1. AI: Can Machines Think? Juntae Kim Department of Computer Engineering Dongguk University

2. What is Intelligence? Understanding Perception (vision, speech), language understanding Reasoning Given facts  new facts Learning Improving performance as it repeats Adaptiveness, Creativity, etc.

3. What is Intelligence? Turing Test Alan Turing (1950) - Can machine be intelligent?

4. What is Artificial Intelligence? Study of how to make computer do things that people do better To find how people think/act intelligently To develop systems that perform intelligent task (perception, reasoning, learning, … ) reasoning speech learning vision

5. History of AI Gestation Artificial neuron (McCulloch & Pitts, MIT) Logic Theorist (Newell & Simon, CMU) Dartmouth workshop  “Artificial Intelligence” Early enthusiasm Checker program (Samuel, IBM) GPS (Newell & Simon, CMU) ELIZA (Weizenbaum, MIT) Machine Translation Project (NRC)

6. History of AI Knowledge-based systems DENDRAL (Buchanan, Stanford) MYCIN (Feigenbaum, Stanford) Commercial expert systems 5th generation computer project (Japan) Current state of the art Practical applications Statistical approaches

7. Areas/Applications of AI Reasoning VLSI verification, Fuzzy control Expert Systems Medical, financial, design, scheduling, law Medicalschedulinglaw Vision Face recognition, autonomous vehicleautonomous vehicle Speech Recognition Voice recognition, speech translationspeech translation Natural Language Understanding Information extraction, machine translationmachine translation

8. Areas/Applications of AI Machine Learning Neural networks, data mining, classification, predictiondata miningclassification Robotics Motion planning, assembly planning Motion planning Game Playing Chess, NPC control ChessNPC control Web agents Personal desktop agent, recommender systems, e- commerce agentrecommender systems

9. Symbolic vs. Connectionist Symbolic AI Physical symbol system hypothesis Intelligence is achieved through Symbol patterns to represent problems Operations on the patterns to generate potential solutions Search to select a solution Logical inference Knowledge-based systems

10. Symbolic vs. Connectionist Connectionist AI Modeling human brain Intelligent behavior is achieved by Large number of connected processing element Learning of the network Neural Networks

11. Conversation with Robots KISMET A robot made in MIT AI lab Vision – object recognition, movement detection Speech recognition Attention/Behavior control Speech generation Introducing emotions, belief, desire Expression examples Focusing Dialog with humans

12. The Chinese Room Experiment John Searl (1980) Experiment Searl’s argument The man in the room does not have any understanding No matter how intelligent-seeming a computer behaves and no matter what programming makes it behave that way, since the symbols it processes are meaningless (lack semantics) to it, it's not really intelligent. It's not actually thinking.

13. The Brain Prosthesis Experiment John Searl (1980), Hans Moravec (1988) Experiment Will you remain conscious? Hans Moravec(1988): Yes. It will show exactly same behavior with real brain. The mechanism involved in consciousness are still operating in the electronic brain, which is therefore “conscious” John Searl(1980): No. You would end up loosing your control over your behavior. (Problem: The consciousness cannot be removed instantaneously)

14. The World’s First Brain Prosthesis: Artificial Hippocampus

15. Can Machines Think? Am I thinking or what? Intelligence? (weak AI) yes, in narrow sense. Consciousness? (strong AI) debate unsolved.