Artificial intelligence (AI) is a process of imitating human intelligence that relies on the creation and application of algorithms executed in a dynamic computing environment. Its goal is to enable computers to think and act like human beings.
Artificial intelligence is a branch of computer science that allows systems to learn and perform tasks normally associated with human intelligence, such as speech recognition, decision-making, or visual perception.
There are three types of AI: artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial superintelligence (ASI).
Narrow AI: ANI is categorized as weak AI because it only specializes in a narrow range of settings or situations, like voice recognition or driverless cars, for example.
Artificial general intelligence: AGI is considered strong artificial intelligence because it works at a higher level, which corresponds to human intelligence.
Artificial superintelligence: Although this type of AI is not currently developed, ASI means that a machine has superintelligence or is smarter than a human.
To achieve this, three components are necessary:
- Computer systems
- Data with management systems
- Algorithms Artificial Intelligence
To get as close to human behavior as possible, artificial intelligence needs a high amount of data and processing capacity.
What are the origins of artificial intelligence?
Since at least the first century Before the Common Era, humans have sought to create machines capable of imitating human reasoning. The term “artificial intelligence” was coined more recently, in 1955, by John McCarthy. In 1956, John McCarthy and his colleagues organized a conference called the Dart Mouth Summer Research Project on Artificial Intelligence, which gave rise to machine learning, deep learning, predictive analytics, and, more recently, prescriptive analytics. A new field of study has also emerged: data science.
Artificial intelligence important in the future
Humans and machines generate data faster than it is humanly possible to absorb and interpret it to make complex decisions. For example, most humans can learn not to lose in a simple game of tic-tac-toe when there are 255,168 possible actions, of which 46,080 lead to a draw. On the other hand, checkers champions are rarer, given that there are more than 500 x 1018 (500 trillion) possible moves. Computers can calculate these combinations and the best possible sorting very efficiently to make the right decision.
Uses of artificial intelligence
AI is present in our daily lives. It is used by the fraud detection services of financial institutions. To predict purchasing intentions and interactions with online customer services. Here are some examples :
Fraud detection.
In the finance industry, artificial intelligence is used in two ways. Apps that score credit applications use AI to assess consumers’ creditworthiness. More advanced AI engines are responsible for monitoring and detecting fake credit card payments in real time.
Virtual Customer Service (VCS)
Call centers use Service Customer Virtual to predict and respond to customer requests without human interference. Voice recognition and a human dialogue simulator provide the first point of interaction with customer service. More complex requests require human intervention.
When an Internet user opens a dialogue window on a web page chatbot, their interrogator is often a computer running a form of specialized AI. If the chatbot cannot interpret the question or resolve the problem, a human agent takes over. These interpretation failures are sent to the machine learning system to improve future interactions of the AI application.
Data analysis with artificial intelligence
As the gold standard in hybrid cloud data management, Net App understands the importance of data access, management, and control. Net App Data Fabric provides a unified data management environment that stretches endpoints, data centers, and multiple hyper-scale clouds. It allows companies, whatever their size, to accelerate mission-critical applications, improve data visibility, optimize data protection, and improve functional agility.
Net app AI solutions are built on key building blocks:
- ON TAP Software enables AI and deep learning to be leveraged locally and in the hybrid cloud.
- Fabric Attached Storage (FAS) systems accelerate AI and deep learning workloads while eliminating performance bottlenecks.
- ON TAP Select software helps collect data efficiently at the edge using Internet of Things endpoints and assembling points.
- Cloud Volumes can be used to create prototypes for new projects quickly. It allows AI data to be received and sent to and from the cloud.
- Net App has also begun integrating big data analytics and artificial intelligence into its products and services, including Active Intelligence quotient, which uses billions of data points, predictive analytics, and a powerful machine learning engine to provide proactive customer support recommendations for complex IT environments.
- Active Intelligence Quotient is a hybrid cloud application built using the same Net App products and technologies that our customers use to build their AI solutions across multiple domains.