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Artificial Intelligence (AI): Trying to Get Computers to Th PowerPoint Presentation, PPT - DocSlides Download myesha-ticknor | 2017-08-10 | General Stephany Coffman-Wolph, PhD. West Virginia University Institute of Technology (WVU Tech). Assistant Professor, Dept. of Computer Science & Information Systems. Quick Intro on Me. Assistant Professor, WVU Tech, Department of Computer Science and Information Systems. ID: 577622
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Presentations text content in Artificial Intelligence (AI): Trying to Get Computers to Th PowerPoint Presentation, PPT - DocSlides Slide1 Artificial Intelligence (AI): Trying to Get Computers to Think Like Us Stephany Coffman-Wolph, PhDWest Virginia University Institute of Technology (WVU Tech)Assistant Professor, Dept. of Computer Science & Information Systems Slide2 Quick Intro on Me Assistant Professor, WVU Tech, Department of Computer Science and Information Systems Founding member and current co-faculty advisor of AWESOME (Association of Women Engineers, Scientists, Or Mathematician Empowerment) PhD from Western Michigan University, Kalamazoo MI MS from Bowling Green State University, Bowling Green OH BSE from University of Michigan, Ann Arbor MI Dissertation Title: â Fuzzy Search Strategy Generation for Adversarial Systems Using Fuzzy Process Particle Swarm Optimization, Fuzzy Patterns, and a Hunch Factor â Master â s Project: â Predicting Future Class Enrollment Using Neural Networks and other Methods â Slide3 Abstract AI is a field older than most realize â the term was coined in the mid 1950s. The field is comprised of many subfields but the main focus is on building intelligent entities. In order to achieve this goal many subcomponents need to be built, including methods for assisting computers to think like humans. Fuzzy logic is one mathematical method of doing so. This talk will briefly introduce both AI and fuzzy logic, as well as discuss the application of fuzzy logic into algorithms to encompass more human-like decision processes. Adding a â hunch-like â element can further enhance an algorithm â s ability to mimic human decision making. Slide4 What I Am Going to Talk About? Brief Introduction to AI and the Turing Test Brief Introduction to Fuzzy Logic Framework for Creating Fuzzy Algorithms The Hunch Factor Applications of Fuzzy Algorithms and Future Work Slide5 The First Electronic Computers Created in the late 1930s and early 1940s during World War 2 (Pictured ENIAC) Slide6 Artificial Intelligence (AI) Slide7 Artificial Intelligence Movies and TV Shows Slide8 Facts on AI Artificial Intelligence is a multifaceted field focused on mimicking human behavior/mannerisms using computer algorithms AI attempts to both understand how we think and how to build intelligent entities AI became a field soon after World War II The term â Artificial Intelligence â was coined in the 1950s Slide9 4 Categories of Artificial Intelligence Thinking Humanly Thinking Rationally Acting Humanly Acting Rationally
Slide10 Alan Turing Subject of the Film: The Imitation Game & Played by Benedict Cumberbatch Slide11 Acting Humanly: The Turing Test Proposed by Alan Turing in the 1950s Designed to provide an operational definition of machine intelligence The basics of the test: A human interrogator poses written questions. If the interrogator cannot distinguish between the written responses of a human or a computer - then we consider the computer to be intelligent Slide12 The Total Turing Test Note: the original avoided physical simulation of a human This version includes a video signal so that the interrogator can test the subject â s perceptual abilities Also this version allows the interrogator to â give â the test subject physical objects Slide13 The 6 Disciplines of AI Between the two Turing Test versions, it was determined that to be considered intelligent, the computer would need 6 capabilities - each became a discipline/area of AI Natural language processing: ability to communicate Knowledge representation: store what it knows/hears Automated reasoning: use stored information to answer questions and draw new conclusions Machine learning: adapt to new circumstances and detect/extrapolate patterns Computer vision: perceive objects Robotics: manipulate objects and move Slide14 The 6 Disciplines of AI My personal research focuses on 3 of the 6 Natural language processing: ability to communicate Knowledge representation: store what it knows/hears Automated reasoning: use stored information to answer questions and draw new conclusions Machine learning: adapt to new circumstances and detect/extrapolate patterns Computer vision: perceive objects Robotics: manipulate objects and move Slide15 What is Fuzzy Logic? Slide16 Fuzzy Logic Introduced in the 1960s by L. Zadeh as an expansion of Boolean logic Slide17 Fuzzy Logic Defined Extremely popular in control systems (e.g., toasters, high-speed train controls, camera filters) A set of rules and techniques for dealing with logic beyond a two-value (yes/no, on/off, true/false) system An abstraction of two-value logic designed to mimic a more human like approach to decision making Allows for not only multiple values but also an overlap of values between fuzzy sets Slide18 Fuzzy Logic = Degrees or Ranges of Values Slide19 Membership Functions Membership functions are used to represent the degree of membership an element has to a Fuzzy set and the values range from 0 to 1. 