Overview
This first lecture introduces the field of Artificial Intelligence (AI), covering foundational concepts, definitions, main topics, core characteristics, multidisciplinary roots, and influential contributors.
Course Introduction & Structure
- The primary textbook is "Artificial Intelligence" by Russell and Norvig; "Artificial Intelligence" by Luger is also used for specific topics.
- Topics covered include intelligent agents, search algorithms, logic, planning, learning, AI languages, and natural language processing (NLP).
- The course models human cognitive processes and aims to build systems that exhibit intelligent behavior.
Definitions & Goals of AI
- AI is a branch of computer science focused on creating computer systems that display characteristics associated with intelligence.
- Definitions include: making machines do tasks considered intelligent if done by humans, and simulating human reasoning and decision-making.
- Alan Turing's Turing Test evaluates if a machine's actions are indistinguishable from a human's.
Key Topics in AI
- Knowledge representation: storing and structuring facts and rules.
- Reasoning, learning, and planning: drawing conclusions, acquiring new knowledge, and devising sequences of actions.
- Logic: propositional and other advanced logics.
- Machine learning: supervised, unsupervised, and reinforcement learning.
- AI languages: e.g., LISP and PROLOG for knowledge processing.
- NLP and communication between agents and humans.
- Application areas: game playing, theorem proving, robotics, computer vision, expert systems.
Intelligence & Essential Capabilities
- Intelligence involves learning, reasoning, solving novel problems, acting rationally, and adapting to new situations.
- AI systems should perceive, understand, reason, plan, and interact with real-world environments using sensors and models.
Multidisciplinary Foundations
- AI draws from philosophy (logic, rationality), mathematics (algorithms, probability), economics (decision/utility theory), neuroscience (neural networks), psychology (cognition, learning), computer engineering, control theory, and linguistics (language understanding).
Influential Figures in AI
- John McCarthy—created LISP, contributed to non-monotonic reasoning.
- Marvin Minsky—founded AI lab at MIT, developed frames knowledge structures.
- Herbert Simon—developed the general problem solver and social network analysis.
- Arthur Samuel—coined "machine learning," pioneered AI in game playing.
- Allen Newell—developed logic theorist and chess machine.
- Nils Nilsson—created A* search algorithm, contributed to AI planning.
The Turing Test
- The Turing Test assesses if a computer can communicate in natural language such that a human cannot distinguish it from another human.
- Required capabilities: natural language processing, knowledge representation, automated reasoning, machine learning.
Key Terms & Definitions
- Artificial Intelligence (AI) — computer science field aiming to create intelligent machines.
- Turing Test — evaluation of machine intelligence based on indistinguishability from human responses.
- Intelligent Agent — an entity that perceives its environment and acts rationally.
- Knowledge Representation — methods for storing facts and rules in AI.
- Supervised Learning — machine learning with labeled data.
- Unsupervised Learning — machine learning without labeled data.
- Reinforcement Learning — learning by trial and error and receiving feedback.
- Natural Language Processing (NLP) — enabling computers to understand human language.
Action Items / Next Steps
- Obtain and start reading chapters from Russell & Norvig’s "Artificial Intelligence."
- Review definitions of intelligence and familiarize yourself with the Turing Test.
- Prepare for the next lecture on the history of AI.