Overview
This lecture explores the field of artificial intelligence (AI), focusing on expert systems, knowledge representation, language processing, and real-world applications, with demonstrations and insights from leading experts.
Foundations of Artificial Intelligence
- Early AI aimed to duplicate human thought, but now focuses on producing intelligent results.
- The physical symbol system hypothesis is foundational, asserting computers can process symbols and abstract concepts.
- AI involves inference (logical reasoning) and symbolic knowledge, not just numerical calculations.
Expert Systems and Knowledge Engineering
- Expert systems mimic human experts, asking users questions and providing advice based on symbolic reasoning.
- Applications range from oil drilling advisors to medical diagnosis and troubleshooting.
- Systems can explain their reasoning when prompted, boosting transparency and trust.
- Newer tools allow systems to be built using examples instead of explicit rules, easing the knowledge engineering process.
Natural Language Processing
- AI struggles with the ambiguity and complexity of human language.
- Natural language interfaces require large processing power and currently work well only in limited domains.
- Programs analyze sentence structure and context to determine meaning.
Practical Applications and Demonstrations
- Expert systems are now accessible on personal computers, aiding tasks like medical screening or software recommendations.
- Demonstrations showed graphical control panels for managing complex systems like nuclear reactors, connected to knowledge bases.
- Systems use symbolic reasoning and heuristics for decision making, often running on languages like LISP.
Challenges and Future Directions
- Key challenges: determining what knowledge to represent, how to encode it, and how to use it effectively.
- Current systems are "brittle," often failing when faced with situations requiring common sense or context awareness.
- Future trends include improving natural language communication and expanding the robustness of expert systems.
Industry Updates (Random Access)
- Export restrictions tighten on high-tech computers to certain countries.
- National Semiconductor faces investigation over chip testing failures.
- IBM predicts strong sales for its personal computers and portables.
- Atari announces layoffs and restructuring.
- Japan-US trade tensions over software copyright protections.
- New educational and gambling software are highlighted.
Key Terms & Definitions
- Artificial Intelligence (AI) — The field focused on creating machines that perform tasks requiring human intelligence.
- Expert System — A computer program that emulates decision-making abilities of a human expert.
- Knowledge Engineering — The process of building systems that use expert knowledge for reasoning.
- Physical Symbol System Hypothesis — The idea that computers can manipulate symbols to represent knowledge and reasoning.
- Natural Language Processing (NLP) — AI's ability to understand and process human language.
- LISP — A programming language designed for symbolic processing, widely used in AI.
Action Items / Next Steps
- Review examples of expert systems in medicine and engineering.
- Explore LISP language basics and its role in AI.
- Read about current AI challenges in knowledge representation and natural language understanding.