The Impact of AI Programming Languages in the World of AI

Erhan Arslan
9 min readOct 30, 2023

Although artificial intelligence has existed in the technology world for a long time, its real impact was not felt until recent years. The main reason for this is that the supporting technology components have not reached a sufficiently advanced stage. However, as technology has made significant advances, tech world has been also witnessing remarkable advances in the field of AI. In recent years, AI has begun to have a meaningful impact on the technology field. As artificial intelligence continues to develop rapidly, the choice of programming language is vital to benefit from its capabilities to the maximum extent. In this study, four basic artificial intelligence programming languages will be digged in: Python, R, Java and C++. These languages each have their own distinct characteristics, areas of application, strengths, and weaknesses in the field of AI.

AI Programming Languages

When choosing programming languages, programming languages with different features were preferred. Because, every projects are needed in different areas with different priorities. Therefore, comparing programming languages with different features and levels will be more useful to better understand the picture.

Python

Python is a big player in AI and ML. When a tech-worker thinks about programming in AI and ML, the first thing that comes to mind for many technology workers is Python. It can be said that it is especially loved by software developers because it is easy to use and learn, flexible. In addition, the most first useful libraries were published in Python. So, this made it more popular.

R

R is a type of programming language that is good at doing statistics, working with data, and creating graphs, and building predictive models. And, it is not needed a good programming experience. 4 Recently, in addition to IT employees, many other professions have started to produce outputs using R.

Java

Java is a versatile computer language that is also used in the field of artificial intelligence. Applications are developed with Java can run on many devices. It has very powerful solutions that provide solutions in a wide range of areas such as smartphones, IoT, Drone technology, Enterprise Backend Applications etc.

C++

C++ is a powerful and fast programming language. Like the C language, it does not need a middleware to run. This why C++ has been choosen when the speed is top priority.That’s why many hardware providers(Apple,Microsoft) develop their OS in C or C++.

Critical Evaluation of Each Programming Language

Key Features;

Python: Python is characterized by its readability and simplicity. It supports different programming styles, and its strong community contributes to its rapid development.

R: R’s main features lie in its powerful statistical and data analysis capabilities. It excels in data manipulation, visualization, and statistical modeling.

Java: Java is known for its performance, scalability, multithreading, platform independent, portable, robust and distributed. It is a good fit for real-time applications, Enterprise Solutions and IoT.

C++: C++ is known for its efficiency and speed, which is critical in high-performance computing scenarios.

Advantages

Python:Python is a versatile language that excels in artificial intelligence development. Readable and easy to understand, it is especially advantageous for rapid prototyping and software 5 development. It is a dynamic language like Javascript. For instance, a developer does not need to care datatype or exact code blocks like Java or C++. To define and Integer value, “a=7” is enough for decleration. Python has a rich ecosystem of libraries, like opencv,TensorFlow and PyTorch, which are widely used in every field of AI. This comprehensive library support simplifies the implementation of complex AI algorithms and reduces development time. Moreover, Python’s large and active community provides continuous improvements, making it an excellent choice for AI projects of all sizes. Also, Python is based on C language. So, it can be created embedded Apps for small gadgets. This makes enable to create AI solutions for small devices.

R: In the field of artificial intelligence, the R programming language is known for its strong skills in statistics and data visuals. With some useful tools like ggplot2, dplyr, and caret, it’s pretty handy for tasks like digging into data and building models. While it’s not the first choice for deep learning or computer vision, it’s really good at rapid data analysis, which is a big part of AI research. Like Java, R can be easily integrated with other systems, so it is useful for artificial intelligence projects that focus on data analysis and visuals and need to integrate with different systems. Therefore, in recent years, the professions who are from different backgrounds than IT have been using the R language in their data-related tasks.

Java:Java is well-known for being fast and platform independency. Also, its powerful multi-threading capabilities enable efficient use of CPU resources, which is crucial for massive AI calculations. This is especially useful for AI tasks that need quick responses, like in the IoT. It’s also popular for making AI-powered apps for Android or platform independence solutions, like drone tech. Besides, Java is famous for building strong, safe, and large-complex solutions that help businesses. This makes Java a good choice for AI in different areas.

C++:C++ is a strong choice in the field of AI because it is super fast and efficient; This is crucial for AI where needed fast calculations. Although, it does not offer libraries as strong as Python. But, it 6 offers AI libraries that will meet the needs in areas where it is strong to help AI developers create advanced ML solutions. This combination of technical features and top-rated performance makes C++ ideal for AI projects that require fast, computationally intensive processes.

