Best AI Programming Languages: A Review of the Top 10

The market for Artificial Intelligence (AI) is rapidly expanding as more organizations use it to improve business workflows.

According to Statista, the AI market value is expected to hit $2 Trillion by 2030 growing at a Compound Annual Growth Rate (CAGR) of 21.6% in the forecast period. Based on this data, it’s worth exploring how Artificial Intelligence will impact the future of Software Development.

Being an emerging technology, it is essential for Software Developers to understand its dynamics and get familiar with the tools, frameworks, and programming languages that are driving AI Research and Development.

Essentially, the languages you specialize in determine the frameworks you work with and the scale of Development projects you are able to handle.

This is down to the fact that some programming languages are great for Developing and deploying Cloud-based environments, while others specialize in extremely low-latency scenarios.

This article explores the Top 10 AI programming languages commonly used for Development projects.

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Top 10 AI Programming Languages

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Top 10 AI Programming Languages

Python

Python is the most popular and fastest-growing programming language of recent times. Its popularity is due to its high-level coding interface and a vast library of frameworks.

Python has become the number 1 choice for young students wanting to learn to program, as it is used for web development, data, and statistical analysis, as well as Artificial Intelligence.

Python includes several libraries that ease the process of building AI applications. Some common libraries include

Pandas: Used for exploratory data analysis

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Scientific Toolkit (SciKit-Learn): An all-in-one machine learning toolkit for training machine learning algorithms.

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Keras: A framework for building and training deep neural networks.

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NLTK: An NLP framework for processing text data for AI applications.

These libraries are highly optimized for their tasks and allow AI training in a few lines of code.

However, Python has its downsides when it comes to AI programming despite the vast ecosystem and use cases, due to performance limitations. Consequently, it is often avoided in applications requiring quick responses. As a result, several large-scale AI projects prefer lower-level languages for better performance.

For instance, Tesla's autopilot system requires a strictly real-time response. This is why Tesla relies heavily on C++, C, and CUDA for hardware-level implementation of their Deep Learning models rather than Python.

C++

Developed in 1979, C++ is one of the older languages on this list. It is a low-level language, which makes it highly efficient and performant in code execution. C++ hosts several popular machines and deep learning libraries like OpenCV and Tensorflow and can be used for statistical analysis.

Its high-speed capabilities make it ideal for developing AI applications for near real-time scenarios.

It is important to note that the performance of modern deep learning models largely depends on hardware acceleration (GPUs and FPGAs, etc.) on top of the development language used.

With that being said, C++, being a low-level, high-performance language, does come with a steep learning curve. However, its many advantages outweigh this challenge, making it a great choice for hardware-level AI programming.

Java

Java is a highly popular language that is used by Developers globally for web, mobile, and AI Programming. It is a high-performance, platform-independent language which means it can be run on any platform that has a Java Virtual Machine (JVM).

Java hosts several AI libraries including;

Tensorflow: A JVM compatible implementation of the popular Deep Learning framework. It supports both CPU and GPU execution and can run on any JVM for building, training, and running machine learning models.

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Neuroph: A tool to build neural networks with GUI interaction. It includes implementation for popular networks like multi-layer perceptron, Hopfield NN and MaxNet. The library has been used for building popular applications like chess engine.

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Java Machine Learning Library: Includes implementations for several Machine Learning algorithms such as K-Nearest Neighbour, Linear Regression, and Weka Clustering.

Java is most popular for its Mobile Development capabilities. This makes Java ideal for developing AI applications for mobile platforms.

Rust

Similar to C++, Rust is a low-level language and, according to a StackOverflow survey, is the most-loved language by developers.

Some of Rust’s key features include

High Performance
Memory Safe
Multi-threaded

Rust includes packages for AI development such as Mosec, Leaf, and Gluon. Tch-rs is another popular project that provides RUST bindings for Pytorch’s C++ API. RUSTs multi-threaded abilities make it particularly useful for tree-based algorithms.

R

R is a popular scripting language for statistical analysis. It may not be ideal for AI programming, but its vectorial computations and functional programming make it great for working with large-number arrays. It is primarily used for generating numeric facts and figures regarding data trends and forecasts but also includes libraries for Machine Learning processing:

CARAT: Includes models for Classification and Regression Training. Additionally, it also allows the use of GridSearch for hyperparameter tuning.

