Top 8 Programming Languages for AI Development in 2024

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With formerly Facebook coming up with new technological innovations like Meta, it’s worth exploring how artificial intelligence will impact the future of software development.

Aside from the 2001 science fiction film with Haley Joel Osment, artificial intelligence is a complex and profound subject area.

There’s a vast world of possibilities when machines are intelligent beings.

In marketing alone, employing artificial intelligence can make a grand difference.

And recent research suggests that the majority of artificial intelligence projects are market-oriented.

It should be self-explanatory as to why these projects would appeal to a growing business such as yours.

But that’s not all artificial intelligence can do. And once you know how to develop artificial intelligence, you can do it all.

Today, you’ll be acquainted with eight programming languages for tackling artificial intelligence projects:

  1. Python
  2. Lisp
  3. Java
  4. C++ 
  5. R
  6. Julia
  7. Prolog
  8. Haskell

To learn more about what these languages can do for you, stay tuned!

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What Is Artificial Intelligence?

If you think that artificial intelligence makes for some scary alternate realities, you’re not alone.

Anyone who’s seen Terminator knows good and well that when you start giving machines human traits, things go south — quick!

In reality, the chance of killer robots threatening your livelihood anytime soon is quite small.

At its core, artificial intelligence (AI) refers to intelligent machines.

Really, if you’ve ever worked with a digital device that didn’t know how to tell up from down or do a simple task, you’d probably quite like artificial intelligence.

No doubt, you’re already using artificial intelligence as is. You know that robot voice you talk to in your phone? That’s AI technology at your fingertips.

In a business capacity, artificial intelligence has a number of benefits. Most marketing automation tools, for one, rely on AI.

A robotic hand with a blue, cube-shaped digital object emerging from a background split with electronic circuit patterns and a blue and yellow geometric design.
Artificial intelligence refers to intelligent machines.

This is how the best tools create and orchestrate campaigns and gather insights to improve your effectiveness as a brand. And this is just one example.

In this era of digital transformation, you’re bound to see AI pop up in numerous scenarios, working together with humans and providing proactive solutions to everyday problems.

8 Best Programming Languages for AI Development

AI development is not for the faint of heart.

Building artificial intelligence into your software requires a certain skill set, and on that note, an adjacenct tech stack, for development to run smoothly.

Below are eight programming languages well-suited for AI development.

An infographic titled
8 Best Programming Languages for AI Development

1. Python

Python is a high-level general-purpose programming language.

Developers cherish Python for its simple syntax and object-oriented approach to code maintainability.

But although Python seems friendly, it’s well-equipped to handle large and complex projects.

Data engineering remains a frequent use case for utilizing Python, as does machine learning. 

Machine learning is a subset of AI that involves using algorithms to train machines.

TensorFlow is Python’s most popular framework for AI development. It’s an open-source machine learning library where you can train deep neural networks.

Other pre-existing libraries and frameworks include:

  • Pandas
  • SciPy
  • nltk
  • PyTorch
  • Keras
  • Theano
  • scikit-learn

Besides machine learning, you can use these technologies for natural language processing (NLP) and evaluating advanced mathematical expressions.

NLP is what smart assistants applications like Google and Alexa use to understand what you’re saying and respond appropriately.

Another great thing about Python is that it is one of the easiest languages to learn, in spite of what it’s capable of!

2. Lisp

Lisp (historically stylized as LISP) is one of the oldest languages in circulation for AI development.

Originating in 1958, Lisp is short for list processing, one of its original applications.

By 1962 and with the aid of creator John McCarthy, the language worked its way up to being capable of addressing problems of artificial intelligence.

In the present day, the language is just as capable, but because of its difficult syntax and complicated libraries, it’s rare that developers flock to Lisp first.

That said, there are a variety of AI projects where Lisp’s expertise could be a great asset, including but not limited to:

  • Rapid prototyping
  • Dyanmic object creation
  • Executing data structures as programs
  • Modifying programs as data
  • Mandatory garbage collection

Overall, Lisp is known in recent history for being immensely flexible, and turning thoughts into reality.

3. Java

Java is a treasured server-side language. Its key feature is that you can use Java almost anywhere, on any platform, through its virtual machine.

Though Android developers have the option to work with Kotlin as well, Java is a native language for Android app development.

Mobile app developers are well-aware that artificial intelligence is a profitable application development trend.

