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Fortran is one of the oldest programming languages; it was specifically designed for scientific and engineering applications back in the 1950s. Despite being a legacy language, Fortran is still uniquely relevant in high-performance computing, especially within the scientific computing domain. Yet in the artificial intelligence and machine learning world, people often wonder if Fortran is relevant at all. This article discusses the strengths of Fortran, its position in scientific computing and AI, and whether it can continue to keep pace with the newer languages.
Fortran is the old terminology for Formula Translation as used in scientific computing. The Fortran language was extensively used for several decades in most of the fields of physics, climate modeling, and computational chemistry. Actually, it has been the principal language for nearly two decades to handle high-performance applications in large-scale numerical calculations.
Fortran was the perfect selection for scientific simulations and numerical models because it emphasizes mathematical computation, giving the most optimized compilers of the time. Most of the present scientific libraries and algorithms still use Fortran, which have had enough time to test them and optimize them. Even, the world's fastest computers still run Fortran codes because of their computing powers.
One of the strong points of Fortran is that it is highly efficient for floating-point calculations and array-based operations, which are the essentials of scientific computing. So, its design favors performance over other considerations, so one can handle massive data sets with impressive speed compared to more general-purpose languages like Python or Java.
Fortran does support parallel computing, though this is pretty much critical for simulating across processors. Of course, High-performance computing is one of the primary areas where the simulation of complex systems often calls for heavy-duty application of computers.
Fortran remains unparalleled concerning scalability as well as efficiency and remains specifically only for math model-based tasks, for example, differential equations or all computationally-intensive calculations as common to most applications in the sciences.
While Fortran does great work in traditional scientific computing, this is not much of a stake when it comes to AI and machine learning. Typically, the projects of AI and machine learning are based more on modern languages like Python since the library ecosystems are developed, including TensorFlow, PyTorch, and scikit-learn. All of these provide high flexibility while making it easy to use-it's an advantage that might be more immediate for the developers and researchers who proceed to develop algorithms related to AI.
Although Fortran cannot contribute much to AI, it can still be applied to domains where algorithms are targeted for their high performance. Among areas in AI, some large-scale simulations might exploit high-performance tasks with the help of efficiency delivered by Fortran codes together with other languages, like Python or C++.
Applications involving heavy numerical computation such as climate modeling, fluid dynamics, or even physics simulation can still exploit Fortran's robust performance. At such times, Fortran can be considered an aid to AI technology since Fortran can absorb the computational intensity that gives rise to complex models.
Despite its merits, Fortran has several drawbacks in the current programming world. The most relevant of these is the relatively small community with few modern libraries that fit the needs of AI and machine learning applications. Fortran does not compare to Python in this regard, which boasts a very rich ecosystem with strong support for many frameworks related to AI.
This also makes Fortran not so easy a language for new learners, especially when compared to the more readable syntax of Python or JavaScript. Due to this reason, it has become less popular among the new generation of programmers, who prefer modern languages that give faster development cycles.
For their language, scientific computing and artificial intelligence have been adapted into a code called Python, C++, Julia, and Python became extremely popular not only because the library can be extensive, but this programming also has very easy-to-write syntax. The adoption became immediate for data scientists and artificial intelligence researchers because Julia also has the power to emulate Fortran-like performance features but modern functionalities which offer a bright new promise.
Fortran remains one important tool in scientific computing specifically with those functions involving large-scale numerical simulation and high-performance computations. Therefore, even though Fortran was never the first choice with programs involving AI and Machine learning, areas where it really found usefulness is an area that incidentally ties up with AI:.
Even such a new modern language as Python or Julia, although enjoying very lively development and having acquired the characteristic of user friendliness, will certainly restrict the activity of Fortran in the AI direction exclusively to a niche field. And with the forever changing nature of AI, certain trends would appear, implemented using both performing and bearing modern facilities for programmers. Fortran is destined to play the role of scientific application's language, taking a place outside of the mainstream of developments in AI.
