The 5 best programming languages for AI development

As a result, Python has become a popular choice for both beginners and experienced users. None of this will occur, however, without the enablement of flexible AI programming languages. Such programming languages are indispensable for writing AI algorithms bolstered by quantum computing to create advanced applications with the power to transform the use cases for which they’re deployed. Well, Google recently released TensorFlow.js, a WebGL-accelerated library that allows you to train and run machine learning models in your web browser. It also includes the Keras API and the ability to load and use models that were trained in regular TensorFlow. This is likely to draw a massive influx of developers into the AI space.

• Julia is a high-performance programming language that is focused on numerical computing, which makes it a good fit in the math-heavy world of AI.
• The problems they solve will reach a complexity defying that of traditional approaches to advanced machine learning.
• By providing developers with direct control over the mapping of computations onto the hardware, Mojo enables the creation of highly optimized AI implementations.
• We sorted the list in ascending order of the best programming languages for machine learning applications.
• It is commonly used in cloud projects since it’s lightweight, compiles in a flash, and handles a bunch of tasks at once.

Mojo, developed by Modular AI, focuses on high performance, scalability, and ease of use for building and deploying AI applications. Swift for TensorFlow, an extension of the Swift programming language, combines the high-level syntax and ease of use of Swift with the power of TensorFlow’s machine learning capabilities. These languages represent a growing trend towards specialized tools and abstractions for AI development. Lisp, one of the pioneering programming languages for AI, has a long-standing history and remains relevant today. Developed in 1958, Lisp derived its name from ‘List Processing,’ reflecting its initial application.

Additionally, Mojo offers memory management with pointers, giving developers fine-grained control over memory allocation and deallocation. These features contribute to Mojo’s performance optimizations and help developers write more efficient and error-free code. In 2025, this programming language remains popular due to its ease of use, versatility, and extensive community support. It is widely used to write instructions, manage sensors, and control the movement of robots.

Rust

Julia is a high-performance programming language that is focused on numerical computing, which makes it a good fit in the math-heavy world of AI. While it’s not all that popular as a language choice right now, wrappers like TensorFlow.jl and Mocha (heavily influenced by Caffe) provide good deep learning support. If you don’t mind that there’s not a huge ecosystem out there just yet, but want to benefit from its focus on making high-performance calculations easy and swift. More recently, a new wave of AI-first languages has emerged, designed from the ground up to address the specific needs of AI development. Bend, created by Higher Order Company, aims to provide a flexible and intuitive programming model for AI.

Swift

By providing developers with direct control over the mapping of computations onto the hardware, Mojo enables the creation of highly optimized AI implementations. As AI keeps pushing the envelope, it’s also pushing the limits of our most popular programming languages, Java, C and Python. And, like everything else, AI is another problem just begging for a new programming language to solve it. This time however, history suggests it might not be such a bad idea.

Additionally, Java supports multithreading, which is beneficial for robotics projects that require multiple tasks to run simultaneously. For instance, it can handle tasks like moving motors, collecting sensor data, and managing user input concurrently. While Java may not offer as much direct hardware access as C++, it remains a stable and reliable language that can be utilized across various platforms. A fundamental aspect of robotics is programming, where selecting the appropriate coding languages is crucial.

Inference latency is critical in real-time AI applications like autonomous driving or live video analysis. However, Python’s Global Interpreter Lock (GIL) prevents multiple native threads from executing Python bytecodes simultaneously, leading to suboptimal performance in multi-threaded environments. ROS lets developers create systems where different components of a robot (such as sensors, cameras, and motors) can communicate effectively with each other. Testing plays a significant role in robotics, particularly in education and research. MATLAB offers robust tools for simulating how a robot will behave in various environments. While MATLAB is not always used in real programs, it is very helpful when planning and testing.

As a free, open-source programming language it’s also very accessible; you can learn or study it in a lot of detail and there is plenty of community support thanks to a plethora of tutorials, courses, resources, and forum posts. When the AI field experienced a long period of diminished funding and interest in the 1970s and 1980s, the so-called “AI Winters”, the focus on specialized AI languages like LISP began to fade. It is not the first time AI has driven a wave of new programming languages.

TensorFlow, PyTorch, and JAX: Choosing a deep learning framework

C/C++ is unlikely to be your first choice when developing an AI application, but if you’re working in an embedded environment, and you can’t afford the overhead of a Java Virtual Machine or a Python interpreter, C/C++ is the answer. When you need to wring every last bit of performance from the system, then you need to head back to the terrifying world of pointers.

Quantum computing

Prolog’s declarative nature and emphasis on logic make it particularly well-suited for tasks that involve knowledge representation, reasoning, and rule-based systems. Its ability to efficiently handle symbolic computations and pattern matching sets it apart in the AI and NLP domains. Prolog’s built-in backtracking mechanism allows for elegant problem-solving approaches. With Prolog, developers can focus on specifying the problem’s logic rather than the algorithmic details.

• Mojo is a new programming language that promises to bridge the gap between Python’s ease of use and the lightning-fast performance required for cutting-edge AI applications.
• As more developers build tools, libraries, and frameworks for Mojo, the language’s appeal will grow, attracting potential users who can benefit from rich resources and support.
• One of Mojo’s most exciting aspects is its potential to accelerate AI development.
• You can also build serverless apps and add machine learning to cloud services.
• Python’s dynamic typing and automatic memory management can increase memory usage and fragmentation.

Google unfortunately stopped development and the project is now archived, which shows just how difficult it can be to get user traction with a new language development, even for a giant like Google. One of Mojo’s most exciting aspects is its potential to accelerate AI development. With its ability to compile to highly optimized machine code that can run at native speeds on both CPUs and GPUs, Mojo enables developers to write complex AI applications without sacrificing performance. The language includes high-level abstractions for data parallelism, task parallelism, and pipelining, allowing developers to express sophisticated parallel algorithms with minimal code. In large-scale AI applications, efficient memory management is crucial to maximize the use of available resources.