More and more people are cashing in on the act of using Artificial Intelligence to do all manner of deeds.

In fact, AI is dubbed as the new big thing — the ‘woke’ in technology.

Shying away from its use is not kosher.

Consequently, people are encouraged to embrace its use in education and across industries.

So, here I am in a source conversation with bing.com searching to find out what AI has to say about itself, and this is what it throws up:

AI stands for Artificial Intelligence — the ability of a computer or a machine to perform tasks usually done by humans, such as learning, reasoning, or understanding natural language.

AI can help to solve many challenging problems, such as recognizing faces, translating languages, driving cars, or creating art.

Artificial Intelligence also covers a broad field that aims to create machines and systems that can perform tasks typically requiring human intelligence.

For example, AI can help computers understand natural language, recognize images, play games, or make decisions.

It can also help solve complex problems in various domains, such as health, education, business, or entertainment.

AI is not just one thing but a collection of methods and techniques that can be applied to different problems and goals.

Some of the sub-fields of AI include machine learning, deep learning, computer vision, natural language processing, speech recognition, robotics, and more.

Each of these sub-fields has its own challenges and applications.

AI is constantly evolving and improving as new research and data become available.

Difference between machine learning and deep learning

Machine learning and deep learning are both types of artificial intelligence that use data and algorithms to learn and improve over time.

The main difference between them is that machine learning can work with smaller and simpler data sets, while deep learning requires large and complex data sets.

Machine learning relies more on human intervention to correct and guide the learning process, while deep learning can learn independently from the environment and past mistakes.

Machine learning makes simple and linear correlations between inputs and outputs, while deep learning makes non-linear and complex correlations that can capture more nuances and details.

Machine learning can train on a regular CPU (central processing unit), while deep learning needs a specialized GPU (graphics processing unit) to train faster and more efficiently.

Machine learning and deep learning have different applications and challenges, depending on the problem and the goal.

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Readers Bureau, Contributor

Edited by Jesus Chan

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