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10 Best AI Programming Languages in 2024

Table of Contents
Best AI Programming Languages

Creating a digital presence is essential for every organisation and individual today. In business, it is crucial for building brand image, boosting customer awareness, and driving prospects. Many retailers have shifted from physical stores to online shops to stay competitive in the ever-changing market.

Websites and web apps are now the lifeblood of online presence. It is a standard practice for businesses to create their own websites. From start-ups to large enterprises, all recognise that web development is key to their success.

Developing web pages or modern web applications is complex. It requires deep expertise and skills. That is why many developers and webmasters use web development frameworks.

Python

Python is the leading language for AI programming. PYPL stats show Python has a 28.11% market share, making it the most popular language. It’s a general-purpose language used for web app development, scientific computing, automation, IoT, and more.

Python has a strong community and many frameworks for AI algorithms. It remains relevant by adding new features and ensuring backward compatibility. Popular machine learning libraries include:

  • Scikit-learn: A user-friendly library for classical ML algorithms like classification, regression, clustering, and dimensionality reduction.
  • TensorFlow: An open-source library for building machine learning and deep learning applications. It offers tools for neural networks, distributed training, and model deployment.
  • Theano: A mathematical library for creating neural networks and other algorithms without manually coding equations.

JavaScript

JavaScript is popular for its flexibility and ability to run on client-side web browsers. It’s mostly used in web development but also has AI programming libraries:

  • Dialogflow and Rasa: Machine learning frameworks for building AI-powered chatbots.
  • TensorFlow.js and Brain.js: Libraries for implementing ML algorithms directly in the browser.
  • ConvNetJS: A library for building neural networks.
  • Math.js: A library with various mathematical and statistical functions.
  • Node-fann: An open-source machine learning library for artificial neural networks.

Java

Java is a good choice for AI projects, especially those that are heavy on object-oriented programming. It has an extensive library, runs on various hardware, and integrates well with other tools, making it ideal for big data. Java libraries include:

  • Apache Flink: A framework and engine for stateful computations over data streams, useful for stream-processing applications.
  • Deeplearning4j: An open-source, distributed deep-learning library for building neural networks and ML models.
  • Encog: An ML framework supporting neural networks, support vector machines, and genetic algorithms.
  • PredictionIO: An ML server for creating predictive engines for recommendation and classification tasks.
  • Weka: An open-source library for data mining, classification, regression, clustering, and more, suitable for beginners.

These programming languages and their libraries offer powerful tools for developing AI applications, each with unique strengths and use cases.

C#

C# (pronounced “C sharp”) is a modern, object-oriented programming language created by Microsoft. It is part of the .NET framework and was introduced in 2000. C# is used in web development, desktop app development, and game development with Unity.

Although C# is not as common in AI development as Python or Java, it is still a good choice for certain projects. Its robust features and integration capabilities make it compatible with various AI frameworks and libraries.

Some popular C# frameworks include:

  • Accord.NET: A machine learning framework with algorithms like SVMs, decision trees, and clustering. It also supports computer vision and signal processing.
  • ML.NET: An open-source library from Microsoft for building models, including deep neural networks.
  • Microsoft Cognitive Toolkit (CNTK): A machine learning library for both deep learning and traditional algorithms. It supports parallel computations.
  • TensorFlow.NET: A .NET binding for TensorFlow, enabling the use of TensorFlow’s capabilities in C# applications.

R

R is a top language for building machine learning algorithms, especially in academic research. It is ideal for projects needing statistical models for machine learning.

Popular R libraries include:

  • RevoScaleR: For statistical analysis on large datasets, compatible with Spark and Hadoop.
  • MASS: Contains functions and datasets for statistical analysis and machine learning.
  • dplyr: A package for data manipulation designed to handle large datasets efficiently.

C++

C++ is a good choice for projects needing efficient hardware interaction. It combines low-level efficiency with high-level abstractions, making it suitable for writing performance-critical code.

Popular C++ libraries include:

  • OpenCV: A library for image and video processing, essential for computer vision tasks.
  • Shark: A library focusing on efficient ML algorithms and data structures, offering supervised and unsupervised learning techniques.
  • Eigen: For linear algebra operations, used in numerical computations and machine learning algorithms.

Go

Go, or Golang, is gaining traction in AI applications due to its CPU and memory optimisations. It offers high-level features for parallel computing.

Popular Go libraries include:

  • Gorgonia: For building neural networks and symbolic reasoning, well-suited for deep learning.
  • Go-Torch: Provides Go bindings for the PyTorch framework.
  • GoNN: For creating and training neural networks using evolutionary computation.
  • GoLearn: An open-source library with tools and examples for building machine learning applications in Go.

These programming languages and libraries provide strong tools for AI development, each with its unique strengths and use cases.

Matlab

Matlab is a proprietary programming language, so it is not open-source. It is known for its powerful numerical computing capabilities and extensive libraries for mathematical operations and image processing. Matlab can be expensive, but its range of functionalities and specialized toolboxes make it valuable for professionals needing advanced mathematical computations and data analysis.

Popular Matlab libraries include:

  • MATLAB Coder: Converts programs into standalone C or C++ versions.
  • Image Processing Toolbox™: Tools for image processing and analysis.
  • Deep Learning Toolbox: Tools for building and training deep neural networks. It offers various architectures, algorithms, and pre-trained models for tasks like image classification and object detection.

Julia

Julia is a newer high-level programming language gaining popularity in AI development. It is useful for big data and large-scale projects because it can run on databases like Hadoop and Spark and is easily distributed across multiple machines.

Julia has a vast ecosystem of packages and libraries for AI-related domains like data science, machine learning, and optimisation. These packages are managed through its built-in package manager called Pkg.

Popular Julia libraries include:

  • DataFrames: Provides a data structure similar to an R or Python data frame, making large datasets easier to work with.
  • Distributions: Functions and methods for generating random numbers and samples, including distributions like normal and uniform.
  • JuMP: A modeling language for mathematical optimisation problems.

Scala

Scala stands for “scalable language,” reflecting its design goal to grow with users’ needs. It combines object-oriented and functional programming paradigms. Scala is gaining traction in AI development due to its strong type safety and interoperability with Java.

Consider these Scala libraries for AI projects:

  • Breeze: A robust library for numerical computing, offering functions for scientific computing, machine learning, and data mining, including linear algebra, matrix manipulation, and optimisation algorithms.
  • Almond: An open-source library providing a high-level API for creating neural networks. It integrates with deep learning frameworks like TensorFlow and PyTorch.
  • Apache Spark: A distributed computing framework that uses Scala as its primary language. Spark supports big data processing, machine learning, and real-time analytics. Its MLlib library is used for scalable machine learning tasks on large datasets.

Other Languages

These languages are just a few options for AI development. If none of these fit your needs, you can explore others like Kotlin, Prolog, Rust, Common Lisp, and more.

Conclusion

The potential of artificial intelligence is limitless, so start your AI project today.

Choosing the right programming language for your AI project can be tough with many options available. Pick one that aligns with your project goals and your team’s expertise. If you need to expand your team or create a new one with AI-specific skills, let EZtek Software help.

We are a leading software outsourcing company based in Vietnam. At EZtek, we offer a wide range of services, from custom software development to QA testing and IT staff augmentation. You can trust us with your outsourcing needs. For more information, feel free to contact us, and we’ll respond within three business days.

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