Artificial Intelligence

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Artificial Intelligence

• CS105

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Team Meeting Time (10 minutes)

• Find yourself a team
• Find your team leader
• Talk about topics and responsibilities

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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

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Expert systems

• Knowledge based system that uses specialized set
  of information to solve problems
• 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

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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
• 1. The infection which requires therapy is
  meningitis, and
• 2. organisms were not seen on the stain of the
  pending CSF culture, and
• 3. the type of infection is bacterial, and
• 4. 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).

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Recognizing images

• Intelligent machines (Computer Vision)

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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?
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Classifying data
positive
x1
Some equation
negative
x2
Linear Classifier A technique that uses an
objects feature(s) to classify which group it
belongs to
Positive or negative?
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Can this be classified?
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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

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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

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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

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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, thats terrible!

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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
• George My aunt is in the hospital. I went to see
  her today and, took her flowers.
• Computer George, thats 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?

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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

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How do we process information?
FRONTAL LOBE Behavior Problem Solving Planning Att
ention Abstract Thinking Judgment Inhibition
PARIETAL LOBE Touch Sensory combination and
comprehension Number area
OCCIPITAL LOBE Vision
TEMPORAL LOBE Audition Language
CEREBELLUM Balance, Posture Cardio, Respiratory
centers
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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-gt inhibited state or
  vice versa

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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
If effective weight of each neuron is above a
certain threshold, output is 1
Receives many inputs Assigned a numeric weight
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Artificial neural networks

• Training The process of adjusting the weights
  and threshold values
• Series of comparisons to desired results

0 0 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 1 0 0
0 1 1 1 1 0
0 0 0 1 0 0
0 0 0 0 0 0
w
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 1 0 0
0 1 1 1 1 0
0 0 0 1 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 1 0 0 0
1 1 1 1 0 0
0 0 1 0 0 0
0 0 0 0 0 0
w
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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