9 Best Programming Languages for AI Development in 2024

1. Python

Python is widely recognized as a leading artificial intelligence programming language, given its simplicity, extensive libraries, and vibrant community support.  The general-purpose programming language for AI development, Python has gained popularity because of its analytics and data visualization features. Data scientists and analysts will find it easier to understand because of its user-friendly syntax.
Garbage collection ensures automatic memory management in the language, and interpreted execution enables rapid iterations in development without requiring recompilation.

Thanks to libraries like NumPy, Python has strong support for matrices and scientific computing, which is one of its advantages. This offers a high-performance basis for several AI methods, such as neural networks and statistical models.  The language has a plethora of AI-specific libraries and frameworks spanning deep learning, high-level neural network APIs, and core machine learning, such as scikit-learn, TensorFlow, and PyTorch.

Python Use in AI

• Python is the language of choice for deep learning, a subset of machine learning that focuses on neural networks. Frameworks like TensorFlow and PyTorch enable the development of complex neural network architectures for tasks like image recognition, natural language processing, and more.
• Python is extensively used in NLP applications, which involve the interaction between computers and human language. Libraries such as NLTK (Natural Language Toolkit) and spaCy make it easier to process and analyze textual data.
• Python, along with libraries like OpenCV (Open Source Computer Vision Library), is widely used in computer vision applications. This includes tasks like image and video processing, object detection, and facial recognition.

2. R

R is a popular language for data science applications and is the standard for statistical computation. It performs exceptionally well when used with AI algorithms including data analysis, simulations, and probabilistic modeling.
R’s package ecosystem makes it possible to manipulate and visualize data, which is essential for the creation of AI. The caret package adds tools for preprocessing and validation to improve machine learning capabilities.
Additionally, the flexible layered syntax of graphics concepts provided by ggplot2 can be used to create professional graphics and customized data visualizations. The TensorFlow for R package bridges into TensorFlow’s capabilities to enable scalable production-grade deep learning.

R Use in AI

• R is renowned for its statistical capabilities, making it a preferred language for statistical analysis. It is often used in AI projects that involve the analysis of data distributions, hypothesis testing, and other statistical techniques.
• R provides powerful visualization libraries like ggplot2, which are used to create high-quality graphics and plots. Visualization is crucial in AI for understanding data patterns, model performance, and conveying results to stakeholders.
• Before feeding data into machine learning models, preprocessing is often required. R is used for tasks such as cleaning and transforming data, handling missing values, and preparing datasets for analysis and modeling.

3. JAVA

AI systems that must interface with current business systems and runtimes use Java. The JVM maintains codebase compatibility while enabling the implementation of AI. Because of its vast ecosystem, which offers a wealth of developers, cross-platform portability, stable runtimes, large libraries, and tools, creating and implementing AI apps that function on a variety of hardware and software platforms is simple.
For example, JVM neural network architectures are supported by DeepLearning4j. Classification, regression, and clustering methods are gathered by the Weka machine learning package, and Mallet provides natural language processing for AI systems.

Java Use in AI

• Java can be employed for developing machine learning algorithms and models. While Python is more commonly associated with machine learning, Java has libraries like Deeplearning4j and Weka that enable the implementation of machine-learning solutions.
•  Java is known for its robustness and efficiency, making it suitable for handling large datasets and performing data preprocessing tasks. Apache Hadoop, a popular framework for distributed storage and processing of large datasets, is implemented in Java.
•    Java is used in NLP applications for text processing, information retrieval, and language understanding. Libraries like Apache OpenNLP provide tools for implementing NLP functionalities.
• In the field of robotics and automation, Java can be employed to program and control robotic systems. Java’s portability and platform independence make it suitable for developing software that can run on different hardware platforms.

4. Julia 

Julia makes functions more versatile without sacrificing speed by employing a multiple dispatch mechanism. Additionally, it makes employing several cores and parallel programming inherently quick. It performs admirably whether distributed across numerous machines or utilized in multiple threads on a single machine.

The fact that Julia plays well with pre-existing R and Python code is one of its strongest characteristics. This enables you to take advantage of Julia’s advantages and work with established Python and R libraries.

Furthermore, Julia’s core libraries for machine learning (Flux.jl), data visualization (Plots.jl), data manipulation (DataFrames.jl), and optimization (JuMP.jl) are developing. Jupyter Notebook capability is readily integrated by the IJulia project.

Julia’s Use in AI

• Julia’s primary strength lies in numerical and scientific computing. It provides a convenient syntax for mathematical operations and supports fast execution of numerical algorithms. This makes Julia suitable for tasks such as linear algebra, optimization, and statistical analysis, which are fundamental to many AI applications.
• Julia has a growing ecosystem of packages and libraries for machine learning and data science. MLJ.jl, for example, is a machine learning framework in Julia that provides a unified interface for various models, data preprocessing, and evaluation metrics.
• Julia is designed with a focus on performance, allowing developers to write code that is both high-level and fast. This makes it suitable for optimizing and implementing performance-critical components of AI algorithms, such as neural network layers or optimization routines.

5. C++

C++ is an alternative if you wish to implement an AI model in a low-latency production setting. C++ can run machine learning programs quickly with extremely little memory usage since it is a compiled language that allows developers to control memory. This makes it suitable for high-processing-demanding AI applications.

Furthermore, independent executable programs that consistently tap high performance across all operating systems and CPUs like AMD and Intel may be created from any C++ code. It enables the reliable deployment of sophisticated AI applications with hardware acceleration anywhere.

Regarding the libraries, TensorFlow’s machine-learning capabilities can be directly plugged in via the C++ interface. Another library designed especially for deep learning tasks is called Caffe2. A common neural network exchange format called ONNX makes it simple to move models between tools. Furthermore, OpenCV offers crucial computer vision building blocks.

