4 Programming Languages That You Need To Know As a Gen AI Professional

Introduction

With several organizations across industries laying off employees on a large scale, many young professionals are often distraught and anxious about the current job market. However, contrary to popular belief, these layoffs are not happening due to AI replacing jobs. They happen mostly because employees are not scaling up to handle AI tools and know valuable skills in AI. Regardless of whether you are a data scientist, a product manager, or a team lead, you need to understand the nuances of generative AI and the programming languages used in generative AI development. This is sure to help you establish a strong footing in the current industry and become an indispensable asset in the company that you are working for.

Programming Languages for Generative AI

In this blog, you will learn the merits of learning the following languages for generative AI and data science applications:

•   ● Python
•   ● Java
•   ● Data-driven decision making
•   ● Python vs JavaScript for AI
•   ● R
•   ● Julia

Python

Did you know that Python empowers some of the world’s most innovative AI solutions from Google’s TensorFlow-driven applications to Netflix’s recommendation algorithms? Its simplicity is what makes it the top choice for AI and machine learning development. With a clean and straightforward syntax, Python can help you work with increased focus thanks to its easy algorithm development and seamless problem-solving skills instead of navigating through a complicated syntax structure.

Another salient feature of Python is that it helps you easily integrate with other languages like C++ for performance-critical aspects of an AI system. It also works with big data frameworks like Apache Spark. It can also be paired with GCP or AWS cloud services. This makes it a highly-scalable AI solution across various business applications. Its cross-platform capability helps developers to develop AI applications on macOS, Windows, and Linux, making them implement it across diverse environments with minimal compatibility issues. Here’s a quick recap on Python.

•   ● Extensive libraries and frameworks
•   ● Easy to use
•   ● Seamless integration
•   ● Cross-platform capability

Java

Yet another widely used programming language in gen AI applications is Java. With several large-scale enterprise applications depending on Java, developers have started widely-using it for backend and data processing operations. Popular platforms like LinkedIn use Java for messaging, and established banks like Citigroup use it in their trading platforms.

Java Virtual Machine is making it relevant in projects that are scalable, performance oriented, and compatible in cross-platform applications. Just like Python, Java also supports cross platform compatibility, but where it stands apart is its libraries like Deeplearning4j, Apache Spark’s MLlib for big data processing, and Weka for machine learning. Java is a compiled language which means that it is usually faster than interpreted languages like Python, making it the perfect candidate for real-time processing and gen AI applications. Also, it is scalable and integrates seamlessly.

Here’s a quick summary of the advantages.

•   ● Cross-platform compatibility
•   ● AI libraries that support deep-learning
•   ● Good performance
•   ● Scalability in enterprise

Python vs JavaScript for AI

Now that we have understood why Java and Python are important languages that you need to know for Gen AI, here are:

Python	Java
● Slower execution speed as it is an interpreted language ● Simpler syntax with a lot of shortcuts for easy usage ● More AI libraries which is suitable for using gen AI projects	● Faster execution speed ● Complex and verbose syntax which is harder to learn and work with ● Fewer AI libraries

Common limitations: Both Python and Java consume a lot of memory and can struggle in handling large datasets. As gen AI models tend to work on a lot of data, you cannot expect quick turnaround times.

R

Another widely used programming language for organizations grappling with more data heavy operations is R. Useful in performing statistical computing and data analysis, R is used by developers who work with large data sets and obtain deep statistical insights. It is used in developing machine learning models and is a stalwart in data mining, modeling, and forecasting. R is equipped with libraries like ggplot2 and ploty, which empower developers to create detailed customizable graphs and charts. Here’s a quick peek into R’s core features.

• ● Suitable for data visualization and analytics
• ● Library package for AI
• ● Strong in statistical computation

Julia

Now that you have understood the importance of some of the commonly used programming languages in Gen AI, here is one lesser-known programming language that is known for its scientific computing. Julia is used in critical gen AI applications due to its vast range of AI libraries.

Did you know that top organizations like NASA rely on mission-critical simulations using Julia? This speaks volumes of its reliability. It is used to run large-scale calculations across different processors or machines. It can also be used for quick model development with high-level syntax. If you are in a sticky situation where you need to integrate code in different platforms, Julia helps you to integrate existing codebases with other languages. Here are some key features of Julia.

• ● Native support for distributed computing
• ● Easy to use with high-level syntax
• ● Wide range of AI libraries
• ● Compatibility with other languages

These are the four key languages used for deep learning and AI applications. Now that you have learnt these programming languages for generative AI, you may find it challenging to choose the right language for the applications that you are working on. You could also face challenges in learning these languages and applying them in various gen AI projects.

Our mentors are non-academicians with decades of experience in AI and data science applications. We offer a deep dive of all programming languages that power gen AI and data science applications. Our mentors guide you in performing capstone projects that help you stand out from the competition. They also help you face tricky interview questions and help you land the right job for your skill set.