Python and Artificial Intelligence – Growing Together

Technologies come about and evolve based on needs and opportunities that arise and often they are related to other technologies. As innovation sparks change, that change creates new obstacles and thus opportunities for other technological evolutions. We saw the mobile revolution set the stage for Cloud and now the Cloud is facilitating IoT. A similar, though less noticed, tech affinity is between Artificial Intelligence (AI) or Machine Learning (ML) and the Python scripting language.

Scripting languages existed in some form for nearly 30 years before web development spurred further growth adoption. Scripting language users have remained relatively steady as a proportion of the developer population, growing from nearly 57% (7.9M) in 2007, to 59% of the population (14.2M) in 2019. Among scripting languages, Python is now one of the fastest growing and that’s largely due to it’s ties to data, analytics and AI/ML.

The chart here shows the number of developers worldwide who are working on AI and ML algorithms as well as the much larger number who are using AI and ML in their apps, going back to 2013. Notice the change in the pattern that begins in about 2016 when the number of developers working with AI begins to increase dramatically. This same change is noticeable at about that time in a chart of the adoption of Python.

Most developers use multiple languages, so to measure language use we ask developers to tell us what percent of the time they use a particular language. This chart shows the percent of developers who use scripting languages who use Python any percent of the time.  This chart compares three of the most popular scripting languages and, as we can see, while Javascript is the most used, Python is currently the fastest growing.

But Python is not just growing overall with casual users, the number of developers who use Python as their primary language; i.e., more than 50% of the time, has more than doubled since 2016. Python’s use for reporting, scientific, and mathematical purposes sets the stage for Python’s numerous libraries, frameworks, and tools with data science and machine learning. Notably, Theano is one example of where Python intersects not only with machine learning, but also with math library acceleration for CPU and GPGPU. More recently, PyTorch’s release (2016) and Tensorflow’s adoption of Keras (2017) are other AI-related changes that have created opportunities for developers to leverage Python. For purposes of AI and machine learning development, Intel has even included Python in its optimization technologies, including Intel Distribution for Python, which is supported by Intel Parallel Studio, and a Python-specific distribution of Intel’s Data Analytics Acceleration Library.

So, as more developers create and integrate AI and ML capabilities into their apps, we can expect to see more and more use of Python.

Data Source:
Global Development Survey, © Evans Data Corp 2021
Worldwide Developer Population and Demographics Study, © Evans Data Corp, 2020