What is artificial intelligence?
Artificial intelligence (AI) is the foundation from which human intelligence processes are mimicked by creating and applying algorithms created in a dynamic computing environment. Or, simply put, AI is about trying to make computers think and act like humans.
To achieve this, 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.
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 a few others organized a conference called the “Dartmouth Summer Research Project on Artificial Intelligence.” This meeting led to the creation 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 that is generated, both by humans and by machines, greatly 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, of which 46,080 end in a draw. Far fewer could become grandmasters of checkers, with more than 500 x 1018 or 500 trillion different possible moves. Computers are extremely efficient at calculating these combinations and permutations to arrive at the best decision.
Artificial intelligence case studies
AI is applied in our day-to-day lives, such as in financial services, fraud detection, merchant purchase predictions, and online customer support interactions. These are some examples:
- Fraud detection. The financial services industry uses artificial intelligence in two different ways. The initial rating of credit applications uses AI to find out what the creditworthiness is. More advanced AI engines are needed to monitor and detect 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, along with simulated human dialogue, is the first point of interaction in a customer service inquiry. In the most difficult queries they are redirected to a person with whom they can interact directly.
- When a person initiates a dialogue on a web page using a chat (chat bot), the interaction is often done with a computer running a specialized AI system. If there is a point where the chatbot is 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 as such understands the value of data access, management and control. NetApp® Data Fabric provides a unified data management environment that spans all types of edge devices, data centers, and multiple hyperscale clouds. Data Fabric enables organizations of all sizes to accelerate critical applications, gain data visibility, optimize data protection, and increase operational agility.
NetApp AI solutions are 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 AI and deep learning workloads and eliminate performance bottlenecks.
- ONTAP Select software makes data can be collected efficiently at the edge, using IoT devices and points of aggregation.
- Cloud Volumes can be used to quickly prototype new projects and provide the opportunity to move AI data in and out of the cloud.
Additionally, NetApp has begun to incorporate 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 technology environments. Active IQ is a hybrid cloud application built on the same NetApp products and technologies our customers use to create AI solutions for a wide range of use cases.
