Information about Artificial_Intelligence

Published on December 11, 2009

Author: Heli9090



Slide 1: Artificial intelligence Slide 2: What is Al…? Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it. Major AI textbooks define the field as "the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines.“ Next Slide 3: Artificial intelligence History of AI Prediction about AI Problems of AI Knowledge Representation Planning Motion and manipulation Perception Social intelligence Natural language processing Slide 4: Approaches to AI Cybernetics and brain simulation Robotics Application of AI Slide 5: History of AI research In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning. Back Slide 6: Prediction about AI 1965, H. A. Simon: "Machines will be capable, within twenty years, of doing any work a man can do. 1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved.“ Back Slide 7: Problems of AI (1) Knowledge representation Knowledge representation and knowledge engineering are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects, situations, events, states and time, causes and effects. knowledge about knowledge (what we know about what other people know) and many other things. Back Slide 8: Planning Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or "value") of the available choices. Next Slide 9: In some planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be. However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty. Back Slide 10: (3) Motion and manipulation The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub-problems of localization (knowing where you are), mapping (learning what is around you) and motion planning (figuring out how to get there). Back (4) Perception : (4) Perception Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual input. A few selected sub problems are speech recognition, facial recognition and object recognition. Back (6) Natural language processing : (6) Natural language processing Natural language processing gives machines the ability to read and understand the languages that the human beings speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval and machine translation. Back Slide 13: (1) Cybernetics and brain simulation The human brain provides inspiration for artificial intelligence researcher. In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Back Slide 14: Robotics Slide 15: ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs. Next Slide 16: Kismet, a robot with rudimentary social skills Next Slide 17: TOPIO, a robot that can play ping-pong, developed by TOSY. Back Slide 18: Applications of Artificial Intelligence Artificial intelligence has successfully been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery, video games, toys, and Web search engines. It may also become integrated into artificial life. Next For Example : For Example Facial recognition system Definition Techniques (1) Traditional (2) 3-D (3) Skin texture analysis Additional Uses Other Fields of AI Slide 20: A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems Back Slide 21: Techniques (1) Traditional Some facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face detection. Back Slide 22: (2) 3-D A newly emerging trend, claimed to achieve previously unseen accuracies, is three-dimensional face recognition. This technique uses 3-D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin. One advantage of 3-D facial recognition is that it is not affected by changes in lighting like other techniques. Back Slide 23: (3) Skin texture analysis Another emerging trend uses the visual details of the skin, as captured in standard digital or scanned images. This technique, called skin texture analysis, turns the unique lines, patterns, and spots apparent in a person’s skin into a mathematical space. Back Slide 24: Additional Uses In the 2000 presidential election, the Mexican government employed facial recognition software to prevent voter fraud. Some individuals had been registering to vote under several different names, in an attempt to place multiple votes. By comparing new facial images to those already in the voter database, authorities were able to reduce duplicate registrations. Next Slide 25: Similar technologies are being used in the United States to prevent people from obtaining fake identification cards and driver’s licenses. Next Slide 26: There are also a number of potential uses for facial recognition that are currently being developed. For example, the technology could be used as a security measure at ATM’s; instead of using a bank card or personal identification number, the ATM would capture an image of your face, and compare it to your photo in the bank database to confirm your identity. Next Slide 27: This same concept could also be applied to computers; by using a webcam to capture a digital image of yourself, your face could replace your password as a means to log-in. Back Slide 28: Others Fields of AI Automatic number plate recognition Biometric technology in access control Face perception Mass surveillance Pattern recognition, analogy , case-based reasoning Template matching Three-dimensional face recognition Last Slide 29: Thanking You Heli Patel NVPAS, VVN Gujarat-India

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