So the infamous FaceApp in addition to the utilitarian Google Assistant both serve as examples of Android apps with artificial intelligence built-in through Java. 

Even outside of mobile apps, Java has quite a few machine learning libraries for deep learning and natural language processing.

Check out the following if you plan to use Java for your machine learning needs:

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

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.

4. C++

C++ has been around for quite some time and is admittedly low-level.

This means C++ works well with hardware and machines but not so well for the more theoretical side of software.

Yet, in practice, C++’s capacity for low-level programming makes it perfect for handling AI models in production.

You can build neural networks from scratch using C++ and translate user code into something machines can understand.

Come to think of it, many of the most notorious machine learning libraries were built with C++.

For these reasons, C++ still gets a lot of attention in the artificial intelligence world. Don’t underestimate what C++ can do!

5. R

R is a popular language for budding and professional statisticians alike.

To be fair, R isn’t the best language for artificial intelligence. But it is pretty handy at crunching numbers.

In AI development, data is crucial, so if you want to analyze and represent data accurately, things are going to get a bit mathematical.

R can be your shining light in an otherwise dark tunnel. In terms of getting your numbers straight, it’s actually better than Python.

Here are some R packages you may want to look into:

  • Gmodels — provides tools for model fitting
  • Tm — a framework for text mining applications
  • OneR — used to implement the One Rule Machile Learning classification

6. Julia

Julia is one of the newer languages for AI development. But that shouldn’t deter you from making it your language of choice for your next AI project.

Although its community is small at the moment, Julia still ends up on most lists for being one of the best languages for artificial intelligence.

You can chalk its innocent fame up to its dynamic interface and arresting graphics for data visualization.

Add in memory management, debugging, and metaprogramming to the mix, and you’ll soon understand what all the hype’s about.

Julia’s wide range of quintessential features also includes direct support for C functions, a dynamic type system, and parallel and distributed computing.

Strictly speaking in regards to AI development, Julia is stacked with premade models, algorithm help, and multiple packages for probabilistic programming, namely:

  • MLJ.jl
  • Flux,jl
  • Turing,jl
  • Metalhead

7. Prolog

Prolog, a portmanteau of logic programming, has been here since 1972. Despite its age, Prolog works well for the right projects.

In Prolog, instead of using coded sequences, you simply chart out a basic set of facts, rules, goals, and queries. 

As a result, Prolog is apt at pattern matching, especially when NLP is involved.

The first chatbot in history, for example, was called ELIZA and written in Prolog.

Prolog’s system of logic played a big part in ELIZA’s ability to assign values to words and reorder them to form a question.

The basic tenets of Prolog are as follows:

‣ facts define true statements
‣ rules define statements with additional conditions
‣ goals define where the submitted statements are placed given a knowledge base
‣ queries define how to make statements true and the final analysis of facts and rules

Amazingly, these fundamentals are the crux of Prolog’s prowess.

Nowadays, cloud technology makes it so chatbots have a whole store of data to access new and old information, meaning chatbots are worlds more intelligent than in the time of Prolog.

8. Haskell

Haskell is a statically typed and purely functional programming language. What this means, in summary, is that Haskell is flexible and expressive.

Its abstraction readiness mitigates the need for spending large amounts of time debugging errors.

And Haskell’s efficient memory management, type system, and code resusability practices, only add to its appeal.

But one of Haskell’s most interesting features is that it is a lazy programming language.

Thus, Haskell only evaluates pieces of code when necessary. Of course, this can be a pro or a con depending on how you look at it.

Altogether, the theme of Haskell’s attractiveness for AI developers is that the language is efficient. You’ll spend more time thinking than typing.

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Use AI in Your Next Project

Artificial intelligence is no joking matter. You use machines every day to make things easier, from driving to putting appointments in your digital calendar.

Well, what happens when your machines know exactly what you want and have the intelligence to meet your needs without prompting?

On a personal level, such a prospect is glorious. On a business level, there’s even more to be excited about.

Deploying one of the languages above in your tech stack is only a minor part of building competent AI software.

The first step is finding a team that can make sure your project is successful. Our work here at Trio is to deliver the best developers in the market.

Contact Trio now to learn more about what we can do!

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With over 10 years of experience in software outsourcing, Alex has assisted in building high-performance teams before co-founding Trio with his partner Daniel. Today he enjoys helping people hire the best software developers from Latin America and writing great content on how to do that!
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