How To Create Artificial Intelligence: The Basics To Know

Since 2015, when artificial intelligence (AI for short) gradually became popular, people began learning how to create artificial intelligence - a trending concept in the world technology market. 

We will explore the article below to get the most general view of how AI is built.

>>>> See More: What does AI Stand For? The Impact Of AI On Life

Principle of AI

Types of AI combine large data sets with algorithms, which are intelligently processed iteratively to learn from patterns and features in the data they analyze.

Every time the AI ​​system runs a data processing cycle, it checks and measures its performance, thereby developing more in-depth knowledge.

How to create artificial intelligence: The basic foundation

The first step on how to create artificial intelligence is to build its potential form. The form is built by combining the activities of two platforms, Deep Learning and Machine Learning. Each of these platforms is made up of a combination of various complex data and mathematical techniques.

Fundamentals of Artificial Intelligence (AI)

The basic foundation of AI

Machine Learning is a functional platform that uses data and algorithms to mimic how humans learn. From there, it will gradually improve the accuracy of the actions over time whenever new data is exposed.

The most practical example of Machine Learning in real life is the Google search engine. When we search for a keyword, it will return a lot of results. As we spend more time looking at something, it can be interpreted as helpful information. Hence, if we find that keyword later, it will adjust itself to give more and more accurate results.

Deep Learning is an artificial neural network with many layers. These layers’ function is learning and improving, in order to make conclusions about objects and events. This is quite similar to how the human brain thinks. Deep Learning works by testing all the computer algorithms and continuously analyzing the data according to a specific logical structure.

For example, Facebook's image recognition technology will automatically send you a notification when a stranger posts a picture of you on their personal Facebook page, which is typical of Deep Learning. Because Facebook has continuously analyzed your photos every time you post on your profile, through those images, Facebook can draw conclusions about exactly which is your face.

>>>> See More: History of artificial intelligence and the most significant milestones

1. Common mathematical models in AI development

1. Heuristics.

This technology is capable of solving problems faster than classical methods. Also, it can find solutions that classical methods cannot. Therefore, heuristics are applied to AI to help solve complex problems.

2. Support Vector Machines.

These are supervised algorithms used for regression analysis or classification. This method is widely used in facial, text, and image recognition AI systems.

3. Artificial Neural Networks.

This technique simulates artificial neural networks similar to the human brain. These networks help identify and learn patterns from data. They are used in Machine Learning, Deep Learning, and pattern recognition.

4. Markov Decision Process (MPD).

As a framework for modeling decision-making, MDP is developed to assist decision-makers by indicating how specific actions should be taken.

5. Natural Language Processing.

A technique used by computers to understand, interpret and manipulate human language. This technique is useful for AI speech recognition and speech synthesis.

>>>> See also: Answer questions about artificial intelligence

Popular mathematical techniques in AI

Popular mathematical techniques in AI

2. Programming languages ​​- The foundation for building AI

After successfully developing many types of AI in its potential form, the next step is creating it in a specific application form. This is mainly done using four basic languages to program AI application platforms.

1. C++

As a mid-level general-purpose programming language with features: procedural programming, generic programming, polymorphic programming, etc., C++ was chosen for AI programming because it is quite flexible and often used for AI development in the manufacturing sector.

2. Java

It is a type of programming language that focuses on providing advanced features. Besides, Java has compatibility with most platforms, flexibility, ease of use, and rich support, so it is quite favored in developing artificial intelligence.

AI programming languages

AI programming languag

3. Python

It is a widely used, high-level, general-purpose programming language with a simple syntax. Python is capable of handling complex processes for different tasks and platforms, making it well-suited for AI development.

4. Lisp

It is a brilliant and adaptable programming language that is especially suitable for problem-solving, writing self-modifying code, creating automated objects, and rapid prototyping. Lisp programming language is mainly used in Machine Learning.

Hope this article has given you a basic idea of how to create artificial intelligence. If you are interested and want to know more about artificial intelligence, please visit Viindoo regularly to keep up-to-date. 

>>>> See Also:

How To Create Artificial Intelligence: The Basics To Know
Viindoo Technology Joint Stock Company, Van Anh Nguyen June 23, 2022