Python, R, Julia & More

If you’re interested in pursuing a career in artificial intelligence (AI), you’ll need to know how to code. But where do you start? What is the best programming language for AI? And what platforms should you begin with? This article will provide you with a high-level overview of the best programming languages and platforms for AI, as well as their key features.

As it turns out, there’s only a small number of programming languages for AI that are commonly used.

Key Takeaways

1. Python emerges as one of the leading AI programming languages, valued for its simplicity, extensive libraries, and strong community support, making it an excellent starting point for beginners.

2. R and Julia are highlighted for their specialized capabilities in statistical analysis and high-performance numerical computing, respectively, catering to specific needs in AI development.

3. While traditional languages like C++ and Java retain their relevance for performance-intensive applications, emerging languages and platforms offer new possibilities, emphasizing the importance of choosing a language based on the project’s specific requirements and the developer’s expertise.

What Is Artificial Intelligence?

Artificial intelligence is one of the most fascinating and rapidly growing fields in computer science. It’s already transforming the world around us as we speak. And it’s as hot a job market as you can get (see Gartner forecasts).

AI aims to create intelligent computer systems. It’s essentially the process of making a computer system that can learn and work on its own.

Machine learning (ML), on the other hand, deals with the methods and techniques used so that a computer system can “learn” how to perform certain tasks and even predict certain outcomes, without being explicitly programmed for it.

A (Very) Brief History of AI

The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy, when the field of artificial intelligence research was founded as an academic discipline. In the years since, AI has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding.

For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals, application domains (such as medicine and robotics), and/or particular implementation approaches (such as symbolic computation and connectionism).

The traditional problems (or goals) of AI research include:

Today’s and Tomorrow’s AI

Today, AI is used in a variety of ways, from powering virtual assistants like Siri and Alexa to more complex applications like self-driving cars and predictive analytics.

In the foreseeable future, artificial general intelligence (AGI, also called strong AI, full AI, or general intelligent action) is still among the field’s long-term goals — with common approaches including statistical methods, computational intelligence, and traditional symbolic AI.

Best Programming Languages for AI Development

Here are the most popular languages used in AI development, along with their key features.

Python

 

 

Python is the most popular AI programming language it’s one of the hottest languages going around, and it’s also easy to learn!

Python is an interpreted, high-level, general-purpose programming language with dynamic semantics. Its high-level, built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for rapid application development (RAD), as well as for use as a scripting or glue language to connect existing components together.

Key features of Python:

• It has simple, easy-to-learn syntax that emphasizes readability (and therefore reduces the cost of program maintenance).
• It supports modules and packages (which encourages program modularity and code reuse).
• It has an extensive standard library (available in source or binary form without charge for all major platforms).

What makes Python good for AI:

• It has a rich set of libraries for data analysis and manipulation, such as Pandas, making it easy to work with the data.
• It has a number of libraries specifically for machine learning, such as TensorFlow and Keras.
• It has solid scientific and computation libraries, such as scikit-learn and NumPy.
• It can even be used to program microcontrollers with projects like MicroPython, CircuitPython, and Raspberry Pi.

Example Projects:

• Project: TensorFlow and Keras
  • Description: Open-source libraries for machine learning and deep learning, developed by Google. Used in various applications, from voice/image recognition to drug discovery.

If you’re starting with Python, it’s worth checking out the book The Python Apprentice, by Austin Bingham and Robert Smallshire, as well as other the Python books and courses on SitePoint.

R

 

 

R is a programming language and free software environment for statistical computing and graphics that’s supported by the R Foundation for Statistical Computing.

The R language is something of a lingua franca among statisticians, as it’s widely used in official statistics (see uRos2020 and uRos2022), but also on data mining and for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R has an active user base of about two million people worldwide.

Key features of R:

• It has an integrated suite of software facilities for data manipulation, calculation, and graphical display.
• It has an effective data handling and storage facility, providing a wide variety of operators for calculations on arrays, lists, vectors and matrices.
• It offers extensive graphical facilities for data analysis and display, either directly at the computer or on hardcopy devices — including interactive web-based interfaces such as Shiny.
• It’s a well-developed, simple and consistent programming language that includes conditionals, loops, user-defined recursive functions, and input/output facilities (including connections to relational databases).

What makes R good for AI:

• It’s designed specifically with statisticians in mind (unlike Python, which was designed as a general-purpose language).
• It has many features that are useful for AI development, such as linear and nonlinear modeling, time series analysis, classification, and clustering.

Example Projects:

• Project: Microsoft R Server
  • Description: An enterprise-scale server for running and managing R workloads, used in big data analytics, including machine learning projects.

Julia

 

 

Julia is a relatively new (launched in 2012), high-level, high-performance dynamic programming language for technical computing, with syntax that’s familiar to users of other technical computing environments.

