Transcript Faculty of Electrical Engineering & Informatics Technical

Intelligent technologies
Why ? How ?
(Long version)
Prof. Peter Sincak – TU Kosice
Center for Intelligent Technologies
http://www.ai-cit.sk
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Siemens AG Vienna, Austria
Maria-Curie Fellowship within
the 5. FP of European Union
November, 2001 – May, 2002
Project :
„Computational Intelligence in
Real World Applications“
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Goal of this talk
 Explain
or remind the basic principles of
Intelligent technologies
 Basic principles and features of neural
networks, fuzzy systems, evolutionary
computing and hybrid systems
 Point out application potential and domains
 Some notes to the future technologies
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Basic principles
 Historical
background
 What is intelligence ??
 Basic features of Intelligent systems and
Intelligent technologies
 What type of tasks could be under
consideration using intelligent technology ?
 Can you call your product intelligent ?????
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What is historical background
of AI ????
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Basic facts about history of AI
First mentioning of the brain
Edwin Smith Papyrus
from 1700 BC – info from 2625 BC
Imhotepa – first surgeon
( also – building pyramids and astr.)
Ebers papyrus - 110 pqges qbout anatomy
Includig brain and ist function
• Aristoteles (355 BC ) - his important
work on memory, dreams and so on.
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Basic facts about history of AI
First AI people – fortune tellers – extrapolating „life“
Blaise Pascal's Pascaline (first calculating machines) - 1642
G.W. Liebnitz (1646-1716)
he was first
Who said – brain is based on mathematics
*Calculemus*... "Let us calculate!"
Goerge Boole (1815-1864)
in book “Investigation of Minds laws ..”
„The mathemathics what has to be
discovered is mathematics of the human
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intellect“
Basic facts about history of AI
A. M. Turing - (1912-1954)
Computers and Intelligence
„Turingov test“
W. McCulloch W. Pitts (´43)
Artificial neural network
N. Wiener (1948)
C. Shannon (1953)
Ashby (1952)
Book „Cybernetics“ - > AI
How you program the machine
To achieve ability to learn ???
Establishing AI – as research
Field
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More about history – Quo Vadis AI ???
Univ. Dartmouthe (´56)
Artificial Intelligence – leading
edge of technolgy
M. Minski
A. Turing
N. Wiener
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What is Artificial (machine)
intelligence ?
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Terminology – non-unified
Non-unified USA, Japan, Európa
Machine Intelligence
Prof. Lotfi Zadeh
Artificial Intelligence
Computational Intelligence
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What is intelligence ?
It is very complex notion but .....
„Intelligence is a feature to learn from experience“
What is Artificial Intelligence – number of tools of AI
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Basic tools of Computational Intelligence
Computational Intelligence
(Prof. Bezdek)
NN
FS
Alife
GA, GP
Virtual Intelligence
Softcomputing
(Prof. Zadeh)
Hybrid Systems
VI + virtuálna realita
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Workshop on VI - Sweeden :
http://msia02.msi.se/~lindblad/vi-dynn/vi-dynn.html
Basic features of Intelligent
systems and Intelligent
technologies
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What are the main features of
Intelligent Systems ???
knowledge representation
& archivation
learning
reasoning - problem solving
Intelligent Systems
Intelligent Technologies
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Why Intelligent ???
Intelligence
knowledge
(Biologically inspired systems, brain-like systems)
Knowledge
 Knowledge
 Knowledge
chaos

– in data (neural networks)
– in experience (fuzzy logic)
– in state space – heuristic search,
(evolutionary computing, evolution)
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Knowledge from data
How to obtain some new info from data
(Datamining ) ?
 How to Model complex system based on data ?
 How to make Rules extraction from data ?
 How Clusters in the hyperdimensional space ?
 How to handel data if you do not know their
statistical distribution ?
 How to handle data – non-statistical approach
(model-free, can do everything as statistics
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– and more )

Knowledge from experience
 How
to „extract knowledge“ from human experts?
 How to incorporate their knowledge into system?
 How to create link – human experience – machine?
 How to make a knowledge fusion from more
experts and also knowledge replication ?
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Knowledge in state space
 How
to find a solution – e.g. optimal
coeficient if there is no idea – how large is
a state space ?
 How efectively search a space to find an
optimal parameters ?
 If other approaches (including statistical)
are failing to find a optimal or suboptimal
values what should we do ?
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What type of tasks could be
under consideration using
intelligent technology ?
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What type of problems ?
 Classification
from data and human
experience
 Modelling from data or human experience
 Prediction – forecast
 Optimalization – finding the optimal values
 Human-machine interface (human-centered)
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In which areas IT were used and have
application potential ? (Business)
Credit rating and risk assessment
 Insurance risk evaluation
 Fraud detection
 Insider dealing detection
 Marketing analysis , Mailshot profiling
 Signature verification , Inventory control
 Prediction of prices, electricity load and
discharge

