Artificial intelligence Write For Us
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Deep Tech Bytes majorly covers Data Science, Machine Learning, Artificial Intelligence, Technology, AI software, Programming, Business News, Startup Stories, and topics that matter to the masses. If you want to contribute an interesting article around these topics, we welcome you to submit your report in line with our editorial guidelines listed below.
How did Artificial Intelligence Originate?
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, prescriptive analytics. It also gave rise to a new field of study, data science.
Why is Artificial Intelligence Important?
Today, the amount of data generated by humans and machines far outpace 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. For 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. Fewer folks would be considered grand champions of checkers, with more than 500 x 1018, or 500 quintillions, different potential moves. Computers are highly efficient at calculating these combinations and permutations to make the best decision. AI (and its logical evolution of machine learning) and deep learning are the foundational future of business decision-making.
Artificial Intelligence Write for us use cases
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 reply to client inquiries outside human interaction. Voice recognition, joined with simulated human dialog, is the first point of interaction in a customer service inquiry. Higher-level questions 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 particularly AI. If the chatbot can’t understand or address the inquiry, a human intervenes to communicate directly with the person. These noninterpretive examples are fed into a machine-learning computation system to improve the AI application for future connections.
Advancements in AI for applications like natural language dispensation (NLP) and computer dream (CV) are helping industries like financial services, health care, and automotive accelerate invention, improve customer experience, and reduce costs. Gartner estimates that up to 70% of people will interact with conversational AI stages daily by 2022. NLP and CV provide a valuable link between humans and robots: NLP helps computer plans understand human speech.
NetApp and Artificial Intelligence Write for us
As the data authority for the hybrid cloud, NetApp understands the value of data access, management, and control. The NetApp® data fabric provides a unified data running environment across edge devices, data centers, and multiple hyper-scale clouds. The data fabric allows organizations of all sizes to hasten critical applications, gain data visibility, streamline data protection, and increase operational agility.
NetApp AI solutions are based on the following fundamental building blocks:
ONTAP software enables AI and profound learning both on-premises and in the hybrid cloud. AFF all-flash systems accelerate AI and deep knowledge workloads and also remove performance blocks.
ONTAP Select software enables efficient data collection using IoT devices and also aggregation points at the edge. Cloud Volumes can rapidly prototype new projects and provide the skill to change AI data to and from the cloud.
In addition, NetApp has begun joining big data analytics and artificial intelligence into its harvests and services. For example, Active uses billions of data points, prognostic analytics, and powerful machine learning to deliver proactive customer support references for complex IT environments. Active IQ is a hybrid cloud request built by means of the same NetApp products and also technologies our clienteles use to build AI solutions for various use cases.
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