Top 10 AI Programming Languages: A Beginner's Guide to Getting Started In 2024

As artificial intelligence (AI) continues to transform industries, many beginners are eager to get involved. Choosing the right programming language is an important first step. In this blog post, I’ll introduce you to the top 10 AI programming languages. I’ll also cover how they are used in AI, highlight their top packages and libraries, discuss the AI roles that primarily use them, and provide resources to help you get started.

What Are AI Programming Languages?

AI programming languages are instruments for creating software that mimics human intelligence. These languages enable the development of algorithms with decision-making and data-learning capabilities. The development of tasks like speech recognition, data interpretation, and autonomous driving is facilitated by the libraries and frameworks that are specific to each language.

Picking a language that makes coding easier and has libraries designed for specific tasks like data analysis and machine learning is crucial. For statistical analysis, R is preferred, while Python is well-known for libraries like TensorFlow and PyTorch.

AI programming languages are the foundation of intelligent systems because they combine the capacity to efficiently perform difficult AI-specific tasks with code.

Top 10 AI Programming Languages

Here’s my curated list of the top 10 AI programming languages in 2024. Leading the list by a significant margin is Python.

1. Python

The most widely used and approachable programming language in the AI space is called Python. It is useful because of its extensive library of packages and simple syntax.

How is Python used for AI? Python is widely used in AI for its comprehensive range of applications, spanning the entire spectrum of artificial intelligence.

Top Python AI packages and libraries:

NumPy: It can handle complex mathematical structures effortlessly.
pandas: It provides user data structures such as DataFrames for analyzing and manipulating data.
Matplotlib: It is a visualization tool widely used for creating static, animated, and interactive graphs and charts.
scikit-learn: It offers a range of tools and algorithms for machine learning.
TensorFlow: Google's brainchild provides a robust foundation for constructing and implementing neural networks.

AI roles that use Python:

Machine learning engineers use Python libraries like TensorFlow, PyTorch, and scikit-learn to create and apply machine learning models.
Data scientists: Using packages like pandas and Matplotlib, they use Python for tasks like data cleansing, analysis, and visualization.
Natural language processing engineers create and implement systems that can comprehend and interpret human language in order to perform tasks like machine translation.
Computer vision engineers enable machines to efficiently interpret images. Video processing functions are provided by libraries such as TensorFlow.
Research scientists develop and use novel artificial intelligence algorithms using Python. The Python packages and libraries serve a wide range of science-related topics.

Top Python resources to get started:

2. R

R is a statistical program that focuses on data analysis (including cleaning, visualization, and interpretation), machine learning, and statistical modeling.

How is R used for AI? R has strong statistical capabilities, and its collection of data visualization features has made it a popular option for creating models and developing prototypes for machine learning algorithms.

Top R AI packages and libraries:

Tidyverse: It is not a single library. It is a set of packages that collaborate smoothly on data science tasks.
caret: It is a one-stop shop for machine learning tasks in R. It provides a unified interface for various machine learning algorithms, allowing users to easily train, tune, and evaluate different models.
XGBoost: It is a powerhouse for gradient boosting, a popular machine-learning technique known for its accuracy and efficiency.
randomForest: As the name suggests, randomForest implements the random forest algorithm, a popular ensemble learning technique that combines the predictions of multiple decision trees.
IML (Interpretable Machine Learning): It provides a framework for building statistical models in R.

AI roles that use R:

Data scientist (R specialist): R continues to be involved in jobs involving statistical analysis and modeling, even if Python is used extensively in data science.
AI researcher (specializing in statistical modeling): Due to R's computing and visualization powers, researchers frequently like using it. R can be used by them to create, test, and assess models that advance AI algorithms.
Bioinformatics scientists: R is used for tasks like variant detection and gene expression analysis, which are crucial for AI-driven genomics and bioinformatics applications.
Econometricians: They use R to create models to foresee market behavior, evaluate risks, and predict trends.
Social data scientists: They analyze text and social networks using R.

3. Julia

Julia is ideal for sectors requiring substantial data analysis because of its fast processing speed and capacity to handle intricate computations.