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In which areas IT were used and have
application potential ? (engineering)
Machinery defect diagnosis
 Signal processing , Character recognition
 Process control & supervision , fault analysis
 Speech , vision and color recognition
 Radar signal classification
 Aircraft control, Car brakes
 Integrated circuit layout
 Image compression
 Prediction of signals and values in engineering

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Can you call your product
intelligent ?? Why not ???
Will it have impact on demand
and sales ???????
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Home appliances
Company: BPL
Product : washing machine
ABS 50F
Fuzzy system decides the type
of Program & amount of water
and washing ingredients
Company : BPL
Product : washing machine
ABS 60 NF
Neuro-fuzzy system detects a type
of material in the machine and
decides the type of the program and
amunt
of
water
and
washing
ingredients.
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Home Appliances
Company : Videocon-international
Product : washing machine –
V-NA- 45 FDX
The same as before – just 996
different cycle to choose from .
Which on is decided
By neuro-fuzzy system
Company : Videocon-internetional
Product : Washing machine
Fuzzy control of the machine
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Home appliance
Company : Sanyo
Product : washing machine
ASW-F60T
The same concept – made by
company
Company : LG
Product : Refrigerator
Neural fuzzy system controls
the freezing procedures in
the refrigirator
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Home appliance
Company Sanyo
Cook , owen – cooker ECJ-5205SN
According to the senszors of infra, thermal
senzor a huminity senzor it estimate a meal quality
and determine A time of cooking.
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Electronics
Company: Sharp
Product : microwave owen
Accoding to the analysis of the inside air the
lenght of the cooking is controlled. The
analysis of the Food smell during cooking is
matter of interest.
Company: Videocon
Product : air-conditioner
Neuro-fuzzy control of air-conditioner to
keep equal temperature within the room
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Electronics
Company : Cannon
Product : videocamera
Canon uses fuzzy system with 13 rules to
focus the objectives based on the
information in the image characteristics
Company : Mitsubishi
Product : TV set
Make a neural controller to adjust the
image contrast according to the
broadcast image. This adaptive
approach produce a very good User
feeling while seeing TV program.
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Electronics
Company : Samsung
Product : Blod pressure measurement
Fuzzy system controls the overall process
of
Blood measurement
Company: Samsung
Product : Camera
Fuzzy control of image focusing
& sharpening
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Electronics
Company: JVC
porduct: car-radio
Using neural networks it is able to control
car radio with high reliability and adapt to
the voice of the speaker.
Company: IntelaVoice
Product : switcher controled by voice
Using neural networks it is able to control
the switch with high reliability and adapt to
the voice of the speaker.
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Copy machines
Company: Canon
Product : Copy Machine
Series of CLC700 a CLC800 have a fuzzy control
of the toner to achieve the best results
Company: Panasonic
Product : Copy machine
In the series FP-1680 up to FP-4080 is
implemented a neuro-fuzzy system to
control various parameters to get the best
copy results as possible
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Car industry
Companies : Mercedes & Hyundai
Mercedes in model CLK use Automatics transmission
based on Highly adaptive technology to adapt to the
style of the driver. Similar approach is in XG Hyundai
model.
Company : BMW
BMW uses long time a fuzzy approach in ABS brake
system which adapts the braking process with the aim
to avoid blocking phase. Also in other advance systems
these technologies are used.
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Car Industry
Company : Siemens AG
Product : Smart Airbag
Smart airbags – for persons safety
uses some parts of intelligent
technologies including adapting
safety measures to the people.
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Internet sources – aproximate measures
Applications neural
engineering – 56 %
Applications of
Applications
of fuzzy - 35 % of EC – 9 %
Aproximate estimation of number of
Intelligent technologies applications
based on Google search engine
neural
fuzzy
evolutionary
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Basic principles of neural
technology
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What is Neural network ???
It is massively parallel processor
which tends to
store knowledge
It is biologicky inspired system –
„tries“ to simulate the Brain
functionality because it has :
• Interneural connections and
network topology – used storing
knowledge
• it learns by examples (data)
In neural technology theory
simulation
implementation
What kind of neural networks we do have
Recurent NN
Input
output
Feedforward NN
output
layer
neurons
input
layer
hidden
layer
Synaptic weights
Neuron – basic processing element
wm
F
i
Fa
Fo
output
w1
input
activation
w0 input
function function
-1
output
Function
Basic approaches in neural
technology
Supervised training by examples – so you are
getting neural network a tool for classification,
modelling , prediction and etc. – You have to have
a data
(input-output examples)
 Unsupervised training – so you are able to neural
network for clustering, dimentionality reduction,
compression etc.
(only input data)

