2. What is AI ?
• Artificial Intelligence is concerned with the
design of intelligence in an artificial device
• The term was coined by McCarthy in 1956.
3. What is intelligence?
• Is it that which characterize humans?
• Behave as intelligently as a human
• Behave in the best possible manner
• Behavior strategies
• Thinking?
• Action?
4. Scope and view of Artificial
Intelligence
Ideal Performance
(rationality)
Human-Like
Performance
Thought/ Reasoning
Behavior
System that think like
Humans
(Turing Test)
System that think
Rationally
(Laws of thought/logic)
System that act
Humans
(Cognitive Science)
System that act
Rationally
(Rational Agents)
5. Scope and view of Artificial
Intelligence.
• One view is that artificial intelligence is about
designing systems that are as intelligent as
humans.
• Trying to understand human thought
• Effort to build machines that emulate the
human thought process.
• Cognitive science approach to AI.
6. Scope and view of Artificial
Intelligence
• Turing test for intelligence.
– ‘Imitation game‘
– Turing argued that if the interrogator could not
distinguish them by questioning, then it would be
unreasonable not to call the computer intelligent.
8. Turing test
• There are two rooms, A and B.
• One of the rooms contains a computer. The other
contains a human.
• The interrogator is outside and does not know which
one is a computer.
• He can ask questions through a teletype and receives
answers from both A and B.
• The interrogator needs to identify whether A or B are
humans.
• To pass the Turing test, the machine has to fool the
interrogator into believing that it is human
9. Scope and view of Artificial
Intelligence.
• Logic and laws of thought
– Deals with studies of ideal or rational thought
process and inference.
– The emphasis in this case is on the inference
mechanism, and its properties.
– That is how the system arrives at a conclusion, or
the reasoning behind its selection of actions is
very important in this point of view.
10. Scope and view of Artificial
Intelligence.
• Rational agents.
– Deals with building machines that act rationally.
– The focus is on how the system acts and performs,
and not so much on the reasoning process.
– A rational agent is one that acts rationally, that is,
is in the best possible manner.
11. Typical AI problems
• “Intelligent entity” need to perform both
“common-place” tasks as well as expert tasks
• Common task are done routinely by people
and some other animals.
• Common task examples
– Recognizing people
– objects.
– Communicating (through natural language)
Navigating around obstacles on the streets
12. Typical AI problems
• Expert tasks cannot be done by all people, and
can only be performed by skilled specialists.
• Expert tasks include
– Medical diagnosis
– Mathematical problem solving
– Playing games like chess
13. Intelligent behavior
• Perception involving image recognition and
computer vision ƒ
• Reasoning (With information we have) ƒ
• Learning
• ƒUnderstanding language involving natural
language processing, speech processing ƒ
• Solving problems
14. What’s easy and what’s hard?
• It’s been easier to mechanize many of the high level cognitive tasks
we usually associate with “intelligence” in people
– e. g., symbolic integration, proving theorems, playing chess,
some aspect of medical diagnosis, etc.
• It’s been very hard to mechanize tasks that animals can do easily
– walking around without running into things
– catching prey and avoiding predators
– interpreting complex sensory information (visual, aural, …)
– modeling the internal states of other animals from their
behavior
– working as a team (ants, bees)
15. Practical Impact of AI
• AI components are embedded in numerous
devices
• AI systems are in everyday use
– Copy machines
– Identifying credit card fraud,
– Advising doctors
– Recognizing speech
– Helping complex planning tasks
– Systems that provide students with personalized
attention
16. Approaches to AI
• Strong AI
– Aims to build machines that can truly reason and
solve problems
– Self aware
– Intellectual ability need to be indistinguishable
from that of a human being.
17. Approaches to AI
• Weak AI
– Intelligence that cannot truly reason and solve
problems
– Acts as if it were intelligent
• Applied AI
– Aims to produce commercially viable "smart"
systems such as, for example, a security system
that is able to recognize the faces of people who
are permitted to enter a particular building.
18. Approaches to AI
• Cognitive AI: Computers are used to test
theories about how the human mind works
• For example,
– Theories about how we recognize faces and other
objects
– About how we solve abstract problems.
19. Limits of AI Today
• What can AI systems do ?
– In Computer vision, the systems are capable of face recognition
– In Robotics, we have been able to make vehicles that are mostly
autonomous.
– In Natural language processing, we have systems that are capable of
simple machine translation.
– Today’s Expert systems can carry out medical diagnosis in a narrow
domain
– Speech understanding systems are capable of recognizing several
thousand words continuous speech
– Planning and scheduling systems had been employed in scheduling
experiments with the Hubble Telescope.
– The Learning systems are capable of doing text categorization into
about a 1000 topics
– In Games, AI systems can play at the Grand Master level in chess
(world champion), checkers, etc.
20. What can AI systems NOT do yet?
• Understand natural language robustly (e.g.,
read and understand articles in a newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Construct plans in dynamic real-time domains
• Exhibit true autonomy and intelligence
