Top 5 most popular languages for Artificial Intelligence (AI) programming

Top 5 most popular languages for Artificial Intelligence (AI) programming

With a plethora of opportunities for both application and development, it is safe to say that Artificial Intelligence (AI) has found a profitable place in the business world.

Even so, the growth we have seen in recent years does not compare with what the near future holds and it is here where it is expected that both companies and developers will be able to make the right decisions regarding the programming languages they will implement in order to develop the full potential of this disruptive technology in their businesses.

What does artificial intelligence (AI) aim to achieve?

The fundamental goal of artificial intelligence is to get machines to simulate human reasoning through computational calculation and programming languages are the most effective tool we have to dictate to both machines and systems what tasks we hope to solve through them. Being a diverse and complex task, it tests the capabilities not only of the development teams but also of each language that is why we have created a list of the most suitable languages to carry out AI deployments.

Although artificial intelligence is not an innovation resulting from the recent wave of technological growth, it has been implemented in the business world where it has shown great performance providing benefits such as: intelligent decision making, improved user experience, process automation in both information technology (IT) and operation (OT) and data analysis among many others.

What does artificial intelligence represent in the business environment?

If you ever wondered why ads on social networks seem designed exclusively for you or why on certain occasions the Uber service is more expensive, you can thank artificial intelligence that, through Machine Learning or machine learning has allowed companies like Facebook, Uber or Airbnb to create algorithms to recognize behavioral patterns, convert them into data and then cross them with others such as: weather, schedule, special dates or current traffic to make autonomous decisions and set the prices of their services dynamically or offer precisely the products that a particular user needs in a timely manner.

If your company is looking to integrate an artificial intelligence solution and undertake its own development, we have created a list of the best programming languages for deploying AI systems that all developers should have in their technology toolkit.

Our top 5 most popular programming languages for producing AI applications:

1. Julia

Designed exclusively for producing numerical analysis and computational science software Julia continues to gain popularity thanks to its agility, convenient syntax and great computational power. Created in 2012, this is a relatively young language and is constantly expanding the number of its Machine Learning libraries thanks to its growing and highly participative support community.

Its proven high performance in technical and scientific fields has allowed Julia to serve as a platform for developing AI in which its users highlight features such as:

  • Dictionaries and introspective code
  • Common numeric data types
  • Macro management and meta programming
  • Robust mathematical functions
  • Built-in package manager
  • Ability to work concurrently for parallel and distributed computing.
  • Support for C functions

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.

2. Java

This is one of the most widely used programming languages in the world for various purposes, artificial intelligence is undoubtedly one of them. Among its greatest advantages are its powerful Virtual Machine Technology, which facilitates its implementation processes, and its multiplatform compatibility, which eliminates the need to rewrite code when using different systems. Java has libraries and frameworks created specifically for AI among which are:

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

Java is one of the most user-friendly languages and has a large number of opensource libraries making it a flexible language that offers simple code debugging, support for large-scale deployments and graphical representation of data. In addition Java is quite popular in web and application development, thanks to the fact that most of these frameworks are supported by artificial intelligence systems choosing Java for AI development is the best choice we as programmers could make, currently this language has positioned itself as one of the most popular choices for maintaining, moving securing and deploying robust artificial intelligence applications.

3. C++

C++ is a very popular language among the developer community because it is one of the most flexible and current low-level languages available. This allows it to work well with hardware and machines making it an ideal language for managing artificial intelligence models. Also, many of the machine learning libraries have been created with C and this is a great advantage when creating neural networks in a clearer way for machines.
Among the advantages of C++ for the development of AI applications are:

  • Multiparadigm language
  • Object-oriented
  • Extensive library of functions
  • Ease of solving complex problems
  • Extensive collection of programming tools

In addition, C++ allows the use of extensive algorithms and is considerably effective in the use of statistical techniques for AI which puts it above other languages in this feature alone.

4. R

If you want to program applications that require data analysis and representation, R is the best alternative we can advise you. This language is open source and has been used to develop solutions in industries such as education, telecommunications, finance and health sciences among others.

Among its features R comes with built-in functional programming, object-oriented structure and vector computation and although it has few libraries for AI all are very advanced in both its utility and performance. Among these are:

  • TM
  • OneR
  • gmodels

R has integrated data and graph modeling support that allows developers to work on Deep Learning in a practical and agile way.

5. Python

The most popular language among both novice and expert developers is also an ideal AI tool when creating code for Machine Learning and neural network connections. Its modern syntax is also minimalist and flexible, which is why it has been implemented in web development as well as in DevOps, even so, its area of greatest influence and scope is artificial intelligence where it has managed to excel.

For this purpose, Python has frameworks exclusively for AI, among which the following stand out:

  • PyTorch
  • Keras
  • Scikit-learn
  • Theano

In any context Python has managed to position itself thanks to the ease with which users find it easy to learn and deploy. It also has a large community that offers support, library development and countless forums around its implementation.

To close it is important to make clear that in the current context the adoption of artificial intelligence is imperative in companies seeking automation and digital transformation that gives the ability to create business solutions of maximum precision in the search for benefits such as reduced operating costs, increased efficiency in its current market, penetration into new markets or development of business alternatives.

For all of the above if your company is looking to accelerate growth or improve the delivery of products and/or services through an AI deployment focused on your business objectives, DreamCode offers you a consolidated team of technology specialists with the necessary experience to plan and execute the IT developments that dynamize your processes in favor of your digital competitiveness. Visit our website now, get to know our work methodologies and make the right technological decision to overcome the challenges of business digitalization.