10 Best AI Programming Languages To Know in 2024

Artificial intelligence (AI) is transforming numerous sectors, leading to improved decision-making, cost reduction, and enhanced productivity.

AI can autonomously perform repetitive and time-consuming tasks, allowing individuals to focus on other core activities. It can also analyze vast amounts of data quickly and provide stakeholders with valuable insights.

However, AI isn’t made from magic.

When you push through the large data sets, you’ll find that underlying frameworks and programming languages contribute to its functionality. Python is among the most popular programming languages for AI development due to its rich ecosystem of libraries and fast learning curve. But other programming languages like Java, C++, and Haskell also play a fundamental role in creating AI systems.

Read on as we discuss the top AI programming languages in detail and explain how to choose a suitable language for your next AI project.

1. Python

One of the most popular programming languages in AI development is Python. It features a user-friendly syntax that’s easy to learn and get. It also has a vast ecosystem of libraries and frameworks like NumPy, Scikit-learn, and Matplotlib. These dependencies save you time since you can import and use them in your project—rather than creating everything from scratch.

Python is also a highly versatile language with wide use cases for data scientists and programming professionals. For instance, it’s used to develop neural networks and machine learning algorithms using Python-based frameworks like TensorFlow and PyTorch. It can also be used in data analysis to discover patterns, trends, and relationships.

The open-source nature of Python paired with its large community has also contributed to its growth in software development. If you’re just starting with Python, you can find help from active users on platforms like GitHub and Stack Overflow.

Best for: General-purpose AI development, data science, and machine learning

2. Java

Java is a general-purpose programming language for creating mobile, desktop, web, and cloud applications. It’s portable and platform-independent, meaning Java applications can run on any computer system with the Java Virtual Machine (JVM) installed.

Its object-oriented nature makes it easier to write efficient code that’s maintainable. Object-oriented programming also facilitates modularity, allowing software developers to break down their applications into smaller and more manageable files.

Various in-built modules and libraries like JUnit and Apache Commons save development time and facilitate the creation of high-performance applications. For instance, Java is commonly used in developing simulations, robotics, and embedded AI applications. A Java extension like RTSJ allows developers to create real-time systems like bots.

Java also supports multithreading (crucial for AI tasks that need to run concurrently) and is highly scalable. It can be used to create simple programs or large applications that scale according to user demands. Plus, a large community of Java users gives you access to tutorials and learning resources.

Best for: Building large-scale, platform-independent AI applications and embedded systems

3. JavaScript

JavaScript is mostly used in creating highly interactive browser-based applications. It’s primarily a client-side scripting language that runs directly on users’ browsers. As a result, it can help perform various computation tasks and improve the performance of AI applications.

It’s also valuable when implementing real-time AI applications like virtual assistants, chatbots, and recommendation engines. Developers can use JavaScript to integrate AI services and functionalities into web applications using application programming interfaces (APIs).

This client-side execution can help offload computation tasks from servers to users’ browsers, which may lead to more responsive applications, especially when dealing with real-time interactions.

With the release of Javascript-based frameworks like TensorFlow.js, Brain.js, and ConvNetJS, developers can directly integrate machine learning capabilities in web development projects. These frameworks enable the training and deployment of machine learning models in the browser, extending the possibilities for AI applications.

Best for: Developing interactive, browser-based AI applications and integrating AI into web projects

4. Prolog

Prolog is recognized for its strength in logic programming and symbolic reasoning, which facilitates the development of AI applications, particularly regarding knowledge representation and problem-solving.

Its declarative nature allows developers to specify the logic of computations without worrying about the underlying control flow. Developers can represent rules, relationships, and facts in a straightforward manner, making it a suitable choice when creating knowledge bases in AI systems.

Powerful pattern-matching capabilities allow developers to scan knowledge bases quickly and find matching patterns based on logical relationships. This feature can be valuable in fields like natural language processing (NLP) where it helps computer systems find answers to different queries. The pattern-matching feature also makes Prolog a suitable language for building various algorithmic solutions.

Best for: Symbolic reasoning, knowledge representation, and logical problem-solving in AI

5. Lisp

Lisp is a high-level functional programming language suitable for AI development, specifically due to its adaptability and support for symbolic expression processing. Symbolic expressions can represent both code and data, which can be primitive types like strings and numbers, or complex data structures like lists and arrays. This unique feature helps Lisp applications perform complex computations and data manipulations on large data sets, making it particularly suited for AI applications.

