Artificial intelligence (AI) is the foundation from which human intelligence processes are mimicked by creating and applying algorithms designed in a dynamic computing environment. Or, simply put, AI is about trying to make computers think and act like humans.

Three fundamental components are needed:

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

The more similar to human behavior we want to achieve, the more data and processing power will be required.

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How did Artificial Intelligence Originate?

Since at least the 1st century BC, humans have considered creating machines that mimic the human brain. Back in modern times, John McCarthy coined the term “artificial intelligence” in 1955. In 1956, McCarthy and also a few others organized a conference called the “Dartmouth Summer Research Project on Artificial Intelligence.” This meeting led to machine learning, deep learning, predictive analytics, and prescriptive analytics. It also spawned an entirely new field of study: data science.

Why is Artificial Intelligence Important?

Today, the amount of data that remains generated by humans and machines dramatically exceeds the ability of people to absorb, interpret, and make complex decisions based on that data. Artificial intelligence is the foundation of all machine learning and the future of all complex decision-making processes. For example, most humans can figure out how not to lose when playing tic-tac-toe. Even though there are 255,168 unique moves, 46,080 ends in a draw, far fewer could become grandmasters of checkers, with more than 500 x 1018 or 500 trillion different possible moves. Computers are incredibly proficient at calculating these combinations and variations to arrive at the best decision.

Artificial intelligence Case Studies

AI remains applied in our day-to-day lives, such as in financial services, fraud detection, store purchase predictions, and online customer support interactions. These are some examples:

Fraud detection

Financial services manufacturing uses artificial intelligence in two different ways. The initial credit application ranking uses AI to find out what your creditworthiness is. More advanced AI engines are needed to monitor fraudulent card transactions when making payments in real-time.

Virtual Customer Help (VCA)

Call centers use VCAs to predict and respond to customer inquiries without human interaction. Speech recognition and simulated human dialogue are the first point of business in a customer service inquiry. In more complicated queries, they remain redirected to a person with whom they can interact directly.

When a person initiates a dialogue on a web page using a chat (conversational bot)

The interaction remains often done with a computer running a specialized AI system. If a point remains reached where the chatbot remains unable to interpret or address the question, a person steps in and will contact them directly. These non-interpretive instances feed a machine learning computing system that enhances the application of AI in future interactions.

NetApp and Artificial Intelligence

NetApp is the data benchmark for the hybrid cloud and understands the value of data access, management, and control. NetApp® Data Fabric provides an integrated data management environment that spans all edge devices, data centers, and multiple hyper-scale clouds. Data Fabric enables organizations of all sizes to have the ability to accelerate critical applications, gain data visibility, optimize data protection, and increase operational agility.

NetApp AI solutions remain built on the following fundamental pillars:

  • With ONTAP ® software, AI and deep learning can be present on-premises and in the hybrid cloud.
  • AFF All-Flash systems accelerate deep learning and AI workloads and eliminate performance bottlenecks.
  • ONTAP Select software enables data to remained collected efficiently at the edge, using IoT devices and also aggregation points.
  • Cloud Volumes can remained used to quickly prototype new projects and also provide the opportunity to move AI data in and out of the cloud.

Additionally, NetApp has begun incorporating Big Data analytics and artificial intelligence into its products and services. For example, Active IQ® uses billions of data points, predictive analytics, and powerful machine learning to deliver proactive customer support recommendations for complex technology environments. Active IQ is a hybrid cloud application built with the same NetApp products and also technologies our customers use to create AI solutions for many use cases.

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