5.8.4 Attention in deep learning
	References
6 Language cognition
	6.1 Introduction
	6.2 Oral language
		6.2.1 Perceptual analysis of language input
		6.2.2 Rhythm perception
			6.2.2.1 Prosodic features
			6.2.2.2 Prosodic modeling
			6.2.2.3 Prosodic labeling
			6.2.2.4 Prosodic generation
			6.2.2.5 Cognitive neuroscience of prosody generation
		6.2.3 Speech production
	6.3 Written language
		6.3.1 Letter recognition
		6.3.2 Word recognition
	6.4 Chomsky’s formal grammar
		6.4.1 Phrase structure grammar
		6.4.2 Context-sensitive grammar
		6.4.3 Context-free grammar
		6.4.4 Regular grammar
	6.5 Augmented transition networks
	6.6 Concept dependency theory
	6.7 Language understanding
		6.7.1 Overview
		6.7.2 Rule-based analysis method
		6.7.3 Statistical model based on corpus
		6.7.4 Machine learning method
			6.7.4.1 Text classification
			6.7.4.2 Text clustering
			6.7.4.3 Case-based machine translation
	6.8 Neural model of language understanding
		6.8.1 Aphasia
		6.8.2 Classical localization model
		6.8.3 Memory-integration-control model
		6.8.4 Bilingual brain functional areas
	References
7 Learning
	7.1 Basic principle of learning
	7.2 The learning theory of the behavioral school
		7.2.1 Learning theory of conditioned reflex
		7.2.2 Learning theory of behaviorism
		7.2.3 Association learning theory
		7.2.4 Operational learning theory
		7.2.5 Contiguity theory of learning
		7.2.6 Need reduction theory
	7.3 Cognitive learning theory
		7.3.1 Learning theory of Gestalt school
		7.3.2 Cognitive purposive theory
		7.3.3 Cognitive discovery theory
			7.3.3.1 Learning is active in the process of the formation of cognitive structures
			7.3.3.2 Emphasize the learning of the basic structure of discipline
			7.3.3.3 The formation of cognitive structures through active discovery
		7.3.4 Cognitive assimilation theory
		7.3.5 Learning theory of information processing
		7.3.6 Learning theory of constructivism
	7.4 Humanistic learning theory
	7.5 Observational learning
	7.6 Introspective learning
		7.6.1 Basic principles of introspection learning
		7.6.2 Meta-reasoning of introspection learning
		7.6.3 Failure classification
		7.6.4 Case-based reasoning in the introspective process
	7.7 Reinforcement learning
		7.7.1 Reinforcement learning model
		7.7.2 Q Learning
	7.8 Deep learning
		7.8.1 Introduction
		7.8.2 Autoencoder
		7.8.3 Restricted Boltzmann machine
		7.8.4 Deep belief networks
		7.8.5 Convolutional neural networks
			7.8.5.1 Feed-forward propagation of the convolutional layer
			7.8.5.2 Feed-forward propagation of subsampling
			7.8.5.3 Error back-propagation of the subsampling layer
			7.8.5.4 Error back-propagation of the convolutional layer
	7.9 Cognitive machine learning
		7.9.1 The emergence of learning
		7.9.2 Procedural knowledge learning
		7.9.3 Learning evolution
	References
8 Memory
	8.1 Overview
	8.2 Memory system
		8.2.1 Sensory memory
		8.2.2 Short-term memory
			8.2.2.1 Classic research of Sternberg
			8.2.2.2 Direct an access model
			8.2.2.3 Double model
		8.2.3 Long-term memory
	8.3 Long-term memory
		8.3.1 Semantic memory
			8.3.1.1 Hierarchical network model
			8.3.1.2 Spreading activation model
			8.3.1.3 Human association memory
		8.3.2 Episodic memory
		8.3.3 Procedural memory
		8.3.4 Information retrieval from long-term memory
			8.3.4.1 Recognition
			8.3.4.2 Recall
	8.4 Working memory
		8.4.1 Working memory model
		8.4.2 Working memory and reasoning
		8.4.3 Neural mechanism of working memory
	8.5 Implicit memory
	8.6 Forgetting curve
	8.7 Complementary learning and memory
		8.7.1 Neocortex
		8.7.2 Hippocampus
		8.7.3 Complementary learning system
	8.8 Hierarchical temporal memory
		8.8.1 Memory prediction framework
		8.8.2 Cortical learning algorithm
	References
9 Thought
	9.1 Introduction
	9.2 Hierarchical model of thought
		9.2.1 Abstract thought
		9.2.2 Imagery thought
		9.2.3 Perceptual thought
	9.3 Deductive inference
	9.4 Inductive inference
	9.5 Abductive inference
	9.6 Analogical inference
	9.7 Causal inference
	9.8 Commonsense reasoning
	9.9 Mathematics mechanization
	References
10 Intelligence development
	10.1 Intelligence
	10.2 Intelligence test
	10.3 Cognitive structure
		10.3.1 Piaget’s schema theory
		10.3.2 Gestalt’s insight theory
		10.3.3 Tolman’s cognitive map theory
		10.3.4 Bruner’s theory of classification
		10.3.5 Ausubel’s theory of cognitive assimilation
	10.4 Intelligence development based on operation
		10.4.1 Schema
		10.4.2 Stages of children’s intelligence development
			10.4.2.1 Sensorimotor period (0–2 years old)
			10.4.2.2 Preoperational stage (2–7 years)
				10.4.2.2.1 Preconceptual stage (2–4 years)
				10.4.2.2.2 Intuitive stage (4–7 years)