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E1071: Package includes an implementation for complex ML algorithms like Naive Bayes and SVM.

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gmodels: Package includes tools for model fitting.

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OneR: Implements the One Rule (OneR) Machine Learning classification algorithm with enhancements.

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TM: A framework for text mining.

Julia

Julia is a relatively newer language that is designed for high-performance numerical computing. It is a fast, dynamic, and easy-to-use language. It supports distributed computing and parallelism, which makes it a great choice for Machine Learning Development.

It includes several ML-related packages, such as

Flux.jl: A machine learning library implemented entirely in Julia. It comes with several tools for ML and statistical analysis.

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Tensorflow.jl: A Julia wrapper for TensorFlow.

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MLBase.jl: Tools to support ML development, including data pre-processing and performance evaluation.

However, it must be noted that because Julia is relatively new to the market, which means some of the libraries may not be as stable as those in more prevalent languages like Python.

Scala

Scala is a fast and efficient programming language often compared to Java. It runs on the Java Virtual Machine (JVM), making it platform independent and has a simpler coding interface than Java. Scala integrates well with Java which makes it great for building AI applications for mobile platforms.

Scala is increasingly being used in Data Science applications due to its ability to deal with distributed data sources and fast execution. It also comes with useful libraries;

Scala-Spark: A wrapper for Apache Spark that is used for data exploration and analysis in distributed systems.

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Smile: A machine learning engine for data analysis, statistics, and NLP.

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BigDL: An AI framework for big data applications.

Haskell

Haskell is a general-purpose, purely functional language. It is statically typed, providing the performance boost desired by AI applications.

Overall Haskell has been gaining popularity amongst AI engineers due to its features including;

Parallel Computing: It is great for developing programs that leverage multi-core systems.

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Lazy: It only evaluates pieces of code when necessary making it efficient in its tasks.

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Security: It is one of the most secure languages for developing applications in.

Haskell has a rich library of ML frameworks such as Grenade which allows the Development of neural networks with a few lines of code. Haskell also provides bindings for using Tensorflow from a native codebase.

However, despite its advantages, Haskell is a complex language with a steep learning curve.

Prolog

Prolog has been around since 1987, and despite its age, it still fits many modern problems. It supports several data structures and has built-in features like backtracking for undoing mistakes.

It is important to highlight that Prolog is a declarative language that declares facts, rules, goals, and queries instead of code statements. This fact and rule-based approach makes it great for NLP applications like chatbots, pattern matching, and constructing tree data structures.

It also includes native libraries for data processing and feature selection.

Despite not being as popular as it once was, today Prolog is used by several research scientists for statistical and rule-based analyses. Moreover, it was used to develop the IBM Watson NLP system which won the 2011 Jeopardy Man vs. Machine contest.

Lisp

Developed in 1960, lisp is the second oldest programming language. It was created to model mathematical notations in the form of computer programs and was used for solving complex mathematical theorems and NLP problems.

Lisp was a major player in the early developments in the field of AI, and John McCarthy, the creator of Lisp, coined the term Artificial Intelligence. Although it is not frequently used for AI programming today, Lisp offers several benefits to AI Engineers in relation to;

Dynamic typing
Mandatory garbage collection
Data structures

It may not be the most popular today but LISP has been used in some major and popular projects including;

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Conclusion

The field of AI is rapidly evolving, and Engineers like you must be equipped with the right tools to keep up. General-purpose programming languages like Python are great for getting started with Data Science and building Machine Learning models.

However, certain use cases will require a different approach. Low-level languages like C++ and Rust are difficult to learn but are great for quick processing, while specialized languages like R and Scala are great for statistical analysis.

To fulfill industry needs, you must diversify your skillset and explore opportunities in different domains.

Best AI Programming Languages: A Review of the Top 10

Blog

Table of Contents

Top 10 AI Programming Languages

Python

C++

Java

Rust

R

Julia

Scala

Haskell

Prolog

Lisp

Conclusion

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