This topic will cover the fundamentals of artificial intelligence, including its definition, history, and various applications in modern society.
Introduction to Artificial Intelligence (AI)
Course‑level overview – concise, organized, and easy to read
| Definition | Key Idea |
|---|---|
| Artificial Intelligence is the field of computer science that creates systems capable of performing tasks that normally require human intelligence. | • Perception (seeing, hearing) • Reasoning & decision‑making • Learning from data • Natural language understanding • Motor control (robotics) |
Alternative terms: Machine Intelligence, Cognitive Computing, Intelligent Automation.
| Year | Event | Significance |
|---|---|---|
| 1950 | Alan Turing’s “Computing Machinery and Intelligence” (Turing Test) | First formal question: Can machines think? |
| 1956 | Dartmouth Workshop (John McCarthy, Marvin Minsky, etc.) | Birth of AI as a research discipline; term “Artificial Intelligence” coined. |
| 1966–1974 | Early expert systems (e.g., DENDRAL, MYCIN) | Demonstrated rule‑based reasoning in narrow domains. |
| 1980s | Rise of connectionism – neural networks (back‑propagation) | Shift from symbolic AI to data‑driven learning. |
| 1997 | IBM Deep Blue defeats world chess champion Garry Kasparov | First high‑profile victory of AI in a complex game. |
| 2012 | AlexNet wins ImageNet competition | Deep learning breakthrough; GPUs enable large‑scale neural nets. |
| 2016 | AlphaGo defeats Go champion Lee Sedol | Demonstrates AI’s ability in highly strategic, intuitive tasks. |
| 2020‑present | Generative models (GPT‑3/4, DALL·E, Stable Diffusion) | AI can produce human‑like text, images, and code. |
| Sub‑field | Primary Goal | Typical Techniques |
|---|---|---|
| Machine Learning (ML) | Learn patterns from data without explicit programming | Supervised/unsupervised learning, reinforcement learning, decision trees, SVMs |
| Deep Learning | Build hierarchical representations using multi‑layer neural networks | Convolutional NN, Recurrent NN, Transformers |
| Natural Language Processing (NLP) | Enable computers to understand and generate human language | Tokenization, embeddings, language models, parsing |
| Computer Vision | Interpret visual information from images/video | CNNs, object detection, segmentation, optical flow |
| Robotics | Integrate perception, planning, and actuation to interact with the physical world | SLAM, motion planning, control theory |
| Knowledge Representation & Reasoning | Model facts and infer new knowledge | Ontologies, logic programming, Bayesian networks |
| AI Ethics & Policy | Study societal impact, fairness, accountability | Bias mitigation, explainability, regulation frameworks |
Agents
Search & Optimization
Learning Paradigms