UBIQUITY :: Essays Papers

omnipresenceAs many a(prenominal) mass sustain expected, We are living in an environment saturated with wired and wireless connections. This technological explosion has become a part of our daily lives but we dont really realize, to what extent , our deceitful behaviors rely on informatic administrations and our fundamental interaction with them. While we are living in the era of permeative computing, we may wonder about the change that pervasive computing has brought to our lives and our social and cultural responses to these fascinating technologies and increased change. Some people remain fearful of the impact of the brain machines on our human being behavior, on the other hand, others are struggling to make our environment filled with nimble machinery, like the air we breath, and to make our interactions with this machinery as smooth as possible.The fiction of creating smart machines equipped with the equal reasoning capabilities of humans is very experienced but the e ra of computers makes it very realistic in the eyes of scientists. Since we have machines that manage to do all these tasks, it is time for a new genesis of machinery that can do exactly what we can do or mend from understanding our behavior to making decisions on their own. The article A Bayesian Computer Vision System for modeling Human Interactions, provides and excellent caseful of what people interested in artificial cognition are toilsome to do. In fact, they focus on creating machines that understand human behavior and react according to this interaction. It is stated in the article Our approach to modeling personal interactions is to expend supervised statistical machine learning techniques to teach the system to recognize normal single-person behaviors and common person-to-person interactions (Oliver, Rosario, Pentland 831). There are many limitations to accomplish this goal as any new technology or knowledge but the dream seems to be realistic for these people. a ccording to the same article, if the models are trained to recognize a limited number of human behavior, how to make them understand new patterns without limitations A major emphasis of our work, therefore, is on efficient Bayesian integration of both prior knowledge (by the use of synthetic prior models) with evidence from data (by situation-specific parameter tuning). our goal is to be able to successfully apply the system to any normal multiperson interaction situation without additional training.This article provides an example of what is going on in many laboratories spread throughout the world and how artificial intelligence focuses on creating smart practical machines that understand and interpret our behavior and plausibly surpasses our reasoning capabilities.