Introduction to Artificial Intelligence for Bradley University – CS 521

1. Introduction to Artificial Intelligencefor Bradley University – CS 521 Anthony (Tony) J. Grichnik Visiting Scientist to Bradley University Caterpillar Inc.

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24. Are you ready?

25. Quiz • Who goes first? • How many digits are there? • What is the object of the game? • How did you figure this out?

26. What do you think of when you think of Artificial Intelligence (AI)?

27. To understand AI is to move beyond programming. The key to AI is the “I” – Intelligence! - for computers to understand, adapt and improve. AI moves us toward understanding ourselves as much as it does solving problems.

28. Goals for This Class • Gain a basic overview of the three major fields in Artificial Intelligence (AI). • Complete one project in each field to build up your academic resume. • Become sufficiently exposed to AI to see if you’d like to pursue a career in the field or go deeper into AI academia. • You will not be an expert at the end of the class…or at the end of your life…there should always be something left to learn. • You will be “dangerously competent” if you complete all the projects successfully.

29. Expectations of Students • Learn! (Well duh….) • If you’re given something to read, read it and read all of it. • If you don’t “get it,” speak up! (It won’t get better if you keep plodding along and are lost from the beginning.) • If you don’t know, ask. • We can’t help much if you don’t! • Don’t show up with things that don’t compile or execute. • They may run wrong or poorly, but this is a Master’s level class. Basic programming should be old hat by now. • Complete the assignments to the best of your ability.

30. Introduction to Artificial Intelligencefor Bradley University – CS 521 Anthony (Tony) J. Grichnik Visiting Scientist to Bradley University Caterpillar Inc.

31. Outline • Introduction: The Clans of Artificial Intelligence (AI) • Logical – Be the Expert • Statistical – Would you like to play a game? • Biological – Solutions…naturally • The Future – Hybrids and more

32. Introduction(Welcome to the neighborhood…) • Contrary to popular belief, AI is not a new field. • In fact it’s founded on principles that are hundreds – sometimes thousands – of years old. • Modern computers simply implement the techniques faster, more effectively and on a broader range of problems. • They key is that computers make AI a practical part of daily life. • In general there are three major fields of AI. • Until recently (the last 25 years or so) they absolutely couldn’t stand each other – and in some ways still can’t stand each other. • Think of them like warring clans bent on each other’s destruction, because for generations they have competed for resources as such. • As a result, if you find a “deep expert” in one one the three fields, the following things will likely be true. • They will be able to tell you what is really great about their clan’s approach. • Their techniques will probably be able to solve your problem, although how efficient and effective the solution will be may not be optimal. (Generally the tools from any clan can be bent into doing whatever you need done.) • They will be able to tell you why the other clans are worthless pigs and should be ignored. Conversely they rarely know what the other clans do well.

33. Statistical Logical Biological The three major clans of AI Bayesian Inference Surfaces & Splines Support Vector Machines Clustering Regression Models Fuzzy Logic Coevolution Ants & Swarms * Expert Systems Inference Engines Neural Networks Genetic Algorithms “BSB” models Heuristic systems *heretics

34. The Statistical Clan • Foundation • Based on mathematical processes dating back hundreds of years • Common Methods • Regression Methods – Data is fit to a equation of known form, then metrics (R, R2, covariance, etc.) are used to measure the quality of the fit. (See http://www.statsoft.com/textbook/stgrm.html ) • Bayesian Inference – Probabilities are calculated to confirm or reject future conditions based on previous (posterior) data. (See http://yudkowsky.net/bayes/bayes.html ) • Clustering Models – Common groupings of vectors are identified via search. (See http://www.statsoft.com/textbook/stcluan.html ) • Key Strength • Statistical methods work well for large data sets, allowing abstraction to a few key parameters like mean, standard deviation, moment and kurtosis. • Key Weakness • Statistical methods must make distribution assumptions to be effective. When these are not met statistical methods often break down. • Common Uses • Political and economic analysis (like trending, consumer metrics) • Transactional processing (like banking – especially fraud detection) • Medicine (drug testing, etc.)