Recursive programming allows coders to create advanced AI algorithms capable of analyzing and processing vast amounts of data. It also offers automatic garbage collection; this feature enables programmers to delegate memory management tasks to computer systems so they can focus more on core activities.

Being a dynamically typed language, Lisp allows the interpreter to assign types to variables at runtime. This feature provides a lot of flexibility and supports the manipulation of complex data structures, which is essential in handling the diverse and dynamic nature of data in AI.

Lisp is among the earliest languages to support functional programming. Its simple syntax supports high expressiveness and extensibility—which facilitates improved experimentation in AI and other research-focused fields.

Best for: AI research and experimentation, especially in fields requiring symbolic expression processing

6. Julia

Julia is popular in the AI community, primarily because of its high performance and rapid prototyping capabilities. Julia offers fast processing abilities, essential in AI development because of the vast amounts of data involved. It also has a user-friendly syntax comparable to Python, making it easier to learn.

Julia is also compatible with other programming languages like R, C, and Python. This means that developers can leverage powerful libraries and dependencies like TensorFlow and PyTorch, which are written in other languages, and use them in their applications.

Plus, parallelism and distributed computing out of the box enable AI platforms to handle heavier computational workloads and large data sets without significant performance degradation.

Julia is open-source, meaning developers can benefit from other community-controlled libraries, saving development time and effort. Some tools that Julia offers include Plots.jl and Gadfly.jl for data visualization and MLJ.jl for building deep learning and machine learning models. Additionally, Julia has a library, Scikitlearn.jl, which serves as an interface to the Python library scikit-learn.

Best for: High-performance AI applications that require fast processing and easy integration with other languages

7. Haskell

Haskell operates on a functional programming paradigm with core principles like pattern matching, high-order functions, and immutability, which are conducive to processing and manipulating complex data structures in AI development.

It’s built on a strong mathematical foundation, which facilitates the creation of complex algorithms and potentially deep learning models. Its characteristic feature of lazy evaluation ensures that expressions are evaluated only when required, which can lead to more efficient use of computational resources.

Haskell’s strong type system and type safety prevent programmers from assigning incompatible values to variables, contributing to the robustness and reliability of AI applications developed in this language.

Best for: Developing AI systems with complex algorithms and a strong emphasis on functional programming principles

8. C++

C++ is instrumental in developing complex software systems with a high level of abstraction. It’s gained traction in AI development due to its versatility, performance, and low-level capabilities that facilitate fast communication between software and hardware components.

While C++ can interact with machine learning libraries like TensorFlow and PyTorch through bindings or APIs, these libraries are primarily designed for use with Python. However, the ability to use these libraries from C++ allows developers to integrate advanced functionalities into their applications rather than creating everything from scratch. C++ supports parallel computing and multithreading abilities, which are essential when creating high-performance AI applications.

AI developers have more control over memory allocation. For example, they can allocate more memory to functions responsible for complex computations, reducing lagging and other performance issues. Frameworks like OpenCL and CUDA support hardware acceleration, enabling machine learning applications to boost performance by taking advantage of computer hardware like GPUs.

Best for: Creating high-performance AI applications with low-level hardware control and memory management

9. R

R is a programming language for statistical computing and data analysis. It has a wide range of data visualization libraries, such as Ggplot2, Lattice, and Shiny, which allow developers to quickly create graphs, charts, and other visuals with their data. Its in-built functions, like glm and lm, allow professionals to engage in statistical modeling.

The Caret package makes it possible to build, train, and test machine learning models. Caret also allows hyperparameter tuning, enabling AI developers to optimize the performance of their ML algorithms.

Programmers can also use R’s performance evaluation tools, like resampling methods and cross-validation, to gauge the performance of their AI models and make suitable changes. Plus, R is compatible with other programming languages like Java and Python, leading to a rich ecosystem consisting of a wide range of libraries for developing different applications.

Best for: Statistical computing, data analysis, and visualization in AI projects

10. Scala

Scala’s functional and object-oriented programming features make it a suitable language for AI development. It allows programmers to write clear and efficient code, leading high-performance apps.

Scala also supports immutability out of the box, ensuring that objects don’t change their state once they’ve been declared. As a result, AI developers can track and work with these objects throughout the software development life cycle. Like C++, Scala supports multiprocessing and parallel computing, enabling AI engineers to build programs capable of performing different tasks simultaneously.

