it presents you
1.Introduction to Artificial Intelligence
2.History and Evolution
3.Speech synthesis
4.Robots and Image processing
5.Sensor fusion
6.Innovation in Artificial Intelligence
7.conclusion
2. AGENDA
• Introduction to Artificial
Intelligence
• History and Evolution
• Speech synthesis
• Robots and Image processing
• Sensor fusion
• Innovation in Artificial Intelligence
• conclusion
3. ARTIFICIAL INTELLIGENCE
• To think like human
• To act like human
• To think rationally(logically)
• To act rationally.
4. ARTIFICIAL INTELLIGENCE
METHODS
• SYMBOLIC AI:
Focus on development
of knowledge based system.
• COMPUTATIONAL INTELLIGENCE:
Neutral networks,
fuzzy systems and evolutionary
computing.
7. HISTORY OF AI
JOHN MCCARTHY
• Precursors
• The birth of AI(1952-1956)
• The golden years(1956-1974)
• The first AI winter(1974-1980)
• Boom(1980-1987)
• Bust: second AI winter(1987-1993)
• AI(1993-2001)
• Deep learning. big data & artificial general
intelligence(2001-current).
8. GOALS OF AI
• Reasoning, problem solving
• Knowledge representation
• Planning
• Learning
• Natural language processing
9. TOOLS OF AI
• Search and optimization
• Logic
• Probabilistic methods for
uncertain reasoning
• Classifiers and statistical
learning methods
• Control theory
• Evaluating progress
10. EXPLOSIVE GROWTH OF AI
• Growth in positive side:
Useful to society.
• Growth in negative sides:
Harmful to society.
11. APPLICATIONS
• Robotics
• Heavy industries
• Medicines
• Telecommunications
• Gaming
• Satellite control
• Military activity control
• Network management
13. WHAT IS SPEECH SYNTHESIS?
• process of converting an acoustic
signal to a set of words.
• serve as the input to further linguistic
processing
• achieve speech understanding
14. SPEECH PROCESSING
• Signal processing:
Convert the audio wave into a sequence of feature
vectors
• Speech recognition:
Decode the sequence of feature vectors into a
sequence of words
• Semantic interpretation:
Determine the meaning of the recognized words
• Dialog Management:
Correct errors and help get the task done
• Response Generation
What words to use to maximize user understanding
• Speech synthesis (Text to Speech):
Generate synthetic speech from a ‘marked-up’
word string
15. What you can do with Speech
Recognition?
• Transcription
dictation, information retrieval
• Command and control
data entry, device control, navigation, call
routing
• Information access
airline schedules, stock quotes, directory
assistance
• Problem solving
travel planning, logistics
16. Transcription and Dictation
• Transcription:
transforming a stream of human speech into
computer-readable form
Example:
Medical reports, court proceedings, notes
Indexing (e.g., broadcasts)
• Dictation:
interactive composition of text
Example:
Report, correspondence, etc.
17. speech recognition system:
• Pre-processing:
conversion of spoken input into a
form the recogniser can process.
• Recognition:
identification of what has been
said.
• Communication:
to send the recognised input to
the application that requested it.
18. USERS AND SPEAKER
MODELS:
Different Kinds of Users
One time vs. Frequent users
Homogeneity
Technically sophisticated
speaker models
Speaker Dependent
Speaker Independent
Speaker Adaptive
19. Speech Recognition as Assistive
Technology
• Main use is as alternative Hands Free Data
entry mechanism
• Very effective
• Much faster than switch access
• Mainstream technology
• Used in many applications where hands are
needed for other things e.g. mobile phone
while driving, in surgical theatres
20. Speech recognition and
understanding
• Sphinx system
– speaker-independent
– continuous speech
– large vocabulary
• ATIS system
– air travel information retrieval
– context management
21. Applications:
• Speech Recognition
Figure out what a person is saying.
• Speaker Verification
Authenticate that a person is who she/he claims to
Limited speech patterns
• Speaker Identification
Assigns an identity to the voice of an unknown person.
Arbitrary speech pattern
22. ABILITY TO DREAM
• MEMORY FORMATION
•SUPERVISED LEARNING & UNSUPERVISED
LEAENING
•SUPERVISED-TRAINING DATA
•UNSUPERVISED-HUMAN ACTS
26. o ASSESING PEOPLE HEALTH THROUGH SENSORS
o CHEMICAL SENSORS & SILLICON RUBBERS
o MICROFLUIDICS
o DECTECTORS CAN BE CONNECTED TO
SMARTPHONES
o EXTEND TO OTHER FLUIDS LIKE TEARS &
SALAIVA
34. Multi-sensor Fusion and Integration
The synergistic combination of data from multiple
sensors
Provide more reliable and accurate information
Sensor data can be incomplete, erroneous and
uncertain
35. Types of multi-sensor data
fusion
1.Complementary Fusion:
Resolves incompleteness of sensor data.
E.g. fusion of several range sensors pointed in different directions.
2.Competitive Fusion:
Fusion of uncertain sensor data from several sources
E.g. heading from odometer and magnetic compass. Reduces the
effect of uncertain and erroneous measurements.
3.Cooperative Fusion:
E.g. a touch sensor refines the estimated curvature of an object
previously sensed by range sensors
36. Architecture for a Multi-sensor Data Fusion
System
Generic multi-sensor data fusion architecture
38. Integration with three different
types of sensory processing
1.Fusion:
Sensor registration converts the sensor data common internal
representation
2. Separate Operation:
Data provided by a sensor may be significantly Different
Influences the other sensors indirectly via the system
controller and the world model.
3.Guiding or Cueing:
data from one sensor is used to guide or cue the operation of
the other sensors e.g. tactile bump sensors, IR light sensors
4.Sensor selection:
used to select the most appropriate configuration of sensors to
suit the environment conditions
39. Estimation methods
Usage:
Signal level fusion
Non-recursive:
Weighted Average
Least Squares
Recursive:
Kalman Filtering
Extended Kalman Filtering
Classification methods
Usage:
Extracting features & matching
at pixel and feature level fusion
Parametric Templates
o Match extracted features to classes in a
multidimensional feature space
Cluster Analysis
o Similar to SOFM
o Learn geometrical relationships
MULTI –SENSOR FUSION ALGORTHIMS
40. Classification methods Learning Vector Quantization (LVQ)
o Another type of NN
K-means Clustering
o Competitive NN
Kohonen Feature Map (SOFM)
ART,ARTMAP,Fuzzy-ART Networks
Inference methods
Usage:
Symbol level fusion –
evidential reasoning
Bayesian Inference
o Information combined according to the rules
of probability theory
o Bayes formula
o between sample data sets
Dempster-Shafer Method
o Rectifies some instances where probabilities
may become unstable in Bayesian inference
Generalized Evidence Processing
o Unifies Bayesian and Dempster- Shafer
methods
41. A
Artificial intelligence methods
Usage:
Can be used at different levels
of fusion
Expert System
o Performs inferences using a data set and rule-
based knowledge base
Neural Networks
o Adaptive
o Backpropagation
Fuzzy Logic
o Multiple-valued logic where variables are
assigned degrees of membership between 0
and 1
“may be” exists between "yes” and “no”
42. 12
Output to system controller
Symbol
Level
Feature
Level
Signal&
Pixel
Level
Implementation of target tracking system integrating visual
detection and ultrasonic sensory data