March 12, 2024

How to Choose the Best AI Programming Language?

Get the latest information about the different languages used to make AI products and find out about the best AI programming language for particular projects.

Written by
Serhii Uspenskyi
CEO
AI & ML
Technologies
AI & ML
Technologies

We can’t imagine our business world without artificial intelligence and machine learning. Can we? For the last the past couple of years, we have obviously noticed a boom in demand for developers who can create projects built around artificial intelligence technology. Almost any business, from small startups to large corporations, wishes to get their hands on all sorts of AI products. Some require computer vision tools to check the quality of their products better, while others need ChatGPT integration.

According to S&P Global, there are over 1,500 companies operating solely in the AI software space.

This high demand for AI development services has programmers thinking about the skills they should have to succeed in this growing field. One of the biggest questions they have is, “What’s the best language for AI projects?”. We’re here to answer this and provide insights into it based on previous development experience at Springs.

Table Of Contents:

How To Choose the Best Language To Code AI?

Before we discuss the different programming languages and technologies that are used in generative artificial intelligence, let’s take a look at the criteria of our search. Each language has a set of strengths and weaknesses that need to be balanced to fit project goals and requirements. A good programming language should possess the following qualities for a programmer to consider it:

  • Requirements. When it comes to artificial intelligence, the best language is the one most suitable for a particular task. Consider the kinds of projects you wish to be involved with. For the first time being, it's better to focus on languages with the widest range of applications.
  • Easy learning. If you only start a career in this field, stick to languages that don’t take forever to learn. Python is one of the world’s most accessible many AI developers begin with and build successful careers.
  • Easy to use. Additionally, the language used for AI development should be easy to use and read. Python has one of the clearest syntaxes that is approachable and effortless to compile.
  • Flexibility. Another thing to look for in a language is flexibility. It should be adaptable enough to work in different scenarios and projects without having to change code too much. 
  • Community. Some of the best AI programming languages have robust communities, offering various frameworks and libraries that ease the development process. For example, Python has one of the largest programming communities with dedicated support for AI-related or LLM projects.
  • Scalability. It's vital to find a language that makes software projects easy to scale when working with larger datasets and user numbers. This way, it will be possible to upgrade products without having to rewrite or adjust large chunks of code.

Best Programming Tools for AI Development According To Our CTO

Of course, many programming languages more or less fit this category. We’ve decided not to list them all one after the other and focus on the ones most suitable for AI projects our company had the pleasure to develop. Here are our CTO’s, Yurii Puliaiev, thoughts on choosing the proper programming language for AI integration to automate your business:

“From my experience, most of the programming in the field of artificial intelligence is done with three things: Python, JavaScript, and NodeJS. The combination of this language and runtime environment worked in 100% of the projects we delivered. 

Whether our clients needed a text-to-video app or a facemask recognition solution, these tools were pretty much everything we needed. Of course, other developers work with more languages and environments, but we find this practice unnecessary.” — Yurii Puliaiev, CTO at Springs.

The Best Languages To Code AI: Springs Web Toolkit

At Springs, our AI developers use a mix of frameworks, environments, and programming languages to create versatile state-of-the-art AI solutions with a proper approach. Let’s take a closer look at their capabilities.

Python

This is one of the best languages for AI creation used by programmers worldwide. Python has an accessible syntax that’s easy to learn and get used to. Developers gain access to various frameworks and libraries tailored for these types of solutions. Some of the most popular ones include Matplotlib, Scikit-learn, and NumPy. It’s possible to use these pieces of premade code to finish projects faster. Python frameworks like PyTorch and Tensorflow are common components in ML algorithms, especially for computer vision and predictive analytics.

JavaScript

This programming language helps AI applications perform computation tasks and improve their overall performance. Springs team uses JavaScript for coding recommendation engines, AI chatbots, and AI Virtual Assistants. This language also helps us add AI capabilities to web applications through API integration. Frameworks like Brain.js, ConvNetJS, and TensorFlow.js introduce ML capabilities to web projects.

NodeJS

Being not strictly the best artificial intelligence language, NodeJS is a powerful tool that can be found in many Springs’ projects. It’s an intelligent environment that helps web developers bridge different components of AI applications. For example, to integrate AI frameworks and libraries into web apps and create connections between AI code and web applications.

