Artificial Intelligence PDF
Preview Artificial Intelligence
2017 Predictive Analytics Symposium Session 36, Artificial Intelligence--Science Fiction or Reality? Moderator: Satadru Sengupta Presenter: Boyi Xie,Ph.D. SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer General Public Release Artificial Intelligence Science Fiction or Reality? Boyi Xie, SOA Predictive Analytics Symposium, 09/15/2017 General Public Release Artificial Intelligence When most of us try to picture an artificial intelligence, of course we think first of a robot. Human performance Rationality Thought processes and System that think like Systems that think rationally reasoning humans Systems that act like Behavior Systems that act rationally humans • Foundations of Artificial Intelligence – Philosophy – Economics – Psychology – Control theory and Cybernetics – Mathematics – Neuroscience – Computer engineering – Linguistics • Weak AI: Machines could possibly act intelligently, or act as if they were intelligent. • Strong AI: Machines that do so are actually thinking, as opposed to simulating thinking. Boyi Xie | PA Symposium | 09/14/2017 2 General Public Release Deep Blue • A chess-playing computer developed by IBM. • It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls. Deep Blue studies thousands of master games. It has an evaluation function that determines how important a position is compares to other alternatives. It search to a depth between six to eight moves to a maximum of twenty or even more moves in some situations. Interestingly, a bug in Deep Blue’s software led to a seemingly random move which Kasparov misattributed to “super intelligence”. https://www.extremetech.com/wp-content/uploads/2014/12/kasparov-vs-ibm-deep-blue-640x414.jpg Boyi Xie | PA Symposium | 09/14/2017 3 General Public Release AlphaGo • A program developed by DeepMind (found in 2010 and acquired by Alphabet’s Google in 2014) that plays the board game Go. • In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional. • It was chosen by Science as one of the Breakthrough of the Year runners-up. Uses a combination of machine learning and tree search techniques Implemented using deep neural network technology It was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves. Later trained by being set to play large numbers of games against itself, using reinforcement learning to improve https://en.wikipedia.org/wiki/AlphaGo Boyi Xie | PA Symposium | 09/14/2017 4 General Public Release Turing Test • Devised in 1950 by mathematician, computer scientist, and World War II code breaker Alan Turing to determine whether a machine can exhibit intelligence. • The test is for a program to have a conversation (via online typed messages) with an interrogator for 5 minutes. The interrogator then has to guess if the conversation is with a program or a person. • The program passes the test if it fools the interrogator 30% of the time. Few AI researchers pay attention to the Turing Test, preferring to concentrate on their systems’ performance on practical tasks, rather than the ability to imitate humans. Boyi Xie | PA Symposium | 09/14/2017 5 General Public Release Chinese Room • In 1980, John Searle described a hypothetical system that is clearly running a program and passes the Turing Test, but does not understand anything of its inputs and outputs. Assume you only speak English, and image yourself in a room with two slits, a rule book written in English, and some scratch paper. Someone slides you some Chinese characters through the first slit, you follow the instructions in the book, transcribing characters instructed onto the scratch paper, and slide the resulting sheet out the second slit. To people outside, it appears the room speaks Chinese, yet you do not understand a word of Chinese. • His conclusion is that running the appropriate program (i.e., having the right outputs) is not a sufficient condition for being a mind. • The aim of the Chinese Room argument is to refute strong AI – the claim that running the right sort of program necessarily results in a mind. Boyi Xie | PA Symposium | 09/14/2017 6 General Public Release ELIZA • ELIZA – A Computer Program for the Study of Natural Language Communication between Man and Machine • ELIZA was intended to simulate – or caricature – the conversation between a psychoanalyst and a patient, with the machine in the role of analyst. ELIZA: Good afternoon. Please type in your name. [The visitor did so, and ELIZA continued with some polite preliminaries.] ELIZA: What brought you here to see me today? VISITOR: Oh, nothing much. I’m feeling a bit tired, that’s all. ELIZA: Why do you think you’re feeling a bit tired? VISITOR: Well, I’ve been traveling a lot, and away from home. Partly ELIZA had to do with the illustrations of mutual ELIZA: Tell me about your family. understanding as Joseph Weizenbaumexplained: “What I mean here is the cocktail party conversation. [The conversation suddenly became intimate. The Someone says something to you that you really don’t visitor began to disclose his worries about his fully understand, but because of the context and lost of wife, his children, his distance – both geographical other things, you are in fact able to give a response and emotional – from them.] which appears appropriate, and in fact the conversation continues for quite a long time.” Boyi Xie | PA Symposium | 09/14/2017 7 General Public Release IBM Watson • A question answering system developed by IBM’s DeepQA project. • It was specifically developed to answer questions on the Jeopardy! show. • In 2011, Watson completed the Jeopardy! winning the first prize of $1 million. • Watson had access to 200 million pages of structured and structured content of four terabytes, including the full text of Wikipedia https://en.wikipedia.org/wiki/File:DeepQA.svg Boyi Xie | PA Symposium | 09/14/2017 8 General Public Release NELL: Never Ending Language Learning • Research Goal – To build a never-ending machine learning system that acquires the ability to extract structured information from unstructured web pages. If successful, this will result in a knowledge base (i.e., a relational database) of structured information that mirrors the content of the Web. We call this system NELL (Never-Ending Language Learner). Never-Ending Learning. T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling. In Proceedings of the Conference on Artificial Intelligence (AAAI), 2015. Boyi Xie | PA Symposium | 09/14/2017 9
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