Key features of Julia:

• It’s designed for high-performance numerical and scientific computing.
• Its easy to use and learn.
• It can call C and Fortran libraries directly (there’s no need to write wrappers or interface code).
• It can be used for parallel and distributed computing.
• It comes with an extensive standard library, including differential equations, optimization, and machine learning.

What makes Julia good for AI:

• In addition to being fast, Julia is also very flexible (which makes it easy to experiment with different models quickly).
• It has a number of well-developed machine learning libraries, such as Flux, MLJ, and KNet.

Example Projects:

• Project: Celeste
  • Description: A project aimed at cataloging the visible universe, processing petabytes of data from the Sloan Digital Sky Survey. Demonstrated Julia’s capability for high-performance scientific computing.

C++ and C

 

 

C++ is a general-purpose programming language with a bias towards systems programming, and was designed with portability, efficiency and flexibility of use in mind. The main competitor to C++ is Microsoft’s C#, which is very similar in syntax but offers some additional features and benefits like a more managed memory model as well as LINQ (Language Integrated Query) support out of the box, amongst other things.

 

 

C++ has also been found useful in widespread domains such as computer graphics, image processing, and scientific computing. Similarly, C# has been used to develop 3D and 2D games, as well as industrial applications.

Key features of C++ and C#:

• They’re both object oriented, supporting data abstraction using classes, objects and inheritance.
• C++ is a compiled language, meaning that it’s converted directly into machine code that can be run on a computer. C# is a managed language and runs on top of a virtual machine, which makes it portable across different platforms.
• C++ offers more control over memory management than C#, but this also means there’s more room for error.

What makes C++ and C# good for AI:

• Both languages are widely used in game development, and many games make use of AI.
• They’re both high-performance, due to being compiled languages and offering low-level control when necessary.
• C# has a wide range of available libraries and tooling support from Visual Studio.

Example Projects:

• Project: Game AI in Unreal Engine (C++)
  • Description: The Unreal Engine, widely used for game development, leverages C++ for its AI capabilities, including pathfinding and decision-making in complex environments.
• Project: Microsoft Cognitive Toolkit (C#)
  • Description: A deep learning framework used to train algorithms to think like the human brain, applied in areas such as speech recognition and search relevance.

Java

 

 

Java is a versatile and powerful programming language that enables developers to create robust, high-performance applications.

Key features of Java:

• It’s object-oriented (supporting encapsulation, inheritance, and polymorphism).
• It’s platform-independent (bytecode can run on any platform that has a Java Virtual Machine).
• It’s statically typed (ensures type safety at compile time).
• It has a large standard library.
• It supports exception handling (which helps to manage errors and exceptional conditions gracefully) and multi-threading (which enables concurrent execution of multiple parts of a program).

What makes Java good for AI:

• It’s fast, reliable, and has great tooling support (making it easy to develop complex AI applications quickly and efficiently).
• It’s used in many mission-critical applications, so it’s battle-tested.
• It can be used for both desktop and mobile apps (using the Android Studio).

Example Projects:

• Project: Deeplearning4j
  • Description: An open-source, distributed deep-learning library for Java and Scala, used in industry for fraud detection, text mining, and image recognition.

JavaScript: A Dynamic Force in AI Development

Despite its roots in web development, JavaScript has emerged as a versatile player in the AI arena, thanks to an active ecosystem and powerful frameworks like TensorFlow.js. As the only programming language native to web browsers, JavaScript enables AI applications to run directly in the user interface, facilitating real-time user interactions and data processing without the need for server-side computation.

Key Features:

• Versatility and Accessibility: With Node.js, JavaScript extends its reach from client-side to server-side, allowing for a unified language across the full stack of development.

• Frameworks and Libraries: TensorFlow.js, Brain.js, and Synaptic provide robust tools for machine learning, neural networks, and deep learning, making advanced AI technologies accessible to web developers.

What Makes JavaScript Good for AI:

• Real-Time Data Processing: Ideal for applications requiring immediate data analysis and feedback, such as interactive web experiences and live data visualization.

• Wide Adoption and Community Support: A vast and active developer community drives innovation and provides extensive resources for learning and troubleshooting

Example Projects:

• Project: Brain.js
  • Description: A JavaScript library for neural networks that enables AI development directly in the browser or on Node.js, making machine learning accessible to web developers.

Scala: Bridging Functional and Object-Oriented Programming for AI

Scala, a language that combines functional programming with object-oriented programming, offers a unique toolset for AI development. Its ability to handle complex data types and support for concurrent programming makes Scala an excellent choice for building robust, scalable AI systems. The language’s interoperability with Java means that it can leverage the vast ecosystem of Java libraries, including those related to AI and machine learning, such as Deeplearning4j.

Key Features:

• Hybrid Paradigm: Scala’s integration of functional and object-oriented programming paradigms allows developers to choose the best approach for their task.

• Immutability: Scala encourages the use of immutable objects, which can simplify development and reduce errors in concurrent applications.

• Type Inference: Scala’s sophisticated type inference system reduces verbosity, making code cleaner and more readable.

