What is the Best Programming Language for AI?

The realm of artificial intelligence (AI) is rapidly transforming the technology landscape, creating growing demand for engineers, developers, and the like. A question that is often asked by newcomers to this space: which is the best programming language for AI?

As you may have guessed, the answer to this question is not straight forward or a one size fits all scenario. Although there is a top favorite, the ‘best’ language for AI is dependent on what your goals and projects are.

In this guide, we'll dive into the top programming languages that are leading the charge in AI, and which are best for various goals and project types.

Python: The Quintessential Best Language for Machine Learning


Python is frequently crowned as the best programming language for AI and the top choice for ML practitioners worldwide. Its dominance in the AI and ML space is not without reason. Python is intuitive and clean, which promotes readability and reduces the learning curve for newcomers. This makes Python an excellent entry point for those looking to dive into the world of AI and machine learning.

The language's real power lies in its vast ecosystem of libraries and frameworks designed to streamline AI and ML development. With industry-standard tools like TensorFlow, Keras, PyTorch, and Sci-kit learn at your disposal, building sophisticated ML models becomes more accessible than ever. These libraries offer pre-built components, algorithms, and a high level of abstraction that can significantly accelerate the development process.

Moreover, Python's popularity has fostered a vibrant community of developers and researchers who continuously contribute to the growth of the language's AI capabilities. The ample availability of resources, tutorials, and documentation ensures that help is just a search query away, making Python arguably the best language to learn for machine learning.

C++: The Speed Demon for Performance-Centric AI


An important limitation of Python to note are its limitations in computational speed – a key benefit of C++ which provides ideal execution speed, latency, and performance. In fact, many AI Python libraries are using C++ code under the hood because of the better performance.

When it comes to performance-intensive AI and ML applications, C++ might very well be the AI best programming language. Its unmatched control over system resources and memory management makes C++ the go-to language for situations where speed and efficiency are non-negotiable, such as in real-time systems and robotics.

C++ offers ML libraries like PyTorch, TensorFlow, mlpack, Caffee, and OpenNN, which, albeit fewer in number than Python's offerings, provide a powerful set of tools for those looking to push the boundaries of performance. The language's speed and efficiency are essential in use cases where milliseconds matter.

However, the trade-off comes in the form of complexity. C++ has a steeper learning curve due to its intricate syntax and lower-level programming model, which requires a more in-depth understanding of memory management and system architecture. This complexity can make C++ less suitable for quick prototyping or projects with tight timelines. Nevertheless, if you are willing to invest the time to master it, C++ offers the potential for creating some of the most performant AI and ML applications available.

R: Best for Data Analysis


R holds a special place in the statistical and data analysis community. Designed specifically for statistical computing, R provides an excellent environment for developing ML models that are heavily reliant on statistical methods.

The language's strength lies in its comprehensive suite of packages for data manipulation, statistical modeling, and visualization, such as XGboost, Gmodels, mlr, and the ggplot2 library. R's data visualization capabilities are particularly noteworthy, as they enable insightful and high-quality graphical representations of data.

R's steep learning curve may be challenging for some, and it is not suitable for production environments. However, for those with a background or keen interest in statistics, R stands as one of the best programming languages for AI and ML, particularly when statistical rigor is a priority.

Java: The Enterprise Choice for Scalability and Performance


Java has long been a staple in enterprise software development, known for its stability, scalability, and rich set of features. It is a contender for the best programming language for AI if you are working on large-scale and commercial projects. Java's object-oriented nature makes it adept at handling complex systems, a common characteristic of AI applications. It comes with many libraries that can be leveraged to create neural networks and preprocess data tasks.

Libraries such as Deeplearning4j, Deep Java Library, and Apache OpenNLP provide a solid framework for ML. While Java might not be as agile or concise as Python, its performance, enabled by the Just-In-Time (JIT) compiler, and its widespread use in the industry make it a reliable choice for developers looking to work on robust AI systems.

Similar to previous languages covered, Java comes with a steep learning curve which may deter some developers, but those who are already familiar with its environment will find it a powerful ally for creating complex enterprise solutions.

JavaScript: The Choice for “Full-stack” Flexibility and Accessibility


JavaScript's role in AI and ML might come as a surprise to some, but its flexibility and ubiquity make it an important language to consider, especially for those looking to integrate AI into web applications. It allows you to execute code on the client-side in the browser, as well as on the server-side such as with Node.js. This makes it a convenient and flexible ‘full-stack’ option. Another value add of JavaScript is its ability to add AI feature to your application natively.

JavaScript is one of the most popular programming languages, but it’s important to note where its limitations are. It is not recommended for intense AI workloads, is lacking in extensive data science and machine learning libraries, and also experiences restrictions when executing workloads in the browser.

Julia: The Possible New Upcomer


A newer programing language Julia was released in 2012 with an intriguing promise to be as general as Python, as fast as C, and as statistics-friendly as R. Based on the 45 million+ downloads and growing community use it seems it has delivered on its promise. Let’s break down some simple comparisons.

Where Python is interpreter-based, Julia uses a JIT (just in time) compilation – allowing it to yield faster execution. Similar to JavaScript, it is a dynamically typed programming language and has a built-in package manager and superior syntax compared to C and C++. Notable libraries for Julia include Flux, Pluto, Makie, MLJ, and Tensorflow.

While Julia boasts many advantages, it may come with more time investment toward writing code from scratch where there are no libraries. However, this newer programming language is an evolving story and is one to keep an eye on as it continues to grow in popularity.

Conclusion: The Best Language is the One That Fits Your Goals

In summary, the best language to learn for machine learning and AI is the one that aligns with your project requirements, skill set, and personal or organizational goals. Each language covered above has its unique strengths and is best suited to particular types of tasks within the AI and ML landscapes.

For those looking to get started in AI and ML, Python's ease of use, extensive libraries, and strong community support make it an excellent first choice. For performance-critical applications, C++ may be the ideal option. Java remains a strong contender in enterprise settings, and R is unbeatable for tasks requiring heavy statistical analysis. JavaScript is key for adding AI features to web applications, and Julia can’t be overlooked as an intriguing newcomer.

Additional Resources

Whether you’re just getting started or are looking to enhance your skills in programmability with AI, check out the below learning resources: