Artificial Intelligence Technology has remained inert for several years but has recently entered a period of acceleration. Recent trends bear evidence of the fact that a career in AI has been greatly sought after and companies are providing job opportunities for those with good AI skills. People who have particularly been interested in data science and software engineering are also keen to learn more about AI and gain AI skills.

AI is seen to come to use in different sectors, be it the commercial world or the educational sector. AI helps in increasing productivity, boosting the quality of the products and consumption. Hence the sectors where the contribution of AI would be actually noteworthy are healthcare, retail and financial service. Apart from that the role of AI in Education is also becoming increasingly significant as well. AI has opened a new world of possibility for companies as well as the engineers. If you’re one of them who wants to make most use of this technology that is going to be a future reality, you might be looking for the fastest ways in which you can build a career in this arena. Even if you’re just a student and have interest in AI, remember that learning the best programming language for AI is where you need to start from.

Presently, there is no particular designated best programming language for this field of technology. But, there are a multitude of programming languages that are popular and can be supported by AI. It is still advised that while thinking of learning an AI programming language ensure that they are supported by the various available deep learning and machine learning libraries. Learning an AI language that takes advantage of a healthy ecosystem of tools and a big community of programmers is advantageous and worthwhile. These are the 5 best programming languages that one can explore while thinking of exploring their interest in AI.

Amongst all the AI programming languages Python is by far the best choice because of its community support and the pre-built libraries like NumPy, SciPy, Pandas and the like that help in expedition of AI development. One can easily leverage the proven libraries like scikit-learn for Machine learning and also use the daily updated libraries like Apache MXNet, TensorFlow and PyTorch for Deep Learning.

For Natural Language Processing, one can take advantage of either NLTK or SpaCy. Python is proved to be the best coding language for Natural Language Processing because of the simple syntaxes, its structure and the text processing tools. Python has the most comprehensive frameworks for Deep Learning as well as Machine Learning. This AI language is flexible as well as platform agnostic and hence it’s easy to use once learnt. This would also help as one would not require to make major changes in the code while running it in a different operating system.

 

The object-oriented programming language has to be one of the best programming languages for AI. With several rich content libraries, Java is also a transparent, portable and maintainable language. Java is a good option as it is user-friendly, has easy ways of debugging and runs across all platforms without need of additional recompilation.

While working with Natural Language Processing, the support provided by the community build around it is immense. Java also allows access to big data platforms namely Apache Hadoop and Apache Spark which indicates how it has fixed its position within the data analytics linked AI development. The other advantages of learning Java are its ways of working beautifully with search engine algorithms and improving the interconnections of the user.

 

This is a language developed by MIT that happens to look into high-performance numerical analysis and computation. If there are tasks that demand this, then Julia is by far the best programming language, one can depend upon. This programming was created to primarily look into numerical calculations that are needed by AI so one can get the outcome without needing a separate compilation.

It is not preferred that one should start with Julia instead of Python and Java, because it is still not supported with rich libraries or a vibrant growing community. Although Julia’s popularity as an open source language is increasing, slowly. The main advantage of using Julia is its capability to translate the algorithms from the research papers directly into code without any loss. This helps in improving safety and also reducing the model loss. While exploring AI programming, Julia can come handy as it will help in cutting down the costs and errors by combining known syntax and easy use of the language.

 

This is a standardized static typing language with non-strict semantics. It is popularly used in several academic circles but tech hotshots like Facebook and Google have also used it considerably. Haskell is known to support embedded domain-specific languages that play a big and vital role in AI. Hence Haskell is mainly used for programming language research and other research projects.

Haskell can easily participate in abstract mathematical calculations as it permits efficient libraries to design AI algorithms. When you can code these algorithms in other programming languages as well, Haskell ensures to maintain its difference by making them more expressive while also ensuring an acceptable level of performance on its part. Haskell also serves as an excellent host for probabilistic programming and helps to identify the errors easily during the compilation. The only shortcoming of Haskell is that it is not a very popular programming language, the community support is not as great as that of either Python or Java.

 

You often hear the saying “old is gold”; this is applicable in case of Lisp. This has been the primary programming language that was used for AI development ever since it started out. Most programmers consider this to be the most suited language for AI development because of its peculiar features that help in the effective processing of information with symbolism.

Lisp is more like a practical mathematical notion for the computer programs with rapid prototyping abilities. It supports symbolic expressions, is very flexible and adapts easily to the problem-solving requirements. As it permits easy dynamic creation of new objects with a feature of automatic garbage collection, it is a go to AI programming language. While the program is running one can even switch on the interactive evaluation of the expressions simultaneously.

 

Other coding languages like R, C++ and Prolog can also be used for AI development. The 5 languages Python, Java, Julia, Haskell and Lisp are the best programming languages but it is also important to understand the demands of the projects before starting off with a programming as each is unique in a certain way.