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Arti cial Intelligence | An Introduction
The most common answer that one expects is “to make computers intelligent so that they can act intelligently!”, but the question is how much intelligent? How can one judge the intelligence?
…as intelligent as humans. If the computers can, somehow, solve real-world problems, by improving on their own from the past experiences, they would be called “intelligent”. Thus, the AI systems are more generic(rather than speci c), have the ability to “think” and are more exible.
Intelligence, as we know, is the ability to acquire and apply the knowledge. Knowledge is the information acquired through experience. Experience is the knowledge gained through exposure(training). Summing the terms up, we get arti cial intelligence as the “copy of something natural(i.e., human beings) ‘WHO’ is capable of acquiring and applying the information it has gained through exposure.”
Intelligence is composed of:
Reasoning Learning Problem Solving Perception Linguistic Intelligence
Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI eld draws upon computer science, mathematics, psychology, linguistics, philosophy, neuro-science, arti cial psychology and many others.
Need for Arti cial Intelligence
- To create expert systems which exhibit intelligent behavior with the capability to learn, demonstrate, explain and advice its users.
- Helping machines nd solutions to complex problems like humans do and applying them as algorithms in a computer-friendly manner.
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Applications of AI include Natural Language Processing, Gaming, Speech Recognition, Vision Systems, Healthcare, Automotive etc.
An AI system is composed of an agent and its environment. An agent(e.g., human or robot) is anything that can perceive its environment through sensors and acts upon that environment through effectors. Intelligent agents must be able to set goals and achieve them. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that cannot only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. Natural language processing gives machines the ability to read and understand human language. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation. Machine perception is the ability to use input from sensors (such as cameras, microphones, sensors etc.) to deduce aspects of the world. e.g., Computer Vision. Concepts such as game theory, decision theory, necessitate that an agent be able to detect and model human emotions.
Many times, students get confused between Machine Learning and Arti cial Intelligence, but Machine learning, a fundamental concept of AI research since the eld’s inception, is the study of computer algorithms that improve automatically through experience. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as a computational learning theory.
Stuart Shapiro divides AI research into three approaches, which he calls computational psychology, computational philosophy, and computer science. Computational psychology is used to make computer programs that mimic human behavior. Computational philosophy is used to develop an adaptive, free- owing computer mind. Implementing computer science serves the goal of creating computers that can perform tasks that only people could previously accomplish.
AI has developed a large number of tools to solve the most di cult problems in computer science, like:
Search and optimization Logic Probabilistic methods for uncertain reasoning Classi ers and statistical learning methods Neural networks Control theory Languages
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Article Tags : Advanced Computer Subject GBlog Machine Learning
Practice Tags : Machine Learning
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