Artificial intelligence Ch1
- 2. ObjectiveObjective To understand AI and related concept (Knowledge base and intelligent system) Understand the components of AI system Get a feel of application areas of AI Get a feel of scholars view to define AI Briefly discuss the difference between Expert system and other systems 2
- 3. Data, Information, and Knowledge • Data: Unorganized and unprocessed facts; static; a set of discrete facts about events • Information: Aggregation of data that makes decision making easier • Knowledge is derived from information in the same way information is derived from data; it is a person’s range of information • What is Data and Information? Are they different from Knowledge? 3
- 4. What is Knowledge • Knowledge includes facts about the real world entities and the relationship between them – It is an Understanding gained through experience – familiarity with the way to perform a task – an accumulation of facts, procedural rules, or heuristics • Characteristics of Knowledge: – It is voluminous in nature and requires proper structuring. – It may be incomplete and imprecise. – It may keep on changing (dynamic). 4
- 5. Types (Categorization) of Knowledge • Shallow (readily recalled) and deep (acquired through years of experience) – necessary to make decision/solve problem in complex situations • Procedural (repetitive, stepwise) versus Episodically (grouped by episodes or cases) • Explicit (already represented/documented) and tacit (embedded in the mind) – Up to 95% of information is preserved as tacit knowledge 5
- 6. Explicit and Tacit Knowledge • Explicit (knowing-that) knowledge: – knowledge codified and digitized in books, documents, reports, memos, etc. • Tacit (knowing-how) knowledge: – knowledge embedded in the human mind through experience and jobs 6
- 7. Knowledge base • Knowledge base is used to store facts and rules. • In order to solve problems, the computer needs an internal model of the world. – This model contains, for example, the description of relevant objects and the relations between these objects. – All information must be stored in such a way that it is readily accessible. – Various methods have been used for KR, such as logic, semantic networks, frames, scripts, etc... 7
- 8. Knowledge base systems (KBSs) • Deal with treating knowledge and ideas on a computer. – Emphases to the importance of knowledge. • Use inference to solve problems on a computer. – Knowledge-based systems describes programs that reason over extensive knowledge bases. • Have the ability to learn ideas so that they can obtain information from outside to use it appropriately. – The value of the system lies in its ability to make the workings of the human mind understandable and executable on a computer. 8
- 9. Attributes of KBS •Learn –The more you use knowledge, the smarter they get and the smarter you get, too •Improve with use –Knowledge is enhanced rather than depleted when used and they grow up instead of being used up •Anticipate –Knowing what you want, KBS recommend what you want next •Interactive –There is two-way communication between you and KBS •Remember –KBS record and recall past actions to develop a profile •Customize –KBS offer unique configuration to your individual specifications in real time at no additional cost 9
- 10. AI vs. KBS • Knowledge based system is part of Artificial Intelligence • AI also requires extensive knowledge of the subject at hand. – AI program should have knowledge base – Knowledge representation is one of the most important and most active areas in AI. – AI programs should be learning in nature and update its knowledge accordingly. 10
- 11. Intelligence Intelligence is the capability of observing, learning, remembering and reasoning. Intelligence is a general mental capability that involves the ability to reason, plan, solve problems, think abstractly, comprehend ideas and language, and learn. Intelligence draws on a variety of mental processes, including memory, learning, perception, decision- making, thinking, and reasoning. Memory of Intelligent system is used to store knowledge base which is the key for success for artificial intelligent systems. AI attempts to develop intelligent agents. 11
- 12. Characteristics of Intelligent system • Use vast amount of knowledge • Learn from experience and adopt to changing environment • Interact with human using language and speech • Respond in real time • Tolerate error and ambiguity in communication 12
- 13. Human Intelligence How do people Reason? They create categories They use specific rules – if ‘a’ then ’b’ if ‘b’ then ‘c’ a b c They use Heuristics - “Rule of thumb” They use Past Experience – “CASES” - Similarities of current and previous case - Store cases using key attributes They Use “Expectations” How does our brain work when we solve a problem? Do we think it over and suddenly find an answer? What do we do when solving a complicated factorization problem, a puzzle or a mystery? 13