How is Julia used for AI? Julia's speed and efficiency make it suitable for sophisticated machine learning models, particularly those involving numerical and scientific computing.

Top Julia AI packages and libraries:

Flux.jl: A popular machine learning library offering a straightforward environment for creating and optimizing neural networks.
Mocha.jl: Ideal for tasks involving deep learning.
DiffEq.jl: Provides a powerful framework for solving various differential equations.
StatsBase.jl: Julia's go-to library for statistical functions.
Makie.jl: Facilitates efficient data visualization, making it easier to understand complex data and model behaviors.

AI roles that use Julia:

AI Engineer: Leverage Julia's capabilities to build and train robust models.
Algorithmic Optimization Researcher: Prototype and evaluate algorithm performance before deploying to production languages.
Machine Learning Engineer: Develop AI solutions for sectors like finance using libraries such as Flux.jl.
Robotics Engineer: Integrate real-time data with robotics frameworks like ROS (Robot Operating System).
Financial Analyst: Develop AI-driven trading algorithms and risk management models using Julia's numerical computing strengths.

4. Scala

Scala excels in machine learning and AI due to its scalability and integration with Apache Spark. Its functional programming capabilities and compatibility with Java enhance its effectiveness in AI system development.

How is Scala used for AI? Scala handles large-scale AI applications effectively, particularly when working alongside Apache Spark.

Top Scala AI packages and libraries:

Apache Spark: Provides distributed computing, data streaming, and machine learning features.
Spark MLlib: Offers a range of machine learning algorithms for various tasks.
Breeze: Includes linear algebra operations, statistical functions, and optimization methods.
Deeplearning4j (DL4J): A Java-based deep learning library with a Scala interface.
Scopt: Helps design command line interfaces for managing AI models.

AI roles that use Scala:

Machine Learning Engineer: Create and implement large-scale systems and machine learning models.
Data Scientist: Build custom machine learning pipelines, cleanse, and manipulate data.
Spark Developer: Work on AI projects using Apache Spark for big data processing.
AI Researcher: Prototype and test new algorithms.
Scala Backend Developer: Develop server-side features using Scala for AI integration.

Top Scala resources to get started:

Introduction to Scala

5. Java

Java is commonly used in AI for developing sophisticated software applications, known for its cross-platform adaptability and integration with AI libraries.

How is Java used for AI? Java builds large-scale, cross-platform applications with integrated AI functionalities.

Top Java AI packages and libraries:

WEKA: A collection of machine learning algorithms.
DeepJAVA: Tools for building and training deep neural networks.
H2O: Utilizes distributed computing for efficient model training.
Deeplearning4j (DL4J): Deep learning library for convolutional and recurrent neural networks.
Smile: Efficient and clear syntax for developing machine learning models.

AI roles that use Java:

Big Data Engineer: Construct and manage AI pipelines with Java frameworks.
Enterprise AI Developer: Create and integrate AI models into enterprise systems.
Android AI Developer: Incorporate AI functionalities into Android applications.
AI Infrastructure Engineer: Develop APIs to expose AI models and monitor their operations.
Software Engineer: Integrate AI models or libraries into Java-based applications.

6. C++

C++ is known for its powerful processing capabilities, making it ideal for AI assignments like computer vision and robotics.

How is C++ used for AI? C++ handles tasks requiring computational power and precision in managing hardware resources.

Top C++ AI packages and libraries:

Eigen: An advanced linear algebra toolkit.
Armadillo: Another algebra library with MATLAB-like syntax.
Dlib: Ideal for creating real-time AI applications embedded in devices.
OpenCV: Provides algorithms and tools for image handling and object detection.
TensorFlow C++ API: Manages TensorFlow functions with C++ speed.

AI roles that use C++:

High-Performance Computing (HPC) AI Engineer: Build and train complex models with precise hardware control.
Embedded Systems Engineer: Develop AI models for resource-constrained devices.
Robotics Engineer: Develop core functionalities for robot control and real-time performance.
Game AI Programmer: Implement complex AI behaviors in games.
Computer Vision Engineer: Utilize C++ libraries for image processing and object detection.

7. JavaScript

JavaScript develops interactive AI features in web browsers, like chatbots, and prototypes AI ideas quickly.

