artificial engineering the future of computing
- 1. A.I. IS THE FUTURE OF COMPUTING!
- 2. Defining Artificial Intelligence “AI is the study of how to make computers do things which, at the moment, people do better.” (Rich) “AI is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligent human behavior. understanding language, reasoning, solving problems, and so on.” (Barr) “AI is the study of ideas which enable computers to do things which make people seem intelligent.” (Winston) AI is the study of intelligence using the ideas and methods of computation.” (Fahlman)
- 3. “A bridge between art and science” (McCorduck) “Tesler’s Theorem: AI is whatever hasn’t been done yet.” (Hofstadter) “AI is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior, and with the creation of artifacts that exhibit such behavior.” (Shapiro) Defining Artificial Intelligence
- 4. An Attempted Definition AI – the branch of computer science that is concerned with the automation of intelligent behavior (Luger & Stubblefield) AI is based on theoretical and applied principles in computer science like Data structures for knowledge representation Algorithms of applying knowledge Languages for algorithm implementation Problem What is Intelligence? This course discusses The collection of problems and methodologies studied by AI researchers
- 5. Brief Early History of AI Aristotle – 2000 years ago The nature of world – changes frequently Logics – certain propositions can be said as “true” Modus ponens and reasoning system Eg:- If we know that “All men are mortal” & “ Socrates is a man”, then we can conclude that “Socrates is mortal”. Copernicus – 1543 Split between human mind and its surroundings – our ideas about the world are distinct from its appearance Descrates (1680) Developed the concepts of Thought and mind Separate mind from physical world – must find a way to reconnect both Mental process can be formalized by mathematics
- 6. Modern History Formal logic Leibniz – introduced formal logic & constructed a machine for automating its calculation Boole – mathematical formalization of the laws of logic (Boolean Algebra) Turing Frege – first-order predicate calculus Graph theory Euler – represents the structure of relationships in the world State space search
- 7. What is AI? Think like humans Think rationally Act like humans Act rationally The science of making machines that:
- 8. Scientific Goals of AI AI seeks to understand the working of the mind in mechanistic terms, just as medicine seeks to understand the working of the body in mechanistic terms. The mind is what the brain does. -- Marvin Minsky The strong AI position is that any aspect of human intelligence could, in principle, be mechanized
- 9. 9 The Turing Test If the interrogator cannot distinguish the machine from the human, then the machine may be assumed to be intelligent. The interrogator •cannot see and speak to either • does not know which is actually machine •May communicate with them solely by textual device
- 10. Acting Like Humans? Turing (1950) ``Computing machinery and intelligence'' ``Can machines think?'' ``Can machines behave intelligently?'' Operational test for intelligent behavior: the Imitation Game Predicted by 2000, a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, language understanding, learning
- 12. Brains ~ Computers 1000 operations/sec 100,000,000,000 units stochastic fault tolerant evolves, learns 1,000,000,000 ops/sec 1-100 processors deterministic crashes designed, programmed
- 13. Institute of Computing Areas of Artificial Intelligence Perception Machine vision Speech understanding Touch ( tactile or haptic) sensation Natural Language Processing Natural Language Understanding Speech Understanding Language Generation Machine Translation
- 14. Intelligent Agents Intelligence is reflected by the collective behaviors of large numbers of very simple interacting, semi- autonomous individuals or agents. Agents are autonomous or semi-autonomous Agents are “situated” – knowledge is limited to the task performed Agents are interactional – cooperate on a task The society of agents is structured – final solution will be seen as collective and cooperative
- 15. Areas of Artificial Intelligence ... Robotics Planning Expert Systems Machine Learning Theorem Proving Symbolic Mathematics Game Playing
- 16. Perception Machine Vision: It is easy to interface a TV camera to a computer and get an image into memory; the problem is understanding what the image represents. Vision takes lots of computation; in humans, roughly 10% of all calories consumed are burned in vision computation. Speech Understanding: Speech understanding is available now. Some systems must be trained for the individual user and require pauses between words. Understanding continuous speech with a larger vocabulary is harder. Touch ( tactile or haptic) Sensation: Important for robot assembly tasks.
- 17. Robotics Although industrial robots have been expensive, robot hardware can be cheap: Radio Shack has sold a working robot arm and hand for $15. The limiting factor in application of robotics is not the cost of the robot hardware itself. What is needed is perception and intelligence to tell the robot what to do; ``blind'' robots are limited to very well-structured tasks (like spray painting car bodies).
- 18. Natural Language Understanding Natural languages are human languages such as English. Making computers understand English allows non- programmers to use them with little training. Applications in limited areas (such as access to data bases) are easy. Natural Language Generation: Easier than NL understanding. Can be an inexpensive output device. Machine Translation: Usable translation of text is available now. Important for organizations that operate in many countries.
- 19. Planning Planning attempts to order actions to achieve goals. Planning applications include logistics, manufacturing scheduling, planning manufacturing steps to construct a desired product. There are huge amounts of money to be saved through better planning.