- 14. Artificial Intelligence • AI is the branch of Computer Science that deals with ways of: – representing knowledge using symbol rather than numeric value and with rule-of-thumb and method of processing information • AI is the effort to develop computer based system that behave as human. –Such system should be able to learn Natural Language. –Able to do text processing, communicate in natural language and speech 14
- 15. Views of AI • AI is found on the premise that: – workings of human mind can be explained in terms of computation, and – computers can do the right thing given correct premises and reasoning rules. Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally 15
- 16. Views of defining AI • What is AI (Artificial Intelligence) – Different scholars define AI differently (A) AI as a system that think humanly (C) AI as a system that Act humanly (B) AI as a system that think rationally (D) AI as a system that Act rationally Concerned with thought processing and reasoning Concerned with behaviors of agents 16
- 17. Views of defining AI (A) AI as a system that think humanly (C) AI as a system that Act humanly (B) AI as a system that think rationally (D) AI as a system that Act rationally measures success of AI in terms of human being performance measures success of AI in terms of ideal concept of intelligence (rationality) 17
- 18. Thinking humanly: The Cognitive Modeling • Reasons like humans do –Programs that behave like humans • Requires understanding of the internal activities of the brain –see how humans behave in certain situations and see if you could make computers behave in that same way. Example. write a program that plays chess. –Instead of making the best possible chess-playing program, you would make one that play chess like people do. 18
- 19. Acting humanly: The Turing Test Can machines act like human do? Can machines behave intelligently? • Turing Test: Operational test for intelligent behavior – do experiments on the ability to achieve human-level performance, – Acting like humans requires AI programs to interact with people • Suggested major components of AI: knowledge, reasoning, language understanding, learning 19
- 20. Acting humanly: Turing Test Turing (1950) on his famous paper "Computing machinery and intelligence": "Can machines think?" "Can machines behave intelligently?" Operational test for intelligent behavior: the Imitation Game Predicted that by 2000, a machine might have a 30% chance of fooling a person for 5 minutes Anticipated all major arguments against AI in following 50 years Active areas of research to achieve this: Machine learning, NLP, Computer vision, etc 20
- 21. Thinking Rationally: The Laws of Thought • A system is rational if it thinks/does the right thing through correct reasoning. • Aristotle: provided the correct arguments/ thought structures that always gave correct conclusions given correct premises. – Abebe is a man; all men are mortal; therefore Abebe is mortal – These Laws of thought governed the operation of the mind and initiated the field of Logic. 21
- 22. Acting rationally: The rational agent • Doing the right thing so as to achieve one’s goal, given one’s beliefs. • AI is the study and construction of rational agents (an agent that perceives and acts) • Rational action requires the ability to represent knowledge and reason with it so as to reach good decision. – Learning for better understanding of how the world works 22
- 23. History of AI •Formally initiated in 1956 and the name AI was coined by John McCarthy. •The advent of general purpose computers provided a vehicle for creating artificially intelligent entities. –Used for solving general-purpose problems •Which one is preferred? –General purpose problem solving systems –Domain specific systems 23
- 24. History of AI • Development of knowledge-based systems: the key to power – Performance of general-purpose problem solving methods is weak for many complex domains. – Use knowledge more suited to make better reasoning in narrow areas of expertise (like human experts do). – Early knowledge intensive systems include: • The Dendral program (1969): solved the problem of inferring molecular structure (C6H13NO2). • MYCIN (1976): used for medical diagnosis. • etc. 24
- 25. History of AI • Shifts from procedural to declarative programming paradigm. – Rather than telling the computer how to compute a solution, a program consists of a knowledge base of facts and relationships. – Rather than running a program to obtain a solution, the user asks question so that the system searches through the KB to determine the answer. • Simulate human mind and learning behavior (Neural Network, Belief Network, Hidden Markov Models, etc. ) 25
- 26. Applications of AI and KBS Solving problems that required thinking by humans: •Playing games (chess, checker, cards, ...) •Proving theorems (mathematical theorems, laws of physics, …) •Classification of text (Politics, Economic, sports, etc,) •Writing story and poems; solving puzzles •Giving advice in medicine, law, … (diagnosing diseases, consultation, …) 26
