5 Exceptional Programming Languages for Artificial Intelligence Integration

Kristijan Pušić | February 16th, 2024

Are you interested in AI and wondering which programming language to learn in 2024? You are in a situation where the first thing you need to do is to choose a framework for this quest and not the language. Usually, the choice between Google (TensorFlow) and Meta (PyTorch) in this area of AI frameworks will satisfy most developers out there. Both support most of the programming languages commonly used in IT.

Which one to choose, which way to go? The best advice would be to start your new AI project with a technology you are already familiar with. If the idea is also to learn a new language while doing an AI project? In that case, let’s see which new language would be most useful to learn in 2024. What could benefit your company the most in the coming years, not strictly related to existing AI projects? What would be the most useful for the company and future customers? What would be the most tactful language to learn?

#1 RUST: An aggressive newcomer to the corporate world

Mozilla, Microsoft, Amazon Web Services (AWS), Google, Facebook (Meta), Apple, Dropbox, Cloudflare, Discord, Twitter/X. The list of Rust companies is quite impressive! AI assistants have accelerated the learning of RUST. It is a low-level language after all and this AI assistant change will trigger faster Rust adoption. The next big thing is finally here and living its corporate life, far away from blockchain, contracts and gossip. A serious language for serious programming. TensorFlow likes RUST, so dive into a great framework TensorFlow with RUST as the most promising enterprise IT language. Macros in Rust generate code based on input, simplifying repetitive patterns and making code more concise. Rust macros simply allow us to write code that writes more code, also known as meta-programming. Macros are used extensively in Rust. Enough from me, check out what Linus Torvalds has to say about RUST and Linux.

#2 Typescript: frontend devs favorite

A superset of JavaScript. TypeScript provides strong static typing. This feature is particularly useful in AI development, where large data sets and complex algorithms are often used. It’s widely used in front-end development around the world, and is gaining traction for full-stack development thanks to Node.js. It was developed by Microsoft and released in 2012.

 #3 Golang: fast adoption

Go is a high-level, statically typed, compiled programming language developed at Google in 2009. It is widely used in enterprise IT ecosystems. Go’s speed and performance are well suited to the CPU-intensive, highly compute-intensive computations required by AI solutions. It is faster than Python, easier to use than other machine learning platforms, and offers several machine learning libraries.

#4 Java: it is never too late to learn java

The undisputed ruler of the programming world by the number of applications written in it.  With compatibility with TensorFlow and hosting libraries such as the Deep Java Library, Kubeflow and OpenNLP, Java is well equipped for AI tasks. Its simplified debugging, user-friendly syntax and support for object-oriented patterns make it a versatile choice for AI developers. The year is 2024, for the record!

#5 Python: A Pioneering Force in AI

Despite predating the AI boom, Python is one of the most popular languages for AI development. Known as the primary language for machine learning, Python owes its popularity to its origins as a robust data analytics tool, well-established in the realm of big data. In particular, Python’s dominance in AI is reinforced by dedicated frameworks such as TensorFlow, scikit-learn, PyTorch, Keras and Theano, which are explicitly designed for machine learning and deep neural network tasks. Their simplicity adds to their appeal, making them an ideal choice for developers new to AI.

Best choices for Individual Developers

1. Diversify with Rust: Given its growing adoption in enterprise environments and its compatibility with TensorFlow, learning Rust could significantly enhance your value in the job market. It’s especially relevant if you’re interested in systems programming, IoT, or performance-critical applications.
2. Embrace TypeScript for Web Projects: If your focus is on front-end or full-stack development, mastering TypeScript can offer a significant advantage. It provides a more structured approach to JavaScript development, making it invaluable for projects involving complex data handling and UIs.
3. Explore Golang for Backend Services: For developers looking to build efficient, scalable backend services, Golang is an excellent choice. Its simplicity, coupled with powerful concurrency support, makes it ideal for microservices architectures and cloud-native development.

Best choices for IT companies

1. Invest in Python for AI Initiatives: Python’s extensive library ecosystem (TensorFlow, PyTorch, scikit-learn) makes it the best starting point for AI projects. Companies should consider investing in Python training for their teams to expedite AI development.
2. Java for Enterprise-Level Applications: Organizations with a strong Java footprint should leverage Java’s robust ecosystem for their AI projects. Java’s wide-ranging library support, including for AI tasks, allows for seamless integration with existing enterprise systems.
3. Rust for Performance-Critical Applications: Companies working on performance-sensitive products (such as embedded systems, networking hardware, or high-frequency trading platforms) should consider adopting Rust. Its safety and performance features can lead to more reliable and efficient applications.

Conclusion: Empowering Growth with AI

While these five languages have different strengths, choosing the right one depends on the specific AI project and the overall idea behind it. Python is the obvious winner, followed by Java, with a number of AI projects being undertaken in these languages. For organizations looking to integrate AI development services, the journey begins with the strategic adoption of one of these languages. With a skilled development team, the possibilities are limitless and AI becomes a catalyst for accelerated business growth. Want more unified front-end solutions? Choose Typescript. Interested in IT enterprise customers for development outsourcing? Hop on the Java train. Want to be an AI development company? Then definitely some Python! Fast learning curve in OOP and microservices and concurrency is your choice? Google’s Golang loves you. Want the best value for you and your company? Check out what Linus Torvalds has to say. RUST!

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