Top 5 Programming Languages for AI Engineers in 2021

Artificial Intelligence resides in an integral part of our everyday lives. It is highly beneficial due to its thousands of unique uses and situations. AI has made things simple and easy for us. The recent boom in AI has come a long way, helping businesses grow to the next level and achieve their ultimate heights. Although this achievement is possible due to Artificial Intelligence advancements but the underlying credits go to the programming languages.

With the adequate boost in AI, skilled programmers and engineers skyrocketed in the improvements in programming languages. There is a huge heap of programming languages to start with developing on AI. You must know that no single programming language is one station solution for Artificial Intelligence programming. It requires various objectives and specific approaches for every project.

Let’s jump on the track and discuss some of the popular programming languages for AI Engineers in 2021.

1. Python

No matter how many languages come and go, Phyton will be the most powerful language you can read. Developed in 1991, Phyton has been suggested by over 57% of the developers as compared to C++ for their programming language development of AI solutions. Python is easy-to-learn and offers an easy entry into the world of AI development for data scientists and programmers alike.

Python not only has excellent community support but also has an extensive set of libraries. It also provides flexibility to the programming language. The most beneficial factor from python is platform independence and extensive frameworks for Machine Learning and Deep Learning. Python has surpassed Java and secured the 2^nd most popular language as per GitHub. In short terms, Python is the fastest growing programming language in today’s tech world.

The most popular libraries are –

• Scikit-learn: It is for training machine learning models.
• TensorFlow: It is for working with machine learning workloads and datasets.
• PyTorch: Natural language processing and Computer vision.
• Keras: Works as the code interface for highly complex mathematical operations and calculations.
• MXNet: An apache library for deep learning workflows.
• SparkMLlib: Like Apache Spark’s Machine Learning library, it helps make machine learning easy with tools like algorithms and utilities.
• Pybrain: It is for powerful machine learning algorithms.
• Theano: It is the library for optimizing, defining, and evaluating several mathematical expressions.

2. Java

Java is all about “write it once and hold the ability to run anywhere.” It is considered one of the best programming languages in the world for the last 20 years. Java has been comprised of flexible nature, high user-friendliness, and platform independence. It can be used in various ways for developing AI. Let’s check out its features.

• Deep Java Library: Amazon builds it for creating and deploying deep learning abilities to use Java.
• TensorFlow: TensorFlow has a variety of supported programming languages, including Java with an API. It isn’t a super-rich feature as other supported languages.
• Kubeflow: It facilitates easy management and deployment of Machine Learning stacks, provides ready-to-use ML solutions.
• Java Machine Learning Library: It provides several machine learning algorithms.
• OpenNLP: OpenNLP is Apache’s machine learning tool for natural language processing.
• Neuroph: It makes designing neural networks through an open-source framework of Java with Neuroph GUI’s help.

3. R

Created by Ross Ihaka and Robert Gentleman, R was launched in 1995. It is the implementation of S programming language that aids in developing data analysis and statistical software. Nowadays, R is being maintained by the R Development Core Team.

R is a good fit for AI programming language among developers. Let’s see how. First, R’s fundamental feature is crunching huge numbers with the help of its comparatively unrefined NumPy package. Secondly, with R, you can work on various programming paradigms like vectorial computation, functional programming, and object-oriented programming.

There are various AI programming packages available for R:

• Tm: It is a programming language framework to text mine the applications.
• GModels: It provides a collection of different tools for model fitting.
• OneR: OneR implements One Rule Machine Learning for the classification algorithms. It is useful for machine learning models.
• RODBC: Works as n ODBC interface for R.

R is used widely among Statisticians and Data Miners for its feature. Such as a wide variety of packages and libraries for extending its functionalities. It also has an active and supportive community. R works in tandem with Fortran, C, and C++. It has several packages that help in extending the functionalities. This programming language supports the production of high-quality graphs.

4. JavaScript

JavaScript is recognized as the most popular programming language in the GitHub and Stack Overflow Developer Survey. Brendan Eich developed it in 1955. JavaScript is an object-oriented programming language that was used to render web pages interactively and dynamically. It comes with a set of ready-to-use codes, prebuild application design and toolset, to build a software application. Thus, the software development process is fast-forwarded, saving money, time, and effort for businesses. JavaScript frameworks are tools responsible for making JavaScript easy and smooth to use. AngularJs is the popular JavaScript framework.

5. Kotlin

Kotlin is a cross-platform, statically type inference programming language. It is a general-purpose, free, and open-source pragmatic language designed for JVM and Android combined functional features. Its main focus is on clarity, safety, interoperability, and tooling support. Kotlin is the official language used for the development of android apps. Its rank might not be as high as that of other programming languages. It stands as the 15^th most active programming language on GitHub. Although Kotlin isn’t the highest-paid language, a Kotlin developer gets an average salary of $57k per year. All thanks to Google for considering it as the primary language for Android applications.

Conclusion

With a wide array of AI programming languages available, choosing the right one might be tricky. AI engineers and Computer scientists pick the right one that fulfills the needs of their project. But you must note that every AI programming language has its own fair share of pros and cons. Although no language is perfect, some improvements made to the programming language developing AI have become very comfortable and quite easy. It will become way easier so that people would join this AI wave of innovation.