Best Programming Languages for AI

• Clark Finley
• December 09, 2022

Artificial intelligence has been around for a long time; it’s not something new in 2022. Experts successfully use AI tools in various fields, becoming more and more perfect every day. Many modern companies are leaning towards implementing artificial intelligence in their activities because it improves customer experience, provides intelligent decision-making, automates routine procedures, minimizes errors, etc. But it’s not enough to decide on using artificial intelligence; you must also understand how to choose a language to develop such systems. Let’s discuss the possible options, pros, and cons in more detail.

Which programming language to choose for AI?

Artificial intelligence is not an area where universal solutions can suit everyone. When choosing the optimal programming language, it is necessary to consider the project’s scope and requirements. R will be an excellent solution if you need to work with a significant volume of information. Experts have created such a database to process large numbers quickly and fast. Python is better than R if you plan to implement machine learning algorithms in production. Each language has its capabilities and restrictions in artificial intelligence programming. Some variants ​​are efficient at natural language processing, while others easily parse numbers. Let’s discuss what database ​​you may choose when programming AI.

Java

Java is a famous server-side language. Its main feature is the ability to utilize it anywhere on any platform. Using Java to create artificial intelligence allows for achieving application scalability. This programming language guarantees easy debugging and simplifies large projects when developing artificial intelligence. All information can be presented in graphical elements to ensure a good user experience.

Python

Python is a minimal code programming language. It appeared long ago and has shown itself well as a general-purpose language suitable when dealing with front-end and back-end. There are other reasons why you should use this dataset to create AI mechanisms:

• Availability of ready-made libraries to make advanced calculations.
• It is an open-source language supported by developers from all over the world. You can find many Python forums and tutorials.
• Simple machine learning.
• Python is a flexible dataset that can be easily adapted to different platforms.

Specialists use this programming language because it allows them to focus on critical areas of development, including innovation, problem-solving, and design thinking. At the same time, the easy syntax and many libraries make it easy to generate code.

R

Such a language is suitable for beginners and experienced programmers. It makes sense to choose R to create AI-based systems if complex calculations are needed. Artificial intelligence and machine learning require processing a vast amount of information. R is acceptable to explore and present such data efficiently. In most cases, R interacts more effectively with statistics than Python.

Julia

Julia is a relatively young system of notation. Despite the small community, this is one of the best variants if you want to create AI products. Julia’s popularity is influenced by its dynamic interface and unusual graphical component to visualize information. Such a programming language offers memory management, metaprogramming, and debugging. Julia successfully translates algorithms from research work into code, reducing modeling risks and increasing security.

C++

C++ is a low-level programming language that has been around for a long time. It successfully interacts with machines but not with the latest conceptual programs. The potential of low-level programming allows C++ to manipulate primitive AI models easily. It can be used to create neural systems and translate programming into machine-readable form.

Which languages should be avoided?

Some systems of notation should be abandoned when creating artificial intelligence. These are, first of all, niche databases and low-level variants. Experts recommend avoiding such sets of formal rules:

• PHP is a scripting language that is used in web development. When creating an AI, it may not be flexible and reliable enough.
• Web development also often uses Ruby tools. But if Python guarantees rapid prototyping, creating a functional system in the Ruby environment requires a lot of time.
• C is a standard low-level solution among system administrators and specialists who develop embedded projects. But this is not the best option for artificial intelligence due to the lack of high-level functionality.
• Fortran is the variant preferred by researchers and engineers. Such a dataset for engineering is challenging to master and lacks many essential features.

Before choosing a programming language, it is necessary to evaluate the tasks and results of the project. Determine the main goals and the resources needed to achieve them. Then learn the tools and libraries of each programming language to make your final choice. Beginners often choose Python, Java, and C ++ but analyze each option’s pros and cons given the work plan. Other popular programming datasets may be used to create AI-based applications, such as Lisp, Prolog, Haskell, etc.