Seven Popular Programming Languages for AI Development

While we find that Python, NodeJS, and JavaScript are sufficient to make artificial intelligence products successfully, these aren’t the only tools developers use. When programming developers use many other programming languages for custom development. Let’s have a look at hem.

  1. C++

Programmers often use this tested language to develop different AI solutions. C++ is considered the best AI programming language for voice recognition or NLP systems, allowing them to process audio information. Developers often use this tool to work on deep learning libraries faster. These features make C++ a versatile tool for artificial intelligence products.

  1. Haskell

AI developers often turn to this language when working on processing and complex data structures for AI solutions. Haskell is built on mathematical principles that are used in making deep-learning models and complex algorithms. The language has features that prevent developers from assigning incomparable values to variables, making the programming process easier.

  1. GoLang

Backend programmers often use Go to compile code for AI projects that require strong computational capabilities. This programming language supports parallelism and concurrency, which are great things to have in apps that work with large amounts of data. It’s also a common component in projects that require scalability and work with microservices. Go is used in tasks such as API interactions, data preprocessing, and model predictions.

  1. Java

Another programming language that API developers heavily use is Java. It’s open-source and cross-platform, allowing the development of apps for web, desktop, cloud, and mobile environments. Offshore engineers utilize Java to develop robotics and simulations in AI applications. Its extensions, like RTSJ, allow the making of real-time systems like assistants and chatbots.

  1. Lisp

AI Chatbot developers praise Lisp for its high adaptability and support for symbolic expression processing. The nature of one of the world’s best languages to code AI allows apps based on Lisp to make complex computations and dataset manipulations. Programmers use it to make predictive analysis and analytical tools that deal with huge amounts of information.

  1. Prolog

Fullstack programmers work with this language thanks to its symbolic reasoning and logical programming capabilities. Prolog is often used in making knowledge bases in AI systems thanks to the fact that it represents facts, rules, and relationships in a straightforward way. The language is also valuable for NLP tasks.

  1. R

This programming language is mostly used in data analysis and statistical computing. R has many data visualization libraries like Shiny, Lattice, and Ggplot2, allowing frontend developers to turn information into charts and graphs quickly. With the help of its Caret library, experts optimize the performance of machine learning algorithms. Additionally, R is compatible with Java and Python.

What Programming Languages For AI Development You Shouldn't Use

Despite the fact that there are many best languages for AI to work with, there are some that programmers, especially ChatGPT developers,  shouldn’t use. They are not as versatile, efficient, or easy to use to make such solutions. Among the most ill-suited for AI development are the following.

PHP

PHP is mostly used in web development and doesn’t have specialized ML and AI libraries. But, it’s popular for making web solutions. The language is not designed for data manipulation and scientific computing, both common tasks in AI development. In this regard, NodeJS and Python are far more effective tools.

Ruby

Despite its syntax and readability rate, Ruby lacks potent machine learning and artificial intelligence ecosystems. Since it is an interpreted language, programs built using Ruby are slower than those made using C++, Java, or other compiled languages.

C

C’s greatest limitation is that it's a foundational low-level language. It’s ok if web programmers need to build apps with low-level hardware integration. However, it doesn’t have AI-specific frameworks and libraries. C’s data structure can cause memory leaks, resulting in potentially unreliable applications. 

Perl

Perl is another programming language that’s unsuitable for AI development. It lacks an adapted framework and library ecosystem, unlike NodeJS and Python. Additionally, Perl’s syntax and programming style is a challenge for less experienced programmers.

Fortran

Scientists and engineers prefer working with this language. Unlike JavaScript and Python, Fortran is fairly hard to learn, which slows down the development process. Additionally, the language lacks many modern features, making it unsuitable for ML programming.

Conclusion

AI development is a complicated process that requires preparation and attention to detail. If you are already familiar with some of the programming languages used for AI/ML development, we wish you luck in this growing and highly profitable field. Those who are new to programming should invest their time in learning more approachable languages like Python and JavaScript.

We strongly recommend using only top-notch AI technologies for building AI products. We will be glad to help you with building your product, idea or startup.

Customer retention is the key

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What are the most relevant factors to consider?

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Don’t overspend on growth marketing without good retention rates

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What’s the ideal customer retention rate?

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Next steps to increase your customer retention

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