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Intelligent technologies Why ? How ? (Long version) Prof. Peter Sincak – TU Kosice Center for Intelligent Technologies http://www.ai-cit.sk 1 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“ 2 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 3 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 ????? 4 What is historical background of AI ???? 5 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. 6 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 7 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 8 More about history – Quo Vadis AI ??? Univ. Dartmouthe (´56) Artificial Intelligence – leading edge of technolgy M. Minski A. Turing N. Wiener 9 What is Artificial (machine) intelligence ? 10 Terminology – non-unified Non-unified USA, Japan, Európa Machine Intelligence Prof. Lotfi Zadeh Artificial Intelligence Computational Intelligence 11 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 12 Basic tools of Computational Intelligence Computational Intelligence (Prof. Bezdek) NN FS Alife GA, GP Virtual Intelligence Softcomputing (Prof. Zadeh) Hybrid Systems VI + virtuálna realita 13 Workshop on VI - Sweeden : http://msia02.msi.se/~lindblad/vi-dynn/vi-dynn.html Basic features of Intelligent systems and Intelligent technologies 14 What are the main features of Intelligent Systems ??? knowledge representation & archivation learning reasoning - problem solving Intelligent Systems Intelligent Technologies 15 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) 16 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 17 – 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 ? 18 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 ? 19 What type of tasks could be under consideration using intelligent technology ? 20 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) 21 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 22 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 23 Can you call your product intelligent ?? Why not ??? Will it have impact on demand and sales ??????? 24 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. 25 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 26 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 27 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. 28 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 29 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. 30 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 31 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. 32 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 33 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. 34 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. 35 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 36 Basic principles of neural technology 37 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) 41 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 43 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 44 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 45 Fuzzy system (controller) – basic tool Crisp output Experience From the Expert Crisp input De-fuzzification Rule - Base (made by expert) fuzzification 46 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. 47 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 48 What is the basic feature of these technologies to have them useful ??? Basic feature is : Universal aproximation theorem Universal unknown function aproximators 49 Basic principles of evolutionary technology 50 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 51 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 52 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 53 Evolutionary programming Data GP Analytical expression Function approximation abilities 54 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 55 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 ....... 56 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 58 Conclusion I believe that computational Machine Intelligence tools should be subject of research Intelligent System Application !!! 59 What could be the future trends ??? Applications Intelligent technologies Computers – 2 Ghz Hardware implementation ???????????????????? 60 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 61 Thank you for beeing with me ! Peter Sinčák 62