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Difference between AI and Neural Network
Once thought of as science fiction or a technology of the future, artificial intelligence (AI) is now a reality. What was formerly reserved for academic inquiry has broken out into the open. Artificial intelligence (AI) is already present in nearly every aspect of modern life, from the workplace to the classroom to the bank to the hospital to your mobile phone. These are the minds behind the sensors in self-driving vehicles, the personalities of virtual assistants like Siri and Alexa, the experts who predict the weather and perform robotic surgery, and so much more. These days, AI can be found just about wherever in today's world. In the last several years, artificial intelligence has emerged as a key component of many different types of IT technology.
Later on, a sophisticated AI method called machine learning evolved, which lets computers learn from the data they're provided without any prior instruction. It doesn't stop there. The programming techniques were based on a set of computational methods known as deep learning.
While the term "deep learning" has only been around for the past few years, the underlying technology was established in the 1960s by the area of biologically motivated neural networks. Neural networks have made a strong comeback in recent years, although under new names and definitions and applied to new areas of study within the current wave of AI and deep learning.
What is Artificial Intelligence?
Machines that exhibit intelligence similar to that of humans are said to have artificial intelligence (AI), also known as machine intelligence. It's the concept of building machines with intelligence on par with or above that of humans. Although the principle is centuries old, it has become widespread with the emergence of digital computers.
AI a subfield of computer science concerned with designing intelligent machines that can do specific jobs at par with or better than human beings, without the need to simulate human thought processes. AI may be found in a wide range of shapes, sizes, and algorithmic complexity. It is used in nearly every industry today, from manufacturing to education to banking to healthcare to even your toothbrush, your automobile, and your smartphone.
What is Neural Network?
The term "Neural Networks" is used to describe a system of virtual neurons or nodes that is loosley modelled after the neural networks that make up the brains of various animals. A lot of today's AI has its roots in this technique. In fact, research indicates that the current implications and applications of AI are just the result of the evolution of the special qualities of neural networks (such as machine learning, deep learning, etc.).
Computer science, physics, information science, psychology, and engineering all have a hand in developing and refining the neural network paradigm. Neural networks are networks of nodes whose functioning is inspired by animal neurons but only in a very general way. Neural networks are widely employed in many fields today, from issue solving and consumer research to data validation and sales forecasting and risk management.
Differences: AI and Neural Network
The following table highlights the major differences between AI and Neural Network −
Characteristics |
AI |
Neural Network |
|---|---|---|
Definition |
Artificial Intelligence (or AI) is a branch of computer science that emphasizes on the creation of smart machines that embody a sort of intelligence, as opposed to the natural intelligence demonstrated by humans. AI refers to machines that are able to mimic human cognitive skills. |
Neural Networks, on the other hand, refers to a network of artificial neurons or nodes vaguely inspired by the biological neural networks that constitute animal brain. Neural networks are a network of interconnected nodes, whose functionality is loosely based on animal neurons. |
Goal |
AI basically works around strong AI and weak AI. Weak AI refers to the use of advanced algorithms to carry out certain problem solving tasks within a limited set of functions whereas strong AI supports the view that machines can really develop human consciousness equal to humans. |
Neural networks represent a well- grounded paradigm rooted in many disciplines, including computer science, physics, information science, psychology, and engineering. The purpose of neural networks is to learn to recognize patterns in your data for making a decision. |
Applications |
Today AI is a ubiquitous part of the modern society. AI is used across a wide range of application areas from self-driving cars to Siri and Alexa, and weather forecasting and clinical diagnosis. AI is used to solve complex tasks across all kinds of industries, including education, health, entertainment, transport, and utilities. |
Neural networks develop algorithms using brain as a model to recognize complex patterns. The applications of artificial neural networks include text classification and categorization, named entity recognition (NER), paraphrase detection, pattern recognition, fraud detection, natural language processing, and more. |
Conclusion
AI and NN are two distinct concepts that are often confused with each other. AI is a broader field that encompasses various methods and technologies for simulating human intelligence, while NN is a specific type of AI algorithm that models the structure and function of the human brain. NNs are used for complex tasks such as computer vision, speech recognition, and pattern recognition, while AI has a wider range of applications in various industries.
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