Artificial Intelligence
Artificial Intelligence
Early Foundations (1940s–1950s)
Theoretical Basis
Computation Theory
Turing Machine Concept
Can Machines Think?
Key Figures
Alan Turing
John McCarthy
Marvin Minsky
Milestones
Dartmouth Conference (1956)
Birth of AI as Field
Symbolic AI Era (1950s–1980s)
Core Idea
Intelligence as Symbol Manipulation
Rule-Based Systems
Technologies
Expert Systems
Logic Programming
Knowledge Representation
Limitations
Brittleness
Knowledge Engineering Bottleneck
Poor Scalability
Statistical Learning (1980s–2000s)
Core Shift
From Rules to Data
Probabilistic Models
Methods
Bayesian Networks
Support Vector Machines
Decision Trees
Drivers
More Data Availability
Improved Computing Power
Deep Learning Era (2010s)
Neural Networks with Many Layers
Representation Learning
Breakthroughs
ImageNet Success (2012)
Speech Recognition Advances
Computer Vision Progress
Key Models
CNN (Convolutional Neural Networks)
RNN / LSTM
Foundation Models (2020s–Present)
Core Paradigm
Large-Scale Pretraining
General-Purpose Models
Transformers
Large Language Models (LLMs)
Multimodal Models
Capabilities
Text Generation
Code Generation
Reasoning & Planning
Examples
GPT Series
Gemini
Claude
Challenges & Limits
Technical
Hallucination
Data Dependence
Compute Cost
Theoretical
Lack of True Understanding
General Intelligence Gap
Ethical
Bias & Fairness
Privacy Issues
Alignment Problem
Future Directions
Technical Trends
AGI Exploration
Smaller Efficient Models
Agent Systems
Integration
AI + Robotics
AI + Science
AI + Healthcare
Paradigm Questions
Scaling vs New Architectures
Data vs World Models
Symbolic + Neural Hybrid