Artificial intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Stated simply, AI is trying to make computers think and act like humans.

Achieving this end requires three key components:

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

The more humanlike the desired outcome, the more data and processing power required.

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, predictive analytics, and now to prescriptive analytics. It also gave rise to a whole new field of study, data science.

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 10¹⁸, 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.

Applications of AI can be seen in everyday scenarios such as financial services fraud detection, retail purchase predictions, and online customer support interactions. Here are just a few examples: 

• Fraud detection. The financial services industry uses artificial intelligence in two ways. Initial scoring of applications for credit uses AI to understand creditworthiness. More advanced AI engines are employed to monitor and detect fraudulent payment card transactions in real time.
• 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.
• 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 communicate directly with the person. These noninterpretive instances are fed into a machine-learning computation system to improve the AI application for future interactions.
• Advancements in AI for applications like natural language processing (NLP) and computer vision (CV) are helping industries like financial services, healthcare, and automotive accelerate innovation, improve customer experience, and reduce costs. Gartner estimates that up to 70% of people will interact with conversational AI platforms on a daily basis by the year 2022. NLP and CV provide a valuable link between humans and robots: NLP helps computer programs understand human speech, and CV applies machine learning models to images, and is perfectly suited for everything from selfie filters to medical imaging.

As the data authority for hybrid cloud, NetApp understands the value of the access, management, and control of data. The NetApp^® data fabric provides a unified data management environment that spans across edge devices, data centers, and multiple hyperscale clouds. The data fabric gives organizations of all sizes the ability to accelerate critical applications, gain data visibility, streamline data protection, and increase operational agility.

NetApp AI solutions are based on the following key building blocks: 

• ONTAP^® software enables AI and deep learning both on premises and in the hybrid cloud.
• AFF all-flash systems accelerate AI and deep learning workloads and remove performance bottlenecks.
• ONTAP Select software enables efficient data collection at the edge, using IoT devices and aggregations points.
• Cloud Volumes can be used to rapidly prototype new projects and provide the ability to move AI data to and from the cloud.

In addition, NetApp has begun incorporating big data analytics and artificial intelligence into its own 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 IT environments. Active IQ is a hybrid cloud application that was built using the same NetApp products and technologies our customers use to build AI solutions for a variety of use cases.