What Makes Scala Good for AI:

• Scalability: Its name derived from “scalable language”, Scala is designed to grow with the demands of its users, from small scripts to large systems.

• Rich Ecosystem: Scala benefits from its compatibility with Java, providing access to a plethora of libraries and frameworks essential for AI and machine learning projects.

Example Projects:

• Project: Apache Spark
  • Description: A unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning, and graph processing. Scala’s concise syntax is ideal for data science workflows.

Honorary Mentions: Lisp and Prolog

Lisp and Prolog are not as widely used as the languages mentioned above, but they’re still worth mentioning.

Lisp (also introduced by John McCarthy in 1958) is a family of programming languages with a long history and a distinctive, parenthesis-based syntax. It was originally created for artificial intelligence research. Today, Lisp is used in a variety of applications, including scripting and system administration.

Prolog (general core, modules) is a logic programming language from the early ’70s that’s particularly well suited for artificial intelligence applications. Its declarative nature makes it easy to express complex relationships between data. Prolog is also used for natural language processing and knowledge representation.

Comparison of AI Programing Languages

Language	Good for AI Because…	Key Features
Python	Rich set of libraries for data analysis, ML, and scientific computing. Easy to learn and use.	Simple syntax, vast standard library, extensive support for data manipulation and machine learning (e.g., Pandas, TensorFlow, Keras).
R	Designed for statistical computing and graphics. Extensive graphical facilities for data analysis.	Integrated suite for data manipulation, calculation, and graphical display. Wide variety of operators for calculations on arrays, lists, vectors, and matrices.
Julia	High-performance numerical and scientific computing. Easy to experiment with different models.	Designed for high performance, can call C/Fortran libraries directly, parallel and distributed computing capabilities.
C++ / C#	High performance and control, widely used in game development. C# offers managed memory model and extensive library support.	Object-oriented, low-level control (C++), wide range of libraries and tooling support (C#).
Java	Portable, scalable, reliable, and has great tooling support. Used in many mission-critical applications.	Object-oriented, platform-independent, statically typed, extensive standard library, supports exception handling and multi-threading.
Lisp	Suitable for AI due to its symbolic processing ability. Flexible, supporting multiple programming paradigms.	Symbolic processing capability, supports procedural, functional, and object-oriented programming.
Prolog	Ideal for solving complex AI problems due to its logic programming capabilities. Excels in pattern matching and automatic backtracking.	Declarative nature, easy to express complex relationships between data, used for natural language processing and knowledge representation.
JavaScript	Enables development of AI models directly in the browser with libraries like TensorFlow.js. Growing popularity in AI for web applications.	Flexible, supports asynchronous programming, extensive library ecosystem (e.g., TensorFlow.js for machine learning).
Scala	Combines object-oriented and functional programming paradigms. Suitable for big data processing frameworks like Apache Spark.	Supports advanced features like pattern matching, lazy computation, and immutability, making it ideal for complex data processing tasks.
Haskell	Purely functional programming language, making it excellent for mathematical computations and abstract reasoning.	Statically typed, lazily evaluated, supports high-order functions, which is beneficial for AI algorithms requiring high levels of abstraction.

Programming Languages to Avoid in AI Development

These are languages that, while they may have their place, don’t really have much to offer the world of AI.

• COBOL is an old language that was created in the 1950s and 1960s. It was designed for business applications and isn’t really suited for anything else.
• FORTRAN is another old language, dating back to 1957. It was designed for scientific and engineering applications and, like COBOL, isn’t really suitable for anything else.
• Pascal is a language that was created in the 1970s and isn’t really used much anymore outside of academia.
• Basic is a language that was designed for beginners and has been largely superseded by other languages such as Python.

That said, you might think that a completely left-of-field language such as JavaScript — which is primarily used for web development — also wouldn’t have much to offer to AI, right? And yet, there are quite interesting active projects — such as TensorFlow.js (backed by Google) — for developing machine-learning models in JavaScript, which can be used directly in the browser or in Node.js. So, who knew?!

Conclusion

AI is here; it’s already happened. And as it’s transforming the way we live and is changing the way we interact with the world and each other, it’s also creating new opportunities for businesses and individuals.

As with everything in IT, there’s no magic bullet or one-size-fits-all solution. When choosing a programming language for AI, you’ll have to test the water, possibly switching languages depending on factors such as the task at hand, the platform you and your team are using, and your own preferences and expertise.

That being said, Python is generally considered to be one of the best AI programming languages, thanks to its ease of use, vast libraries, and active community. R is also a good choice for AI development, particularly if you’re looking to develop statistical models. Julia is a newer language that’s gaining popularity for its speed and efficiency. And if you’re looking to develop low-level systems or applications with tight performance constraints, then C++ or C# may be your best bet.

Further reading:

FAQs About Best Programming Language for AI

Now, for the sake of quick informative bites, I’m going to provide a quick set of opinionated answers.