9 Best Programming Languages to Build AI Apps in 2024

Which is the Best Programming Language for AI?

Python is the most preferred language for building AI applications. It has easy-to-learn syntax and libraries like TensorFlow and PyTorch. These libraries are helpful in tasks like data analysis, modeling, and training neural networks. 

Ideal for: Image recognition, facial recognition, machine learning, and other AI apps.

Beyond Python: Exploring Other AI Programming Languages

1. Java

Java might be the basic and old programming language used for different types of software development. But it's still powerful in the age of AI. Big companies like Google, IBM, and LinkedIn trust Java’s capabilities to build their AI/ML models. 

Wondering why? 

• It is object-oriented. That means Java can present even the complex process simply and easily. 
• AI applications deal with sensitive data which means you need a language that ensures data and code security. Rely on Java’s advanced features like sandboxing to get that level of security.  
• Java offers pre-built components means building AI apps is not just easy but faster too.
• It automatically deletes useless data keeping your code clean and lightweight. 

Ideal for: Large-scale enterprise AI projects, especially those involving security-critical data.

2. C++

AI applications demand speed and performance thus they need a language that can offer both. And C++ is the perfect combination to work with other programming languages for building such high-end AI apps. 

It can handle complex calculations, gives precise control over memory usage, and offers several libraries to support AI projects. If real-time performance is important for your AI project, then C++ is a great start. 

Ideal for: AI projects where real-time processing and high performance are crucial, such as robotics and simulations.

3. R

While Java, C++, and Python are the best programming languages for AI, you must also consider R language. Why?

Because most of the AI apps are built for data analysis, data visualizations, and statistical computing. R is designed for this purpose only making it suitable for such apps. It also supports libraries and packages like Ranger, OneR, Gmodels, and so on. 

Though everything is good about R, don’t use it for later stages of AI app development. It lacks compiler and memory management features to support the deployment. 

Ideal for: Performance-critical AI projects requiring real-time processing, like robotics or simulations.

Also Read: 5 Free AI Tools for Coding in Any Language

4. Julia

Julia gives you the best of two worlds: Python and C++. It offers speed with its just-in-time compilation feature and has a clear and concise syntax like Python.

Julia is also flexible and can dispatch multiple functions. This feature helps in writing highly optimized code for different AI algorithms and data structures. However, its ecosystem is still in the growing stage. 

Ideal for: Developers seeking a balance between speed and ease of use, particularly for complex AI algorithms and data structures. 

5. Prolog

Prolog gas is a different approach than other traditional AI programming languages. It works on logic and doesn’t give step-by-step instructions. It represents rules as facts and also defines relationships between them. 

This approach allows the computer to understand and draw conclusions based on the rules. Its symbolic reasoning helps in effectively training the AI models that require decision-making ability. 

However, prolog is not good for computational tasks unlike Python and other AI programming languages. 

Ideal for: Developing AI apps for problem-solving, machine learning, logic reasoning, and decision-making. 

6. Haskell

Where Prolog is a logical language, Haskell is a mathematical one. It focuses purely on calculations and provides data immunity. In other words, data doesn’t change after creation which leads to few errors.  

What makes it different from other languages is its lazy evaluation and pattern-matching feature. It runs calculations, when necessary, which boosts speed and efficiency. 

On the other hand, the pattern-matching feature helps AI models in tasks like data manipulation. 

Idea for: Natural language processing apps, rapid prototyping, statistical analysis, strategy games, and more. 

7. Scala

Write less, do more with Scala. It is a high-level language designed to solve the challenges of Java. Developers usually prefer Scala because it supports functional and object-oriented programming.

It has a big data processing powerhouse, Apache Spark, to handle massive datasets. Thus, Scala is good for developing AI projects with big data handling capabilities. Another reason for using Scala is its interoperability and scalability. 

Ideal for: Data processing in real-time, predictive modeling, AI recommendation systems, sentiment analysis, etc. 

8. Lisp

Despite being one of the oldest, Lisp (short for list processing ) is capable of developing high-performance and intelligent AI apps. It treats code as data and thus doesn’t need a compiler to interpret the code.  

The language is simple, works on multiple platforms, and faster making it a natural fit for AI apps. What’s more? Lisp’s rich ecosystem with libraries like ACL2 and SBCL makes it a good fit for symbolic AI tasks. 

Ideal for: AI chatbots, text analysis, knowledge base systems, proof assistants, strategy games, etc.  

Also Read: A Complete Guide to Master AI Prompt Engineering and Perfectly Train Your AI Model

How to Choose the Right AI Programming Language?

Picking one is not essential or mandatory as you may have to work with more than one language.

The best bet is to start with programming languages in which you already have experience rather than learning a new one. Also, choose the type of language based on your AI project. 

For instance, if your project is more about machine learning and deep learning, Python is best. And if it involves complex mathematical and statistical computing, choose either C++ or Julia. 

Conclusion

AI app development might be new, but the base still relies on traditional programming languages like C++. They are simple and easy to learn. 

But new AI programming languages are also gaining popularity as they offer new capabilities important for new-age apps. Thus, having a mix of both in your developers’ toolkit will be useful for creating apps that understand natural human language and analyze data in real time.