Artificial Intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of embedded algorithms in a dynamic computing environment. Simply put, AI attempts to make computers think and act like humans.
Achieving this goal requires three key elements:
- Computer systems
- Data and data management
- Advanced AI Algorithms (Code)
The more human the desired outcome, the more data and computing power is required.
How did artificial intelligence originate?
At least from the 1st century BC. C., humans were intrigued by the possibility of creating machines that mimic 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. This beginning has led to the development of machine learning, deep learning, predictive analytics, and now prescriptive analytics. A new field of study, data science, has also emerged.
Why is artificial intelligence important?
Today, the amount of data generated by humans and machines far exceeds the ability of humans to assimilate that data, interpret it, and make complex decisions based on that data. Artificial intelligence forms the basis of all computational learning and is the future of all complex decision-making. For example, most people can figure out how not to lose at Tic Tac Toe (three in a row), even though there are 255,168 unique moves, of which 46,080 end in a draw. With over 500 x 1018, or 500 quintillion different moves possible, far fewer people would be considered grandmasters of checkers. Computers are extremely good at calculating these combinations and permutations to make the best decision. AI (and its logical evolution from machine learning) and deep learning are the fundamental future of business decision-making.
Artificial intelligence use cases
Artificial intelligence applications can be seen in everyday scenarios such as financial services fraud detection, retail purchase forecasting, and online customer service interactions. These are just a few examples:
fraud detection. The financial services industry uses artificial intelligence in two ways. Initial loan application scoring uses AI to understand creditworthiness. More advanced artificial intelligence engines are used to monitor and detect fraudulent payment card transactions in real time.
Virtual Customer Service (VCA). Call centers use VCA to predict and respond to customer requests outside of human interaction. Speech recognition, with simulated human dialogue, is the first point of interaction in a customer service request. Top level requests are routed to a human.
When a person initiates a dialogue on a website through a chatbot, the person is often interacting with a computer running a specialized AI. If the chatbot cannot interpret or answer the question, a human steps in to communicate directly with the person. These non-interpretative instances are fed into a machine learning computing system to improve the application of AI for future interactions.
Advances 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 by 2022, up to 70% of people will interact with conversational AI platforms daily. NLP and CV represent a valuable link between humans and robots: NLP helps computer programs understand human language, and CV applies machine learning models. to images and is perfect for everything from selfie filters to medical imaging.
NetApp and artificial intelligence
As the authority on hybrid cloud data, NetApp understands the value of data access, management and control. The NetApp® Data Fabric provides a unified data management environment spanning edge devices, data centers, and multiple large-scale clouds. The Data Fabric gives organizations of all sizes the power to accelerate critical applications, gain insight into data, improve data protection and increase operational agility.
NetApp AI solutions are based on the following key components:
- ONTAP® software enables AI and deep learning both on-premises and in the hybrid cloud.
- AFF 100 flash systems accelerate AI and deep learning workloads and eliminate performance bottlenecks.
- ONTAP Select software enables efficient data collection at the edge using IoT devices and aggregation points.
- Cloud Volumes can be used to quickly prototype new projects and provide the ability to move AI data to and from the cloud.
Additionally, NetApp has begun integrating 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 provide proactive customer support recommendations for complex IT environments. Active IQ is a hybrid cloud application built with the same NetApp products and technologies that our customers use to build AI solutions for a variety of use cases.
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White Clover Markets
Artificial Intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of embedded algorithms in a dynamic computing environment. Simply put, AI attempts to make computers think and act like humans.
Achieving this goal requires three key elements:
The more human the desired outcome, the more data and computing power is required.
How did artificial intelligence originate?
At least from the 1st century BC. C., humans were intrigued by the possibility of creating machines that mimic 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. This beginning has led to the development of machine learning, deep learning, predictive analytics, and now prescriptive analytics. A new field of study, data science, has also emerged.
Why is artificial intelligence important?
Today, the amount of data generated by humans and machines far exceeds the ability of humans to assimilate that data, interpret it, and make complex decisions based on that data. Artificial intelligence forms the basis of all computational learning and is the future of all complex decision-making. For example, most people can figure out how not to lose at Tic Tac Toe (three in a row), even though there are 255,168 unique moves, of which 46,080 end in a draw. With over 500 x 1018, or 500 quintillion different moves possible, far fewer people would be considered grandmasters of checkers. Computers are extremely good at calculating these combinations and permutations to make the best decision. AI (and its logical evolution from machine learning) and deep learning are the fundamental future of business decision-making.
Artificial intelligence use cases
Artificial intelligence applications can be seen in everyday scenarios such as financial services fraud detection, retail purchase forecasting, and online customer service interactions. These are just a few examples:
fraud detection. The financial services industry uses artificial intelligence in two ways. Initial loan application scoring uses AI to understand creditworthiness. More advanced artificial intelligence engines are used to monitor and detect fraudulent payment card transactions in real time.
Virtual Customer Service (VCA). Call centers use VCA to predict and respond to customer requests outside of human interaction. Speech recognition, with simulated human dialogue, is the first point of interaction in a customer service request. Top level requests are routed to a human.
When a person initiates a dialogue on a website through a chatbot, the person is often interacting with a computer running a specialized AI. If the chatbot cannot interpret or answer the question, a human steps in to communicate directly with the person. These non-interpretative instances are fed into a machine learning computing system to improve the application of AI for future interactions.
Advances 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 by 2022, up to 70% of people will interact with conversational AI platforms daily. NLP and CV represent a valuable link between humans and robots: NLP helps computer programs understand human language, and CV applies machine learning models. to images and is perfect for everything from selfie filters to medical imaging.
NetApp and artificial intelligence
As the authority on hybrid cloud data, NetApp understands the value of data access, management and control. The NetApp® Data Fabric provides a unified data management environment spanning edge devices, data centers, and multiple large-scale clouds. The Data Fabric gives organizations of all sizes the power to accelerate critical applications, gain insight into data, improve data protection and increase operational agility.
NetApp AI solutions are based on the following key components:
Additionally, NetApp has begun integrating 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 provide proactive customer support recommendations for complex IT environments. Active IQ is a hybrid cloud application built with the same NetApp products and technologies that our customers use to build AI solutions for a variety of use cases.
Read Also : Does Netflix Announce Scott Pilgrim Anime Starring Michael Cera and Full Movie Cast?