What is Artificial Intelligence? Types and Uses of AI

What is Artificial Intelligence? Types and Uses of AI

Artificial Intelligence (AI) is similar to Machine Learning (ML), however, AI requires no programming. AI algorithms have been created to perform specific tasks without any human intervention. There are three types of AI; rule-based, statistical modeling, and neural networks. Rule-based systems use keywords or rules to determine what actions need to take place at various times. Statistical models are designed to make predictions using data analysis. Neural networks create their own model of how things work. Once developed, these algorithms can then be adapted by humans to suit our needs. While each of these methods has its advantages, they do share some similarities. Both require a training phase where the algorithm learns about the environment in which it operates.

What Is Machine Learning?

Machine learning is a sub-field of AI that takes information from past experiences or data to predict future outcomes. To train the ML algorithm, we give examples of how situations play out in order to provide the necessary information for predictions. These predictions are then used to adjust behavior in subsequent interactions. The goal of this type of learning is to automate routine tasks. In contrast, AI uses programs to achieve goals (i.e., playing chess).

Why Use AI?

The application of AI is vast and continues to expand in many different fields. According to Gartner Inc., it will become a $36 billion market by 2020. As AI becomes more prevalent, businesses will look to leverage it to increase productivity and efficiency. It may seem obvious to use AI in business to assist workers in repetitive tasks, however, AI can also help employees who deal with complex problems. Businesses using AI can reduce costs, improve customer service, and enhance operations.

Types of AI

There are four major types of AI: rule-based, statistical modeling/machine learning, deep learning, and natural language processing. Each of these has its strengths and weaknesses. Let’s briefly discuss them below.

Rule Based Systems

This traditional method of AI relies on programmed rules to carry out certain tasks. These rules can be written in plain English or coded into software. A rule base system might read text, scan images, and identify objects in a scene. When applied to the real world, it would detect faces, recognize animals, and search for objects.

Statistical Modeling/Machine Learning

A statistical modeling approach uses mathematics to predict future states. It works off of observations and previous data to make decisions. Examples in business could be predicting how much profit a company should expect or assessing the success of marketing campaigns. Similar to rule-based systems, a statistical model might analyze data and make predictions. However, it does not rely on preprogrammed rules. Instead, it develops its own set of rules over time.

Deep Learning

Deep learning technology is a subset of AI. It refers to AI techniques that operate at multiple levels of abstraction. Deep learning involves layers of processing units that can interpret input patterns and classify data based on abstractions. An example of this type of AI would be facial recognition software. If a user uploads a picture of himself to the app, it will compare the image to several others stored online. Then, it will find matches based on features like eyes, nose, mouth, and skin tone. Finally, the program pulls together all the clues and determines if the person in the photo is the owner of the account.

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