1. Artificial Intelligence Introduction Chapter

2. 2 Data Driven Methods What is Artificial Intelligence? Common AI techniques Choosing between AI techniques Pre and post processing Powered by DeSiaMore

3. What is AI? Various definitions: – Building intelligent entities. – Getting computers to do tasks which require human intelligence. But what is “intelligence”? Simple things turn out to be the hardest to automate: – Recognising a face. – Navigating a busy street. – Understanding what someone says. All tasks require reasoning on knowledge. 3Powered by DeSiaMore

4. 4 What is AI? Machines that perceive, understand and react to their environment – Goal of Babbage, etc. – Oldest endeavor in computer science Machines that think – Robots: factory floors, home vacuums – Still quite impractical Powered by DeSiaMore

5. Who does AI? Many disciplines contribute to goal of creating/modelling intelligent entities: – Computer Science – Psychology (human reasoning) – Philosophy (nature of belief, rationality, etc) – Linguistics (structure and meaning of language) – Human Biology (how brain works) Subject draws on ideas from each discipline. 5Powered by DeSiaMore

6. Typical AI Problems Intelligent entities (or “agents”) need to be able to do both “mundane” and “expert” tasks: Mundane tasks - consider going shopping: – Planning a route, and sequence of shops to visit! – Recognising (through vision) buses, people. – Communicating (through natural language). – Navigating round obstacles on the street, and manipulating objects for purchase. Expert tasks are things like: – medical diagnosis. – equipment repair. Often “mundane” tasks are the hardest. 6Powered by DeSiaMore

7. 7 AI vs. humans AI applications built on Aristotlean logic – Induction, semantic queries, system of logic – Human reasoning involves more than just induction Computers never as good as humans – In reasoning and making sense of data – In obtaining a holistic view of a system Computers much better than humans – In processing reams of data – Performing complex calculations Powered by DeSiaMore

8. 8 Successful AI applications Targeted tasks more amenable to automated methods – Build special-purpose AI systems Determine appropriate dosage for a drug Classify cells as benign or cancerous – Called “expert systems” Methodology based on expert reasoning Quick and objective ways to obtain answers Powered by DeSiaMore

9. 9 Data Driven Methods What is Artificial Intelligence? Common AI techniques Choosing between AI techniques Pre and post processing Powered by DeSiaMore

10. 10 AI Technique Intelligence requires Knowledge Knowledge posesses less desirable properties such as: – Voluminous – Hard to characterize accurately – Constantly changing – Differs from data that can be used AI technique is a method that exploits knowledge that should be represented in such a way that: – Knowledge captures generalization – It can be understood by people who must provide it – It can be easily modified to correct errors. – It can be used in variety of situations Powered by DeSiaMore

11. 11 The State of the Art Computer beats human in a chess game. Computer-human conversation using speech recognition. Expert system controls a spacecraft. Robot can walk on stairs and hold a cup of water. Language translation for webpages. Home appliances use fuzzy logic....... Powered by DeSiaMore

12. 12 Fuzzy logic Fuzzy logic addresses key problem in expert systems – How to represent domain knowledge – Humans use imprecisely calibrated terms – How to build decision trees on imprecise thresholds Powered by DeSiaMore

13. 13 Advantages of fuzzy logic Considerable skill for little investment – Fuzzy logic systems piggy bank on human analysis Humans encode rules after intelligent analysis of lots of data Verbal rules generated by humans are robust – Simple to create Not much need for data or ground truth Logic tends to be easy to program Fuzzy rules are human understandable Powered by DeSiaMore

14. 14 Where not to use fuzzy logic Do not use fuzzy logic if: – Humans do not understand the system – Different experts disagree – Knowledge can not be expressed with verbal rules – Gut instinct is involved Not just objective analysis A fuzzy logic system is limited – Piece-wise linear approximation to a system – Non-linear systems can not be approximated Many environment applications are non-linear Powered by DeSiaMore

15. 15 Neural Networks Neural networks can approximate non-linear systems – Evidence-based Weights chosen through optimization procedure on known dataset (“training”) – Works even if experts can’t verbalize their reasoning, or if there is ground truth Powered by DeSiaMore

16. 16 Advantages of neural networks Can approximate any smooth function – The three-layer neural network Can yield true probabilities – If output node is a sigmoid node Not hard to train – Training process is well understood Fast in operations – Training is slow, but once trained, the network can calculate the output for a set of inputs quite fast Easy to implement – Just a sum of exponential functions Powered by DeSiaMore

17. 17 Disadvantages of neural networks A black box – The final set of weights yields no insights – Magnitude of weights doesn’t mean much Measure of skill needs to be differentiable – RMS error, etc. – Can not use Probability of Detection, for example Training set has to be complete – Unpredictable output on data unlike training – Need lots of data – Need expert willing to do lot of truthing Powered by DeSiaMore

18. 18 Recap: Fuzzy logic – Humans provide the rules – Not optimal Neural network – Humans can not understand system – Optimal Middle ground? – Genetic Algorithms – Decision Trees Powered by DeSiaMore

19. 19 Genetic algorithms In genetic algorithms – One fixes the model (rule base, equations, class of functions, etc.) – Optimize the parameters to model on training data set – Use optimal set of parameters for unknown cases Powered by DeSiaMore