C++ Use in AI

• C++ is well-suited for performance-critical tasks, making it an ideal choice for AI applications that demand high computational efficiency. 
• Some machine learning frameworks, such as TensorFlow and PyTorch, provide C++ APIs alongside their primary Python interfaces. C++ can be used for integrating machine learning models into applications where low latency and high performance are essential.
• C++ is a dominant language in the field of game development, and AI plays a crucial role in creating intelligent non-player characters (NPCs) and decision-making systems within games. 
• C++ is employed in computer vision applications and image processing tasks where real-time performance is critical. 

6. Javascript

JavaScript is used in situations where online systems require flawless end-to-end AI integration. Enabling AI applications through recognizable web programming is the aim. It is well-liked for full-stack development and integrating AI functionalities into user interactions on websites.

Furthermore, JavaScript updates pages and processes user inputs in real time without lag by utilizing an event-driven approach. Because types are dynamic rather than strict and the language may prototype code quickly, it is flexible.

Regarding its libraries, TensorFlow.js converts Google’s machine learning framework to JavaScript for use with Node.js and browsers. Deep learning is made flexible by the neural network API offered by brain.js. Architecture-neutral neural networks are provided by Synaptic.js. The model integration is further made simpler by Node-RED’s visual workflow.

Javascript Use in AI

• JavaScript is commonly used to develop AI applications that run in web browsers. With the advent of libraries like TensorFlow.js and Brain.js, developers can implement machine learning models directly in the browser, enabling tasks such as image recognition, natural language processing, and more.
• JavaScript is employed in creating browser extensions that utilize AI functionalities, such as language translation or content recommendation. It’s also used for building chatbots and virtual assistants that can interact with users on websites.
• JavaScript, especially with libraries like D3.js and Chart.js, is widely used for data visualization. AI projects often involve the analysis and presentation of large datasets, and JavaScript can be utilized to create interactive and dynamic visualizations.

7. Lisp

Lisp is a strong functional programming language well-known for logical reasoning and rule-based artificial intelligence applications. It may even change its code on the fly through metaprogramming, representing knowledge as both code and data in the same symbolic tree structures.

Lisp’s nested list code style also facilitates processing and analysis, which is important for modern machine learning. While maintaining the fundamentals of Lisp, modern versions include useful automation such as memory management.

Concerning important dialects and ecosystems, Clojure permits the utilization of Lisp features on Java virtual machines. Building expert systems is made easier by CLIPS. Lisp retains its symbolic qualities while extending to contemporary statistical approaches like neural networks through interfaces with TensorFlow.

Lisp Use in AI

• Lisp excels at symbolic reasoning and manipulation. Its support for symbolic expressions and the ability to treat code as data make it well-suited for developing AI applications that involve symbolic reasoning, knowledge representation, and rule-based systems.
• Lisp is commonly used in research and prototyping within the AI community. Its expressive syntax and powerful features, such as dynamic typing and garbage collection, allow researchers to quickly experiment with and implement new AI algorithms and models.
• Lisp’s recursive nature and support for recursion make it particularly useful for tasks involving tree structures and hierarchical representations. This is beneficial in AI for handling complex data structures and representing knowledge naturally and flexibly.

8. Haskell

Pure math functions are used in Haskell, a language for functional programming, to create AI algorithms. It lessens bugs and facilitates verification by preventing side effects within functions, which is helpful in safety-critical systems.

The best feature is that it evaluates code lazily, increasing efficiency by only doing calculations when necessary. It also simplifies the process of declaring and abstracting reusable AI components.

Neural networks and machine learning are directly addressed by its libraries, such as HLearn and LambdaNet. Simultaneously, BayesHaskell and Haxcel support the requisite math for probability and linear algebra.

Haskell Use in AI

• Haskell’s strong support for symbolic reasoning and declarative programming makes it well-suited for applications in logic programming. It can be used in the development of expert systems and knowledge representation where logical inference and symbolic manipulation are crucial.
• Haskell’s emphasis on functional purity and immutability makes it suitable for applications that require formal verification. In AI, where correctness and reliability are crucial, Haskell can be used to develop systems that are more amenable to formal methods and proof-based verification.
• Haskell’s expressive type system and functional programming paradigm make it suitable for mathematical modeling and algorithm development. It can be used in AI projects that involve complex mathematical computations, simulations, and numerical analysis

9. Scala

Functional and object-oriented programming approaches are combined in Scala. This enables mathematical clarity through pattern matching and immutability, as well as modular data abstraction through classes and methods.

Furthermore, Scala’s sophisticated type system ensures robustness for scale through static checking and leverages inference for flexibility. The deployment of AI tasks over parallel infrastructure is also made possible by asynchronous processes.

With the provision of neural networks (ScalNet), numerics (Breeze), distributed machine learning on Spark, and interaction with Java ecosystems like DeepLearning4J, specialty libraries expand on Scala’s fundamental benefits for AI. Scala thus combines access to a wide range of technologies with sophisticated language features for increased efficiency.

Scala Use in AI

• Scala is commonly used in big data processing frameworks like Apache Spark. Spark provides APIs in Scala, making it a natural choice for developers working on AI applications that involve processing large datasets, machine learning, and distributed computing.
• Scala is a functional programming language, and functional programming concepts can be advantageous in AI development. Functional programming can simplify code, make it more modular, and enhance the expressiveness of algorithms, which can be particularly useful in certain AI scenarios.
• Scala is statically typed, providing strong type safety. This feature can be beneficial in AI projects, especially when dealing with complex algorithms and data structures, as it helps catch errors during compile-time rather than runtime.