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Artificial intelligence concept map
Basic concept map of artificial intelligence, covering definition, application fields, ethics and law, social impact, research and education, future trends, core technologies, three cores of artificial intelligence, and three major research schools of AI.
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Artificial intelligence concept map
- DeepSeek User Guide Collection
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Artificial intelligence basic concept map
definition
I. The meaning of artificial intelligence
Machine systems that simulate human intelligence
Ability to learn independently and solve problems
II. The origin of artificial intelligence
Early theories and concepts
Turing test
Early research on neural networks
development milestones
1. 1956, Turing Test: Alan Turing proposed the "Turing Test" to explore whether computers can exhibit intelligence similar to humans.
2. In 1997, IBM Deep Blue defeated the world chess champion: The Deep Blue computer developed by IBM defeated the world chess champion Garry Kasparov in a game, marking an important breakthrough in artificial intelligence.
3. In 2011, IBM Watson won the Jeopardy Challenge: IBM's cognitive computing system Watson defeated human contestants in the American knowledge competition show "Jeopardy!" demonstrating the powerful capabilities of artificial intelligence.
4. In 2016, AlphaGo defeated the world champion of Go: AlphaGo developed by DeepMind, a subsidiary of Google, defeated the world champion of Go, Lee Sedol, in the game, triggering global attention and discussion on artificial intelligence.
Three cores of artificial intelligence
Computing power
algorithm
data
Three major research schools of AI
symbolism
Symbolic AI or logical AI
logical reasoning
rule based system
logic programming
knowledge representation
ontology
semantic network
expert system
diagnostic system
decision support system
connectionism
Neural networks
neural network
feedforward neural network
feedback neural network
deep learning
convolutional neural network
recurrent neural network
machine learning
supervised learning
unsupervised learning
behaviorism
Behaviorism in AI
Robotics
sensor technology
motion control
autonomous system
adaptive control
multi-agent system
simulated evolution
genetic algorithm
artificial life
core technology
machine learning
supervised learning
Classification problem
regression problem
unsupervised learning
cluster analysis
Dimensionality reduction
correlation analysis
reinforcement learning
Markov decision process
Q-learning and policy gradient
deep learning
Neural network structure
1. Neural network is a computing model that simulates the structure of neurons in the human brain.
2. Neural network includes input layer, hidden layer and output layer.
3. Activation functions such as ReLU, Sigmoid, etc. are used to introduce nonlinear relationships.
4. The loss function is used to measure the difference between the predicted value and the true value.
5. Optimization algorithms such as gradient descent, stochastic gradient descent, etc. are used to update weights.
6. The backpropagation algorithm adjusts the weights by calculating gradients.
7. Convolutional neural network (CNN) is used to process image data, Recurrent neural networks (RNN) are used to process sequence data.
8. Long short-term memory network (LSTM) combines the advantages of LSTM and CNN and is suitable for processing time series data.
9. Generative adversarial network (GAN) consists of a generator and a discriminator and can generate realistic data.
deep learning framework
TensorFlow
PyTorch
Natural Language Processing (NLP)
language model
BERT
GPT series
speech recognition
ASR system
speech synthesis
future trends
Artificial Intelligence and Human Collaboration
Augmented reality (AR)
job aid
Educational applications
human-computer interaction
Smart Assistant
interactive robot
Popularization of artificial intelligence
smart home
home automation
security monitoring
smart city
traffic management
Energy optimization
Ethical challenges of artificial intelligence
machine bias
Algorithmic fairness
Diversity and Inclusion
Automation unemployment
career reinvention
lifelong learning
research and education
Artificial Intelligence Course
Undergraduate and Graduate Education
Curriculum
Practice projects
Online educational resources
MOOC platform
Professional certification
research institute
Academic Research Center
university laboratory
Scientific research institutions
Corporate R&D Center
technological innovation
Industrial cooperation
social impact
economic impact
Industrial transformation
emerging industries
Upgrading of traditional industries
labor market
Changes in career structure
Shifting skill requirements
Sociocultural impact
human behavior
consumption habits
interpersonal communication
cultural diversity
globalization
cultural integration
political influence
international relations
technological competition
Cooperation and conflict
policy making
science and technology policy
public safety
ethics and law
Artificial Intelligence Ethics
machine autonomy
Robot rights
Machine ethical decision-making
data privacy
Personal information protection
Data encryption technology
laws and regulations
Artificial Intelligence Regulation
international standards
Domestic regulations
Artificial Intelligence Responsibility
product liability
Service Responsibilities
1. Artificial Intelligence Ethical Principles: Ensure the safety, fairness and transparency of AI technology.
2. Data privacy protection: Respect user privacy and prevent data leakage and abuse.
3. Explainable AI: Improve the understandability and predictability of AI systems and enhance user trust.
4. Responsibility for artificial intelligence: clarify the responsibilities of developers, users and regulators of AI technology.
5. Coexistence of humans and AI: Pay attention to the impact of AI technology on employment, education and other fields, and promote the harmonious coexistence of humans and AI.
6. Prevent AI discrimination: Eliminate bias and discrimination in AI technology and protect the rights and interests of different groups.
7. Construction of artificial intelligence regulations: Improve relevant laws and regulations to guide the healthy development of AI technology.
8. International cooperation and exchange: Strengthen research and cooperation on artificial intelligence ethics and law on a global scale.
Application areas
medical health
disease diagnosis
Medical image analysis
Genomics data processing
Intelligent monitoring
wearable devices
Remote health monitoring
Fintech
risk management
credit score
market analysis
Automated trading
high frequency trading system
Robo-advisory
Transportation
Autonomous driving
path planning
environmental awareness
Intelligent logistics
warehouse automation
Logistics optimization
Educational Technology
personalized learning
adaptive learning system
Intelligent tutoring robot
Educational Management
student behavior analysis
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