6 Best Programming Languages ​​for AI

Author(s): Tirendaz Academy

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You should know at least one of these programming languages if you want to build a career in AI.

You’ve probably seen what artificial intelligence (AI) can do in science fiction movies like Terminator or Her. Scientists say that the greatest invention in history is AI.

Soon, there will be no industry without AI. Applications such as driverless cars, computer vision, and natural language processing have been able to develop using AI in recent years. AI is now deciding for us what to watch, what to listen to, and what to buy. If you are planning a career in AI, I would highly recommend that you start learning a programming language first. In this post, I’ll talk about 6 programming languages used for AI.

First of all, let’s take a look at what AI is.

What is AI?

The brain is the most wonderful organ of the human body. The brain controls thought, memory, emotion, motor skills, vision, breathing, and touch. This complex structure of the brain became a source of inspiration for scientists and the concept of AI emerged. AI is the ability of a computer or robot to perform humanoid tasks.

Let me explain the history of AI. First, Alan Turing, a founding father of AI, came up with the question that “Can machines think like humans?”. Later, John McCarthy created the term “Artificial Intelligence” and invented the programming language LISP, played computer chess via the telegraph with opponents in Russia, and invented computer time-sharing. At that time, the computer was big enough to fill a room. But the concept of AI has created great hope and enthusiasm for the world of science and technology.

While AI was seen in the 60s and 70s as a computer skill that could play chess and checkers, perform simple calculations, and solve mathematical problems, in the 80s and 90s it was seen as a risk assessment and decision-making ability, and in the 2000s, with the development of the computational potential of computers, it was understood that learning systems could be possible.

In recent years, AI has focused on tasks that only humans can do, such as image and voice recognition. Thus, the following problems that were previously unsolvable were now overcome with AI.

• Image recognition
• Object recognition
• Language-to-language translations
• Natural language comprehension
• Image and speech recognition
• Assistant assistants
• Driverless cars

Let’s dive into the programming languages ​​you can use for AI projects.

1. Python

Python is the most used programming language to develop AI applications. Python was developed by Guido van Rossum in the 1990s. YouTube, Instagram, Pinterest, and SurveyMonkey are all built using Python.

Python is a syntax-easy, general-purpose, interpretive, and object-oriented programming language. With Python, you can build both AI applications and projects such as website and game development.

In addition, Python is one of the reasons AI has become so popular in recent years. The most used libraries for AI such as Scikit-Learn, Keras, TensorFlow, and PyTorch were written in Python.

Python is taught as a beginner language in many universities because it is easy to learn. You can carry out projects related to AI, data science, machine learning, deep learning, and data analysis with Python

2. Java

Another important programming language for AI is Java. Java was first developed in 1989 under the name Oak. It was later modified in 1995 and became Java as we know it. For server-side application development, Java is undoubtedly king. The most used language, especially for creating Android applications, is Java.

You can use Java for AI projects. There are many frameworks written in the Java language. For example, TensorFlow Java can run on any JVM to build, train, and deploy machine learning models. TensorFlow Java supports both CPU and GPU. Frameworks for neural networks written in Java are Neuroph and Deeplearning4j. Frameworks for natural language processing are Apache OpenNLP, and Stanford CoreNLP. Frameworks for machine learning are Java Machine Learning Library (Java-ML), RapidMiner, Weka, and Deep Java Library (DJL).

3. Julia

Julia is a high-performance, flexible language used for scientific and numerical calculations. Julia’s syntax is similar to Python. So you can learn easily. Julia has been downloaded over 34.8 million times and the Julia community has registered over 6,800 Julia packages for community use. In addition to these, you can easily use libraries from Python, R, C/Fortran, C++, and Java.

As you know, Python is an interpretive language. So if you run a line of code, you can see the output immediately. Unlike R and Python, Julia is compiled, like C or Fortran, so it’s fast.

Complex algorithms run faster the more cores they use. Julia uses all cores. It makes the calculations by utilizing the parallel programming feature by distributing the calculations to the cores. So, you can use Julia to do scientific calculations quickly. Julia can also be integrated with TensorFlow.jl, MLBase.jl, and MXNet.jl.

4. C++

C++ was very popular in the late 20th century because it is a low-level programming language. The performance of low-level programming languages ​​is fast. C++ is still used to develop operating systems, file systems, and other system-level applications.

C++ is a general-purpose, cross-platform programming language created as an extension of C by Bjarne Stroustrup of Bell Laboratories in the early 1980s. C++ is also used for AI, although it is not the language of the first choice. C++ is an advantageous language as it translates user code into computer-readable machine code. You can use frameworks like TensorFlow and Caffe are written in C++ to build AI projects.

5. JavaScript

JavaScript is an object-oriented programming language used to create dynamic websites. You can build dynamic web pages and also develop web applications with this language. Thanks to AI libraries written in JavaScript, you can develop AI applications without the need for another programming language. With AI, JavaScript developers can build smarter web applications.

JavaScript has machine learning libraries and modules. TensorFlow.js is a JavaScript framework for training and deploying machine learning models in the browser and Node.js. This library aims to build machine learning models for mobile, desktop, cloud, and web. Other JavaScript frameworks you can use for your AI projects are Brain.js, ConvNetJS, and Neataptic.

6. R

If you’ve done machine learning or data analysis projects, you’ve probably heard of the R programming language. R was originally developed by Ross Ihaka and Robert Gentleman in 1993 to implement the S programming language, which focuses on statistical computation and graphical modeling.

Over the years, R has become an open-source language that allows statistical data analysis and graphing. R became popular very quickly. R is used not only by statisticians but also by economists, geneticists, agronomists, biologists, and the business world. R also works quite well with code from other programming languages ​​such as C, C++, Python, Java, and .NET.

Although R is mostly used for data analysis and machine learning, it is also used for AI projects. You can build a model and deploy it with TensorFlow using R. You can use R packages such as caret, mlr, and XGBoost for machine learning projects

Closing Thoughts

AI is an automatic process where the computer can do the things that human intelligence can do. AI is a great field and is used in many industries. Many companies integrate their work with AI. Thus, companies save both time and money. You first need to learn a programming language for AI projects. In this post, I talked about 6 programming languages ​​for AI. Among these languages, Python is the most used. I would say that other programming languages are good in some cases. But Python has become the lingua franca of data scientists and researchers for performing experiments in deep learning. If you’re just getting started with AI, I highly recommend learning Python.

That’s it. Thank you for reading. I hope you enjoy it. Don’t forget to follow us on YouTube | GitHub | Twitter | Kaggle | LinkedIn. You may be interested in the following posts.

• 8 Best Seaborn Visualizations
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