5 Popular Programming Languages for Artificial Intelligence

11 Jan 2021

According to Statista, the AI market is expected to generate $126 billion of revenue by the year 2025. The revenue generated by the AI software market currently stands at around $22 billion.

Statistics never lie, and it’s evident that the Artificial Intelligence software market is going to boom in upcoming years.

AI Industry Stats
Source: Statista

There is a lot of room for new developers to break into this immense market. The AI market is still in its development stage, and many companies are hiring AI developers to enhance their traditional systems.

If you believe that AI is going to bring about the next big technological evolution and you want to be a part of it, then here are a few languages that you can learn to prepare yourself for future opportunities:

1. Python

Python is a leading choice for beginners and professional programmers alike because of its flexibility and its multiple uses. The language is being widely used to program AI-based software for robots and other machinery.

Python is an open-source language with large community support that keeps introducing new libraries for various purposes.

There are numerous Python frameworks and libraries used for app development, game development, and even web development. If we specifically explore AI libraries, then here are our best options:

  • NumPy
  • Pandas
  • Pybrain
  • SciPy
  • Atplotlib
  • Nltk
  • SimpleAI

One benefit of Python is its easy learning curve due to its simple syntax. Moreover, the language also supports machine learning concepts using Tensorflow and PyTorch. This platform-independent language also works perfectly in combination with other AI languages.

If you want to try Python as a beginner, you should check these Python courses to get an overview of how Python connects with AI.

2. Java

Java has been around for over 25 years, and it is used in billions of devices. The language offers a virtual machine technology that makes it easier to implement on various platforms.

Java incorporates ease of use, fast debugging, automatic memory manager, and package services, which makes it an ideal choice for AI development. Moreover, Java has a wide range of applications, making it one of the most versatile programming languages used.

If we talk about the AI libraries and frameworks available for Java, then TensorFlow is on top of the list, as it also supports Java using API. Other popular library options include:

  • Deep Java Library
  • Kubeflow
  • OpenNLP
  • Java Machine Learning Library
  • Neuroph

If you want Java to be your go-to option for AI development, then go ahead and check out these best Java courses to understand language basics and algorithm concepts.

3. Lisp

Lisp stands for List Processing and it is one of the oldest programming languages, dating back to the age of Fortran. The language was introduced in 1958 to deal with mathematical notations in programs.

With the advancement of AI, Lisp caught the eye of many developers due to its concepts and support for Artificial Intelligence. Lisp became highly popular due to its supportive AI and Machine Learning features. These features include:

  • Dynamic Object Creation
  • Flexibility
  • Garbage Collection
  • Rapid Prototyping

Lisp has a range of uses but it’s mostly known for AI and machine learning algorithms. Some other popular programming languages such as Julia and R are also influenced by Lisp.

Although Lisp is compatible with AI development, many developers still do not prefer it due to a lack of libraries and numerous flaws. There are much better options available such as C++ and Prolog.

If you still want to learn Lisp for an understanding of some basic concepts, then you can check some Lisp courses to see how this language works with Artificial Intelligence algorithms.

4. Prolog

Prolog is an ideal option for NLP (Natural Language Processings), and it’s also being used for AI development. The language was developed in 1972 and it was mainly used to create chatbots. The first-ever chatbot, “ELIZA,” was also created with Prolog.

Mechanisms and concepts like automatic backtracking and pattern matching are the base for this language. These features facilitate AI development to integrate better solutions. The main use of Prolog language is in logical programming.

If you already know C++, then it is not recommended to go for Prolog because it will mess up your C++ concepts. Some concepts might feel odd or bizarre, but still, it is a great option to consider if you want to study logical programming.

If you just want to get a taste of logical programming, then check Prolog courses to strengthen your logical and AI concepts.

5. C++

C++ works considerably well with search engines, neural networks, and games. Google Chrome is also built on C++, where AI algorithms are used to some extent. The language is known for its quick execution and ability to develop complex algorithms.

C++ is not a good choice for beginners because of its complex syntax. Another downside of C++ is that it does not support garbage collection, but it still manages to handle large projects efficiently.

There are numerous implementations of C++ in game development, browsers, banking applications, and cloud systems, etc. If you want to use it for machine learning concepts, then the language API can also be used with TensorFlow.

The language is overall used for productivity and fast loading applications with a touch of AI. If you already have a programming background, then these best C++ courses will aid you throughout your development journey.

To Sum up

All of these languages have their own pros and cons. Some of them are outdated, while other languages have limited libraries. The best option at this moment is Python because it offers various AI libraries to work with, and the language is also fairly easy to learn.

The second-best option, according to our preference, is Java. If you want to take advantage of future AI development opportunities, then do not forget to check the best AI courses list for complete guidance about language choices and scope.

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