			10.4.2.3 Concrete operational stage (7–11 years)
			10.4.2.4 Formal operational stage (12∼15 years)
	10.5 Intelligence development based on morphism category theory
		10.5.1 Category theory
		10.5.2 Topos
		10.5.3 Morphisms and categories
	10.6 Psychological logic
		10.6.1 Combined system
		10.6.2 INRC quaternion group structure
	10.7 Artificial system of intelligence development
	References
11 Emotion intelligence
	11.1 Introduction
		11.1.1 Difference lies in requirement
		11.1.2 Difference lies in occurrence time
		11.1.3 Difference lies in reaction characteristics
	11.2 Emotion theory
		11.2.1 James-Lange’s theory of emotion
		11.2.2 Cognitive theory of emotion
		11.2.3 Basic emotions theory
		11.2.4 Dimension theory
		11.2.5 Emotional semantic network theory
		11.2.6 Beck’s schema theory
	11.3 Emotional model
		11.3.1 Mathematical model
		11.3.2 Cognitive model
		11.3.3 Emotion model based on Markov decision process
	11.4 Emotional quotient
	11.5 Affective computing
		11.5.1 Facial expressions
		11.5.2 Gesture change
		11.5.3 Speech understanding
		11.5.4 Multimodal affective computing
		11.5.5 Affective computing and personalized service
		11.5.6 The influence of affective understanding
	11.6 Neural basis of emotion
		11.6.1 Emotion pathway
		11.6.2 Papez loop
		11.6.3 Cognitive neuroscience
	References
12 Consciousness
	12.1 Overview
		12.1.1 Base elements of consciousness
		12.1.2 The attribute of consciousness
	12.2 Global workspace theory
		12.2.1 The theater of consciousness
		12.2.2 Global workspace model
	12.3 Reductionism
	12.4 Theory of neuronal group selection
	12.5 Quantum theories
	12.6 Information integration theory
	12.7 Consciousness system in CAM
		12.7.1 Awareness module
		12.7.2 Attention module
		12.7.3 Global workspace module
		12.7.4 Motivation module
		12.7.5 Metacognition module
		12.7.6 Introspective learning module
	12.8 Conscious Turing machine
	References
13 Brain–computer integration
	13.1 Overview
	13.2 Modules of the brain–computer interface
	13.3 Electroencephalography signal analysis
		13.3.1 Electroencephalography signal sorting
		13.3.2 Electroencephalography signal analytical method
	13.4 Brain–computer interface technology
		13.4.1 Visual-evoked potential
		13.4.2 Event-related potential
			13.4.2.1 P300 potential
			13.4.2.2 Event-related desynchronization
		13.4.3 Spontaneous electroencephalography for action training
		13.4.4 Self-regulation of steady-state visual-evoked professional
	13.5 P300 brain–computer interface system
		13.5.1 Architecture
		13.5.2 Visual elicitor subsystem
		13.5.3 Electroencephalography acquisition subsystem
		13.5.4 Electroencephalography analysis subsystem
	13.6 ABGP agent
	13.7 Key technologies of brain–computer integration
		13.7.1 Cognitive model of brain–computer integration
		13.7.2 Environment awareness
		13.7.3 Autonomous reasoning
		13.7.4 Collaborative decision-making
		13.7.5 Simulation experiment
	References
14 Brain-like intelligence
	14.1 Introduction
	14.2 Blue Brain Project
		14.2.1 Brain neural network
		14.2.2 Cerebral cortex model
		14.2.3 Super computational simulation
	14.3 Human Brain Project
		14.3.1 Research contents of the project
			14.3.1.1 Data
			14.3.1.2 Theory
			14.3.1.3 The technology platform of information and communication
			14.3.1.4 Applications
			14.3.1.5 Social ethics
		14.3.2 Timing plasticity of peak potential
		14.3.3 Unified brain model
	14.4 Brain research in the United States
		14.4.1 Human connectome project
		14.4.2 MoNETA
		14.4.3 Neurocore chip
	14.5 China Brain Project
		14.5.1 Brain map and connectome
		14.5.2 General intelligent platform
		14.5.3 Artificial intelligence chip
		14.5.4 Tianjic chip
		14.5.5 Decoupled NEUTRAMS
	14.6 Neuromorphic chip
		14.6.1 The development history
		14.6.2 IBM’s TrueNorth neuromorphic system
		14.6.3 British SpiNNaker
	14.7 Memristor
		14.7.1 Overview
		14.7.2 In-memory computing
	14.8 Development roadmap of intelligence science
		14.8.1 Elementary brain-like computing
			14.8.1.1 Natural language processing
			14.8.1.2 Image semantics generation
			14.8.1.3 Speech recognition
			14.8.1.4 Language cognitive
		14.8.2 Advanced brain-like computing
			14.8.2.1 The perception, assessment, and presentation of emotion
			14.8.2.2 Promote mood in the process of thinking
			14.8.2.3 The understanding and feeling of mood
			14.8.2.4 Adjust maturely to emotion
			14.8.2.5 Maintain the harmonious interpersonal relationship
			14.8.2.6 Deal with frustration
		14.8.3 Super-brain computing
			14.8.3.1 High intelligence
			14.8.3.2 High-performance
			14.8.3.3 Low energy consumption
			14.8.3.4 High fault-tolerance
			14.8.3.5 All-consciousness
	References
Index