The Best Programming Languages for AI

AI has changed how everything works in almost every sector. Before you start your next company project, learn more about the best programming languages for AI.

Artificial Intelligence has been around since the mid-years of the twentieth century. Still, it's only been in the last ten years or so that it's become accessible to developers looking to build AI into their applications.

Programming languages are the backbones of artificial intelligence development projects because they help software developers create innovative AI solutions without learning the highly specialized language that scientists use to communicate with each other. 

Do you know the AI adoption rate among the industries worldwide?

Source: Statista - AI Adoption

AI developers

In this article, we'll run through some of the best programming languages for AI and why they're so helpful.

Here's the list:

1) Python

Python is excellent for AI because of its robust data science and machine learning capabilities. Its computational elegance and readability have made it a top choice among data scientists, who can use it to analyze even large amounts of complex datasets without having to worry about computational speed.

Python has an extensive list of packages related to artificial intelligence, such as PyBrain, NeuralTalk2, and PyTorch. While TensorFlow is more widely used than PyTorch right now, its popularity might be on par with PyTorch in a few years due to all of its unique features that got recently released.

These include variable precision in addition to GPU acceleration for deep learning networks. The newest version also gives you multi-device support. Ultimately, Python is easier to learn than C++ or Java (and thus preferred by beginners). It's also one of the most popular languages that professionals use.

It doesn't lag behind other languages in development since developers are constantly adding new libraries or upgrading existing ones; often, they build their own to bring some advantage to their company while working on specialized projects.

Furthermore, suppose you know C++ or Java already but aren't quite familiar with neural networks or deep learning methods. In that case, you can easily pick up what you need, thanks to over 830 classes present in its Numpy library alone.

Benefits of Using Python for AI

  • Python has many available libraries that simplify programming with machine learning.

  • If you are working on an AI-based project, the chances are that there is already a library in Python that does what you need.

  • If you're a new developer, there's a good chance you chose to learn Python because it has an easier learning curve than most programming languages.

  • It has a robust machine learning framework called PyBrain and boasts an active user community, making it possible to ask questions and get help with development questions.

2) R

R, an open-source programming language, supports statistical analysis and scientific computing. The R programming language proves helpful in producing interactive graphics and other advanced visualizations. Its design handles all types of data analysis, from simple linear regressions to complex 3D simulations. Anyone can use R.

As a programming language, R has been designed from statistical computation to machine learning techniques. R comes with object-oriented programming, a high level of extensibility, memory-efficient non-stop computing, comprehensive functionality, a huge user base, and is widely used for forecasting.

Benefits of Using R for AI

  • R's computational power is an exception in dealing with large amounts of data.

  • Its ability to apply mathematical functions also makes it more desirable when creating programs with complex decision-making processes.

  • Regarding performance, you can even expect better results using an open-source tool like R since it doesn't require licensing fees.

  • It's also good at finding patterns in big data sets, making it popular with companies who want to analyze their customer information for marketing purposes or identify significant risks in operations.

3) Java

Java is considered one of today's most sought-after programming languages. With its object-oriented nature, Java can quickly allow you to get things done with little effort; it's easy to make use of threads and multi-threading functionality in Java because of its built-in support for concurrency.

You can use many programming languages like Ruby on Rails, Python, and Node.js in conjunction with Java because they all offer comprehensive frameworks that work together with it.

Since it's one of the more popular programming languages at play these days, you should consider learning how to use Java if your current role or future aspirations involve using artificial intelligence solutions.

Benefits of Using Java for AI

  • The Java programming language has several characteristics that make it particularly suitable to develop AI programs. Java is a high-level, object-oriented programming language.

  • It is highly readable, making sense when you consider that developers often work on large projects with many other team members working at different speeds in different time zones.

  • It is a level 5 programming language that ensures programmers with multiple benefits.

  • As AI applications use machine learning algorithms, code written from scratch instead of premade libraries takes longer to compile than something coded in Java. 


