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

1. Submitted by,
S.Nivathika
S.K.Ramya
S.Sundra meena
S.Uma Megeshwari
K.Vijayalakshmi
Guided by,
Ms.Girija,
Assistant Professor,
Computer Science department

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.

5. KNOWLEDGE BASED SYSTEMS

6. NEURAL NETWORKS

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

12. SPEECH SYNTHESIS
In artificial intelligence

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

23. HUMAN TOUCH

24.  SENSATIONS ARE MOTORIZED
 OPTICAL WAVEGUIDES
 ELASTOMERIC SENSORS
 OPTOELECTRONIC PROSTHESIS

25. SWEAT DETECTORS

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

27. EYE PHONES

28.  INTERSCATTER COMMUNICATION
 BACKSCATTER
 LENS WITH TINY ANTENNA
 SMART CONTACT LENS

29. BEST ROBOTS OF 2016-2017
 PEPPER
KURI
YUMMI

30. PEPPER
 HUMANOID HELPER
 HONDA
 SHARE UPGRADES
 SHOPING MALLS IN
SAN FRANSICO & SAN
JOSE

31. KURI
• MAYFIELD ROBOTICS
• LASER DEPTH PRECISION
SYSTEM
• REMOTE CONTROLLED VIA
KURI APP
• BUILT IN CAMERA
• MOVES, LISTENS
• SPEAKS & ENTERTAINS

32. YUMMI
• FRENCH STARTUP
• HUMANOIDS FOR ELDERLY
CARE
• VERBAL CUES
• ASSIST WITH COOKING &
MAKE CONTACTS

33. Multi-sensor Fusion and Integration

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

37. Multi-sensor Integration
Functional diagram of multi-sensor fusion and integration

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