Artificial intelligence
Nowadays Artificial intelligence is one of the emerging fields associated with science and technology and it makes the world of technology hacked after the intervention of fifth generation of computers. The word artificial intelligence is mostly associated with machines and its uses in daily life but the most significant term associated with Artificial intelligence is the word of automation and machine learning. It is the process or frame work in which the machine is specifically instructed to come up with learning phenomenon at its own.
The machines perceives its environment and then perform different actions using the information it gets from its surrounding means the people actually don’t need to train the machine
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It works on the phenomenon associated with the instructions the programmer already fed into the machine. Some of the sample voice patterns are already installed in machine prior to the phenomenon of learning. Now the machine of Artificial intelligence recognize the voice of human by matching it with the patterns already fed into it. The other examples associated with the working of this new technology includes autonomous cars and content delivery networks, interpreting complex data and military simulations.
AI research is divided into subfields that focus on specific problems, approaches, the use of a particular tool, or towards satisfying particular applications.The basic purpose of artificial intelligence is to research which includes planning, knowledge, natural language, perception and general processing abilities to manipulate and move objects. The field of artificial intelligence also includes other fields of knowledge like linguistics, neuroscience and mathematics. AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and
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The major goal of AI is also concern with the working of machine automatically or with a very little human efforts. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.
Problem solving and reasoning:
Problem solving and reasoning is also associated with AI. If we talk about the problem solving methods it generally considers the field of computer science and hence the reasoning, reasoning is the branch of psychology and the mixture of both these fields of knowledge will make the recognition of AI. There are different programs and skill associated with AI that need to be studied in depth for understanding the basics of AI. Normally we considers the branch of software likely to be the major part of
In chapter 7 the main topics that were discussed were thinking, language and intelligence. The aspect of cognition is defined as the mental activities involved in acquiring, retaining and using knowledge. Thinking involves be to manipulate internal, mental representation of information to be able to draw inferences and conclusions. With thinking there are two kinds of mental representation present which are, mental images and concepts. There are also types of concepts that are used with thinking.
Dr. Cabrera suggests six types of thinking: “Creative Thinking; Critical Thinking; Systems Thinking; Interdisciplinary Thinking; Scientific Thinking; and Emotional
A good reasoning is a reasoning that leads to certain, true and valid conclusions. There are two kinds of reasoning, inductive and deductive reasoning. Both processes include the process of finding a conclusion from multiple premises although the way of approach may differ. Deductive reasoning uses general premises to make a specific conclusion; inductive reasoning uses specific premises to make a generalized conclusion. The two types of reasoning can be influenced by emotion in a different manner because of their different process to yield a conclusion.
In the article, the author addresses the steps needed to successfully implement the taxonomy thinking skills, including: Teachers should be familiar with the thinking skills, teachers should identify student needs, and teachers should choose the most relevant skills according to content, curriculum, and developmental levels. Burns addresses the four major thinking skills categories, Analytical Reasoning Skills, Critical Thinking
Based upon the analysis, Parnas’ article is geared more towards people involved in the field of Artificial Intelligence where Eldridge’s article is geared towards people who are not necessarily knowledgeable about Artificial Intelligence yet are interested to learn more about the topic. Throughout the article, Parnas maintains the skeptical attitude towards Artificial Intelligence, literally ending with “Devices that use heuristics to create the illusion of Intelligence present a risk we should not accept” (Parnas, 6). Eldridge on the other hand, maintains a positive attitude throughout the article despite the shortcomings of AI. Together, both authors provide compelling arguments for and against Artificial
This article fits with my research because it talks about the positive outcome artificial intelligence can have in the educational field. It is fairly simple to read and was helpful by proving the positive impact. It address the pros and cons about the educational AI that could be used to help students with courses they are having difficulties
Computer games that involve computer controlled characters, which opposes the character controlled by the human, have a ‘brain’ on their own. To test whether the computer controlled character exhibits human intelligence, an interrogator could play against the computer controlled character as well as a human controlled character. If the interrogator is able to notice a difference between the techniques and strategies used by the two characters, then according to the Turing test theory, the computer controlled character is not ‘intelligent’. Another artificial intelligent application which can be tested using a variation of the Turing test, is the future Google’s self - driving cars. The cars have only a start and stop button, and is relied on a very detailed map, and uses a GPS system to get to a destination.
