Introduction to Artificial Intelligence

1. Introduction to Artificial Intelligence
Lecture-01
Hema Kashyap

2. ∗ Concerned with the design of intelligence in artificial device.
∗ Term was coined by McCarthy in 1956.
∗ Two ideas in the definition
∗ Intelligence
∗ Artificial Device
Basic Concept of AI

3. ∗ As Human
∗ Ideal Performance
∗ Thought Process /Reasoning
∗ Final Manifestation in terms of its Actions
What is Intelligence

4. • Ability to interact with the real world
▫ to perceive, understand, and act
▫ e.g., speech recognition and understanding and synthesis
▫ e.g., image understanding
▫ e.g., ability to take actions, have an effect
• Reasoning and Planning
▫ modeling the external world, given input
▫ solving new problems, planning, and making decisions
▫ ability to deal with unexpected problems, uncertainties
• Learning and Adaptation
▫ we are continuously learning and adapting
▫ our internal models are always being “updated”
 e.g., a baby learning to categorize and recognize animals
What’s involved in Intelligence?

5. ∗ Definition vary along two dimensions
Defining Artificial Intelligence
Thought and Reasoning
Behavior/Actions
Human like
Performance
Ideal Performance
/Rationality
System that Thinks
like Human
Eg: Cognitive
Modeling
System that think
Rationally
Eg: Laws of
Thoughts and
Logic
System That act
like Human
Eg: Turing Test
System that act
Rationally
Eg: Rational Agent

6. ∗ It talks about a program that thinks like human
∗ There are two ways to do so:
∗ Introspection
∗ Through Psychological Experiments
∗ Eg: Program that plays chess like human
Thinks Humanly: The Cognitive
modeling approach

7. ∗ A Greek philosopher Aristotle was the first one to codify “Right Thinking” i.e.
reasoning process.
∗ His syllogism provided patterns for arguments structures that always yielded
correct solutions/conclusions when given correct premises
∗ Eg:
∗ Socrates is a man.
∗ All men are mortal.
∗ Therefore, Socrates is mortal.
∗ This study initiated the field called Logic
∗ Two obstacles to this approach:
∗ State problem in the formal terms using logical notations
∗ There’s a difference between being able to solve a problem “in principle” and
implementing it in real.
Think Rationally: The “Laws of
Thought” process

8. ∗ Proposed by Alan Turing in 1950 to provide a satisfactory
definition of AI.
∗ He suggested a test based on in-linguiability from
undeniably intelligent entity-human beings.
∗ The computer passes the test if a human interrogator ,
posing the written questions, cannot tell whether the
responses came from a person or not.
∗ But to achieve this a computer would need to possess
following capabilities:
∗ Natural Language Processing
∗ Knowledge representation
∗ Automated Reasoning
∗ Machine Learning
Acting Humanly: The Turing Test
Approach

9. ∗ This test includes the video signal so that the
interrogator can test the subject’s perception
abilities.
∗ To pass this computer needs
∗ Computer Vision: to perceive objects
∗ Robotics: to manipulate objects and move about.
Total Turing Test

10. ∗ Room with operator and huge
Chinese literature
∗ Chinese people outside sending in
some Chinese texts.
∗ If the operators on looking up the
literature able to respond /send text
written in front of the text received,
then person outside believes
operator knows Chinese.
∗ Its just the matter of “Translating”.
∗ This doesn’t mean that the person
understands semantics of the
language.
∗ So, Cognition and Understanding is
different thing
Chinese Room Test

11. ∗ Agent(that acts), and a computer program agent is more than just a mere
program, the one
∗ That operates under autonomous control
∗ Perceive their environment
∗ Persisting over a prolonged time period
∗ Adapting for change
∗ Being capable of taking an another’s goal
∗ A Rational Agent is the one that acts so as to achieve the best outcome or when
there is uncertainty, the best expected outcome.
∗ Act Rationally Reason Logically Draws Conclusions
Acts on that conclusion
Acting Rationally: The Rational agent
Approach

12. ∗ Intelligent Agents need to be able to do both
“MUNDANE” and “EXPERT” task.
∗ Mundane Task: planning route actively, recognizing,
communicating etc.
∗ Expert Task(needs domain specific knowledge):
medical diagnosis, mathematical problem solving etc.
Defining: Typical AI problem