Artificial intelligence (AI) is the basis for mimicking human intelligence processes by creating and applying embedded algorithms in a dynamic computing environment.

Put simply, AI seeks to make computers think and act like humans.

To achieve this goal, three key components are required:

  • Computer systems
  • Data and data management
  • Advanced AI algorithms (code)

The more humane the desired result, the more data and computing power are required.

How Did AI Come About?

At least from the 1st century BC. C. People were fascinated by the possibility of developing machines that mimick the human brain.

In modern times, the term artificial intelligence was coined by John McCarthy in 1955. In 1956, McCarthy and others organized a conference called the Dartmouth Summer Research Project on Artificial Intelligence.

That beginning led to the development of machine learning, deep learning, predictive analytics and now prescriptive analytics. It also spawned an entirely new field of study, data science.

Why Is Artificial Intelligence Important?

Today, the amount of data generated by both humans and machines exceeds humans’ ability to take in that data, interpret it, and make complex decisions based on that data.

Artificial intelligence forms the basis of all computer learning and is the future of all complex decision-making.

For example, most people can figure out how not to lose in tic-tac-toe (zeros and crosses), despite the fact that there are 255,168 unique moves, 46,080 of which end in a draw.

With more than 500 x 1018, or 500 trillion different potential moves, far fewer people would remain consider great checkers champions.

Computers are extremely efficient at computing these combinations and permutations in order to make the best decision.

AI (and its logical evolution from machine learning) and deep learning are the fundamental future of business decisions.

Artificial Intelligence Use Cases

Artificial intelligence applications can seen in everyday scenarios such as financial services fraud detection, retail purchase forecasting, and online customer support interactions.

Here are some examples:

Discovery of a fraud. The financial services industry uses artificial intelligence in two ways. The initial loan application score uses artificial intelligence to understand credit worthiness.

More advanced artificial intelligence engines being use to monitor and detect fraudulent payment card transactions in real time.

Virtual customer support (VCA). Call centers use VCAs to predict and respond to customer inquiries outside of human interaction.

Speech recognition, together with a simulate human dialogue, first point of interaction when a customer service inquiry made. Top-level queries remain redirect to a human.

Personal Area Network (PAN): It is an interconnections of private generation devices to communicate over a brief distance, that’s less than 33 feet or 10 meters or inside the range of an man or woman character, usually the usage of some form of wireless technology.

Meanwhile, when a person initiates a dialogue on a website via chat (chatbot), the person is often interacting with a computer running a specialized AI.

However, if the chatbot cannot interpret or answer the question, a human steps in to communicate directly with the person.

Finally, these non-interpreting instances are fed into a machine learning system to improve the AI ​​application for future interactions.

NetApp and Artificial Intelligence

Before, as the data authority for the hybrid cloud, NetApp understands the value of data access, management, and control.

Then, NetApp Data Fabric provides a unified data management environment that spans edge devices, data centers, and multiple hyperscale clouds.

However, the data fabric enables companies of all sizes to accelerate critical applications, achieve data transparency, optimize data protection and increase operational agility.