20. 20 An example genetic algorithm Sources: http://tx.technion.ac.il/~edassau/web/genetic_algorithms.htm http://cswww.essex.ac.uk/research/NEC/ Powered by DeSiaMore

21. 21 Advantages of genetic algorithms Near-optimal parameters for given model – Human-understandable rules – Best parameters for them Cost function need not be differentiable – The process of training uses natural selection, not gradient descent Requires less data than a neural network – Search space is more limited Powered by DeSiaMore

22. 22 Disadvantages of genetic algorithms Highly dependent on class of functions – If poor model is chosen, poor results Optimization may not help at all Known model does not always lead to better understanding – Magnitude of weights, etc. may not be meaningful if inputs are correlated – Problem may have multiple parametric solutions Powered by DeSiaMore

23. 23 Data Driven Methods What is Artificial Intelligence? Common AI techniques Choosing between AI techniques Pre and post processing Powered by DeSiaMore

24. 24 Typical data-driven application Input Data Features f(features) Result How do we find f() Which features? AI application in run-time Powered by DeSiaMore

25. 25 What is the role of the data? Validation – Test known model – Technique: Difference between model output and ground truth helps to validate the model Calibration – Find parameters to model with desired structure – Technique: Tuned fuzzy logic method Genetic algorithms Induction – Find model and parameters from just data – Technique: Neural network methods, bagged/boosted decision trees, support vector machines, etc. Powered by DeSiaMore

26. 26 What is the problem to solve? Do you have a bunch of data and want to: – Estimate an unknown parameter from it? True rainfall based on radar observations? Amount of liquid content from in-situ measurements of temperature, pressure, etc? Regression – Classify what the data correspond to? A water surge? A temperature inversion? A boundary? Classification Regression and classification aren’t that different – Classification: estimate probability of an event A function from 0-1 Powered by DeSiaMore

27. 27 Which AI technique? Do you have expert knowledge? – Humans have a “model” in their head? Should the final f() be understandable? – Create fuzzy logic rules from experts’ reasoning Aggregate the individual fuzzy logic rules Can tune the fuzzy rules based on data – Using regression, decision trees or neural networks for RMS error criterion – Genetic algorithms for error criteria like ROC, economic cost, etc. Many times the original rules are just fine Do you already know the model? – A power-law relationship? Gaussian? Quadratic? Rules? – Just need to find parameters to this model? If linear, just use linear regression If non-linear: use genetic algorithms Use continuous GAs Both of these can be used for regression (therefore, also classification) Powered by DeSiaMore

28. 28 Which AI technique (contd.) Do you know nothing about the data? – Not the suspected equation/model (GA)? – Not the suspected rules (fuzzy logic)? – Use a AI technique that supplies its equations/rules “black box”. For classification, use: – Bagged decision trees or Support Vector Machines If output is probabilistic, remember to apply Platt scaling Summary statistics on bagged DTs can help answer “why” – Neural Networks For regression, use: – Neural networks Powered by DeSiaMore

29. 29 Where do your data come from? Observed data – Compute features – Choose AI technique The 4 choices in the previous two slides Simulated data: – Example: trying to replicate a very complex model – Throw randomly-generated data at model – Compute features – Choose AI technique: GA for parametric approximations NN when you don’t know how to approximate Powered by DeSiaMore

30. 30 Where do you get your inputs? What type of data do you have? – Individual observations? Sample them (choose at random) and use directly – Sparse observations in a time series? Generate time-based features (1D moving windows) Signal processing features from time series – Data from remotely sensed 2D grids? Generate image-based features using convolution filters Do you need: – Pixel-based regression/classification? » Use convolution features directly – Object-based regression/classification? » Identify regions using region growing » Use region-aggregate features Powered by DeSiaMore

31. 31 Typical data-driven application Observed data Features f() Result Signal/image processing;sampling normalize/create chromosome/ determine confidences FzLogic/GenAlg/NN/DecTree Platt method/region-average/threshold A data-driven application in run-time Powered by DeSiaMore

32. 32 Data Driven Methods What is Artificial Intelligence? Common AI techniques Choosing between AI techniques Pre and post processing Powered by DeSiaMore

33. 33 Preprocessing Often can not use pixel data directly – Too much data, too highly correlated – May need to segment pixels into objects and use features computed on the objects Different data sets may not be collocated – Need to interpolate to line them up – Mapping, objective analysis Noise in data may need to be reduced – Smoothing – Present statistic of data, rather than data itself Features need to be extracted from data – Human experts often good source of ideas on signatures to extract from data Powered by DeSiaMore

34. 34 Postprocessing The output of an expert system may be grid point by grid point – May need to provide output on objects Storms, forests, etc. – Can average outputs over objects’ pixels May need probabilistic output – Scale output of maximum marginal techniques – Use a sigmoid function Called Platt scaling Powered by DeSiaMore

35. 35 Summary What is Artificial Intelligence? – Data-driven methods to perform specific targeted tasks Common AI techniques – Fuzzy logic, neural networks, genetic algorithms, decision trees Choosing between AI techniques – Understand the role of your data – Do experts understand the system? (have a model) – Do experts expect to understand the system? (readability) Pre and post processing – Image processing techniques on spatial grids Powered by DeSiaMore

36. AI Introduction END 36Powered by DeSiaMore