35. The Logical Clan • Foundation • Based on principles established in philosophy and rhetoric (logical thinking), in some cases dating back to ancient Greece • Common Methods • Expert Systems – A framework of logic statements capture the experience of a person or people knowledgeable in a particular domain. See http://www.amazon.com/gp/reader/0824799275/ref=sib_dp_pop_ex/102-7050084-5599306?ie=UTF8&p=S00P#reader-link and read the entire “Excerpt.” • Rule Induction / Rule Abduction – Similar to expert systems, Rule Induction works from historical data to extract a framework of rules that describe the underlying knowledge structures present in the data (e.g. “Result R is a consequence of Rule A operating on facts F1..Fn”). Rule Abduction is similar but focuses on explanation of rules that exist (e.g. “Rule A is explained or supported by evidence E1..En”). See http://en.wikipedia.org/wiki/Logical_reasoning and also http://en.wikipedia.org/wiki/Abductive_reasoning • Key Strength • Logical methods are highly explainable to the general public. It is easy to see “why” a logical AI system behaves as it does. • Key Weakness • Logical systems often fail as the number of facts represented increases. A bivalent or Boolean system expands a 2n, where n = the number of conditions evaluated in the system. • Common Uses • Simple control systems (like your microwave or simple industrial automation) • Product selection guides (like those found on many websites or at the auto parts store)

36. The Biological Clan • Foundation • Biology, naturally! More specifically, computing principles derived from biology. These techniques are relatively young, with the oldest dating back 50 – 100 years. • Common Methods • Neural networks – Computational models of brain functions. Generally fall into synthesizing (interpolation) and classifying (categorization) models with hundreds of variations. See http://en.wikipedia.org/wiki/Neural_network as a good starting point. • Genetic algorithms – Maps complex searches into a “survival of the fittest” approach. See http://www.rennard.org/alife/english/gavintrgb.html and especially the Java applet http://www.rennard.org/alife/english/gavgb.html ) • Key Strengths • Biological methods are often faster than equivalent methods from other clans, often by orders of magnitude. • Biological methods are often more powerful solutions to highly complex problems. Biologically-inspired computing techniques are sometimes the only means to solve complex problems in reasonable amounts of time. • Key Weakness • Biological AI often lacks explainability provided by the other two clans. The solutions can often be verified but the process of obtaining the solution can be difficult to follow. • Common Uses • Pattern recognition (like vision systems, voice print analysis) • Optimization processes (like for industrial processes) • Complex control systems (like some advanced aircraft and engines)

37. The Heretics…Hybrid Systems • Foundation • In the last 15-20 years a significant expansion has occurred in hybrid systems containing elements of each major clan. • Common Methods • Fuzzy logic – Combines logical AI with statistical concepts. See http://www-bisc.cs.berkeley.edu/ and view the PowerPoint presentation http://www-bisc.cs.berkeley.edu/BISCProgram/PBIDS.ppt • Heuristic systems – Logical AI combined with biological concepts. See http://www.mpib-berlin.mpg.de/en/forschung/abc/forschungsziele.htm • Swarm computing – Uses independent biological agents (“ants”) to statistically explore potential solutions. See http://en.wikipedia.org/wiki/Swarm_intelligence and linked sites as needed. • Key Strengths • Combines the best elements of each field to make even more powerful AI systems. • Key Weakness • “A camel is a horse designed by committee.”” – Anonymous • If poorly constructed, a hybrid will bring together the worst elements of each clan rather than the best ones. • Common Uses • Few so far…see http://proceeed.statsoft.com for one commercial hybrid.

38. If you learn nothing else from this class, learn this: USE THE RIGHT TOOL FOR THE JOB!!!! Memorize the strengths and weaknesses of each clan and use that to your advantage!

39. Introduction to Artificial Intelligencefor Bradley University – CS 521 Anthony (Tony) J. Grichnik Visiting Scientist to Bradley University Caterpillar Inc.