As Machine Learning and Artificial Intelligence are gaining traction with businesses and individuals, now is a good time to look at which one of the programming languages are best suited for it. We will be looking at the best ones for AI development. Like always, use cases will be a determining factor in choosing one. So, let’s dive into it!

Best Programming Languages for AI Development

1. Python

It is only natural to start by presenting one of the most well-known programming languages. Python has been around for more than 30 years and is one of the best choices for AI development. It is one of the 3 most used programming languages in Machine Learning and AI.

Its advantages include being user-friendly, easy to learn, and high-performance. Its syntax is simple and readable, making prototyping, testing, and debugging fast.

Being so popular means that a vast quantity of prebuilt libraries is available, like NumPy, Scipy, PyTorch, TensorFlow, and visualization tools like Matplotlib. Documentation is easy to find and abundant.

Python is also well-integrated with multiple frameworks and platforms. It is also open source with a large community.

Its downside includes being an interpreted language which can make it slower than other programming languages.

2. Java

Java is a multi-purpose and vastly popular programming language. It’s scalable and easy to implement with Java Virtual Machine Technology.

Its advantages include being easy to debug and implement on large-scale AI applications. It has a large online community with assistance being easy to obtain and open-source frameworks and libraries like Deep Java Library.

Java is also secure. Data in graphics can also be easily represented using Java.

Its downside is the increase in response time due to its low processing speed.

3. C++

C++ is a fast and powerful high-level object-oriented language that was built as an extension of C. It is generally used in complex automated products such as those used in banking and at the enterprise level.

Its advantages include being a compiled language. This results in faster execution times and processing speeds than other programming languages, meaning that it can crunch vast amounts of data quickly. Being around since the late ’70s, it has good support and documentation.

Its downsides include not being as easy to learn or user-friendly as other languages, such as Python. The laborious syntax can result in slower project development.

Many things must be handled directly by the programmer making it tedious. Libraries are also more limited than some of the other languages presented here.

4. Prolog

Prolog is a declarative programming language that can be used for logic-based programming.

Prolog stands for ‘’Programming in Logic,’’ and its advantages include being mainly used for computational linguistics such as Natural Language Processing and AI. It can also be used for symbolic reasoning. It is possible to generate code in Java and C# using Prolog.

Other advantages include being highly efficient because of backtracking and data structuring, and list handling.

Its downsides include not having support for graphics, not being easy to learn and read, and having limited resources and community. Use cases for Prolog are also limited.

5. Lisp

Lisp has been around for a long time and is one of the earliest languages used for AI development. It was initially created for scientific research.

Lisp’s well-known advantages include rapid prototyping, specific problem-solving, uniformity, and flexibility.

Its downsides include being hard to read and write due to a strange syntax. It also has limited resources in the form of frameworks, libraries, and communities.

6. Haskell 

Haskell is a functional programming language that emphasizes type safety and correctness. It also handles error resolution during compilation.

Its advantages include error resolution in an efficient manner, code reuse, and safety.

Its downsides include having small community support and being difficult to learn.

7. Julia

Julia is a more recent language developed for numerical analysis and computational science. Julia can provide the user with good graphics, data visuals, and a dynamic interface.

Its advantages include having native and non-native libraries, a dynamic type system, as well as features like memory management, debugging, and metaprogramming. It is also fast.

Its downside is that it won’t have a wide community and support, being a relatively new language.

Conclusion

So here where the top programming languages for AI. Python is one of the most well-known and supported for AI development, as well as Java. Others have a narrower use case, such as Prolog for NLP. So, finding a suitable AI development tool will depend on your use case.