Introduction

Artificial Intelligence has changed the world in very unexpected ways. Many, especially the newer generation, don't notice it because they feel AI has been in our lives forever. However, every algorithm has only come in recent decades, from personalized recommendations to social humanoid robots. But these are just global impact examples of AI. Hitting closer to home, businesses utilize AI to improve production, operations, and customer experience. This article will go over which programming languages are the best for AI and what can be done with them.

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Python

Python is a high-level, general-purpose, highly readable, and easy-to-learn programming language. Its simple syntax and object-oriented programming help produce the most complex AI programs. Developed by Guido van Rossum in the 1990s, Python now has a flourishing community that innovates with this programing language, continuously producing applications with abundant pre-designed libraries and frameworks such as Pandas, SciPy, Nltk, PyTorch, Keras, and Theano. This is the most popular programming language for AI, which can be read more about here, and it is always the top choice for developers.

However, the best programming language for your project may be entirely different. Let's review other popular programming languages for AI and figure out what's best for you!

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Java

Java almost always ranks second after Python as the best language for AI. Its best quality makes it one of the top candidates for AI development, such as easy debugging and simple syntax. Furthermore, Java offers excellent production value, smooth integration, quick execution, and fast runtimes, all of which AI requires. It is easy to implement on various platforms, which is why it is so loved. The Java community is rich and active, allowing developers to learn from each other and think up new AI solutions faster. The famous FaceApp and Google Assistant are both built with Java. Some libraries that can be used when developing an AI program with Java are Deep Java Library, KubeFlow, OpenNLP, Java Machine Learning Library, and Neuroph. 

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C# & C++

C# and C++ are object-oriented languages containing many tools for building artificial intelligence applications. C++ gets more attention in the AI business than C# as it is a low-level programming language that has been around for a long time. It has a vast application in AI development. Its strengths come from the rapid processing speed that allows it to handle complex machine learning modules and run with high efficiency. You can build a neural network in C++ and translate user code into something machines can understand. Created in 1983, this language has won the title of "the fastest coding language," so the speed for AI development is assured.

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R

Ross Ihaka and Robert Gentleman initially created R in 1993 to implement a language that focused on statistical computation and graphical modeling. R is used by statisticians, economists, geneticists, agronomists, biologists, and the business world. Now, it is also used to develop AI algorithms and is the best programming language choice for companies that want to use statistical models for machine learning. One of the biggest strengths is its interactive environment that facilitates rapid prototyping and exploratory model sections. Some of its most popular libraries are RevoScaleR, MASS, plyr, caret, mlr, and XGBoost.

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JavaScript

JavaScript already has AI libraries written in JavaScript, and you can develop AI applications without the need for another programming language. It has become famous for AI partly because it can run on the client side in web browsers but is also a flexible language in general. This programming language has machine learning libraries and modules that can assist in developing AI. TensorFlow.js is a JavaScript framework for training and deploying machine learning. Other popular libraries and frameworks are ConvNet.js, Math.js, Brain.js, and Node-fann. 

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Julia

Julia is one of the newer languages developed at MIT in 2012 and has only recently become popular in AI development. It has the capacity to handle expensive numerical analyses and large data sets. An exciting feature of Julia is that it can translate algorithms directly from research papers into code. Julia is a good language for big data and large-scale projects because it can be used to run on databases like Hadoop and Spark and can be easily distributed across multiple machines. Julia is a very high-performing language and is perfect for developers with a background in Python and R. Its most popular libraries are DataFrames, Distributions, and JuMP.

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Other Languages for Artificial Intelligence Programming

Aside from the languages mentioned above, many more compete in developing Artificial Intelligence. Those unmentioned heroes include Go, Matlab, Haskell, Prolog, LISP, Wolfram, and Rust, along with their endless frameworks and libraries that help make AI development a dream come true. While the list goes on, there seems to be a unanimous agreement between firms, developers, and businesses that Python is the best language for AI development. 

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Conclusion

Because it has helped many scales up their business and drive better results, more companies are looking to integrate AI into their operations. In essence, AI has been implemented by many companies using different programming languages. Deciding which language suits you best depends on many factors, from what it is about, your background, and your level of comfort with these languages. 

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Deploying a powerful AI requires an extraordinary team of engineers. At Dirox, our developers are highly skilled in using different languages to build AI programs that will be most suitable for you. Aside from the proficient technical skills, developers at Dirox are also great communicators who prioritize collaboration to achieve better productivity and results. Please contact our expert consultants to learn how we can open business opportunities with AI.