4 popular programming languages for Artificial Intelligence

In this post, we will look at 4 most popular programming languages for artificial intelligence popularly referred to as AI. The demand for artificial intelligence (AI) and machine learning ML is growing at a very fast rate. Many companies have adopted machine learning and artificial intelligence for problem solving over humans.

Artificial intelligence is application in almost all fields including medical science, robotics, military. due to the high increase in demand for AI, many programming languages have been created for AI programming, let’s look at the 4 most used programming languages below

Python

Python is the most popular and widely used programming language for artificial intelligence and machine learning. This I can say is due to its simple and concise syntax.

Some Key Features of Python

• AI programming with python is relatively fast.
• Algorithms can be put to test without first implementing them.
• large development community
• numerous libraries that supports object-oriented and procedural programming. 

Some key libraries of python for AI development are TensorFlow, SciKit-Learn, NLTK etc.

C++

The second on the list is C++. It is considered the fastest programming language for AI. C++ can be used for intensive number-crunching in a precompiled C/ C++.

C++ when combined with a hybrid of Python is ideal for flexibility and increased output for general APIs development. Some of C++ libraries for AI includes Microsoft Cognitive Toolkit, mlpack Library, Dynamic Neural Network, Shogun etc.

Prolog

Prolog is usually called the declarative programming language. It is highly used for pattern matching in AI. Prolog is best used for implementing algorithms with a large implicit search of options (finding dependencies, logical inference, searching moves).

Prolog can generate code C++, C#, Ruby and Java.

Java

Java’s cross platform compatibility makes it suitable to code for windows, linux, Andriod & iOS. Java is better and faster while performing neural networks and search engine algorithms with concurrency. It is scalable, easy to debug and has rich libraries.