The Top 5 AI Programming Languages You Need as an AI Engineer

The market for artificial intelligence is forecasted to grow significantly over the upcoming years, skyrocketing to an astounding $407 billion by 2027, up from its already impressive $86.9 billion revenue in 2022. With this new growth, new jobs will also follow—by 2025, the AI sector is expected to employ 97 million people. 

In this rapidly growing space, having the right skills can make all the difference. As an aspiring AI engineer, your toolbox must be as dynamic and innovative as the field. At the top of this list is what programming language you use. In this post, we’ll highlight the top five AI programming languages that are absolute must-learns in 2024 for anyone aiming to make their mark in the field of AI. 

1. Python

Python stands at the forefront of AI programming thanks to its simplicity and flexibility. It’s a high-level, interpreted language, making it ideal for rapid development and testing, which is a key feature in the iterative process of AI projects. 

The real strength of Python in AI comes from its extensive libraries and frameworks like TensorFlow, PyTorch, and Keras, which offer pre-built functions and classes that significantly speed up the development of complex algorithms.

Developed by Google, TensorFlow excels in numerical computation, which is vital for deep learning. With its dynamic computational graph capability, PyTorch allows more flexibility in designing deep learning models, making it popular in research and new AI developments. Finally, as a high-level neural network library, Keras provides an easy entry point for beginners in AI due to its user-friendly interface.

Python’s vast community also means a wealth of tutorials, forums, and third-party resources, making troubleshooting and learning more accessible. Another good solution for practicing is to try out different tools and complete simple tasks, such as automating the boring aspects of your workflow, like generating documents using Python and creating an effective web crawler. Programming libraries like Pandas and NumPy simplify the process when it comes to data handling and manipulation, which is essential in pre-processing data for AI models.

2. Java

Java’s object-oriented approach makes it a strong candidate for AI projects, especially in large-scale, enterprise-level applications. Its platform independence is a major plus since it allows AI systems developed in Java to be easily transferred across different environments without the need for recompilation.

One of Java’s notable strengths in AI is in the growing field of natural language processing and neural networks. Programming libraries like Deeplearning4j offer robust tools for creating complex neural network structures. Java’s strong memory management and high-performance garbage collection mechanisms ensure efficient handling of the large data sets typical in AI.

Java’s widespread use in enterprise environments means integrating AI into existing systems is often smoother. Its well-established ecosystem and strong community support are invaluable, especially when dealing with legacy systems in large corporations.

With its robust and versatile capabilities, Java has been effectively utilized in a wide range of AI projects, showcasing its adaptability and efficiency in various domains. For instance, the open-source Java framework Apache Jena is pivotal in building semantic web and linked data applications. It offers an API for handling RDF graphs, which is essential for complex data structures. 

3. R

R is a powerhouse for statistical analysis and data visualization, making it a top choice for AI projects involving heavy data manipulation and analysis. It’s particularly suited for projects primarily focusing on the data itself, such as predictive modeling and statistical computing.

R’s comprehensive package ecosystem, with tools like ggplot2 for data visualization and dplyr for data manipulation, streamlines the data analysis process. This capability is crucial in AI for understanding data patterns and anomalies, which directly influence the quality of AI models.

As an AI programming language, R is utilized in various sectors covering various industries. In research and academics, it’s used for statistical computations, machine learning, and analyzing large data sets. IT companies employ R for business intelligence, data handling, and machine learning products. 

Furthermore, in the finance sector, it’s used for risk analysis, credit scoring, and financial modeling. E-commerce companies leverage R for improving user experience, cross-product selling, and targeted advertising.

While R might not have the same speed as Python or Java, its advanced statistical capabilities make it indispensable for AI projects where statistical rigor is paramount. The language’s ability to handle large datasets and perform complex statistical operations with ease makes it a favorite among data scientists who are leveraging AI in their research.

4. Prolog

Prolog, which stands for “Programming in Logic,” is a language designed for AI’s more specific needs, particularly in symbolic reasoning, problem-solving, and pattern matching. Unlike imperative languages that follow a sequence of commands, Prolog is declarative, focusing on the relationship between facts and rules.

In the field of AI, Prolog is extensively used for developing expert systems, natural language processing, and proving theorems. Its strength lies in its pattern matching and automatic backtracking features, which are useful in developing complex AI algorithms that involve rule-based logical queries. Prolog’s use also extends to the broader Semantic Web, information retrieval from large databases, and the creation of expert system shells for specialized problem-solving.

Although Prolog might not be as widely used as other AI programming languages like Python or Java, its unique approach to problem-solving and AI development makes it a valuable skill for engineers working on specific AI tasks, like developing chatbots or rule-based systems.

5. C++

C++ is known for its high execution speed and control over system resources, making it ideal for developing AI applications where performance is a critical component. Its ability to run close to the system hardware allows for more efficient processing of AI algorithms, which is essential in real-time applications and systems with limited resources.

AI engineers use C++ in machine learning and neural network development, especially when latency is a key concern. For instance, the efficient matrix operations in TensorFlow are powered by C++, taking advantage of its speed and lower-level operations. This underscores C++’s role in providing the backbone for high-performance computing in AI.

C++ is also commonly used in game development and simulation, areas increasingly adopting AI for more realistic and complex environments. The language’s capability to handle complex calculations at high speeds makes it a go-to for engineers working on AI in gaming and simulation.

Why These 5 AI Programming Languages Are Essential

Proficiency in AI programming languages is just one facet of becoming an accomplished AI engineer. In today’s cloud-centric world, you must also be well-versed in cloud services and infrastructure management. This process includes understanding how to compare the pricing and features of major cloud providers that are out there today.

Whether developing sophisticated machine learning models, integrating AI into enterprise systems, or pushing the boundaries of AI in gaming and real-time applications, these languages are the building blocks of a successful future career in AI and related fields.

Since you’re here…
Curious about a career in data science? Experiment with our free data science learning path, or join our Data Science Bootcamp, where you’ll get your tuition back if you don’t land a job after graduating. We’re confident because our courses work – check out our student success stories to get inspired.