Artificial intelligence is posed to revolutionize the modern world. From business intelligence and robotics to online shopping and self-driving cars, companies use AI technology to optimize, automate, and enhance processes. Computer code forms the backbone of the algorithms powering AI. Through AI programming languages like Python and Julia, developers construct customized AI systems that tackle intricate problems across various domains. Keep reading to explore some of the top AI programming languages and learn how you can take the first step toward a career in AI. 

What Are AI Programming Languages? 

Just as letters and words form the sentences you're reading now, programming languages provide the components and structure needed to generate code. These languages enable developers to create custom software solutions tailored to specific needs. They serve as the foundation for today's most advanced technologies, facilitating the development of tools like OpenAI’s ChatGPT, virtual assistants and chatbots, and personalized e-commerce recommendations. Like spoken languages, each language has its own unique characteristics. Some languages are designed for rapid big data analysis, while others are geared toward artificial intelligence and machine learning. The wide variety of languages available ensures that developers have the right tools to tackle any AI development challenge.

Top 10 AI Programming Languages  

Identifying the best programming language for AI is complex because each one has specific strengths depending on the intended application. For example, R enables robust statistical analysis and has vast applications in data analysis and visualization, while real-time processing in C++ makes the language ideal for high-performance AI applications such as gaming. Programming languages also vary in terms of accessibility. Python has a gentle learning curve for beginner coders but lacks some of the features of harder-to-learn languages like Julia. The ideal language depends on the specific requirements of the project, the developer’s expertise, and the desired outcomes. Each language brings certain benefits; understanding these can help professionals choose the most appropriate tools and systems for their AI endeavors.

1. Python 

Since it was released in 1991, Python has been a go-to choice for creating applications, evaluating data, and building AI code. OpenAI, one of the most well-known AI organizations, uses Python to continuously refine ChatGPT and other generative AI programs. According to several studies, it’s currently the most widely used programming language in existence.  Python’s plain-language syntax makes it easier to read and understand than many other languages. It also includes extensive libraries such as NumPy, Matplotlib, Pandas, and TensorFlow that facilitate complex AI operations. For example, streaming services such as Spotify use AI tools like TensorFlow and the extended TensorFlow Ecosystem to analyze user data and provide personalized recommendations. Retail companies leverage Pandas to analyze sales data and track inventory levels. Developers can use Python for a diverse range of AI coding applications, including machine learning models, large language models (LLMs), computer vision systems, generative AI programs like ChatGPT, and more.  

2. Java  

Java is an object-oriented language, meaning that it organizes code around self-contained units. This allows developers to create enterprise-level AI systems by breaking them into many interconnected components. While Python prioritizes libraries and quick development, Java excels in stability, scalability, and cross-platform compatibility. Its modular nature and powerful debugging tools also make error detection and correction easier. Java is a popular AI programming language because it’s able to handle massive, complex projects across various devices. It also allows programs to process data in real time, enabling applications like chatbots to respond to prompts immediately. The language is commonly used to develop machine learning systems, search algorithms, fraud detection systems for financial institutions, and recommendation systems for e-commerce platforms. 

3. R 

R is highly regarded for big data analysis due to its exceptional statistical computing capabilities. It allows users to conduct complex regression analyses and hypothesis tests, as well as create detailed data visualizations, including heatmaps and graphs. This visual output is invaluable for both exploratory data analysis and presenting findings to stakeholders. R has a substantial library of packages that facilitate specialized statistical modeling tasks as well as assisted coding features like autocomplete. R programmers use the language to build AI solutions such as predictive models and pattern recognition systems in finance, healthcare, and marketing. Its versatility and strength in data-driven decision-making processes make R a valuable tool for professionals across industries.

4. Prolog 

Prolog is a logic-based language that organizes information as rules and facts. It excels in pattern recognition, problem-solving, and automated reasoning. Prolog’s ability to use reason and logic makes it an ideal choice for AI tools that mimic human thinking. Developers can describe a set of rules and then use Prolog to generate solutions to problems automatically. While it’s an older language, it’s still commonly used for natural language processing and knowledge-based AI tools. Prolog has been used in medical diagnosis systems, smart manufacturing, AI-generated schedules, educational tools, and games.  

