The Science and Engineering of Making Intelligent Machines



Author: Gayus Pratama Polunggu
            Today some of you not strange with Computer, Smartphone, and maybe robot. The thing that I already said before is the example of things that can help to doing your work more easy with artificial intelligence inside. Artificial intelligence (AI) is the intelligence exhibited by machines or software, and the branch of computer science that develops machines and software with intelligence.
            It’s no secret that Google has an interest in artificial intelligence (AI); after all, technologies derived from AI research help fuel Google’s core search and advertising businesses. Artificial Intelligence, which commenced publication in 1970, is now the generally accepted premier international forum for the publication of results of current research in this field. AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By this developing of AI, human can work base on the foundations of the field of artificial intelligence.
            There no something perfect in this world, it’s the word that I can say for AI technology. AI or Intelligent Machines make more change and useful for our daily life but there no something without negative comment from civil, Andor said in http://channel9.msdn.com "There are big limitations on what a computer can do in any case - it's not just a case of -we'll write better software and make faster computers and it'll all be OK-". The intelligent from machine is taken from human, because human make machine and teach it become smart for our benefits. Because of that many mistake can happen during working that cause by human error.
            Therefore, you may take the effect of using AI technology, you also get the positive effect beside "make your life more easily" such as deduction, reasoning, and problem solving. Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. For difficult problems, most of these algorithms can require enormous computational resources – most experience a "combinatorial explosion". The search for more efficient problem-solving algorithms is a high priority for AI research.
            Dependence is hard to remove from our self. From dependence to unproductive and make you tired doing something. A new study links after-hours smartphone work to poor productivity the next day, reports the WSJ’s Melissa Korn: “The researchers found that work-related smartphone use in the evening was associated with fewer hours of sleep. The subjects who recorded shorter nights also reported depleted reserves of self-control, and those who felt morning exhaustion also indicated they were less engaged during the day, a domino effect that shows how an unending workday ultimately leads to poorer work”. AI technology make all easy as long as you can take the positive side and it will be dangerous when you use it under control to spend your time with it.
            The social problem above just image of the negative side and the positive side by using AI technology. In third paragraph I tell you why the mistake happen when the machine work. You can find the mistake happen because human error on there. Machine learning is the study of computer algorithms that improve automatically through experience and has been central to AI research since the field's inception. Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Within developmental robotics, developmental learning approaches were elaborated for lifelong cumulative acquisition of repertoires of novel skills by a robot, through autonomous self-exploration and social interaction with human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation. Human and machine is link by one context, that if one doing mistake and the other will take the effect from it as continuance.
            Every technology is create because any reason. For example TV is made to inform and entertain people with every program. AI technology made for machine so it can learn from its environment such as natural language processing, social intelligence, motion and manipulation (physical contact with an object).
            Natural language processing gives machines the ability to read and understand the languages that humans speak. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human-written sources, such as Internet texts. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation. Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer sciences, psychology, and cognitive science. While the origins of the field may be traced as far back as to early philosophical inquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1995 paper on affective computing. A motivation for the research is the ability to simulate empathy. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response for those emotions. Emotion and social skills play two roles for an intelligent agent. First, it must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, in an effort to facilitate human-computer interaction, an intelligent machine might want to be able to display emotions—even if it does not actually experience them itself—in order to appear sensitive to the emotional dynamics of human interaction. 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, or finding out where other things are), mapping (learning what is around you, building a map of the environment), and motion planning (figuring out how to get there) or path planning (going from one point in space to another point, which may involve compliant motion - where the robot moves while maintaining physical contact with an object).
            Artificial intelligence techniques are pervasive and are too numerous to list. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.
            In conclusion, Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? The question can answer with the quotes by Turing’s polite convention “We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being”.

0 komentar:

Posting Komentar