Introduction

Artificial intelligence (AI) systems are developed and operated using coding languages.

While FORTRAN, BASIC, and COBOL are all in use today and have a rich history, newer coding languages are emerging as better options for creating AI. Python and R are two languages well-known in the AI industry. Python's ease of use, adaptability, and extensive library ecosystem has helped it establish itself as the default programming language for AI. It is the best option for tasks involving data processing, analysis, and machine learning thanks to its simple syntax and wide range of libraries, including NumPy, Pandas, and Scikit-learn. Python can describe complicated AI algorithms clearly and intelligibly due to its readability, which speeds up research and makes it available to a broader spectrum of programmers. Furthermore, Python has a sizable and vibrant community that guarantees ongoing assistance, regular upgrades, and the availability of various AI-specific packages like TensorFlow and PyTorch(Zulunov & Soliev, 2023).

Another well-liked AI language, R, provides extraordinary benefits to quantitative computing and data presentation. It has many libraries like ggplot2 and dplyr that excel in interactive information analysis and is created primarily for data analysis. R is exceptionally well suited for applications like regression analysis, grouping, and time series evaluation because of its emphasis on statistical modelling and interactive nature. R is an excellent option for statisticians and academics working on AI projects due to its tight interaction with artificial intelligence packages like Caret and randomForest(Dalmasso et al., 2020). Additionally, thriving communities that actively contribute to their growth and upkeep benefit Python and R. With this capability, these languages are constantly updated with the most recent developments in AI, enabling them to adapt to evolving demands. In comparison, older languages' usability could be improved by a lack of assistance from the community and out-of-date libraries.

Conclusion

In summary, the advancement of AI has been transformed by the rise of languages like Python and R. Compared to more ancient languages like FORTRAN, BASIC, or COBOL, they are better suited for AI tasks due to their straightforwardness, extensive libraries, and vibrant communities. R's emphasis on statistical computation makes it an excellent choice for analyzing information and simulation(Baniecki et al., 2021).