Transcript AI Application

Artificial Intelligence
CS105
Team Meeting Time (10 minutes)
• Find yourself a team
• Find your team leader
• Talk about topics and responsibilities
Recap
• Attempt to understand and mimic human
intelligence
• Turing Test
• State of the art AI
• Looked at techniques for representing
knowledge for intelligent problem solving
– Semantic networks and trees
Expert systems
• Knowledge based system that uses specialized set of
information to solve problems
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Doctors, Plumbers, Gardeners, Dentist
Specific problem domain
Applications are specific
Example: the troubleshoot in your windows
• Rule-based system: Based on a set of if-then or logical
rules
– Rules form the knowledge base
• Inference engine: determines which rules to be followed,
tries to derive answers from a knowledge base
Expert systems
MYCIN: Has the patient recently had symptoms of persistent headache or other
abnormal neurologic symptoms(dizziness, lethargy etc)?
HUMAN: Yes
MYCIN: The CSF cultures will be considered to be associated with meningitis.
Please give the date on which clinical evidence(symptoms, signs or lab tests)
of meningitis first appeared. Please enter CSF finding. Is meningitis a
hospital-acquired infection?
HUMAN: Why?
MYCIN: This will aid in determining the organisms (other than those seen on
cultures or smears) which might be causing the infection. It has already been
established that
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4.
The infection which requires therapy is meningitis, and
organisms were not seen on the stain of the pending CSF culture, and
the type of infection is bacterial, and
neurosurgery is not one of the relevant items from the history of this
patient, and
5. the age of this patient is greater than ten days, therefore, if
6. the infection was acquired while the patient was hospitalized then there is
evidence that the organisms (other than those seen on cultures or smears)
which might be causing the infection are e. coli (75), staphylococcus-coag
pos (3), pseudomonas-aeruginosa (5).
Recognizing images
• Intelligent machines (Computer Vision)
Pattern recognition
• Techniques that classify data (patterns) in an attempt to
understand the data and take actions based on that
understanding
– A priori knowledge: Previous knowledge that does not get modified
with new experiences
– Statistical information extracted from the patterns
– Example: Face recognition system – understanding pixels
A priori or statistical
information based?
Classifying data
x1
positive
Some equation
negative
x2
Linear Classifier: A technique that uses an object’s
feature(s) to classify which group it belongs to
Positive or negative?
Can this be classified?
x1
x2
Natural language processing
• Branch of AI concerned with interactions
and human languages
• Natural Language: Set of languages that
humans use to communicate
• This problem is of strong equivalence
– Ability to comprehend languages, extensive
knowledge about the outside world and being
able to manipulate it
– Voice recognition: recognizing human words
– Natural language comprehension: interpreting
human communication
– Voice synthesis: recreating human speech
Voice synthesis
• Artificial production of human speech
– A system used for this purpose is called a speech synthesizer
• How do you synthesize speech?
– Phonemes: The set of fundamental sounds made in any
given natural language
• /K/ in Kit and sKill
• Select appropriate phonemes to generate sound of a word, the pitch
might be tweaked by the computer depending on context
– Recorded speech
• Same words have to be recorded multiple times at
different pitches
Voice recognition
• Sounds each person make is unique
– Vocal tracts: cavity in animals where sound that is
produced at the sound source is filtered
• Systems have to be trained for vocabulary sets
– Acoustic modeling: Statistical models of sounds
• Audio recording of speech and text transcriptions
– Language modeling: capture the properties of a
language, and to predict the next word in a speech
sequence
Natural language comprehension
• Most challenging aspect!
– Natural language is ambiguous – multiple interpretations
– Understanding requires real world knowledge and syntactic
structure of sentences
• Examples:
Time flies like an arrow
The pen is in the box
The box is in the pen
George: My aunt is in the hospital. I went to see her today and, took
her flowers.
Computer: George, that’s terrible!
Natural language comprehension
• Lexical ambiguity: Words have multiple meaning
Time flies like an arrow
• Syntactic ambiguity: Sentences have more than one meaning
The pen is in the box
The box is in the pen
Makes sense
Makes no sense!
George: My aunt is in the hospital. I went to see her today and, took her
flowers.
Computer: George, that’s terrible!
• Referential ambiguity: Ambiguity created when pronouns could
be applied to multiple object
Ally hit Georgia and then she started bleeding
Who started bleeding? Ally, Georgia or someone else?
Natural language comprehension
• Systems must have these common components:
– Lexicon: vocabulary, word and expressions
– Parser: Text analyzer, inbuilt grammar rules, to form an
internal representation of the text
– Semantic theory: study of meaning and relationships
between words, phrases
– Logical inference: Process of drawing conclusions based on
rules applied depending on observations or statistical
models
How do we process information?
PARIETAL LOBE
FRONTAL LOBE
Touch
Sensory combination
and comprehension
Number area
Behavior
Problem Solving
Planning
Attention
Abstract Thinking
Judgment
Inhibition
OCCIPITAL LOBE
Vision
TEMPORAL LOBE
CEREBELLUM
Balance, Posture
Cardio, Respiratory centers
Audition
Language
Neuron
• Brain is made up of neurons
– An electrically excitable cell that processes and transmits information by
electrical and chemical signaling
– An excited neuron conducts a strong signal and vice versa
– A series of excited neurons form a strong pathway
– A neuron receives multiple inputs from other neurons
• Assigns a weight on each signal based on its strength
• If enough signals are weak-> inhibited state or vice versa
Artificial neural networks
• A mathematical model inspired by the structure and/or
functional aspects of biological neural networks
Artificial Neuron or Node
Inputs: 1 or 0
Receives many inputs
Assigned a numeric weight
If effective weight of each
neuron is above a certain
threshold, output is 1
Artificial neural networks
• Training: The process of adjusting the weights and threshold
values
– Series of comparisons to desired results
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Artificial neural networks
• You can train a neural network to do anything
– No inherent meaning to the weights: Making it versatile
• Applications:
– Pattern recognition
– Classification
– Modeling how are brain works