Transcript Chapter13

Chapter 13
Artificial Intelligence
Chapter Goals
• Discuss types of problems that
– humans do best
– computers do best
• Turing test
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Chapter Goals
• Knowledge representation and semantic
networks
• Search Trees
• Expert systems
• Biological and artificial neural networks
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Chapter Goals
• Natural language processing
• Natural language comprehension ambiguities
• Understand the main kinds of AI used in
autonomous robots
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What is Artificial
Intelligence?
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All I have to offer is the truth…
• The truth is, Hollywood movies are great, but
they are not reality!
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AI Today
• No single computer today is close to being
considered “intelligent” like a human
• However, computers can solve particular
human-like tasks are typically considered to
require intelligence, such as:
– Playing chess
– Diagnosing diseases
– Identifying objects in images
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What is Artificial Intelligence?
• This is an important question…
• Computer Science has a particular viewpoint
• There are 2 Big Slices:
– A Practical Definition (Task oriented)
– A Philosophical Definition (Perception Oriented)
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What is Artificial Intelligence?
• Practical Definition: Making a
computer do things tasks that are easy
for humans to do, like:
–Find kitty (aka “Machine Vision”)
–Use natural human languages
–Apply expert human knowledge
–Play chess
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What is Artificial Intelligence?
• Philosophical Definition:
–Making a computer fool humans into
thinking it is a human (Turing test).
–(I perceive that you are intelligent).
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The Turing Test
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The Turing Test
• Alan Turing wrote a landmark paper:
“Can machines think?”
• How will we know when
we’ve succeeded?
• The Turing test used empirically determine if a
computer is “intelligent”
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The Turing Test
Figure 13.2
In a Turing test, the
interrogator must
determine which
respondent is the
computer and which is
the human
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Tasks
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Us vs. The Machines
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Some tasks are easy for computers
Some tasks are hard for computers
Some tasks are easy for humans
Some tasks are hard for humans
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Easy For a Computer…
• Adding a thousand four-digit numbers
• Counting the letters in a book
• Searching a list of 1,000,000 numbers for
duplicates
• Matching finger prints
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Easy for a Human…
Where’s Kitty?
• A computer would have difficulty pointing out the cat in
this picture, which is easy for a human
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Specific AI Tasks
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SPECIFIC AI TECHNIQUES
• !MAGIC FREE ZONE!
• We will look at techniques to perform specific
tasks that we consider to be “intelligent”
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Knowledge Representation
• Humans use knowledge for certain tasks
• AI systems must have a way to represent
knowledge
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Semantic Networks
• Semantic network A knowledge
representation technique that focuses on the
relationships between objects
• A directed graph is used to represent a
semantic network or net
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Semantic Networks
Figure 13.3
A semantic
network
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Search Trees
• Search tree A structure that represents all
possible moves in a game, for both you and
your opponent
• The paths down a search tree represent a
series of decisions made by the players
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Search Tree Example: Nim
Figure 13.4 A search tree for a simplified version of Nim
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Search Trees
• Search tree also work for more complicated
games such as chess
• Because these trees are so large, only a
fraction of the tree can be analyzed in a
reasonable time limit, even with modern
computing power
• Now, the biggest supercomputers compete
against each other
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Expert Systems
• Simulates a Human Expert
– Car Mechanic
– Medical Doctor
– Gardener
• Using:
– A Set of Rules – The “Data”
– An Inference Engine – The SW that asks questions and
applies the rules
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Expert Systems
• Example: What type of treatment should I put
on my lawn?
– NONE—apply no treatment at this time
– TURF—apply a turf-building treatment
– WEED—apply a weed-killing treatment
– BUG—apply a bug-killing treatment
– FEED—apply a basic fertilizer treatment
– WEEDFEED—apply a weed-killing and fertilizer
combination treatment
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Expert Systems
• Questions:
– BARE—the lawn has large, bare areas
– SPARSE—the lawn is generally thin
– WEEDS—the lawn contains many weeds
– BUGS—the lawn shows evidence of bugs
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Expert Systems
• Rules:
– if (BARE) then TURF
– if (SPARSE and not WEEDS) then FEED
– if (BUGS and not SPARSE) then BUG
– if (WEEDS and not SPARSE) then WEED
– if (WEEDS and SPARSE) then WEEDFEED
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Expert Systems
• An execution of our inference engine
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System: Does the lawn have large, bare areas?