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What type of problems especially with NN





classification
neural control – more nelinearity
prediction problems based on history
signal tranformation
clustering in hyper-dimensional space
(diagnostic applications)
 many other
Basic principles of fuzzy
technology
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What is fuzzy system ??
Based on fuzzy logic – fuzzy sets
Ai  {x, Ai ( x)}
Ai  {x}
It is good for expressing verbal values
(small people, mid-size people, tall people)

1
small
140 150
tall
mid
168
175
Height of people
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Why fuzzy set is important ?
You are able to describe a experience or
behavior of the system in the form of
IF .............. THEN ............... rules
e.g.
Preposition
Consequens
IF a car is big AND car is expensive THEN car is fast
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Fuzzy system (controller) – basic tool
Crisp output
Experience
From the
Expert
Crisp input
De-fuzzification
Rule - Base
(made by expert)
fuzzification
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Where is good fuzzy logic ??
Modeling – e.g.
experience in washing
Washing machine
In case when you are not able to get model – and you
Are able to describe behavioral model by fuzzy rule
Behavioral Model of the system
In case if you want to incorparate experience of the expert
In the system – e.g. predictions, decisions etc.
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Aplication domains
 Transpotation
(cars, trains, traffic management...)
 Computing with words – Internet – information
retrieval
 Fuzzy measures – image processing, databases
 Control – easy to design (if you have an expert)
 Felling sensors – Keise problem description by
fuzzy, etc
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What is the basic feature of these
technologies to have them useful ???
Basic feature is :
Universal aproximation theorem
Universal unknown function
aproximators
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Basic principles of
evolutionary technology
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What are the basic tools in
evolutionary computation ?
Genetic algorithms – optimalization tool
Genetic programming – system for data analysis
with aim to provide
analytical expresion
Based on biological inspiration and Darwin theory
of evolution
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Basic tools in Evolutionary
computation
– encoding the problem
 Fittness function – Evaluation function
 Operator – mutation , selection, cross operator ...
 Some chromozoms survive some are destroyed
 Chromozoms
 To
find in heuristic way the - best values
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Evolution – evolutionary solutions
Interactive Evolutionary approach
So e.g. you envolve design of the event
Make few iteration
Stop – human will influence the evolution
Envolving towards
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Evolutionary programming
Data
GP
Analytical
expression
Function approximation abilities
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Where to use Evolutionary
 Optimalization
problems in general
 Planning and scheduling (TSP problem)
 GP for data-mining with aim of analytical
expression
 broad range of engineering, business and
other applications
1.7Ghz ???????
Problem – time consuming process
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Some more tools of intelligent
technologies
All tools related to Machine (Artificial
Intelligence)
NN, FS, EC +
 Expert systems
 Logic programming tools
 Tools of chaos theory
 Tools of Artificial Life
 Tools of Multi-Agent technologies
 Etc .......
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What is the trend in using Intelligent technogies
Computational Intelligence
Fuzzy systems
Neural Networks
Evol. Computat.
Hybrid technologies – ECANSE – the clever
approach to solve the problem using various tools of IT.
Expert systenms …..
Planning , Schedulling
Classical Artificial Intelligence
Answer is simple
When to use Intelligent
technologies ?
Only if the application of IT will make
a product more advace and succesfull
on the market (money)
„Nobody cares what technology –
but new technology must be better“
It is general belief that
Intell. Techology is able to do it
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Conclusion
I believe that computational Machine Intelligence tools
should be subject of research
Intelligent System
Application !!!
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What could be the future trends ???
Applications
Intelligent
technologies
Computers – 2 Ghz
Hardware implementation
????????????????????
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ISTAG – IST EU program
advisory group
Visionary report
“Ambient Intelligence”
The role of Machine Intelligence in
the Information Society
(http://www.cordis.lu/ist/istag.htm) - No MI-people
Participated   
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Thank you for beeing with me !
Peter Sinčák
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