According to the SAS Institute:
“Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.”
Artificial intelligence includes the following elements:
- Models of human behavior
- Models of human thought
- Systems that behave intelligently
- Systems that behave rationally
- A set of specific applications that use techniques in machine learning, deep learning and others
In the larger picture of Data Science, artificial intelligence (AI) can encompass (among others):
Other Definitions of Artificial Intelligence Include:
- “Strategy to make data analytics tools smarter.” (Narrative Science)
- “Learning platform that simplifies and accelerates the process of gaining insights from a large quantity of natural language inundating and enterprise.” (Lexanalytics)
- “The ability to mimic the rational thinking of the human brain and suitably alter reactions when environmental parameters change.“ (Paramita Ghosh)
- “Technology that appears to emulate human performance typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialogs with people, enhancing human cognitive performance (also known as cognitive computing) or replacing people on execution of nonroutine tasks. (Gartner)
- “Technologies that supply digital services, once done by humans.” (The Economist)
- “The field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.” (AWS)
Businesses Use AI to:
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