Interoperability with Java means developers can leverage Java-based libraries and tools like Apache Hadoop and Spark for big data processing. This allows professionals to find hidden patterns and relationships from vast amounts of data and make informed choices.

Scala’s pattern-matching abilities also make it suitable for tasks like NLP, where AI systems must process user inputs and generate relevant responses. Its object-oriented nature makes it a good choice for toolkit creation. For instance, you can use Scala to develop tools for prototyping and research.

Best for: Building scalable, high-performance AI applications that integrate with big data processing frameworks

Programming languages to avoid for AI

While many programming languages can be used in AI projects, some might not be as user-friendly, efficient, or versatile as others. Some programming languages to avoid when engaging in AI development include:

• PHP. This language is mainly used in web development. It misses out on artificial intelligence programming because it lacks specialized AI and machine learning libraries.
• Ruby. While Ruby has an elegant syntax and high readability rate, it lacks a robust AI and machine learning ecosystem. Ruby is also an interpreted language, meaning its execution is much slower than compiled languages like Java and C++.‍
• C. This foundational low-level programming language can be useful for building apps or embedded systems requiring low-level hardware integration. However, its suitability for AI development is affected by the absence of high-level AI-specific libraries and frameworks. Additionally, the manual memory management principle that C advocates for can lead to memory leaks if not handled properly, potentially resulting in unreliable applications.

How to choose the right AI programming language

From Python to Scala, you can use many programming languages in your workflows for AI projects. While these languages share some similarities, they have unique features that make them best for specific tasks.

The following steps will help you choose the best AI programming language for your next project:

Assess your project’s needs

Start by assessing the needs and requirements of your AI project. Pay attention to your app structure and the basic features your program requires in order to work.

Determine if you’re building a web-based application, a deep learning model, or a robotics project. Different projects might favor different languages.

For instance, JavaScript might be suitable for creating web-based applications since it supports a lot of browser functionalities by default. On the other hand, C++ could be the right language for creating robotics since it supports low-level hardware integration.

Consider skill level and learning curve

Consider your skill level because programming languages have different learning curves. For example, Python has a flatter learning curve due to its simple and beginner-friendly syntax. C++ has a steeper learning curve, meaning it may take more time and effort to master.

Check available libraries and frameworks

When creating an AI application, use a programming language with a rich ecosystem of tools, libraries, and frameworks to accelerate your development process. This ecosystem provides the abstractions necessary to create AI applications—without creating core functionalities from scratch. Python has more extensive AI, data science, and machine learning libraries, making it a good fit for many AI projects.

Compare performance and stability

Consider memory management techniques and execution speeds of programming languages since they can impact performance and stability. Multithreading and parallel processing capabilities also impact performance.

C++ is more suitable for high-performance tasks because of its multithreading ability and efficient memory management practices. Java is also a good alternative with its automated garbage collector for memory management.

Java also handles scalability well through the microservices architecture, where components are loosely coupled, enabling them to be deployed and scaled easily.

Gauge community and support options

A strong community can help with troubleshooting, resource sharing, and continuous learning. Programming languages with larger communities can also offer better support for budding AI developers.

JavaScript, Python, and Java are currently the languages with the most active users. If you’re a beginner, it might be a good idea to go with languages with big communities.

Consider future compatibility needs

When considering an AI programming language, consider its future relevance in computer science. Also, determine whether the language’s syntax, tools, and frameworks are updated regularly.

Is the language growing in popularity? What are some emerging trends and advancements in the programming language? Future-proofing can help you determine how changes in the programming language will impact your AI project.

Assess integrations and compatibility

If your project requires a combination of different tools, choose a programming language that integrates well with other platforms and libraries. A language like C++ can accommodate libraries developed in other languages. JavaScript can also help you combine APIs running on completely different frameworks.

Find artificial intelligence programmers and jobs on Upwork

AI programming is a complex but exciting and fulfilling activity. You can use your technical skills to create applications to solve various problems. But to create truly efficient programs, you’ll need to use the right programming language.

There’s no one-size-fits-all programming language, so consider your project requirements, ease of use, available libraries, and compatibility with other frameworks to find the best programming language for your project.

If you’re an AI expert looking for a job, start your search on Upwork. With different AI programming jobs posted regularly, you can find projects that align with your skills and earn extra income.

And if you’re looking for software developers to assist with your project, Upwork can connect you to professionals with experience in programming languages like JavaScript, Python, and Java.