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Transcript
Artificial Intelligence Where it has been and where it should be going AI is conceived….(slowly) 6th Century - First mention of any type of automata in Homer’s Iliad, with the mention of Hephaestus’, the God of Fire, workshop. 5th Century- Aristotle codifies the first formal deductive reasoning system: Syllogistic logic. 15th and 16th Century- Mechanical clocks first appear in Europe. 1642- Pascal invents the mechanical calculator, the “Pascaline.” 1673- Leibniz improves upon Pascal’s calculator and envisions a “universal calculus of reasoning” to decide arguments mechanically. 19th Century- AI in literature: Hoffman’s The Sandman, Goethe’s Faust, and Shelley’s Frankenstein. AI is articulated… (sort of) Babbage produces a small working model of the Difference Engine and persuaded the British government to finance a larger model. Babbage couldn’t return what he promised. Babbage ignores the Difference Engine in pursuit of the Analytical Engine, a machine that could do not only arithmetical calculations but also analysis and reasoning. The AI story continues Ava Lovelace, Byron’s daughter, takes an interest in Babbage’s work. 1890- Herman Hollerith performs the US census using a machine that encoded information using punch cards. 1923- The term “robot” is introduced into the English language in Karel Capek’s play, Rossum’s Universal Robots. Turing (this guy didn’t smile very often so this pic was a find) Turing Test: Any machine that could hold a conversation with a person who cannot see who he is talking and cannot distinguish whether it is a machine or human is intelligent. Universal Machine: A machine that has the ability to learn any number of tasks and perform them. (Humans are a universal machine or at least close enough.) Intelligence comes from organization. Cybernetics Communication within both living being and machines Norbert Wiener says this is the source of intelligent behavior. This is obviously not a process limited to human beings and thus intelligence is not a characteristic limited humans. John Von Neumann (no luck on a smile from this guy) 1942- Von Neumann comes to Princeton convinced that high-speed computing is the essential to the future of science and mathematics, and sets to work on the design of the IAS machine. The IAS’s predecessors, ENIAC and EDVAC, were designed for specific tasks (nautical and bombing tables). The IAS could be manipulated to solve a wide class of problems if only they were input properly. 1951- “anything that can be completely and unambiguously described , anything that can be unambiguously put into words is ipso facto realizable by a suitable finite neural network.” The Dartmouth Conference (Dartmouth College… artists rendering) The term “artificial intelligence” is adopted, to the chagrin of many of those present McCarthy proposed the idea of a programming language for use in problems requiring conjecture and self-reference. “We are a long way from even knowing what questions to ask or what aspects to abstract for theory. The present need is for a large population of concrete systems that are completely understood…” The InformationProcessing Model Redefine the computer as an information processor not a calculating machine. symbolic-functioning capabilities of computers. a symbol is something that could stand for an object and its meaning, its uses, and all other pertinent information about it. Games kerplunk -> <- obvious The games computers played at first were the ones that were most interesting from a computational standpoint: chess and checkers. Samuel considered a different approach, instead of imitating a human thought process he wanted to enlist a new thought process all together. “I think you study the way people solve problems to get an insight into what the real problem is…And then you sit down and say, ‘Okay, given the technology available…how best can we solve the problem?” “The machine, therefore, played a perfect ending without one misstep. In the matter of the end game, I have not had such competition from any human being since 1954, when I lost my last game.” The Game: Chess Other programs of the day took advantage of the speed of the computer processor, using it to search the field of possible board configurations after each of its various possible moves and then choosing the optimal configuration. “if all you have is a machine that bests humans by means of speed alone, what really do you have?” Economizing search strategies among a set of possibilities has proven a key element in Artificial Intelligence to date. Although we cannot mimic bird flight, we can still fly anywhere we want. Side Note: In 1997 IBM’s Deep Blue defeated Garry Kasparov, the world’s reigning chess champion at that time. Vs. Pseudo-Intelligent Systems (Cyrus Whitney far cooler than either of the two listed below) PARRY – Imitated a paranoid patient and was made to be interviewed by psychiatrists PROSPECTOR – Analyzes geologic data and makes interpretations and predictions. LUNAR – Contained a wealth of information about moon rocks and was able to answer questions about them written in plain English CYRUS – Was fed news articles about Secretary of State Cyrus Vance and was able to be interviewed as if it were him. They All Fall Short While impressive these systems only manage to simulate a small range of thinking. None of them are able to provide any meaning to the data they present. Still a long way from Turing’s Universal machine. <- Yao Ming Ryan Foss-> Robotics Isaac Asimov this guy wrote a lot of stories about robots… but never met one In the late 1960s three major robotics projects got started in the United States. Each project had its own particular flavor but the general idea was to create some sort of independent thinking, interacting, machine. Robotics (a term coined by Isaac Asimov) was the practical application of AI, and it ended up having a significant impact on the theory. Requirements for AI were rewritten, and a system now had to be able to demonstrate that it had an internal model of the world, be clever enough to answer questions on a wide range of topics (analytical and common), acquire information from the external world and update its model, and adhere to some goals within its range of physical limitation. This new definition posed 3 major problems: How to incorporate and generalize observations, how to represent non-physical real world data (say emotions), how to get knowledge about the world. Language Sorry no clever pictures for this slide One of the most obvious applications of a narrowly defined/capable AI system is interpreting one language into another. “The pen is in the box.” “The box is in the pen.” AI therefore cannot be simply a informationretrieval system, rather it must be something more like question-answering system that not only processes data but also amends and draws inferences from the input. Applied AI DENDRAL- chemist’s assistant in interpreting the data from mass spectrography that operates at the level of a Chemistry Ph.D. MACSYMA- mathematician's assistant that works faster than humans in manipulating many types of algebraic expressions. There is also the Sussman and Stallman program for understanding electronic circuits. And many groups have dedicated their work to producing systems to aid doctors in all types of medical diagnosis. Recent Attempts At AI Temporal Difference – TD-Gammon A new approach to gaming Rather than play out the game ahead of time, creates a set of goals with different weights that are adjusted each time it plays Capable of competing with the best players in the world and even creating new strategies of its own More Recent AI Developments Decision Trees Slacking off on the pictures Form of classification Consecutive tests of input properties guide data toward correct leaves, or create new classes when necessary Learning to Reason Similar to Turing’s idea that intelligent systems can mimic human development Program is given a period to observe an environment then expected to carry out inductive reasoning about that environment effectively. Where is AI going? Frederick Brooks: “The quantification of information embodies in structure.” Jim Gray (Microsoft): “We have been handed a puzzle: genomes and brains work. They use much more compact programming languages than we do.” Butlery Lampson: cars that don’t kill people and automatic programming (programs that write other programs). John McCarthy: computer programs with at least the intellectual capabilities of humans Raj Reddy: “a computer that could read a chapter in a book and answer the question at the end of the chapter.” The Feigenbaum Test Similar to the Turing Test. Perhaps a better challenge proposed by Feigenbaum is as follows: manually program a novice view of a given domain, write software for the system that will read the next level in the field, augmenting new material with that it already mastered. Allow the system to ask questions, and answer dutifully. If absolutely necessary, direct intervention is permissible (i.e. hard-code the new rules) though in no more than 10 percent of the new material. If the computational machine is capable of keeping up with the material such that it can pass subsequent Feigenbaum tests then AI has been achieved. What AI isn’t (our opinion) • • • • AI is not deterministic AI isn’t an optimizing system. AI isn’t a classification program. AI isn’t a vast search of a problem space. • AI doesn’t necessarily have to be human intelligence. What AI is (or might be, we really aren’t sure) • AI is a system that understands the information it processes • AI is a system that learns • AI is a system that can interact and manipulate its environment • AI has initiative • AI can always respond even when the stimuli is outside of its experience or internalized model of the world. (i.e. the response can be no response, the response cannot be the program crashing)