Top 9 Best Programming Languages for Artificial Intelligence

Python

One of the most popular programming languages for artificial intelligence is Python. It has a strong community behind it and offers many frameworks that are meant to make life easier when working with artificial intelligence. The language also continues to stay relevant, adding new features while still ensuring backward compatibility, so old code does not need updating.

Some of its most popular libraries are:

• Scikit-learn: This library contains various tools for machine learning and data mining
• TensorFlow: A popular library for building artificial neural networks
• Theano: An advanced mathematical library that lets developers create neural networks and other sophisticated algorithms without coding the mathematical equations by hand.

JavaScript

Many developers prefer JavaScript when working with artificial intelligence. Its popularity is partly due to the fact that it can run on the client-side in web browsers, but it is also a flexible programming language in general.

Some of its most popular libraries are:

• TensorFlow.js: This library makes it easier for developers to create machine learning workflows using JavaScript.
• ConvNetJS: A JavaScript library for machine learning that lets developers build neural
• Math.js: A library containing various mathematical and statistical functions
• Brain.js: An open-source neural network library written in JavaScript
• Node-fann: Another open-source machine learning library, this one designed specifically with artificial neural networks in mind

C#

C# is another programming language that contains many tools for building artificial intelligence applications. The language is object-oriented and integrates easily into Microsoft’s .NET framework.

Some of the more popular AI frameworks for this language include:

• Microsoft Cognitive Toolkit
• MyCaffe framework
• Tensor Flow .Net

R

R is one of the top languages well-suited to machine learning that’s frequently used in academic research. It’s a good option if the company needs to use statistical models for machine learning. Companies can use either R in conjunction with Hadoop or Spark’s MLLib library to create their own models, or use an R package to access an existing model.

Some of its most popular libraries are:

• RevoScaleR: This is a proprietary library for performing statistical analysis on data that can be used with Spark or Hadoop.
• MASS: A library containing many functions and datasets for machine learning and stats.
• plyr: Another statistical package created to help make manipulating large datasets easier.

Java

Java is another good choice for companies looking to conduct artificial intelligence, especially those that are heavy on object-oriented programming. Because it has an extensive library and can run on all kinds of hardware, Java is highly flexible. It’s also one of the easiest languages to integrate with other tools, which makes it good for big data. Java can be used in conjunction with Hive or Pig for processing large sets of data.

Some of its most popular libraries are:

• MillWheel: This library offers developers an infrastructure for building stream processing applications that can be deployed across multiple servers.
• Deeplearning4j: A library designed to help companies build neural networks and other machine learning algorithms with Java. It also allows them to deploy these models across Spark clusters.
• Thinc: A library that allows developers to build neural networks without needing to build the mathematical models behind these networks.
• PredictionIO: This machine learning engine is still in beta, but it makes it easier for companies looking to create their own recommendation engines or other types of predictive tools.
• Weka: A library for data mining and machine learning.

C++

C++ is best for companies that want to use artificial intelligence in hardware. Since it’s a low-level language, it can be used to write efficient code and get the most out of existing hardware. For this reason, C++ is often used in the video game industry.

Some of its most popular libraries are:

• Theano: A library for building neural networks.
• OpenCV: This is a collection of tools used to build computer vision applications.
• Intel’s Math Kernel Library (xMKL): An advanced math library designed for high-performance computing, especially in areas like artificial intelligence and cryptography. It can be used with C++ to help speed up machine learning applications.
• LIBSVM: An open-source library that trains support vector machines, often used for predictive modeling and classification.

Go

Go is a relatively new programming language that’s starting to be used in artificial intelligence applications, mostly because it offers CPU and memory optimizations. Some experts say that its high-level features make it easier to read code and develop code for parallel computing.

Some of its most popular libraries are:

• The Go Learn Toolkit: An open-source library that offers plenty of machine learning examples, so developers can learn how to build applications from them.
• Gorgonia: This is a package for building neural networks and other machine- learning- related code in Go. It also allows users to run models on both CPUs and GPUs.
• GoNN: A library that uses evolutionary computation to train neural networks and other machine learning models.
• Go-Torch: A library used for machine learning and NLP tasks, especially natural language processing and working with text.training of artificial intelligence models.
• TensorFlow-Go: A library for using TensorFlow in Go.
• ggplot2: An open-source plotting library built to work with the statistical programming language R.

Matlab

As a proprietary programming language, Matlab isn’t open- source; however, it has the best numerical computing library and is good for mathematical operations and image processing. It can be expensive, but it’s worth picking up if your company needs these features while conducting artificial intelligence.

Some of its most popular libraries are:

• MATLAB Coder: A tool that allows users to convert programs into standalone C or C++ versions.
• Image Processing Toolbox™: This is a set of tools for image processing and analysis.

Julia

Julia is a newer language that’s only recently become popular for AI. It’s growing in popularity because it can be used to run on databases like Hadoop and Spark and easily distributed across multiple machines, making it good for big data and large-scale projects.

Some of its most popular libraries are:

• DataFrames: This package provides a data structure similar to an R or Python data frame, which is meant to make large datasets easier to work with.
• Distributions: This library contains functions and methods needed to generate random numbers and samples, including distributions like the normal distribution and uniform distribution.
• JuMP: A modelling language for mathematical optimization problems.

Conclusion

Deciding which programming language for your artificial intelligence project can be a difficult task. There are a number of options available, and it’s important to select the best one that aligns with the company’s goals and expertise of its employees. Some top languages, like R and Matlab, aren’t open source but have a large community that makes it easy for developers to find answers when they’re stuck. Others, like C++, are low-level languages that can be used to write efficient code and maximize the use of hardware. If the company wants flexibility and ease-of-use, JavaScript is a good choice; however, if it needs speed or requires custom hardware, C++ is a better choice. Each of the languages on this list is a good option for artificial intelligence, and it’s worth brushing up on each so you can choose one that aligns with your development goals.