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What is Artificial intelligence & How AI system builds

 

Artificial intelligence

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. Major Artificial intelligence textbooks define the field as "the study and design of intelligent agents", where an intelligent agent is a system that senses its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of building intelligent machines".

Artificial intelligence (AI) was founded on the claim that a central property of man, intelligence—the dexterity of Homo sapiens—can be described so accurately that it can be simulated by a machine. It raises philosophical issues about the nature of the mind and the limits of scientific pride, which have been addressed by myth, fiction and philosophy since ancient times. Artificial intelligence has been the subject of breathtaking optimism, has faced surprising setbacks and today, has become an essential part of the technology industry providing the heavy lifting for many of the toughest problems in computer science.

Artificial intelligence research is highly technical and specialized, so much so that some critics condemn the "fragmentation" of the field. Subfields of Artificial intelligence are organized around particular problems, the application of specialized tools, and long-standing theoretical differences of ideas. The central problems of AI include such traits as reasoning, cognition, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") is still a long-term goal of (some) research, while many researchers no longer believe it is possible.

 

Key Technologies of Artificial intelligence (AI)

For Artificial intelligence (AI) to be successful, it requires machine learning (ML), which is the use of algorithms to parse data, learn from it, and make a determination or prediction without the need for explicit instructions. Thanks to advances in computation and storage capabilities, machine learning (ML) has recently evolved into more complex structured models, such as Deep Learning (DL), which use neural networks for even greater insight and automation. Natural language processing (NLP) is another trend that has driven recent AI advancement, particularly in the field of virtual home and IT assistants. NLP uses vocal and word-based recognition to simplify interfacing with machines through natural language prompts and questions

 

How to build artificial intelligence (AI) system

Without the right artificial intelligence (AI) strategy, IT cannot meet today's stringent network requirements. There are a number of technical elements here that an AI strategy should include.

Data: Any meaningful AI solution starts with a massive amount of quality data. AI continuously builds its intelligence over time through data collection and analysis. The more diverse data is collected, the better the AI ​​solution becomes. In the case of real-time applications involving highly distributed "edge" devices such as IoT and mobile devices, for example, it is important to collect data from each edge device in real time, then process it locally or very close to the edge. Important is computer or cloud using AI algorithms.

Domain-specific expertise: Whether helping a doctor diagnose cancer or enabling an IT administrator to diagnose wireless problems, AI solutions require labeled data based on domain-specific knowledge. These metadata chunks help the AI ​​break down the problem into smaller segments that can be used to train an AI model. This task can be achieved by using design intent metrics, which are structured data categories to classify and monitor the wireless user experience.

Data Science Toolbox: Once the problem has been broken down into domain-specific chunks of metadata, this metadata is ready to be fed into the powerful world of ML and big data. Various techniques, such as supervised or unsupervised ML and neural networks, must be employed to analyze the data and provide actionable insights.

Virtual Network Assistant. Collaborative filtering is an ML technique that many people experience when they select a movie on Netflix or buy something from Amazon and receive recommendations for similar movies or items. Beyond recommendations, collaborative filtering can be applied to sort through large data sets and to identify and correlate those creating AI solutions to a particular problem.

In AI for networking, a virtual network assistant can act as a virtual wireless specialist in a wireless environment that helps solve complex problems. Imagine a virtual network assistant that combines quality data, domain expertise and syntax (metrics, classifiers, root causes, correlations and rankings) to provide predictive recommendations on how to avoid problems and address existing issues provides actionable insights on the way. It can learn the nuances of wireless networks and "what went wrong?" Can answer questions like and and "Why did this happen?" These are the types of automatic progression that AI is enabling.

 

What are the benefits of Artificial intelligence (AL)?

With Artificial intelligence (AL) come a lot of hype and it can be misleading and create false hopes. But AI for networking is very real and is already providing substantial value to companies in nearly every industry. There are many examples of how AI-powered networks can help your environment.

Detection of time series anomalies: Many of the devices that run on today's networks were invented 20 years ago, and they do not support current management messages. AI can detect time series anomalies with correlation that allows network engineers to quickly find relationships between events that would not be obvious even to an experienced network expert.

Event correlation and root cause analysis: AI can use a variety of data-mining techniques to detect terabytes of data in minutes. This capability lets IT departments quickly identify which network feature (for example, OS, device type, access point, or switch) is most related to a network problem, expediting problem resolution.

Predicting user experiences: Today, application bandwidth segmentation occurs largely through capacity planning and manual adjustment. Soon, however, AI will be able to predict a user's Internet performance, thus allowing a system to dynamically adjust bandwidth capacity based on which applications are in use at specific times. Manual planning will give way to predictive analysis which is informed by historical trends and current calendar information.

Self driving: Artificial intelligence enables IT systems to self-improve for maximum uptime and provide instructional actions to fix problems that arise. In addition, AI-powered networks can capture and save data before a network event or outage, helping to expedite troubleshooting.

Today, the convergence of many different technologies is enabling AI to completely disrupt the networking industry with new levels of insight and automation. AI helps reduce IT costs and it helps businesses achieve their goal of providing the best possible IT and user experience.

 

 

Frequently Asked Question (FAQ)

 1. Am I already using Artificial intelligence (AI)?

Ans: - Yes. Almost everyone who has a computer, smartphone or other smart device is already using AI to make life easier

 

2. Why do I need Artificial intelligence (AI) for my business?

Ans: - Artificial intelligence (AI)? is already changing the expectations of your customers. Think of the consumer who lives near Uber, Google, and Amazon. If he goes to a department store to buy a suit, what does it take to provide him with the same level of service he has become accustomed to?

Retailers must know who he is because he has bought something online. They should know its size and choice based on its purchase history. And they should be able to suggest the perfect pair of shoes to go with whatever he or she chooses.

The same principle applies to every type of business. Customers know you have their data. They know everything you can do with it. And they expect you to use it to provide fast, smart, personal engagement in every conversation.

 

 3.  What are the main challenges with Artificial intelligence (AI) technology?

Ans: - Think of AI as an iceberg. What you see as a user is just the tip - but beneath the surface is a vast support system of data scientists and engineers, massive amounts of data, labor-intensive extraction and preparation of that data, and a vast technology infrastructure.

It takes a specialized team of data scientists and developers to access the right data, prepare the data, build the right models, and then integrate the predictions into the end-user experience like a CRM.

 


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