Learn Artificial Intelligence in simple and easy steps starting from basic to advanced concepts with examples. The market for Artificial Intelligence (AI) technologies is flourishing.
Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises.
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal “intelligent” machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.
Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem-solving”.
As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of “artificial intelligence”, has become a routine technology. For example, optical character recognition is no longer perceived as an exemplar of “artificial intelligence”, has become a routine technology.
Capabilities currently classified as Artificial Intelligence (AI) include successfully understanding human speech, competing at a high level in strategic game systems (such as Chess and Go), self-driving cars, and interpreting complex data. Some people also consider Artificial Intelligence (AI) a danger to humanity if it progresses unabatedly.
Artificial Intelligence (AI) research is divided into subfields that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications.
The central problems (or goals) of Artificial Intelligence (AI) research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.
General intelligence is among the field’s long-term goals Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic Artificial Intelligence (AI). Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics.
The Artificial Intelligence (AI) field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, and artificial psychology.
In this Artificial Intelligence tutorial, we will take you through the various concepts of Artificial Intelligence and explain how you can use them in a real-time environment.
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