FINAL EXAM OUTPUT
MWF 2:00-3:00 PM
PROFESSOR MARIDEL OROPESA CASAS
MARCH 9, 2016
First and foremost, I would like to give my unending gratitude to my parents for their love and support in my entire life. Thank you both for giving me the courage, strength, and guidance in reaching my stars and chasing my dreams and goals. I would like to give thanks to my three wonderful brothers, aunties, uncles, and other relatives that always been supportive since in my first day here on earth.
I would also like to give thanks to my Professor in Productive Keyboarding, Professor Maridel Oropesa Casas from Adamson University, for her guidance ...view middle of the document...
It has become an essential part of the technology industry. Research associated with artificial intelligence is highly technical and specialized.
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach. Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with few sub-problems such as facial, object and speech recognition. Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.
II. Computers, Robots, and Artificial Intelligence
In recent years the mushrooming power, functionality and ubiquity of computers and the Internet have outstripped early forecasts about technology’s rate of advancement and usefulness in everyday life. Alert pundits now foresee a world saturated with powerful computer chips, which will increasingly insinuate themselves into our gadgets, dwellings, apparel and even our bodies. Yet a closely related goal has remained stubbornly elusive. In stark contrast to the largely unanticipated explosion of computers into the mainstream, the entire endeavor of robotics has failed rather completely to live up to the predictions of the 1950s. In those days experts who were dazzled by the seemingly miraculous calculational ability of computers thought that if only the right software were written, computers could become the articial brains of sophisticated autonomous robots. Within a decade or two, they believed, such robots would be cleaning our oors, mowing our lawns and, in general, eliminating drudgery from our lives.
Robots that chart their own routes emerged from laboratories worldwide in the mid-1990s, as microprocessors reached 100 MIPS. Most build two-dimensional maps from sonar or laser rangender scans to locate and route themselves, and the best seem...