AI Programming Languages: What to Know in 2024

Written by Coursera Staff • Updated on

AI is an essential part of the modern development process, and knowing suitable AI programming languages can help you succeed in the job market. Explore popular coding languages and other details that will be helpful in 2024.

[Featured Image] A student takes an online course in which she'll learn about various AI programming languages, like Python and Java, on her laptop from home.

The programming world is undergoing a significant shift, and learning artificial intelligence (AI) programming languages appears more important than ever. In 2023, technological research firm Gartner revealed that up to 80 percent of organizations will use AI in some way by 2026, up from just 5 percent in 2023 [1]. 

AI is rapidly evolving. Likewise, AI jobs are steadily increasing, with in-demand roles like machine learning engineers, data scientists, and software engineers often requiring familiarity with the technology. If you’re considering working in AI or want to experiment with it, knowing the right language and remaining up-to-date on the state of programming can help you weather the changes AI brings across various industries. 

Below, we explore some popular AI programming languages. It will also examine the differences between traditional coding and coding for AI and how AI is changing programming. 

Read more: Python vs. C#: Which Language Is Best for AI?

What AI programming languages should you learn?

Different programming languages will be ideal, depending on the use cases. Here are four popular coding languages that are suitable for AI-related applications and technologies: Python, Java, C++, and Julia. 

1. Python

Python is a general-purpose, object-oriented programming language that has always been a favorite among programmers. It's favored because of its simple learning curve, extensive community of support, and variety of uses. That same ease of use and Python's ability to simplify code make it a go-to option for AI programming. It features adaptable source code and works on various operating systems. Developers often use it for AI projects that require handling large volumes of data or developing models in machine learning.

2. Java

Java is typically longer, requiring more lines of code than Python. It has a smaller community than Python, but AI developers often turn to Java for its automatic deletion of useless data, security, and maintainability. This powerful object-oriented language also offers simple debugging and use on multiple platforms. Java’s libraries include essential machine learning tools and frameworks that make creating machine learning models easier, executing deep learning functions, and handling large data sets.

Read more: 10 Machine Learning Algorithms to Know

3. C++

Like Java, C++ typically requires code at least five times longer than you need for Python. It can be challenging to master but offers fast execution and efficient programming. Because of those elements, C++ excels when used in complex AI applications, particularly those that require extensive resources. It's a compiled, general-purpose language that's excellent for building AI infrastructure and working in autonomous vehicles.

Read more: 5 Benefits of AI to Know (+ 3 Risks to Watch Out For)

4. Julia

Julia excels in performing calculations and data science, with benefits that include general use, fast and dynamic performance, and the ability to execute quickly. It's excellent for use in machine learning, and it offers the speed of C with the simplicity of Python. Julia remains a relatively new programming language, with its first iteration released in 2018. It supports distributed computing, an integrated package manager, and the ability to execute multiple processes.

AI vs. traditional coding: What’s the difference? 

Coding is an essential skill for anyone who wants to work in AI. The programming languages may be the same or similar for both environments; however, the purpose of programming for AI differs from traditional coding. With AI, programmers code to create tools and programs that can use data to “learn” and make helpful decisions or develop practical solutions to challenges. In traditional coding, programmers use programming languages to instruct computers and other devices to perform actions.

Additionally, AI programming requires more than just using a language. You also need frameworks and code editors to design algorithms and create computer models.

How does AI-assisted programming change the industry?

As AI continues expanding, the programming landscape is shifting. However, University of California San Diego professor Leo Porter believes AI will increase inclusion and diversity in programming.

In 2022, AI-assisted coding tools like GitHub CoPilot emerged. Although the execution isn't flawless, AI-assisted coding eliminates human-generated syntax errors like missed commas and brackets. Porter believes that the future of coding will be a combination of AI and human interaction, as AI will allow humans to focus on the high-level coding skills needed for successful AI programming.

Educators are updating teaching strategies to include AI-assisted learning and large language models (LLMs) capable of producing cod on demand. As Porter notes, "We believe LLMs lower the barrier for understanding how to program [2]." 

It’s clear that AI will change the way programmers work. Developers could experience a boost in productivity and job satisfaction thanks to AI's assistance. 

Will AI replace coding jobs?

ChatGPT was released in 2022 and quickly surprised the coding community when it successfully created simple HTML websites using written instructions. The concern that coding could soon be obsolete seemed like a natural progression. However, like many jobs, AI will likely transform programming, working with humans to speed development. As Amazon’s Vice President for AI Services told IEEE Spectrum, “I don’t believe AI is anywhere near replacing human developers” [3]

AI will likely assume repetitive, routine tasks in the future. Doing so will free human developers and programmers to focus on the high-level tasks and the creative side of their work. 

How to choose an AI programming language 

To choose which AI programming language to learn, consider your current abilities, skills, and career aspirations. For example, if you’re new to coding, Python can offer an excellent starting point. This flexible, versatile programming language is relatively simple to learn, allowing you to create complex applications, which is why many developers start with this language. It also has an extensive community, including a substantial one devoted to using Python for AI. 

From there, it’s helpful to consider your career goals. For example, if you want to create AI-powered mobile applications, you might consider learning Java, which offers a combination of easy use and simple debugging. Java is also an excellent option for anyone interested in careers that involve implementing machine learning programs or building AI infrastructure. 

If your professional interests are more focused on data analysis, you might consider learning Julia. This relatively new programming language allows you to conduct multiple processes at once, making it valuable for various uses in AI, including data analysis and building AI apps.

However, if you want to work in areas such as autonomous cars or robotics, learning C++ would be more beneficial since the efficiency and speed of this language make it well-suited for these uses. 

Next steps with Coursera

Coding will remain an in-demand skill—both in AI and traditional settings—for years to come. Build your coding skills with online courses like Python for Data Science, AI, & Development from IBM or Princeton University’s Algorithms, Part 1, which will help you gain experience with Java. 

You can also gain a more robust foundation in AI with courses like AI for Everyone from DeepLearning.AI. This beginner-level class will teach you about AI terminology, strategies, and workflows. 

Article sources


Gartner. “Gartner Says More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026,” Accessed March 21, 2024. 

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