Mechanism of artificial intelligence (AI) and its application [Updated]

AI Basics

A new buzzword in the tech world, artificial intelligence (AI) is poised to change the way future generations work. But what exactly is AI and how does it work? You may not be aware of it, but you probably interact with it on a daily basis. From smartphones to chatbots, AI is already pervasive in many aspects of our lives. Increased investment in this area and increased use of AI in the enterprise space show how the job market for AI professionals is warming.

What is AI

Let’s start this tutorial by understanding what AI is and how it works. AI is perhaps one of the most exciting advancements we’re experiencing as humans. It is a branch of computer science dedicated to creating intelligent machines that act and react like humans.

The next section of this tutorial will cover AI types.

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Types of AI

There are four main types of AI. they are:

1. Reactive Machine

This kind of AI is purely reactive and does not have the ability to form ‘memory’ or use ‘past experience’ to make decisions. These machines are designed to perform specific tasks. For example, programmable coffee makers and washing machines are designed to perform specific functions but have no memory.

2. Memory limit AI

This kind of AI uses past experience and current data to make decisions. Limited memory means the machine can’t come up with new ideas. They have embedded programs that run memory. Reprogramming is done to make changes to such machines. Self-driving cars are an example of limited memory AI.

3. Theory of Mind

These AI machines can socialize and understand human emotions, and have the ability to cognitively understand someone based on their environment, facial features, and more. A machine with such capabilities has not yet been developed. A lot of research has been done on this type of AI.

4. Self-awareness

This is the future of AI. These machines become hyperintelligent, sentient, and conscious. They may have their own unique traits, but they can react very much like humans.

The next section of this tutorial will help you better understand how to implement AI correctly.

How to implement AI

Let’s explore the following methods that describe how to implement AI.

machine learning

Machine learning is what gives AI the ability to learn. It does this by using algorithms to discover patterns and generate insights from publicly available data.

deep learning

Deep learning, a subcategory of machine learning, gives AI the ability to mimic the neural networks of the human brain. Understand the sources of patterns, noise, and clutter in your data.

Consider the image shown below.

Labeled photo ai.

Here, we used deep learning to separate different types of images. The machine looks at different features in the photo and distinguishes between them in a process called feature extraction. Based on the characteristics of each photo, the machine classifies them into various categories such as landscape, portrait and others.

Let’s understand how deep learning works.

Consider the image shown below.

hidden layer ai

The image above shows the three main layers of a neural network.

  • input layer
  • hidden layer
  • output layer

input layer

The image you want to separate goes into the input layer. Arrows are drawn from the image to individual dots in the input layer. Each white dot in the yellow layer (input layer) is a pixel in the image. These images fill the white dots in the input layer.

As you progress through this artificial intelligence tutorial, you should have a clear understanding of these three layers.

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hidden layer

Hidden layers are responsible for all mathematical computations or input feature extraction. In the image above, layers shown in orange represent hidden layers. The lines you see between these layers are called “weights”. Each of them typically represents a float or decimal number and is multiplied by the value of the input layer. All weights are added in hidden layers. Dots in hidden layers represent values ​​based on the sum of the weights. These values ​​are passed to the next hidden layer.

You may wonder why there are multiple layers. Hidden layers act as an alternative to some extent. The more hidden layers there are, the more complex the input data and the data that can be generated. The accuracy of the prediction output usually depends on the number of hidden layers present and the complexity of the input data.

output layer

The output layer will give you an isolated photo. The layer sums all these weights to determine if the photo is portrait or landscape.

Example – airfare forecast

This forecast is based on a variety of factors, including:

  • Airlines
  • departure airport
  • destination airport
  • departure date

To train the machine, we start with historical ticket price data. As machines are trained, they share new data that predicts costs. Earlier, when we learned about the four types of machines, we talked about machines with memory. Here we will only discuss memory and how to understand patterns in the data and use them to predict new prices as shown below.


After this tutorial, let’s look at how AI works and the applications of AI.

Mechanism of AI and application of AI

A common AI application we see today is automatic switching of home appliances.

When you enter a dark room, room sensors detect your presence and turn on the lights. This is an example of a non-memory machine. Some of the more sophisticated AI programs can even predict your usage patterns and turn on your appliances before you explicitly tell them to.

Some AI programs can identify your voice and take action accordingly. When you say “Turn on the TV,” the TV’s sound sensor detects your voice and turns it on.

With a Google dongle and a Google Home Mini, you can actually do this every day.

The final section of this artificial intelligence tutorial explores a use case for AI in healthcare.

Use Case – Predicting if a person has diabetes

AI has some great use cases, and this section of the tutorial will help you better understand them, starting with the application of AI in healthcare. The problem statement predicts whether a person has diabetes. In this case, certain information about the patient is used as input. This information includes:

  • number of pregnancies (for women)
  • glucose concentration
  • blood pressure
  • Year
  • insulin level

Check out Simplilearn’s video on “Artificial Intelligence Tutorial” to see how the model for this problem statement is created. This model is implemented in Python using TensorFlow.

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AI is redefining how business processes are executed in areas as diverse as marketing, healthcare, and financial services. Businesses are continually looking for ways to profit from this technology. With calls for improvements to current processes growing, it makes sense for the expert to acquire his AI expertise.

If you found this tutorial helpful, also check out the Caltech AI course. This course teaches fundamental concepts such as AI, data science, machine learning, and deep learning with TensorFlow. Apart from theory, you will also have the opportunity to apply your skills in solving real-world problems through industry-oriented projects. So start your career in AI today and be successful!

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