Top 9 Programming Languages For Artificial Intelligence Blog

It offers innovative solutions to business problems in the form of hundreds of unique use cases, thus making lives easier. Java is popular for its scalability, platform-independent nature, and presence of JVM that allows compiled Java code to run on all platforms that have JVM installed without recompilation. Create a dedicated team of developers, designers, and IT consultants with the right skills and experience. The first thing to consider is the birds-eye view of your project.

  • However, every algorithm has only come in recent decades, from personalized recommendations to social humanoid robots.
  • Even so, the right setup can make Haskell a decent tool for AI developers.
  • Those are the underlying premise of most machine learning algorithms.
  • AI has brought technology to a whole new level and paved the way for innovation for global industries.
  • There are several deep and machine learning libraries built on C++.
  • Python is a high-level, general-purpose, highly readable, and easy-to-learn programming language.

There is increasing interest in using JavaScript for Data Science, but many believe that this is due to the popularity of the language rather than it’s suitability. Prolog supports the development of graphical user interfaces and administrative and network applications. It is well suited for projects like voice control systems and filling templates. It allows expressive and efficient libraries to express AI algorithms. For example, HLearn uses well-known algebraic structures (modules, monoids, etc.) to express and boost the speed of simple machine learning algorithms.

Best programming languages for AI development: Rust

In Python, almost all ideas can be quickly validated via lines of code. Prolog’s nature makes it simple and straightforward to implement facts and rules. It allows you to query the database even when you have thousands of these facts and rules. Since it is functional and stateless, code can be easily executed on different CPUs in the cloud. If we talk about industry adoption, Facebook uses Haskell to fight spam. You can even use pre-built AI models, such as vision and speech recognition, intelligent search, and emotion and sentiment detection.

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best languages for ai has a number of libraries and frameworks for AI development, including TensorFlow, OpenCV, and Caffe. TensorFlow is an open-source library for machine learning and deep learning that provides a wide range of tools for building and deploying models. OpenCV is a library for computer vision that provides a collection of tools for image processing, feature extraction, and object detection.

What Is the Best Programming Language for AI?

It is easier to get started with Python because code is more readable, concise, and clear. C++’s low-level programming capabilities make it ideal for managing simple AI models. For example, developers utilize C++ to create neural networks from the ground up and translate user programming into machine-readable codes. The language appeared in the 60s and has long been one of the main tools of AI researchers.


First, Alan Turing, a founding father of AI, came up with the question that “Can machines think like humans? But the concept of AI has created great hope and enthusiasm for the world of science and technology. Java is also widely used in the industry, and is a popular choice for developing large-scale enterprise systems. Because of its performance and scalability, Java is a good choice for developing AI systems that need to handle a lot of data or handle high traffic. Overall Julia is the most viable choice for developing AI applications that require high numerical computational power. In addition, it is also friendly to novices who have Python skills and want to program AI solutions.

How Much Programming Knowledge is Required to Learn ML?

This responsive language is a top choice for AI programming because it can handle complicated algorithms and stream data at scale. Scala is a popular choice for interfacing with big data processing engines like Apache Spark, which is written in Scala. Over time, many of LISP’s unique features have been folded into other popular programming languages—think Python’s list comprehensions and LINQ in C#.

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. Okay, here’s where C++ can shine, as most games use C++ for AI development. That’s because it’s a fast language that can be used to code high-performance applications.

#8 Prolog

C++ offers several AI and ML libraries such as Caffee, Microsoft Cognitive Toolkit , TensorFlow, DyNet, OpenNN, FANN, Shogun and mlpack library. Recent studies show that businesses are investing more in artificial intelligence. You need to be able to generate executable code in a format that can be run on the operating systems your computing hardware uses. All-in-all, the best way to use this language in AI is for problem-solving, where Prolog searches for a solution—or several.

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Because of its popularity, the resources available for those just getting started in JavaScript are massive. There is not “the best” or “the worst” programming language for AI. But some of them have been more convincing to create AI solutions over the years. That’s because they offer many tools for AI development, which can help you save your time and money. Enterprises worldwide have reported plans to expand their AI strategies.

The Top Programming Languages in AI

In addition, Python works best for natural language processing and AI programs because of its rich text processing features, simple syntax, and scripting with a modular design. Companies are constantly looking to deploy better AI solutions. According to IDC, the AI market will surpass $500 billion by 2024 with a five-year CAGR of 17.5 percent and total revenue of $554.3 billion.

Which Language is Best for AI?

Python is the best language for AI. It is easy to learn, has a large community of developers, and has an extensive collection of frameworks, libraries, and codebases. However, Python has some criticisms—it can be slow, and its loose syntax may teach programmers bad habits.

Though commercial applications rarely use this language, with its core use in expert systems, theorem proving, type systems, and automated planning, Prolog is set to bounce back in 2022. Keras, Pytorch, Scikit-learn, MXNet, Pybrain, and TensorFlow are a few of the specialist libraries available in Python, making it an excellent choice for AI projects. It deserves to be on the list of the most flexible languages. In the context of working with search engines, it reduces response time and improves rankings. Programmers appreciate it for performing fast calculations, which is essential for AI.

choice for developing

The alternatives to R programming language are SPSS, Stata and SAS, However, they are commercial statistical packages while R is a free software under the GNU General Public License. Developers have found that Julia works extremely well for science-guided artificial intelligence and ML. This approach is being used in climate modeling and applications predicting epidemics, for example.

  • And given how many mobile apps take advantage of AI, it’s a perfect match.
  • So if you run a line of code, you can see the output immediately.
  • This language is the first to mention when it comes to complex calculations in the scientific and technical fields.
  • Rather than mimicking human intelligence, the concepts of machine learning apply one of the key behaviors of humans – the ability to learn.
  • Similarly, libraries like Keras, Caffe, and TensorFlow handle deep learning with finesse, keeping AI development with Python perfectly streamlined and easy.
  • The AI capabilities of JavaScript help it interact and work smoothly with other source codes like HTML and CSS.

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