User: No
System: Does the lawn show evidence of bugs?
User: No
System: Is the lawn generally thin?
User: Yes
System: Does the lawn contain significant weeds?
User: Yes
System: You should apply a weed-killing and fertilizer
combination treatment.
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Artificial Neural Network
• Attempts to mimic the actions of the neural
networks of the human brain
• Good at things like:
– Will it rain?
– Is that a Kitty?
– Is that a male or female face?
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Biological Neurons
Figure 13.6 A biological neuron
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Neural Networks
– Each neuron has multiple input tentacles called
dendrites and one primary output tentacle called
an axon
– A series of connected neurons forms a pathway
– The gap between axons and dendrites is called a
synapse
– Strong connections creates a strong pathway
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Biological Neural Nets
• Your brain
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Neural Networks
• Each connection between elements has a
particular strength
• Particular combinations of inputs will make it
through the network and produce an output
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Artificial Neural Nets
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Artificial Neural Networks
• The process of adjusting the connection
strength is called training
• A neural net can be trained to produce
whatever results are required
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Natural Language Processing
• Three separate problems
– Voice synthesis
• Recreating human speech
• Making computers talk
• Easy to do
– Voice recognition
• recognizing human words
• Making computers listen
• Harder to do
– Voice comprehension
• Making computers understanding
• Very hard to do
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Voice Recognition is HARD
• The sounds that each person makes when speaking
are unique
– unique shape to our mouth, tongue, throat, and nasal
cavities that affect the pitch and resonance of our spoken
voice
– mumbling, volume, regional accents, complicate the
problem
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Voice Recognition is HARD
• Humans speak in a continuous, flowing manner
– Words are strung together into sentences
– Sometimes it’s difficult to distinguish between phrases like “ice
cream” and “I scream”
– Also, homonyms such as “I” and “eye” or “see” and “sea”
• Humans can often clarify these situations by the context of
the sentence, but that processing requires another level of
comprehension
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Voice Comprehension is HARD
• Human speech is inherently ambiguous
• 3 kinds of ambiguity
– Lexical
– Syntactic
– Referential
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Lexical Ambiguity
– The meaning of individual words
Time flies like an arrow.
– What do you mean, “flies” ?
– The computer gets confused.
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Syntactic Ambiguity
• Phrases can be put together in various ways
I saw the Grand Canyon flying to New York.
• What is flying, the Grand Canyon or me?
• The computer gets confused.
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Referential Ambiguity
• When using pronouns, for example:
The brick fell on the computer but it is not broken.
• To what does “it” refer to?
• The computer gets confused.
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Robots and AI
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Autonomy
• Some robots use AI
• Some robots do NOT use AI
• The difference is AUTONOMY
• Autonomy – the ability to adapt to new
situations without outside help
Autonomy requires AI
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Robotics
• Not Autonomous 
– Uses “Simpler”
algorithm consisting
of a list of steps
• Autonomous 
– Uses more
“Complex” Artificial
Intelligence
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2 Robotic AI Architectures
• Sense Plan Act
– The “old way”
– A Top-Down approach
• Subsumption
– The “newer way”
– A Bottom-Up approach
– Similar to how nature works
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Robotics - Sense Plan Act Architecture
• In the sense-plan-act (SPA) paradigm the world is
represented in a complex semantic net in which the
sensors on the robot are used to capture the data to
build up the net
• The “old way”
• Very hard to do…
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Robotics - Subsumption Architecture
• Rather than trying to model the entire world all the time, the
robot is given a simple set of behaviors each associated with
the part of the world necessary for that behavior
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