5. JavaScript 

JavaScript is a highly versatile language used for both front-end development and AI development. Initially, the language was created to add interactive elements to websites, such as animated menus and pop-up messages. In 2009, a tool called Node.js expanded JavaScript’s capabilities, enabling the code to run on servers. Years later, libraries like TensorFlow.js and Brain.js made it possible for developers to run AI models such as machine learning or neural nets using JavaScript. Since the language processes data in real time, it enables AI systems to instantly deliver adjusted outputs as new information is integrated. JavaScript also offers built-in debugging tools that help developers quickly find and fix mistakes. The language is now used for many AI applications, including machine learning and analytics programs. 

6. Lisp 

Lisp has been around since the 1950s, making it one of the oldest languages still in use. Originally, Lisp was primarily used for symbolic processing, which allowed users to treat ideas and concepts as distinct symbols that could be manipulated to gain insights and solve problems. The versatility of Lisp has grown significantly since then. Today, Lisp is used for prototyping experimental AI algorithms, designing natural language processing experiments, and powering logic-based systems. Its unique features continue to make it a relevant choice for researchers and developers working on advanced AI projects.

7. Julia 

Julia’s clear syntax, versatility, and speed make it a popular choice for a wide range of AI development applications. While it’s a relatively new programming language, it excels in high-performance computing and mathematical precision. Julia offers several advanced features, including multiple dispatch and differentiable programming. One of its standout characteristics is the ability to write generic code, enabling developers to create functions that automatically adapt to a wide range of data types. This capability significantly enhances code reusability and cuts redundancy in programming workflows. Julia comes equipped with a comprehensive ecosystem of libraries specifically tailored for tasks such as data manipulation, machine learning, parallel computing, and data visualization. Julia has been used for climate modeling, drug discovery, robotics, and other cutting-edge AI applications. 

8. C++

For performance-critical applications, few programming languages can match the speed, efficiency, and level of control that C++ provides. One of the key advantages of C++ is that it allows developers direct control over hardware and memory management, improving AI coding execution speed significantly. The C++ ecosystem has numerous libraries and frameworks that facilitate the development of machine learning applications, including TensorFlow, Caffe, and the Microsoft Cognitive Toolkit. Additionally, C++ is widely used in constructing deep learning frameworks and large language models (LLMs), creating immersive environments in gaming, and developing sophisticated robotics systems. Its combination of high performance, extensive library support, and flexibility makes C++ one of the most popular languages among developers. 

9. Haskell 

Haskell entered the scene in 1990. While it's not the newest programming language on this list, it offers a variety of distinctive features. Haskell uses a pure functional approach and immutable data, which minimizes the chances of errors during software development. Unlike C++, it has automatic memory management, which has significant security benefits. While the learning curve for Haskell is steep, its functionality and accuracy make it worth the effort. Major corporations like Meta, GitHub, and Microsoft rely on Haskell’s functional programming capabilities to build reliable, high-performance AI models.  

10. Scala 

Scala is a statically typed, general-purpose language that integrates both functional and object-oriented programming. The name is short for “scalable language,” and it is specifically designed to manage large, scalable systems. Scala’s flexibility, fast AI prototyping, and impressive performance have made it a popular choice for developers across industries. It is widely used in the financial services sector for algorithmic trading and risk management. E-commerce platforms use Scala to build secure web applications capable of handling a massive number of simultaneous users. In healthcare, it is often used to develop electronic health record systems and medical data analytics tools.

Learn These Programs with WGU 

If you’re considering a career in AI and want to take your AI coding expertise to the next level, WGU can help. We offer online, competency-based IT degree programs that can equip you with the skills you need to thrive in today’s competitive market. Our B.S. Computer Science degree program includes courses in AI and machine learning, computer architecture, data structure and algorithms, scripting and programming, web development, and other industry essentials. WGU’s unique learning model allows you to study and complete assignments on your schedule from the comfort of your own home. Whether you’re a parent, working professional, or simply need to fit a college education into an already busy life, WGU gives you the flexibility you need. Take the first step toward an exciting AI career by applying today.