First created in 1958, LISP is a functional programming language, meaning that everything in it is an expression. In other words, every line of code does something. It may sound a bit complex at first, but because you can write a function to do just about anything you want, understanding and building on LISP syntax are more straightforward than learning a whole new language from scratch.

So if you have any programming experience already — even Python or C++ — picking up LISP will be easy for you. But even if you don't know any languages other than Java or JavaScript, there are resources online to help you get started with functional languages.

Benefits of Using LISP for AI

  • Almost all major deep learning frameworks rely on LISP for their core operations, which gives you lots of flexibility when choosing libraries or tools.

  • Code executes quickly without needing to take into account environmental details.

  • It's ideal for abstractions: using simpler models to explain deeper ones, so programmers don't need to understand how individual components work.

  • It helps you save time later on if predictions based on initial models turn out to be wrong, so rewriting them would become relatively simple without losing much progress along the way.

5) Prolog

Prolog is a declarative programming language in which you describe what you want to achieve instead of how. In Prolog, knowledge gets represented by facts and rules. Facts are statements about objects, such as Jim has two children or Jane's favorite number is eight.

Rules describe when you can infer new facts from existing ones, such as If a person has more than one child, then that person has more than two children. It's an approach to AI that allows programmers to spend less time on algorithms and more time thinking about goals. 

Benefits of Using Prolog for AI

  • Prolog can process vast amounts of data quickly because it uses unification.

  • It has a reputation built into its syntax; people that use it are often more intelligent than average programmers.

  • It has helped increase the speed and accuracy of these projects.

  • If you want your program to become sentient over time (just like Skynet), then Prolog might be just what you need! Being able to modify your program significantly improves its abilities.

6) C++

C++ is a popular, general-purpose programming language. It's a high-level language developed by a team of computer scientists led by Bjarne Stroustrup at Bell Labs. It runs on Windows, Linux, Mac OS X operating systems, and mobile devices such as smartphones and tablets. C++ has been used to develop games, apps, and graphics programs.

It gets used to creating many other software programs, including those designed to work with artificial intelligence technology. However, it's not particularly good for tasks like GUI (graphical user interface) design or rapid prototyping due to its complexity and slow development speed.

Benefits of Using C++ for AI

  • C++ is helpful in machine learning for both prototyping and production because it enables you to load extremely complex models into memory quickly with little effort.

  • It also lets you experiment with new models or redesign existing ones quickly without excessive loading times or sacrificing processing power.

  • It is one of the best choices when developing high-performance code that needs quick access to many data storage spaces.

  • If you need an algorithm or your application requires functionality not built into the language already, chances are someone else has already written what you need. Mostly these modules will be open source which means they're free to use and modify for commercial purposes.

7) Haskell

Haskell is a purely functional-based language, which means that all expressions get evaluated to produce only one value. Since it doesn't have variables, Haskell relies on recursion heavily to create its code, but there are some mutable types, specifically lists, and arrays.

It makes Haskell ideal for developing complex algorithms that rely on several steps before reaching a final result. The syntax can be off-putting since it uses layout to organize code into lines with semicolons at their end instead of an indentation.

One of Haskell's more exciting features is its type system. It has no null value—you can't store anything in a variable or pass nothing as an argument.

Benefits of using Haskell for AI

  • Haskell includes a robust type system to avoid many types of bugs in your code.

  • While other languages make it more challenging to write concise code, Haskell makes it very easy. So, it is suitable for projects involving large sets of data. 

  • It also allows you to work on multiple projects at once due to its conciseness.

  • A significant benefit of using Haskell is its speed. A program written in Haskell will usually run faster than programs written in other programming languages because of how simple it is.

8) JavaScript

JavaScript is a widely-used programming language essential to artificial intelligence and can help you build everything from chatbots to computer vision. JavaScript has quickly become one of AI's most popular languages thanks to its flexibility and deep community of developers.

