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Tech Talk February 21, 2020  |  4 Min Read

Artificial Intelligence and Machine Learning have ushered a new era in technology development. From a narrative in fictional works to the disruption force in the business landscape, both have come a long way and are showing the potential to change the human-machine interface forever.

As per research by PWC, AI has the potential to contribute USD 15.7 trillion to the global economy by 2030, pushing global GDP higher by 14%

For the unversed, Artificial Intelligence aims to create intelligent software/machines that can simulate advanced human behavior like thinking, reasoning, predicting, judging, and more. On the other hand, Machine Learning is just a facet of AI where software/machines would learn from the data provided to them, just like humans, instead of being programmed.

For programming AI and ML software, we need a language that can offer versatility against the complexity inherent in the AI projects. Most of the Artificial Intelligence developers prefer Python, the second choice being JAVA with close competition by R, Prolog, and Lisp. Other languages being used in AI development, although not predominantly, are Scala, Julia, and C++.

Usually, it is the preference of developer, available tech-stack, and project requirement that dictates the choice of language for AI and ML development. But, Python wins hands down. We will now dig deep into why choose the Python programming language for artificial intelligence and ML Development.

Range of Available Libraries

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With a rich tech-stack of libraries and frameworks, Python has probably the most extensively tested and well-structured environment for AI development. This ensures a significant reduction in SDLC because developers don’t have to code from scratch for basic functionalities.

Several Python libraries fit the bill for AI and ML development as they can access, handle, and transform complex data in large volumes. A few examples of such libraries are TensorFlow, Theano, Keras, Scikit-learn, NumPy, PyTorch, and Pandas. The growing popularity of AI-focused libraries has inspired Python development companies to venture into Data Science and Machine Learning.

Soft Learning Curve

Python is easier to learn due to its clear syntax and readability. Being a high-level general-purpose programming language that is also simple and intuitive, it is easily picked up by data-scientists who are not professional developers.

Usually, AI projects require substantial collaboration among developers, database architects, and data scientists. When the whole team has some level of proficiency with one common language, building a prototype becomes easier. Therefore, most of the companies are using python for machine learning and artificial intelligence development.

As per PYPL Popularity of Programming Language index by GitHub, Python is ranked as the number one programming language in Feb 2020.

Flexible Programming Style

It is difficult to find a language as flexible as Python. Developers can choose either scripting or Object Oriented Programming, and can combine Python with other languages seamlessly. Instead of being restricted to a particular function, Python can be applied for linking different data structures together or back-end programming. Because the majority of code would be verified in the IDE itself, it is easier to implement algorithms.

Platform Agnostic

Python is a free, open-source, and redistributable programming language. It can be used on Windows, Macintosh, Linux, Solaris, and more. Both these factors ensure that the modules and even the complete program is largely portable and doesn’t require much modification to run on a system different from the one on which it was developed. Due to this, an Artificial Intelligence development company saves on time and cost of platform specific programming, accelerating SDLC.

Multiple Visualization Options

AI and ML projects require processing and visualization of huge volumes of complex data. Therefore, it is critical for any language to be a candidate for AI and ML development to offer data visualization.

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Python libraries like Matplotlib, Plotly, Seaborn, ggplot, and Altair offer interactive data visualization tools, that make data exhibits vivid and easy to interpret. Since most of the AI and ML apps demand a visual dashboard, data science firms are keenly looking to hire Python developers.

Strong Community Support

Being an open-source programming language, Python has extensive documentation easily available on the internet. There are communities, discussion forums, and blogs where developers have entered valuable information in elaborative threads. Established support community along with flexible programming style, soft learning curve, and range of available libraries ensures that even enterprise-level Artificial Intelligence Python applications enter the market faster when compared to other languages such as Scala or Julia.

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Python with its 164,852 active repositories is only third to JavaScript and Java at GitHub. This rich project documentation denotes the solid community attached to Python, involved in development, testing, and troubleshooting. With readily available collaborators present across the globe, SDLC is accelerated.

Convenient Testing and Debugging

With the widespread use of AI in day-to-day software, it is important to consider the cost of AI development services. The newly developed AI software needs to be tested against massive data with considerable noise. Moreover, tests have to be thorough to remove human bias if any, because with such volume of data an error if left undetected is bound to be amplified. Readily available talent and established community support make it relatively inexpensive and easier to test Python projects.

Summing Up

AI and ML are emerging technologies and yet to achieve a mature tech-stack like mobile app development. Unless other top programming languages for machine learning and AI emerge more versatile, flexible, economic, and with an established peer community; Python will continue to stay the primary coding language for the major chunk of AI and ML development projects.

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