Top Best Artificial Intelligence Programming Languages 2022

Best Programming Languages for AI Development (2022)

Best Programming Languages for AI Development (2022)

Best Programming Languages for AI Development. Building artificial intelligence tools is easier with these AI-focused programming languages. Artificial intelligence is not a field of universal, one-size-fits-all solutions. Your choice of AI programming language will depend on the scope and requirements of your project.

The abundant advantages of AI overpower its mere flaws to provide the best business solutions with maximum precision. Adopting AI into your business operations can increase efficiency, agility, and reduce costs. According to Fortune Business Insights, “the global AI market was valued at USD 27.23 billion in 2019 and is projected to reach USD 266.92 billion by 2027, exhibiting a CAGR of 33.2% during the forecast period”.

In a business capacity, artificial intelligence has a number of benefits. Most marketing automation tools, for one, rely on AI. This is how the best tools create and orchestrate campaigns and gather insights to improve your effectiveness as a brand. And this is just one example. In this era of digital transformation, you’re bound to see AI pop up in numerous scenarios, working together with humans and providing proactive solutions to everyday problems.

What is artificial intelligence?

Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment.

 Types of Artificial Intelligence

  • Reactive Machines
  • Limited Memory
  • Theory of Mind
  • Self Aware.

What is AI used for? Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.


Top Best Artificial Intelligence Programming Languages 2022

Here are some of the best programming languages you can choose from to understand and develop an AI system. Below are 5 options to consider and how they can benefit your smart projects.


1. Python

Python is the ideal coding language used for machine learning, NLP, and neural network connections. This programming language can be used even if you are new to AI development since it is flexible and comes with pre-existing libraries like Pandas, SciPy, and nltk. Python language is lauded for its simple syntax and minimal codes. Python has proven to be effective for a myriad of other areas, such as building websites and creating scripts for DevOps, yet it is with AI/ML where the language has really shined.”


Which programming is best for AI?

Python is one of the most popular AI programming languages thanks to its wide variety of proven, pre-designed libraries that optimize the AI development process. Scikit-learn supports fundamental machine learning algorithms like classification and regression, while Keras, Caffe, and TensorFlow facilitate deep learning.

Python uses readable keywords, provides object-oriented programming, and can be integrated with other languages like Java. The development speed of Python is comparatively faster and allows algorithm testing without actually running them. The python libraries Numpy can be used for scientific computation and PyBrain for machine learning. Python has a standard library in development and a few for AI. It has an intuitive syntax, basic control flow, and data structures. It also supports interpretive run-time, without standard compiler languages. This makes Python especially useful for prototyping algorithms for AI.

Machine learning is a subset of AI that involves using algorithms to train machines. TensorFlow is Python’s most popular framework for AI development. It’s an open-source machine learning library where you can train deep neural networks. Besides machine learning, you can use these technologies for natural language processing (NLP) and evaluating advanced mathematical expressions. NLP is what smart assistants like Google and Alexa use to understand what you’re saying and respond appropriately.

2. Lisp

LISP or list processing was created in 1958 by John McCarthy that is suitable for AI development. It is flexible and adapts to the solution and can be effectively used for machine learning. LISP is known for rapid prototyping and the dynamic creation of new objects. Although LISP is not popularly used now since the codes are harder to learn and it does not have good libraries.

In the present day, the language is just as capable, but because of its difficult syntax and complicated libraries, it’s rare that developers flock to Lisp first.  That said, there are a variety of AI projects where Lisp’s expertise could be a great asset, including but not limited to:

  • Rapid prototyping
  • Dyanmic object creation
  • Executing data structures as programs
  • Modifying programs as data
  • Mandatory garbage collection

Despite its flaws, Lisp is still in use and worth looking into for what it can offer your AI projects. Grammarly, DART, and Routinic are some of its success stories. Overall, Lisp is known in recent history for being immensely flexible and turning thoughts into reality.


3. Java

Java is one of the popular programming languages which has a multitude of open-source libraries. The Java programming language is user-friendly and provides an independent platform and hence is considered good for developing AI. This is a standard and flexible programming language that offers easier debugging of codes, scalability, the ability to support large-scale enterprises, and graphical representation of data. Java is easy to learn, versatile, and its Virtual Machine Technology enables the development of AI language on different platforms.

Java is a treasured server-side language. Its key feature is that you can use Java almost anywhere, on any platform, through its virtual machine. Though Android developers have the option to work with Kotlin as well, Java is a native language for Android app development. Mobile app developers are well-aware that artificial intelligence is a profitable application development trend.

So the infamous FaceApp in addition to the utilitarian Google Assistant both serves as examples of Android apps with artificial intelligence built-in through Java. Even outside of mobile apps, Java has quite a few machine learning libraries for deep learning and natural language processing. There’s more coding involved than Python, but Java’s overall results when dealing with artificial intelligence clearly make it one of the best programming languages for this technology.

Check out the following if you plan to use Java for your machine learning needs:

  • Deep Java Library
  • Kubeflow
  • OpenNLP
  • Java Machine Learning Library
  • Neuroph

4. C++

C++ is an extension of the C programming language and can be used to build neural networks. The speed of C++ is the greatest benefit since AI development has complex computations and this language can make the calculations faster. It has a low-level memory control feature and carries asset-compelled applications, performance-critical applications, etc. C++ has a complex syntax but is cost-efficient compared to other languages like Java and can be used in AI programming for search engine optimization and ranking.

C++ has been around for quite some time and is admittedly low-level. This means C++ works well with hardware and machines but not so well for the more theoretical side of the software.  Yet, in practice, C++’s capacity for low-level programming makes it perfect for handling AI models in production.  You can build neural networks from scratch using C++ and translate user code into something machines can understand. Come to think of it, many of the most notorious machine learning libraries were built with C++.


5. Prolog

One of the oldest programming languages, Prolog or logical programming is a powerful framework that works with three elements- facts, rules, and goals. A developer should define all three elements and then Prolog establishes relations between them to reach a certain conclusion by analyzing facts and rules. Here, the implementation of algorithms happens through logical inferences and searches and this language is great for developing AI systems since the solutions are logical and not just based on pre-existing statements. Prolog is best for creating chatbots, voice assistants, and graphical user interfaces (GUI).

Nowadays, cloud technology makes it so chatbots have a whole store of data to access new and old information, meaning chatbots are worlds more intelligent than in the time of Prolog. In Prolog, instead of using coded sequences, you simply chart out a basic set of facts, rules, goals, and queries. As a result, Prolog is apt at pattern matching, especially when NLP is involved. The first chatbot in history, for example, was called ELIZA and written in Prolog. Prolog’s system of logic played a big part in ELIZA’s ability to assign values to words and reorder them to form a question.



Is Python the best language for AI? A great choice of libraries is one of the main reasons Python is the most popular programming language used for AI. A library is a module or a group of modules published by different sources like PyPi which include a pre-written piece of code that allows users to reach some functionality or perform different actions.

As a programmer, you should get to know the best languages for developing AI. For efficiently building AI systems one should know at least one programming language and let us make the decision easier for you. Building artificial intelligence into your software requires a certain skill set, and on that note, an adjacent tech stack, for development to run smoothly.

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