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What Is Artificial Intelligence (AI)?

How Did Artificial Intelligence Originate?

At least since the first century BCE, humans have been intrigued by the possibility of creating machines that mimic the human brain. In modern times, the term "artificial intelligence" was coined in 1955 by John McCarthy. In 1956, McCarthy and others organized a conference titled the Dartmouth Summer Research Project on Artificial Intelligence. This beginning led to the creation of machine learning, deep learning, and predictive analytics, and now to prescriptive analytics. It has also given rise to a whole new field of study, data science.

Why Is Artificial Intelligence Important?

Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial Intelligence forms the basis for all computer learning and is the future of all complex decision making. As an example, most humans can figure out how to not lose at tic tac toe (noughts and crosses), even though there are 255,168 unique moves, of which 46,080 end in a draw. Far fewer folks would be considered grand champions of checkers, with more than 500 x 10^18, or 500 quintillion, different potential moves. Computers are extremely efficient at calculating these combinations and permutations to arrive at the best decision. AI (and its logical evolution of machine learning) and deep learning are the foundational future of business decision making.

Artificial Intelligence Use Cases

Applications of AI can be seen in everyday scenarios like financial services fraud detection, retail purchase predictions, and online customer support interactions. Here are some examples:

  • Virtual customer assistance (VCA). Call centers use VCA to predict and respond to customer inquiries outside of human interaction. Voice recognition, coupled with simulated human dialog, is the first point of interaction in a customer service inquiry. Higher-level inquiries are redirected to a human.
  • Chatbot. When a person initiates dialog on a webpage via chat (chatbot), the person is often interacting with a computer running specialized AI. If the chatbot can’t interpret or address the question, a human intervenes to speak directly with the person. These noninterpretive instances are fed into a machine-learning computation system to improve the AI application for future interactions.
  • Fraud detection. The financial services industry uses artificial intelligence in two ways. Initial scoring of applications for credit uses AI to understand credit worthiness. More advanced AI engines are employed to monitor and detect fraudulent payment card transactions in real time.

NetApp and Artificial Intelligence

As the data authority, NetApp understands the value of the access, organization, management, and control of data. We have adapted the principles of computational intelligence into our solutions, such as NetApp® OnCommand® Insight, an open platform for on-premises and hybrid cloud data center management, and Active IQ®, a predictive analytics and proactive support application for the hybrid cloud. OnCommand Insight implements machine learning to constantly analyze and provide consistent insight across your data center so you can monitor and manage your hybrid IT multivendor storage, compute, and networking infrastructure. Active IQ builds on the NetApp AutoSupport® set of predictive technologies with artificial intelligence, powerful machine-learning capabilities, and additional deployment options.

Core to these solutions is the NetApp Data Fabric vision. Data Fabric gives organizations of all sizes the ability to accelerate critical applications, gain data visibility, streamline data protection, and increase operational agility.

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Monitor and manage your multivendor hybrid cloud IT infrastructure.

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Predictive analytics and proactive support for your hybrid cloud.