Top Programming Languages To Build AI Apps in 2021

Top Programming Languages To Build AI Apps in 2022

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Are you wondering which language is apt for you to perform AI work? What all things that you can accomplish when you think about doing AI based project. Firstly, if these are the questions that come up to you. You are on the right track. AI and machine learning are one of the most trending projects and technologies that are happing in the world of IT. When it comes to app development,  you need to ensure that your app marks a value for the customer. And provides them with wonderful features that will help the users solve a problem and help them to get on with their lives.


With the amount of technologies that are coming up day after day, it can become quite daunting and confusing to select a particular technology.  When it comes to selecting a particular technology that can develop AI based projects, there are no single technology. Instead, there are many different technologies that work in sync or tandem to fulfill the requirement.

In this blog, we will dissect and select which technology is apt for you to develop AI based projects.

Base Language and AI Questions

When the question is about AI. You should ensure that you have enough experience using a BASE language. This base language will help you to further develop and implement the required features and functionalities in your project. So if you are willing to develop AI based projects you need to know about Python. Python forms the base language here.  People who have some kind of background in other languages such as C, C++ or Java, will they be able to use Python so easily, The answer is Yes.  Even these people will feel comfortable using the base language features and technologies. They can do it without any major hassles and hurdles.

This is because when implementing and using AI based projects, the language does not matter much. It is the other set of questions that matter when implementing the project.

Questions such as:

  • Identify and acknowledge the problem
  • Prepare and collect the data
  • Select and choose the algorithm
  • Train the algorithm
  • Choose the programming language
  • Execute and run on the specified platform

These are some major questions that come out when you want to implement AI based projects.

So let’s jump in and see all the related technologies that can go and help implement AI based projects.


Programming Languages To Build AI Apps:


  • Python:

As we mentioned earlier, Python is detrimental in learning and implementing AI based apps and its related technologies. This forms a base language that helps to run many different technologies. When the question is about Sentimental processing and Natural Language Processing, people usually find Python to be the most apt language to implement and use for AI applications.

As a matter of fact, Python has been the most popular language that has gained popularity among the community of developers and programmers. Right from 2006 till 2020 Python has following an upward trend and has recently beaten Java as the most searched language on the Internet. If you compare the trends, Python has followed a consistent upward route. Python is also used for statistical and mathematical applications. It is the most useful for topics such as advanced computing and artificial intelligence as well as machine learning applications. There are many different languages and tools that run on top of Python based applications such as R. R language requires python to be preinstalled into the machine.

  • Java:

Java is the second most popular language that is used for the implementation of NLP, neural networks and search algorithms. It has long dominated the trend for the development of AI and ML based applications and was recently beaten by Python to be the second most demanded language for AI apps. Java is worth mentioning and it is very good for implementing Next gen AI based apps. Java is also being used predominantly for many different kinds of Artificial intelligence based application. It is the second most based popular environment that fully supports AI and ML based applications.

  • C++:

C++ has predominantly been the oldest language that exists to implement and use AI based apps. It provides a lot of libraries and tools to help developers create AI apps. It ranks in the 9th position when the question is about popularity of AI apps and trends among developers and programmers. C++ along with its object oriented characteristics has come a long way to help enable and develop AI and ML based applications. There are many AI based games that require C++ language under the hood for its development. As a swift and faster platform it is evident that this language is here to stay for a long time to come.

  • R Language:

R is a statistical language that is developed to do statistical applications and it runs on top of Python. So if you want to generate valuable insights using business statistics then R is no doubt the perfect choice. This language has been designed from the ground up to work with data science, neural networks, statistics, and machine learning applications.  If you are a person who has some background in Java, Python, C++, or JavaScript then you will easily be able to master R. If you are into statistical applications development then you should surely check out R and its base language Python.

  • JavaScript:

Whether you are a web developer looking to try out your hands in AI app development, then JavaScript is the right choice for you. JavaScript can be used for training of Neural networks, web and mobile app development, deep learning and other AI based applications. JavaScript has been the backbone of several different applications that help boost AI and ML productivity and development efforts.


Also Read: Application Of Artificial Intelligence in Business


  • Scala:

Scala took the Java Virtual Machine (JVM) climate and fostered a superior answer for programming wise programming. It’s viable with Java and JavaScript, while making the coding system more straightforward, quicker, and more useful.

Because of Scala’s strong elements, similar to high-filling roles, adaptable points of interaction, design coordinating, and program devices, its endeavors to dazzle developers are paying off. It’s presently perhaps the best language to use for AI improvement.

One more advantage to remember is the Scaladex, a list containing any accessible Scala libraries and their assets.

  • Lisp:

Created during the 1960s, Lisp is the most established programming language for AI advancement. It’s actual savvy and versatile, particularly really great for tackling issues, composing code that changes itself, making dynamic articles, and fast prototyping.

However, there are drawbacks. Lisp’s syntax is uncommon contrasted with current coding languages, making it harder to decipher. Pertinent libraries are additionally restricted, also software engineers to exhort you.

Regardless of its imperfections, Lisp is as yet being used and worth investigating for what it can offer your AI projects. Grammarly, DART, and Routinic are a portion of its examples of overcoming adversity.

  • Prolog:

For a more consistent method of programming your AI framework, investigate Prolog. Programming utilizing it follow a fundamental arrangement of realities, rules, objectives, and questions rather than groupings of coded guidelines.

Prolog can comprehend and match examples, find and design information intelligently, and naturally backtrack a cycle to track down a superior way All things considered, the most effective way to involve this language in AI is for critical thinking, where Prolog looks for an answer—or a few.

Consequently its utilization in chatbots and menial helpers like IBM’s Watson. Consider how straightforward however accommodating these types of brilliant correspondence are. Prolog probably won’t be as flexible or simple to use as Python or Java, however it can offer a significant support.

  • Julia:

Julia is another top-of-the-line item that simply hasn’t accomplished the status or local area support it merits. All things considered, its highlights don’t frustrate. This programming language is valuable for general undertakings yet works best with numbers and information investigation.

It offers a few apparatuses for making a unique connection point and great designs to picture your information, for instance. There’s additionally memory the executives, metaprogramming, and investigating for effectiveness.

As far as AI abilities, Julia is extraordinary for any AI project. Regardless of whether you need premade models, assist with calculations, or to play with probabilistic programming, a scope of bundles anticipate, including MLJ.jl, Flux.jl, Turing.jl, and Metalhead.


  • Haskell:

Assuming that you need unadulterated usefulness regardless of anything else, Haskell is a decent programming language to learn. Getting the hang of it for AI advancement can take some time, due partially to restricted help.

Be that as it may, its reflection abilities make it truly adaptable, particularly when managing blunders. Haskell’s proficient memory for the executives and type framework are significant benefits, just like your capacity to reuse code.


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