Since JavaScript was created in 1995, many human-like behaviors have been written using it, such as facial recognition and art generation programs. As businesses continue to move away from old-fashioned legacy systems, JavaScript will remain an essential skill for anyone looking to gain insights into artificial intelligence. 

Benefits of Using JavaScript for AI

  • The high flexibility of JS allows it to be used with various operating systems, browsers, and virtual machines that developers are using.

  • It doesn't have to be ported from one system to another because many operate on similar architecture. 

  • It is also among those rare languages where there's a good chance you can apply it in whatever domain you choose.

  • As it's web-based (and browser-based), coding is relatively lightweight; there aren't too many technical requirements.

9) Julia

AI is a hot area of research, and Julia is well-positioned to tap into that interest. Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and their team built the language from scratch with numerical performance in mind, and it runs on almost any operating system. It's also super easy to learn—it uses many common coding concepts you already know, like loops and conditional statements. 

Julia's development environment isn't ideal at present — the tooling could use some work — but it will get better over time as more people build tools around the programming language.

Benefits of using Julia for AI

  • It is a high-level, high-performance programming language specifically designed for scientific computing. 

  • Julia's syntax is elegant and concise, allowing you to focus on solving your problem rather than writing new code. 

  • By using Julia, You can save time and produce cleaner, faster code with fewer errors.

  • One of Julia's biggest strengths is that it's free and open-source, meaning anyone can access its code. 

Wrapping Up

In conclusion, you can use many programming languages to develop artificial intelligence. No one language excels at everything.

Some specialize in development speed, some have a natural advantage with probabilistic models, and others have better integration with existing software.

The ideal option will depend on what you're trying to build and how long it takes you to get there. 


Question: What should I consider while developing an AI project?

Answer: When designing and implementing AI and machine learning programs, two factors are at play: first, which algorithm you're using, and second, what language it's written in.

Choosing a single algorithm for your program isn't very difficult; only a handful of choices are available. The problem is that each of these algorithms requires its computer language.

So let's say you choose an algorithm called Deep Q Learning. To write that program, you have to know how to code in Java, Python, JavaScript, or C++ — and possibly more than one of those languages if you decide to do something different for GPU operations versus data analytics operations, and so on.

There are over 100 algorithms, each with its language and each with its pros and cons. For example, there are only a handful of good deep learning platforms available, but dozens upon dozens of decision tree algorithms or heuristics.

Question: Which one is better — Python or R programming language?

Answer: R and Python are standard as programming languages for AI, ML, and data analysis. If you're starting in AI and machine learning, chances are you'll encounter both these languages and will have to choose one over another.

These two languages have several things in common: 

  • They're both open-source.

  • They're powerful.

  • They're easy to learn (if you already know a programming language).

  • They come with several libraries that make it easy to import and analyze data.

So which one should you choose? Here's how they compare against each other. 

Comparison Parameter

R Language

Python Language

Primary Objective of the Language

Its primary objectives include statistics & data analysis. 

Its primary objectives include production & deployment. 

Main Users

Research & Development Teams are primary users of the R language.

Software Programmers are the primary users of the Python language. 

Availability of Libraries

Available in high abundance.

Comparatively, it has a lower number of libraries than R.

Database Handling Capacity

High Database Handling Capacity.

High Database Handling Capacity.

Learning Curve of the Language

R is a seemingly complex language to learn at the beginning & has a high learning curve. 

Python is a renowned simple language to learn and has a linear & smooth learning curve.

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Question: What are the five most used programming Languages in 2021?

Answer: As per Statista, the top programming languages in 2021 include the following: 

  • JavaScript has 64.96% of respondents saying yes to the language.

  • HTML/CSS has 56.07% of respondents saying yes to the language.

  • Python has 48.24% of respondents saying yes to the language.

  • SQL has 47.08% of respondents saying yes to the language.

  • Java has 35.35% of respondents saying yes to the language.

Apart from them, Node.js, TypeScript, C#, Shell, and C++ make it to the top 10 programming languages used by developers worldwide.

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