How is JavaScript used for AI? Builds interactive AI features in web browsers and quickly prototypes AI ideas with libraries like TensorFlow.js.

Top JavaScript AI packages and libraries:

TensorFlow.js: Develop and train machine learning models in JavaScript.
Brain.js: User-friendly for newcomers.
Synaptic.js: Flexible API for various neural network architectures.
Keras.js: Utilize Keras’ features in a JavaScript setting.
Ml5.js: Ready-made machine learning models for JavaScript applications.

AI roles that use JavaScript:

Front-End Web Developer: Build interactive and intelligent user interfaces.
Web AI Developer: Create full-fledged AI applications in the browser.
AI Visualization Developer: Visualize complex data from AI models.
Conversational AI Developer: Build chatbots for natural user interaction.
Full-Stack AI Developer: Develop AI-powered web applications.

8. Lisp

Lisp is used in reasoning tasks, such as language processing, due to its adaptability.

How is Lisp used for AI? Used for symbolic reasoning tasks due to its ability to manipulate symbols and data structures.

Top Lisp AI packages and libraries:

Common Lisp User Environment (CL-USER): Platform for engaging with Common Lisp.
Applicative Common Lisp (ACL2): Supports automated reasoning and theorem proving.
McCLIM: Machine learning algorithms with an emphasis on machine learning methods.
OpenCyc: Open-source knowledge base for AI tasks.
Lisp implementations with AI functionalities: Various versions like Apache CLIME and GNU CLisp.

AI roles that use Lisp:

AI Research Scientist: Rapid prototyping in academic settings.
AI Language Engineer: Develop and improve Lisp compilers and libraries.
Legacy AI System Maintainer: Maintain and debug legacy AI systems.
Open-Source AI Developer: Contribute to Lisp-based projects.
AI Educator: Teach core AI concepts using Lisp.

9. Haskell

Haskell offers clean and reliable code, ideal for AI research, prototyping algorithms, and education.

How is Haskell used for AI? Used for research, prototyping algorithms, and education due to clean, reliable code from functional programming and strong typing.

Top Haskell AI packages and libraries:

HLearn: Machine learning algorithms coded in Haskell.
Bayesbayes: Programming for handling uncertainty in AI.
Halcyon: Deals with reasoning and conclusions based on information.
BioCondor: Creates workflows for distributed computing tasks.

AI roles that use Haskell:

AI Researcher: Utilize Haskell for formal verification of AI algorithms.
Probabilistic Programming Specialist: Build models for Bayesian inference and probabilistic reasoning.
Functional Verification Engineer: Create precise specifications for AI systems.
Domain-Specific Language Developer: Develop DSLs for advanced tools.
Research Scientist: Develop deep learning frameworks based on functional programming principles.

10. Mojo

Mojo makes AI development easier with user-friendly code that maintains speed. While still catching up with library support, it promises performance and adaptability.

How is Mojo used for AI? Offers high performance for building efficient and scalable AI applications.

Note: Mojo is still under development and hasn't yet achieved widespread adoption.

How to Learn AI Programming Languages

Starting your AI journey requires mastering key skills like basic mathematics (linear algebra and calculus) and statistics, which are foundational for understanding AI operations and algorithms. Programming knowledge, particularly in Python, is essential due to its simplicity and the extensive resources available for AI. Skills in data manipulation and understanding data structures are integral to implementing AI solutions. Continuously updating these skills through practical projects and targeted learning tracks can effectively build your expertise in AI.

Top Resources to Get Started:

What is AI? A Quick-Start Guide For Beginners: Overview of AI, including its applications and how to start learning.
Implementing AI Solutions in Business: Getting Started with AI: Basics of AI, its evolution, and a framework for implementing AI in business.
Understanding Artificial Intelligence Course: Explores foundational aspects of AI with hands-on exercises.
Introduction to AI Agents: Explores AI agents and tools like Auto-GPT, AgentGPT, and BabyAGI.
AI Fundamentals Certification: Covers essential concepts of AI, including machine learning and generative AI.

Starting with AI programming is an exciting and promising journey. Choose your language, start experimenting, and learn as you go!