- 27. How to make computers act like humans? The following sub-fields are emerged • Natural Language processing (enable computers communicate in human language, English, Amharic, ..) • Knowledge representation (schemes to store information, both facts and inferences, before and during interrogation) • Automated reasoning (use stored information to answer questions and to draw new conclusions) • Machine learning (adapt to new circumstances and accumulate knowledge) • Computer vision (recognize objects based on patterns in the same way as the human visual system does) • Robotics (produce mechanical device capable of controlled motion; which enable computers to see, hear & take actions) • Is AI equals human intelligence ? 27
- 28. Programming paradigms • Each programming paradigms consists of two aspects: –Methods for organizing data/knowledge, –Methods for controlling the flow of computation • Traditional paradigms: Programs = data structure + algorithm • AI programming paradigms: Programs = knowledge structure + inference mechanism 28
- 29. Basic Kinds of Systems • System is a set of components that interact to each other in a logical way to achieve specific goals. • There are different types of system based on the services, the user type, and the method of operations • Some of the systems includes: – Information Systems – Database Management System – Information Retrieval System – Expert System 29
- 30. Expert system • An expert system, also known as a knowledge based system, • a computer program that contains some of the subject- specific (domain specific) knowledge of one or more human experts. • a system with two basic components: • Knowledge base, which model the knowledge of an expert in the area under consideration • Inference engine • When it is used by non expert user, it can serve as an expert that guide the user to make an expert decision. (doctors, engineers, lawyers, etc) 30
- 31. Expert system – Examples: • Dendral, MYCIN, PUFF, ELIZA, BTDS, etc. • Dendral expert system: – The primary aim to aid organic chemists with identification of unknown organic molecules by analyzing information from mass spectrometry graphs and the knowledge of chemistry 31
- 32. Expert system • MYCIN: • Written in LISP around 1970s and derived from Dendral expert system • was designed to diagnose infectious blood diseases and recommend antibiotics, with the dosage adjusted for patient's body weight • It would query the physician/patient running the program via a long series of simple yes/no or textual questions. • At the end, it provides • a list of possible cause bacteria ranked from high to low based on the probability of each diagnosis, • its confidence in each diagnosis' probability • the reasoning behind each diagnosis • It has around ~50 rules 32
- 33. Expert system • PUFF: – PUFF can diagnose the presence and severity of lung disease and produce reports for the patient's file – Is an Expert System that interprets lung function test data and has become a working tool in the pulmonary physiology lab of a large number of hospital 33
- 34. Expert system • ELIZA : • ELIZA is a very well-known artificial intelligence program designed to emulate a Rogerian psychotherapist. • The basic elements of Carl Rogers' new way of therapy was to have a more personal relationship with the patient, to help the patient reach a state of realization that they can help themselves • ELIZA was showcased for a number of years at the MIT AI Laboratory. • ELIZA has no reasoning ability, cannot learn • ELIZA only appears to understand because "she" uses canned responses based on keywords, as well as string substitution 34
- 35. THE DIFFERENCE BETWEEN HUMAN BEINGS AND ARTIFICIALLY INTELLIGENT SYSTEMS Human Brain Natural device Self-willed and creative Basic unit – neuron Storage device – electrochemical Low crunching Advanced detective reasoning Lower speed Emotion-driven Limited volume Fuzzy logic Computers Non-natural device Limited creativity Basic unit – RAM Storage device is electromechanical High crunching Elementary detective reasoning High speed Non-emotional Higher volume Numeric (binary) logic 35
- 36. What computers can do better • Numeric computations • Information storage • Repetitive operation 36
- 37. How do AI differ from Human Intelligence • The successful AI systems are neither artificial nor intelligent • The successful AI is based on: – Human expertise – Knowledge – Selected reasoning pattern 37
- 38. Problems of AI: • The following points are taken as drawbacks of Artificially Intelligent systems: – AI do not come up with new and novel solutions – Existing AI systems try to reproduce the expertise of humans, but do not behave like human experts – Lack of common-sense – Reasoning: the human intelligence performs better in this respect 38