Since the spread of formal schooling and education in human societies, fostering cognitive abilities, such as understanding, reasoning, critical thinking, creativity, problem-solving and judgment has been highlighted [1]. Problem-solving is an essential skill in today’s life [2]. Problem-solving is a goal-directed thinking [3]. It is a mental process, some logical, orderly, intellectual thinking that helps cope with problems, search several solutions and choose the best solution [4]. According to Moshirabadi, problem-solving is a systematic process and a problem-focused situation analysis that indicates the ability of individuals to overcome obstacles and to achieve goals.
Artificial Intelligence and its effect on the workforce Artificial intelligence(AI) is a recent reality of technological advancement affecting human society. To analyze its effect on the workforce we will look back in history for technological disruptions and how they affected the workforce and compare and contrast to the way AI is currently impacting and will continue impacting the human workforce and other aspects of human society such as economics, politics and the general environment. History Throughout history technological advancement has affected human society in its ways of living, working and its environment.
Defining intelligence is a very difficult proposition and one which Alan Turing attempted to avoid answering as regards machine intelligence in the Imitation Game which has become known as the Turing Test (Turing, 1950). He posed the question “Can machines think?” which is he developed to ask if machines are able to converse in a way that can persuade humans they too are human. A machine is declared to have passed the test if human judges are unable to tell the difference between a human and a computer through a typed conversation. He suggested that a machine that persuades 70 per cent of human judges after five minutes of conversation should be deemed to have passed the test.
Artificial Intelligence is the field within computer science to explain some aspects of the human thinking. It includes aspects of intelligence to interact with the environment through sensory means and the ability to make decisions in unforeseen circumstances without human intervention. The beginnings of modern AI can be traced to classical philosophers' attempts to describe human thinking as a symbolic system. MIT cognitive scientist Marvin Minsky and others who attended the conference
Rise of Artificial Intelligence and Ethics: Literature Review The Ethics of Artificial Intelligence, authored by Nick Bostrom and Eliezer Yudkowsky, as a draft for the Cambridge Handbook of Artificial Intelligence, introduces five (5) topics of discussion in the realm of Artificial Intelligence (AI) and ethics, including, short term AI ethical issues, AI safety challenges, moral status of AI, how to conduct ethical assessment of AI, and super-intelligent Artificial Intelligence issues or, what happens when AI becomes much more intelligent than humans, but without ethical constraints? This topic of ethics and morality within AI is of particular interest for me as I will be working with machine learning, mathematical modeling, and computer simulations for my upcoming summer internship at the Naval Surface Warfare Center (NSWC) in Norco, California. After I complete my Master Degree in 2020 at Northeastern University, I will become a full time research engineer working at this navy laboratory. At the suggestion of my NSWC mentor, I have opted to concentrate my master’s degree in Computer Vision, Machine Learning, and Algorithm Development, technologies which are all strongly associated with AI. Nick Bostrom, one of the authors on this article, is Professor in the Faculty of Philosophy at Oxford University and the Director at the Future of Humanity Institute within the Oxford Martin School.
2.3.2 Competitive Intelligence as a process Competitive Intelligence is the processes that made up of phases that are linked together (Nasri 2011). The output of any phase of these phases is the input to the next one (Bartes 2012). The overall output of the CI process is an input to the decision-making processes (Wright et al. 2009). The elements of the intelligence model have been investigated in many academic fields.
The attraction of artificial intelligence for me lies in its breadth of applicability, both as a method of problem solving in itself and in a symbiotic integration with other areas of computer science. A broad spectrum of applications exist within the artificial intelligence field, ranging from intelligent non-player controlled characters in computer game software to a ubiquitous computing solution that intelligently reacts to a variety of users. This diversity is one of the main reasons that I feel compelled to pursue artificial intelligence further. While I have striven to develop my understanding of artificial intelligence during my undergraduate education, the choreographed requirements of a bachelor's degree have restricted my research to only a minute sample of artificial intelligence’s applications. During my exposure to the field, I have often been unsatisfied with the level of interaction artificial intelligence displays in response to prompts of varying complexity.
Artificial Intelligence AI is defined as "the science and engineering of making intelligent machines." Artificial Intelligence mainly deals with the very fundamental concept of Philosophical Logic. It is actually necessary as we need the machine to think like a human and take appropriate actions. Philosophical logic is the branch of study that concerns questions about reference, predication, identity, truth, quantification, existence, entailment, modality, and necessity. Philosophical logic is the application of formal logical techniques to philosophical problems.