What Are the Best Programming Languages for AI Development?

The templates are safer to use, and they provide a better way for generalizing APIs. Although templates are a powerful technique that can simplify most things, they require more time and experience to decide when their usage is appropriate. It was originally developed for Lambda Calculus computation, and since its inception, it has evolved a lot.

  • The knowledge of programming languages such as Python, Java, C++ is very important in becoming proficient in Artificial Intelligence.
  • It has a number of well-developed machine learning libraries, such as Flux, MLJ, and KNet.
  • C# has a wide range of available libraries and tooling support from Visual Studio.
  • But to employ artificial intelligence in your company’s systems and services, you’re going to need software engineers who are up to the task.
  • Python is a high-level, interpreted programming language that is widely used for developing AI and machine learning applications.
  • AI is present in everything, from autonomous driving to phrase correction.

The developers should specify goals and rules which prevent many enthusiasts from prolog programming. Not many people know about this language because it emerged not so long ago. However, the r language has quickly gained popularity due to the complex tasks it is able to solve. Actually, it can deal with huge numbers better than Python, and according to the latest surveys, this language is the most popular among developers who regularly work with big data. It is difficult to find a language that can boast such a variety of features.

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With Julia, it’s quite easy to translate algorithms from research papers into code with no loss in translation – thus decreasing model risk and improving safety. Developed in 1958, Lisp is named after ‘List Processing,’ one of its first applications. By 1962, Lisp had progressed to the point where it could address artificial intelligence challenges.

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By emphasizing precision, the language sets itself apart from existing AI languages. But first, let’s examine the rationale behind their adoption by artificial intelligence engineers. Programming languages come in a wide variety, from Java to Julia, so where do you begin?

What Is the Best Programming Language for AI?

Julia has been downloaded over 34.8 million times and the Julia community has registered over 6,800 Julia packages for community use. In addition to these, you can easily use libraries from Python, R, C/Fortran, C++, andJava. R programming language was written by a statistician for other statisticians. This language currently has more than two million users and 12,000 packages to its name. A LinkedIn group of R users has more than 40,000 members, all speaking to the popularity and usability of this tool. Plus, you do not need to be a fully fledge programmer to use it.

Can AI replace Python?

The short answer is: 'No. ' However, writing lots of lines of code in a specific language will become a smaller proportion of the role of a software engineer.

In summary, Java is a powerful, versatile programming language that is well-suited for developing AI and machine learning applications. Its platform-independence, wide variety of libraries, and large and active community make it a great choice for both beginners and experts. For example, TensorFlow Java can run on any JVM to build, train, and deploy machine learning models. Frameworks for neural networks written in Java are Neuroph and Deeplearning4j.

The Best Programming Language for AI: Read and Find Out!

Even a beginner can easily best ai language and create effective solutions as there is always someone who can answer your questions. Moreover, thousands of useful tutorials are available on the Internet for free. The quest for the creation of an artificial brain was inspired by the natural processes of the human brain. Gradually, the idea turned into a scientific concept and triggered the creation of practical intelligent technologies. Take, for example, AlphaGo — a computer program that beat all human professional players while playing the most difficult board game Go. These factors contribute to the continued popularity of C++ in the artificial intelligence community.

  • Due to its capacity to parallelize workloads and scale quickly, TensorFlow is frequently employed by many AI practitioners.
  • While Julia lacks a sizable user base and community, it provides a wealth of premium tools for creating superior AI.
  • Based on the semantics of the Miranda programming language, Haskell is a functional programming language.
  • Other JavaScript frameworks you can use for your AI projects are Brain.js, ConvNetJS, and Neataptic.
  • Moreover, thousands of useful tutorials are available on the Internet for free.
  • The developers should specify goals and rules which prevent many enthusiasts from prolog programming.

With AI, JavaScript developers can build smarter web applications. C++ is a general-purpose, cross-platform programming language created as an extension of C by Bjarne Stroustrup of Bell Laboratories in the early 1980s. C++ is also used for AI, although it is not the language of the first choice. C++ is an advantageous language as it translates user code into computer-readable machine code.

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According to a survey conducted by Toward Data Science, more than half of all data scientists and ML developers used this language in 2017. C++ is one of the well-known programming languages due to the popularity of C, the programming language it inherits from. Even though Prolog was one of the first logic programming languages, hence one of the oldest, it still holds its position in the industry. It’s also a lazy programming language, meaning it only evaluates pieces of code when necessary.


A beginner’s introduction to AI programming is made even simpler by the abundance of online training resources for well-known languages like Python and Java. Many well-known systems, like Dropbox, Yelp, Firefox, Azure, Polkadot, Cloudflare, npm, Discord, etc., use Rust as their backend programming language. Rust is the ideal language for developing AI and using it in scientific computing because of its memory safety, speed, and ease of expression. It is a strong choice for AI engineers because it supports parallelism and has several excellent artificial intelligence libraries.

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