0 represents not in the set 1 represents entirely in the set Numbers between 0 and 1 represent some level of being part of the set Slide20 Fuzzification Slide21 Fuzzification A method of adding abstraction to data, operators, or a concept Fuzzification of data is the process of taking â raw â /non-fuzzy data and converting it into fuzzy data Fuzzification of operators is the process of converting a mathematical, logical, or comparative operator to its fuzzy counterpart, which operates on fuzzy sets instead of pure numbers Fuzzification of concepts, the most difficult of the three, is the conversion of an idea into a corresponding fuzzy version These three techniques, together, can be used within my framework to create a fuzzy algorithm Slide22 Fuzzy Algorithm Slide23 Fuzzy Algorithm A fuzzy algorithm goes beyond simply fuzzy data and includes fuzzified operators and/or concepts within the algorithm It is trickier to determine if the algorithm is fuzzy when only the operators have been fuzzified because of the fine line between an operator and a fuzzy operator â even when operating on fuzzy data A fuzzy operator is differentiated from the basic operator by being re-written to accommodate the meaning of the fuzzy data A fuzzy operator operates on fuzzy sets (not straight numbers) When the algorithm is modified to include a fuzzy concept, the algorithm is undeniable a fuzzy algorithm Slide24 Fuzzification of Concept Fuzzification of a concept is the most challenging of the 3 presented mainly because a concept can be difficult to define Concept = an essential element from the algorithm Like the fuzzification of data or an operator, the purpose is to create an abstract version of the non-fuzzy â concept â Fuzzification of concepts allows for more human-like decisions and algorithm processing Humans fuzzify concepts without even thinking about it. The human brain categorizes & sees connections between items easily. (Computers fundamentally operate on a No/Yes, False/True, 0/1 level) Slide25 Example: Fuzzification of Sorting A common algorithm is the sorting of a list into alphabetical or numerical order Example Grocery List: bananas, crackers, grapes, potatoes, cheese, apples, pretzels, and powdered sugar Sorted Alphabetical: apples, bananas, cheese, crackers, grapes, potatoes, powdered sugar, and pretzels We can fuzzify the concept of a sorted list by defining a fuzzy sorted list: Contains all the elements in the original list (no lost of information) Sorting is not completely alphabetical â just by the first letter (i.e., grouping the items together by the 26 letters in the alphabet) Fuzzy Sorted List: apples, bananas, crackers, cheese, grapes, potatoes, pretzels, and powdered sugar Slide26 The Hunch Factor Slide27 The Hunch Factor Mathematical attempt at mimicking the human internal intuitive decision process Supplies a human â hunch-likeâ element into the decision-making processes of an algorithmActs as a fuzzy learning component and is continually altered during algorithm executionAlso provides memory for the algorithm The Hunch Factor is stored as a fuzzy value and, thus, represented by a membership function Slide28 The Hunch Factor: How It Works
Guessing well â the hunch factor â encourages â continued behaviors Guessing poorly â the hunch factor â influences â the system to try a different area of the solution space The hunch Factor changes based on observed information and can be manipulated in various ways Height Increase, Width Decrease â More Confidence Height decrease, Width Increase â Less Confidence Slide29 Applications and Future Work Slide30 Successful Applications: Fuzzy Process Particle Swarm Optimization (FP2SO) (with and without the hunch) Fuzzy Patterns Based Approach for Environment Analysis in Adversarial Games Fuzzy Search Strategy Generation for Adversarial Systems Using Fuzzy Process Particle Swarm Optimization, Fuzzy Patterns, and a Hunch Factor Fuzzification of the Special Simplex Method for the Transportation Problem Fuzzification of the Golden Ratio Search (and preliminary research on other search algorithms) Fuzzification of Simple Sorting Algorithms Slide31 Results and Key Observations A fuzzy algorithm: Often performs better than the traditional algorithm because it takes advantage of the quickness of integer calculations over double-precision calculations Finds multiple â good enough â solutions within a controllable range faster than the traditional algorithms Is useful when the end user would prefer several similar answers to select from and not just one answer Slide32 Future Work: Questions to be Answered Continue exploration into the use of Fuzzy Algorithms What algorithms benefit from being made fuzzy and how can the gains be measured? What algorithm characteristics make it a good candidate for becoming a fuzzy algorithm? How do these characteristics impact the solution of a problem when a fuzzy algorithm is used to solve it? Refining and further investigation into the Hunch Factor What membership functions work best? In what situations does it work best? What manipulation works best? etc. Slide33 Picture Citations (in order of appearance) https://www.technologyreview.com/s/601519/how-to-create-a-malevolent-artificial-intelligence/ https://fahmirahman.wordpress.com/2011/04/19/the-history-of-the-eniac-computer/ https://en.wikipedia.org/wiki/ENIAC http://themadraspost.blogspot.com/2014/02/unknown-electronic-do-you-know-which-is.html http://www.computerhistory.org/revolution/birth-of-the-computer/4/78 http://www.columbia.edu/cu/computinghistory/eniac.html http://www.phillyvoice.com/70-years-ago-six-philly-women-eniac-digital-computer-programmers/ http://www.fanpop.com/clubs/star-trek-the-next-generation/images/31158790/title/data-wallpaper http://www.dailymail.co.uk/tvshowbiz/article-2762469/Anthony-Daniels-reveals-initially-refused-play-C-3PO-voice-role-new-Star-Wars-movie.html https://s-media-cache-ak0.pinimg.com/originals/28/91/aa/2891aa51b8fbb124a1478ae03a8008aa.jpg http://coralis101.deviantart.com/art/Wall-E-and-EVE-Icons-97034881 http://also.kottke.org/misc/images/super-toy-teddy.jpg https://www.cloudynights.com/uploads/profile/photo-224707.jpg?_r=0 https://www.mathworks.com/company/newsletters/articles/alan-turing-and-his-connections-to-matlab.html http://www.impawards.com/2014/imitation_game_ver6.html http://zadeh.narod.ru/Lotfi_Zadeh_L.jpg
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