Application Areas

Python: it is used in many areas of AI because of strong support libraries. It can be seen lots of solutions in field of ML, DL, NLP, CV, Reinforcement Learning and Data Science. AI applications developed with Python can be seen in every business field.

R: R shines in specific application areas like statistical analysis, and data visualization. It’s commonly used in tasks like data mining, creating models, and developing machine learning algorithms. Solutions written in R can be seen in area of epidemiology, finance, and genetics.

Java: Java has a wide range of applications. It plays a crucial role for the platform independent applications like IoT,smart devices. For instance, smart homes and industrial automation, healthcare solutions, robotics, remote monitoring and control of various devices and systems. Additionally, Java’s ability to handle real-time processes with minimal delay makes it main player in AI scenarios that require rapid decision-making, as seen in the use of autonomous drones. These drones rely on Java to quickly adapt to dynamic environments, providing rapid responses in situations.

C++: C++ finds extensive application in the field of artificial intelligence (AI), particularly in domains that demand efficiency and speed. It is widely employed in high-performance AI areas. C++ is been used in field of Image and Video Processing, Natural Language Processing, RPA, Game Development.

Limitations

Python: Python can be a bit slower than low-level languages like C++. So, for AI tasks where it is needed super-fast computations, it might not be the best choice.

R: It can slow down when working with big data or complex calculations, which can affect real-time response time. An also, there is no sufficent libraries for every scope of AI. So, it might not be the top choice for all AI areas.

Java: Java has some limitations in the field of AI. One limitation is that it uses a lot of memory, problem for AI when resources are limited. Another limitation is low number of AI libraries. Also, Java can be tough for beginners.

C++: C++ is a strong language, but it has some limits in the AI. One limitation is that it can be a bit complex and hard to learn, which might be hard for beginners. Also, C++ usually needs more lines of code, which can make development process slower. So, it’s not the best choice for the projects that don’t require speed.

Comparative Analysis

We’ve conducted a thorough examination of the main characteristics, strengths, weaknesses, application areas. To make it easier to understand, you’ll find the information in Table 1.

Table 1: Comparative Analysis of Programming Languages

Current Trends and Developments in AI Programming Languages

In the world of AI, it’s crucial to keep up with the latest trends and developments to stay competitive. Additionally, Python is being more and more dominant Programming language from day to day in field of AI. In last 5 years, it have come from third most-used language to first position. Here are some important trends which may have big impact on technology in coming years.

AI on Small Devices

From day to day, the usage of smart devices around are increasing. When the trend goes to the smart and small devices, AI languages and libraries that work well on these small gadgets will be needed. So, Python published versions MicroPython and TensorFlow Lite that are suitable for small devices. This may make Phyton as a good competitor of Java in field of Platform Independence.

Quantum Computing

Quantum computers are becoming popular, and languages like Python and C++ are adapting for quantum programming. An example is Qiskit, a quantum computing framework that uses Python.

AutoML and No-Code AI

Now, there are tools that make building AI models easier, even for people who aren’t experts. A serious amount of professions who are not from tech background try to develop solutions in AI. It seems that this topic will be more popular in closer dates.

Federated Learning

This is a way for machines to learn together without sharing your data. Python’s PySyft is used for this.In a Gartner document, it says most of the AI solutions will not require Data from outside in 2025. So, Sharing Data between the AI Solutions may be a hot topic in coming years.

Explainable AI (XAI)

When the topic is Technology, most of the humans afraid of evolutions in field of technology. So, tech-world has to care more about ethics and understanding AI. So, there are libraries like LIME and 10 SHAP in Python that help explain how AI models work. It seems that this topic will be a popular area in coming years, especially for goverments and corporate companies.

Integration of Programming Languages

If it is very large data that has to be processed, evaluate and make decisions in a very short time, things can get a little difficult. In these cases, a single programming language may be insufficient. Although high-level languages such as Python have versatile and powerful libraries, they do not affect the runtime speed. That’s why technology companies have started combining multiple programming languages into the same solution in an AI project. For example, while they do AI modeling with Python, they code performance-critical parts with C++.

Conclusion

In conclusion, when the topic is about AI programming languages and software, it’s important because of technology trends, being eco-friendly, sustainability, finding developers, and being flexible. Python is a good choice. It works well with new tech trends like AI on small devices and quantum computing. It’s also efficient and can help make eco-friendly AI. A lot of people use Python, so it’s easy to find developers for your AI projects. Plus, it can change and adapt to new things in AI, which makes it a safe choice for long-term projects.

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