9 Best Programming Languages for AI 2023 Project Guide

It ranks among the most popular programming languages after Python, C, and Java. TensorFlow, Caffe, Microsoft Cognitive Toolkit , mlpack Library, DyNet, FANN, OpenNN, Shogun are several popular AI-ML libraries for C++. Though Rust doesn’t have the established machine learning ecosystem that some other languages do for machine learning, several ambitious projects have emerged from it. If you’re looking for a high-performance, fast, and safe software development language, Rust is a good option. Developers who are familiar with C++ find Rust contains similar syntax, although it offers memory protection and prevents garbage collection.

Artificial Intelligence is on everybody’s mind—especially businesses looking to accelerate growth beyond what they’ve previously been able to achieve. With AI, your business can save time and money by automating and optimizing typically routine processes. Once AI is in place, you can be sure that those tasks will be handled faster and with more accuracy and reliability than can be achieved by a human being. Wolfram makes it possible to express complex notions in computational form.

How to Choose a Programming Language for an AI Project

Many machine learning and deep learning libraries are written in C++. C++ is typically used in the context of resource-intensive AI applications that require quick execution. This dynamic programming language is designed to excel at numerical analysis and computational science. Developed by MIT in 2012, Julia is a relatively new language—but its popularity is on the rise thanks in part to its speed, powerful computational capacity, and script-like syntax.

When it comes to AI development, there are several programming languages you can choose from. Among them, here are major 10 programming languages that are used extensively in AI and machine learning development in 2022. Java has been called the “jack of all trades” of programming languages. It is hugely popular with programmers and is starting to gain ground with machine learning practitioners as more programmers with a Java background enter ML. Aside from statistical computing, this language also features a variety of tools and packages that support creating decision trees and simplify data manipulation.

Libraries offered by Java for AI programming is also a benefit. It offers these libraries:

Programmers who don’t mind coding will choose it over Python. As soon as we get information about you and your project, our expert team will swing into action. We will do an indept analysis of your requirements and get back to you with our recommendations and feedback.

best languages for ai programming hinges on quick execution and fast runtimes, both of which happen to be Java’s superpowers. It offers excellent production value and smooth integration with all key analytic frameworks. And Haskell’s efficient memory management, type system, and code resusability practices, only add to its appeal. But that shouldn’t deter you from making it your language of choice for your next AI project. You can build neural networks from scratch using C++ and translate user code into something machines can understand. Plus, Java’s object-oriented design makes the language that much easier to work with, and it’s sure to be of use in AI projects.

R Programming Language

As we can see, AI simplifies everyday life and brings businesses to a new level. It turns manual labor into automatic mode, analyzes unstructured data, identifies patterns, builds forecasts, and warns of violations in equipment and infrastructure. 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. R also has a strong support for data wrangling and data visualization, which is crucial when working with complex datasets. With R, you can easily import, clean, and transform data, and then visualize it in different formats.

Scala debuted in 2004 as a more concise alternative built to address perceived shortcomings in Java’s design. Scala’s source code was created to run on the Java Virtual Machine, meaning that Java and Scala stacks can be integrated interchangeably. Scala supports many JVM libraries and also shares readable syntax features with other popular programming languages.

Need to source and hire remote software developers?

Many popular big data processing tools like Apache Hive, Apache Hadoop, and Apache Spark are written in Java, enabling smooth integration with these key analytic frameworks. Python tends to top the list of best AI programming languages, no matter how you slice it up. The fact that it has been around for so long and has consistently performed well as a general purpose programming language that can be used for front-end or beck-end development. It is easy to learn, produces clear, streamlined code, and works well for new as well as experienced developers.

Java’s Virtual Machine Technology helps the developers build a single version of an app that they can run on other Java-based platforms. Developers can also work on the graphics and interfaces, making them more appealing with the Standard Widget Toolkit. In all, Java helps you maintain, port, and make AI applications secure.

Python Programming Language

It’s Python’s user-friendliness more than anything else that makes it the most popular choice among AI developers. That said, it’s also a high-performing and widely used programming language, capable of complicated processes for all kinds of tasks and platforms. Scala makes the coding process fast, easy, and much more productive. The index Scaladex that has the Scala libraries and resources helps developers create some quality applications. In fact, the very term Artificial Intelligence was coined by John McCarthy, the inventor of LISP.

It has an integrated suite of software facilities for data manipulation, calculation, and graphical display. It can even be used to program microcontrollers with projects like MicroPython, CircuitPython, and Raspberry Pi. For most of its history, AI research has been divided into subfields that often fail to communicate with each other.

• Developed way back in 1983, C++ hold`s special importance in AI programming.
• Those unmentioned heroes include Go, Matlab, Haskell, Prolog, LISP, Wolfram, and Rust, along with their endless frameworks and libraries that help make AI development a dream come true.
• Torch is a machine learning library that provides a wide range of tools for building and deploying models.
• According to him, after such training, computers demonstrate behavior that they were not programmed for.
• C++ offers more control over memory management than C#, but this also means there’s more room for error.
• Here’s another programming language winning over AI programmers with its flexibility, ease of use, and ample support.

It supports a range of libraries such as TensorFlow, MXNet, Keras and more. It leverages CARAT for classification and regression training, randomForest for decision tree generation, and much more. With its simple syntax, abundant libraries, flourishing community and concise coding, Python remains a highly effective AI development programming language. Python is an interpreted, high-level, general-purpose programming language with dynamic semantics.

• R’s interactive environment is also ideal for rapid prototyping and experimentation with new problems.
• ML tools like Tensorflow, Flux, and MVBase are already taking advantage of this, allowing machine learning engineers to save time.
• You can find it in huge banks and corporations software, especially in the USA and Eastern Europe.
• But one of Haskell’s most interesting features is that it is a lazy programming language.
• It was highly influential in GUI, font editors, desktop metaphors, and IDEs.
• C++ comes with limited but highly effective machine learning and deep learning libraries written in C++.

White helps to expand BairesDev’s business at a global scale while managing new market research, overseeing growth projects, and generating leads. There are a number of excellent languages for writing AI models, such as Python, Java, and R. More importantly, the man who created Lisp was very influential in the field of AI, so much of his work had been implemented for a long time. If your company is looking to integrate Artificial Intelligence, there are a few languages you should seriously consider adding to your developer’s toolkit. Business Development Manager Emma White helps BairesDev grow at a global level by expanding the client base and overseeing of growth projects. If your company is looking to integrate Artificial Intelligence, there are a few languages you should seriously consider adding to your developer’s toolkit.

• It is faster than Python, accelerating development processes for organizations.
• Over time, many of LISP’s unique features have been folded into other popular programming languages—think Python’s list comprehensions and LINQ in C#.
• Additionally, it offers a limited number of frameworks and libraries.
• In marketing alone, employing artificial intelligence can make a grand difference.
• Create a dedicated team of developers, designers, and IT consultants with the right skills and experience.
• If we talk about industry adoption, Facebook uses Haskell to fight spam.

Your choice of AI programming language will depend on the scope and requirements of your project. If your project involves extensive data analysis, look to R, which was designed to crunch big numbers with ease. R, however, is not suited for deploying machine learning models in production—Python would be a better pick for that task.