Three Reasons Python Is The AI Lingua Franca
Earlier this year, Python celebrated its 30th anniversary as a programming language. For any software language to last three decades and maintain relevance to developers of all stripes is something special.
Much of what made Python a spectacular achievement when Guido van Rossum released version 0.9.0 in 1991 informs its success today. Python has always been simple and consistent, offering readable code and an entry ramp for developers learning a new language. These aspects of the language, along with its “batteries included” philosophy, paved the way for amateurs and professionals alike to push the boundaries of open source software programming over the last 30 years.
Recently, this has meant integration of artificial intelligence (AI) and machine learning (ML). Python’s initial release came before AI was a broadly accessible business tool, but quite a lot has changed since 1991. The 1996 chess match between IBM’s Deep Blue and Grand Champion Gary Kasparov demonstrated that AI was capable of complex algorithmic problem solving at levels well above even the most skilled human beings. Thereafter, the business of AI began to boom. The market for AI/ML in software development is growing at a rapid pace as AI streamlines industries as diverse as insurance and higher education. According to a Fortune Business Insights report from July 2020, the market size of the global AI market was valued at about $27 billion in 2019 and is projected to reach more than $250 billion by 2027.
Developers should expect AI/ML projects to comprise a greater and greater amount of their overall work in the coming years, and the time is now to learn the best language for artificial intelligence: Python. What makes Python so well-suited to AI and ML? Here are three reasons why Python can be the most important tool in your AI toolbox.
The primary reason Python outstrips other languages goes all the way back to its founding. The “batteries included” nature of the language — meaning Python comes with a large library of useful modules and all the parts required for full usability — makes Python a perfect outlet to spin up a solution in complex use cases.
Python has been called the second-best langu
age for everything. Fittingly, Python overtook Java in the TIOBE index of programming languages last November and became the second-most popular programming language for developers. For any individual task, there might be a better language than Python, but for enterprise companies starting to integrate AI and ML into their codebase there is nothing more flexible than Python.
The two most popular AI requests I encounter when meeting with prospective clients are robotic process automation (RPA) and leveraging data to improve modeling and forecasting. These projects require more collaboration from more developers on my team than app development without AI or ML. If developers aren’t using a broadly popular, flexible language they are severely limiting the makeup of their development team. Because Python code is simple and sleek, I can pull in colleagues to contribute to a complex AI project whenever necessary. They can quickly get themselves up to speed.
The last thing a company wants is to begin integrating AI into their software and be forced to abandon the project. If for some reason a project had to be put on pause, though, an experienced Python developer could pick up where their counterparts left off and polish off an unfinished project with a new team. All of this contributes to a more secure development lifecycle and a faster time-to-market turnaround for the customer.
Leveraging insights from Big Data within a company is a primary use case for AI and ML. The sheer volume of data collected by enterprises has accelerated so quickly that a report by Seagate and IDC notes more data is created every hour than in the average year in the 1990s. But that data doesn’t do any good for anyone unless they uncover the insights within. AI and ML supercharge data analysis by uncovering patterns and trends within the data that can be used by humans to make better business decisions.
The same IDC report notes that 68% of data available to businesses goes unleveraged. That poses a simple question: how does a business leverage the data it collects? One way is to visualize the data and track it over time in charts and graphs. As human beings we are generally quite poor at recognizing relationships within large data sets without the assistance of graphs. Thus, data visualization is a key aspect of any successful use of Big Data.
Python has a wide range of mature tools that create data visualizations. These run the gamut from custom interactive dashboards to funnel visualizations that track the customer journey. AI can help human analysts conduct complex analysis on data sets with multiple variables, but visualization is critical for analysts and executives to better understand the story the AI has uncovered. Different data-centric projects will require different solutions, but Python libraries like Airflow and Pandas offer myriad suggestions for the integration and cleaning of various types of data. This process, known as “Extract, Transform, Load” or ETL is key to prevent mismanagement of data that can ruin a project. “Garbage in, garbage out” goes the saying.
Given the unceasing acceleration of data creation and the demand for AI and ML to assist companies in interpreting that data, any software language must support scaling. Because of its relative simplicity, Python code is generally capable of handling massive scale.
It is the odd tool appreciated by amateurs and professionals alike. A standard Python codebase supports Instagram, the sixth-largest site on the Internet with more than 6 billion monthly visits. Open-source software like Python powers more of the business world than you might think. Gartner research estimates that 90% of enterprises are now using open source software.
Python is capable of scaling to handle increasingly large amounts of data and users, and with its popularity booming, it can also scale to meet more demand for software developers. The Python community is warm and welcoming. Veterans of the language contribute hours of their time to code libraries that serve as the guide for countless successful projects involving AI and ML. A software language that meets all technical standards and supports a community that perpetuates knowledge of the language itself will thrive in the long run.
Lingua franca is a term to describe the trade language used between people whose native languages are different. It’s the lowest common denominator of language. As AI and ML drive more demand for data-centric software development, more developers will be needed in the trade to meet that demand. No matter what language they’ve used before, no matter their level of expertise as a programmer, Python is accessible to them. Python is both a language itself and a bridge between languages.
For 30 years Python has persisted as a force within the development world. It is the preeminent open-source language for developers and businesses, and while it wasn’t specifically designed to handle the demands of AI and ML, it does so with aplomb. Whatever the needs of the next generation of developers, I expect Python to serve them just as well.
About the author: Calvin Hendryx-Parker is the co-founder and CTO of Six Feet Up, a software company that helps organizations build apps faster, innovate with AI, simplify Big Data, and leverage Cloud technology. In 2019, Calvin was named an AWS Community Hero. Additionally, he is the co-founder of IndyPy, the largest Python meetup in Indiana, and the founder of IndyAWS, Indiana’s fastest growing cloud meetup.