- 20. Expert Systems Expert Systems attempt to capture the knowledge of a human expert and make it available through a computer program. There have been many successful and economically valuable applications of expert systems. Benefits: Reducing skill level needed to operate complex devices. Diagnostic advice for device repair. Interpretation of complex data. “Cloning'' of scarce expertise. Capturing knowledge of expert who is about to retire. Combining knowledge of multiple experts. Intelligent training.
- 21. Expert systems are constructed by obtaining the knowledge of a human expert and coding it into a form that a computer may apply to similar problems. domain expert provides the necessary knowledge of the problem domain. knowledge engineer is responsible for implementing this knowledge in a program that is both effective and intelligent in its behavior.
- 22. Deficiencies of Current Expert Systems 1. Difficulty in capturing “deep” knowledge of the problem domain 2. Lack of robustness and flexibility 3. Inability to provide deep explanations 4. Difficulties in verification may be serious when expert systems are applied to air traffic control, nuclear reactor operations, and weapon systems. 5. Little learning from experience
- 23. Theorem Proving Proving mathematical theorems might seem to be mainly of academic interest. However, many practical problems can be cast in terms of theorems. A general theorem prover can therefore be widely applicable. Examples: Automatic construction of compiler code generators from a description of a CPU's instruction set. J Moore and colleagues proved correctness of the floating-point division algorithm on AMD CPU chip.
- 24. Symbolic Mathematics Symbolic mathematics refers to manipulation of formulas, rather than arithmetic on numeric values. Algebra Differential and Integral Calculus Symbolic manipulation is often used in conjunction with ordinary scientific computation as a generator of programs used to actually do the calculations. Symbolic manipulation programs are an important component of scientific and engineering workstations. > (solvefor '(= v (* v0 (- 1 (exp (- (/ t (* r c))))))) 't) (= T (* (- (LOG (- 1 (/ V V0)))) (* R C)))
- 25. Game Playing Games are good vehicles for AI research because most games are played using a well-defined set of rules board configurations are easily represented on a computer Games can generate extremely large search spaces. Search spaces are large and complex enough to require powerful techniques(heuristics) for determining what alternatives to explore in the problem space.
- 26. Characteristics of A.I. Programs Symbolic Reasoning: reasoning about objects represented by symbols, and their properties and relationships, not just numerical calculations. Knowledge: General principles are stored in the program and used for reasoning about novel situations. Search: a ``weak method'' for finding a solution to a problem when no direct method exists. Problem: combinatoric explosion of possibilities. Flexible Control: Direction of processing can be changed by changing facts in the environment.
- 27. Symbolic Processing Most of the reasoning that people do is non-numeric. AI programs often do some numerical calculation, but focus on reasoning with symbols that represent objects and relationships in the real world. Objects. Properties of objects. Relationships among objects. Rules about classes of objects. Examples of symbolic processing: Understanding English: (show me a good chinese restaurant in los altos) Reasoning based on general principles: if: the patient is male then: the patient is not pregnant Symbolic mathematics: If y = m*x+b, what is the derivative of y with respect to x?
- 28. Knowledge Representation It is necessary to represent the computer's knowledge of the world by some kind of data structures in the machine's memory. Traditional computer programs deal with large amounts of data that are structured in simple and uniform ways. A.I. programs need to deal with complex relationships, reflecting the complexity of the real world. Several kinds of knowledge need to be represented: Factual Data: Known facts about the world. General Principles: ``Every dog is a mammal.'' Hypothetical Data: The computer must consider hypotheticals in order to reason about the effects of actions that are being contemplated.
- 29. Representation Systems What is it? Capture the essential features of a problem domain and make that information accessible to a problem-solving procedure Measures Abstraction – how to manage complexity Expressiveness – what can be represented Efficiency – how is it used to solve problems Trade-off between efficiency and expressiveness
- 30. Different representations of the real number π. Representation of
- 31. Logical Clauses describing some important properties and relationships General rule A blocks world Block World Representation
- 32. Logical predicates representing a simple description of a bluebird. Bluebird Representations Semantic network description of a bluebird.
- 33. State Space Search State space State – any current representation of a problem State space All possible state of the problem Start states – the initial state of the problem Target states – the final states of the problem that has been solved State space graph Nodes – possible states Links – actions that change the problem from one state to another State space search Find a path from an initial state to a target state in the state space Various search strategies Exhaustive search – guarantee that the path will be found if it exists Depth-first Breath-first Best-first search heuristics
- 34. Portion of the state space for tic-tac-toe. Tic-tac-toe State Space
- 35. State space description of the automotive diagnosis problem. Auto Diagnosis State Space
- 36. Assignment Create and justify your own definition of artificial intelligence? Describe whether or not you think it is possible to a computer to understand and use a natural language? Describe your own criteria for computer software to be considered “intelligent”. Discuss why you think the problem of machines "learning" is so difficult? List two potentially negative effects on society of the development of AI techniques.