Introduction to Artificial Intelligence

1. Lecture 1: Introduction
1
Introduction to Artificial Intelligence

2. Course Contents
 Introduction toAI
 Components ofAI systems
 Searching
 Learning
 Reasoning
 Logic I and Logic II
 Knowledge representation
 Machine learning
 Natural Language processing
 Intelligent system design
 AI robotics
2 Lecture 1: Introduction

3. Course Assessment
 Quiz 10%
 Project 20%
 Lab 20%
 Assignment 10%
 Midterm 20%
 Final term 20%
3 Lecture 1: Introduction

4. What is Intelligence?

5. What is Intelligence?
 Intelligence is an umbrella term used to describe a property
of the mind that includes many related abilities, such as the
capacities to
 Reason
 Plan
 Solve problems
 Think abstractly
 Comprehend ideas
 Use language
 Learn
5 Lecture 1: Introduction

6. What is Artificial Intelligence?

7. What is Artificial Intelligence?
 Artificial Intelligence is a way of making a machine
(Computer, robot software) to think and behave intelligently
like a intelligent human
 AI study and design a computing systems that can perceives
its environment and takes actions like human beings
 AI term was introduced by John McCarthy in 1956
 AI is defined as a system that possesses at least one of the
abilities mentioned in the previous slide
 AI studies theories and technologies for obtaining systems
that are partially or fully intelligent
7 Lecture 1: Introduction

8. What is Artificial Intelligence?
 Four definations ofAI
 Think humanly – cognition – cognitive science – cognitive
neuro science data driven
 Act humanly
 Think Rationally
 Act Rationally
8 Lecture 1: Introduction

9. Lecture 1: Introduction
Why study AI?
Search engines
Labor
Science
Medicine/
Diagnosis
Appliances What else?
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10. Lecture 1: Introduction
Honda Humanoid Robot
Walk
Turn
Stairs
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11. Lecture 1: Introduction
Sony AIBO
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12. Family Robot
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Pepper, emotional robot

13. Lecture 1: Introduction
Natural Language Question Answering
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14. Lecture 1: Introduction
Robot Teams
Rise Lab Foot ball team
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15. A Brief History of AI
 1943: McCulloch and Pitts propose a model of artificial
neurons
 1956 Minsky and Edmonds build first neural network
computer, the SNARC
15 Lecture 1: Introduction

16. The Dartmouth Conference (1956)
 John McCarthy organizes a two-month workshop for
researchers interested in neural networks and the study of
intelligence
 Agreement to adopt a new name for this field of study:
Artificial Intelligence
16 Lecture 1: Introduction

17. “An attempt will be made to find how to make machines
use language, form abstractions and concepts, solve
kinds of problems now reserved for humans, and
improve themselves. We think that a significant
advance can be made if we work on it together for a
summer.
”
John McCarthy and Claude Shannon
DartmouthWorkshop Proposal
AI’s official birth: Dartmouth, 1956

18. 1952-1969 Enthusiasm
 Checkers player
 Lots of work on neural networks
18 Lecture 1: Introduction

19. 1966-1974 Reality
 AI problems appear to be too big and complex
 Computers are very slow, very expensive, and have very little
memory (compared to today)
19 Lecture 1: Introduction

20. 1969-1979 Knowledge-based systems
 Birth of expert systems
 Idea is to give AI systems lots of information to start with
20 Lecture 1: Introduction

21. 1980-1988 AI in industry:
 First successful commercial expert system
 Some interesting phone company systems for diagnosing
failures of telephone service
21 Lecture 1: Introduction

22. 1990s to the present:
 Increases in computational power (computers are cheaper,
faster, and have tons more memory than they used to)
 An example of the coolness of speed: Computer Chess
22 Lecture 1: Introduction

23. Lecture 1: Introduction
AI State of the art
 Have the following been achieved byAI?
 World-class chess playing
 Playing table tennis
 Cross-country driving
 Solving mathematical problems
 Discover and prove mathematical theories
 Engage in a meaningful conversation
 Understand spoken language
 Observe and understand human emotions
 Express emotions
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24. Sub-domains of AI
 LogicalAI
 Search
 Natural language processing
 Pattern recognition
 Machine learning
 Knowledge representation
 Inference
 Learning from experience
24 Lecture 1: Introduction

25. Sub-domains of AI
 Planning
 Common sense
 Cognitive systems
 Machine consciousness
 Neural networks
 Robotics
25 Lecture 1: Introduction

26. Components of AI
26 Lecture 1: Introduction

27. Components of AI
Lecture 1: Introduction
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28. Agent
 An agent is anything that can perceive its environment
through sensors and acts upon that environment through effectors
and actuators
 Agent includes human, robot, softbot, thermostat, etc.
 A human agent has sensory organs such as eyes, ears, nose, tongue
and skin parallel to the sensors, and organs as actuators such as
hands, legs, mouth, for effectors
 A robotic agent replaces cameras and infrared range finders for the
sensors, and various motors and actuators for effectors
 A software agent has encoded bit strings as its programs and
actions
28 Lecture 1: Introduction

29. An Intelligent Agent
Knowledge
representation
reasoning
planning
learning
input
Natural lang.
vision
effectors

30. Lecture 1: Introduction
Acting Humanly: The Full Turing
Test
 A computer passes the test if a human interrogator, after
posing some written questions, cannot tell whether the
written responses come from a person or from a computer.
 “Can machines think?” → “Can machines behave
intelligently?”
 TheTuring test (The Imitation Game): Operational definition
of intelligence.
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31. Lecture 1: Introduction
Acting Humanly: The Full Turing Test
• Computer needs to posses : Natural language processing, Knowledge
representation, Automated reasoning, and Machine learning
• Problem: 1) Turing test is not reproducible, constructive, and agreeable to
mathematic analysis. 2) What about physical interaction with interrogator and
environment?
• Total Turing Test: Requires physical interaction and needs perception and
actuation.
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32. Lets Think…….!
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 Concepts
 Facts
 Reasoning
 learning

33. Chair?

34. Chair?

35. Chair?

36. Chair?

37. Chair?

38. Chair?

39. Questions…..?
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 How system can identify these images?
 How classification of these images occurred?
 How intelligent system can learn and reason?
 What knowledge base we need for learning and reasoning?

40. Cool things AI is doing now
 Speech recognition
 Face recognition
 Automated reasoning
 Machine learning
 Expert systems
 Intelligent cars
 Voice recognition
 Health monitoring
 Companion robots
 Many more
40 Lecture 1: Introduction

41. Real AI

42. Robots help nurses in hospitals
deliver stuff to different rooms

45. Task 1
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 Sensors?
 Actuators?
 Schemas?
 Functionalities
 How can improve?

46. Q&A
Lecture 1: Introduction
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