Why Python is (Still) a Top Programming Language for AI, ML and Even DL Applications

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Although it is described as a high-level dynamic programming language, Python is simply easier to use than other programming languages, enabling development using considerably less effort. According to a 2019 Stack Overflow Survey, the nearly 30-year old Python was still the fastest-growing major programming language, edging out Java, and is the most “wanted” language of AI developers.

Initially, Python was used to program simple tasks, write automation scrips, or rapidly prototype apps. Today however, it has risen to the status of first-class language. For example, Amazon’s 2018 launch of its DeepLens project to help developers learn machine-learning technique basics is heavily reliant on Python. Other companies that rely on Python include Industrial Light and Magic, Netflix, Reddit, Facebook, Instagram, Google, and Spotify.

Given the rise in Artificial Intelligence (AI) and its subsets Machine Learning (ML) and Deep Learning (DL), developers are harnessing the potential of Python for data science, data analysis, and more. They embrace its simplicity and ease of use, within their complex world.

Figure 1: Programming Example. (Source: MIKHAIL GRACHIKOV/Shutterstock.com)

Although ML enables machines to make decisions by inputting data, it is not appropriate for image recognition and object detection since this requires the implementation of high-dimensional data. Feature extraction is another major challenge, relaying to the machine the features it should look for to precisely predict an outcome.  Feature extraction is currently a manual process in Machine Learning. Deep Learning, in comparison, is primarily used to deal with high-dimensional data. It is based on the concept of Neural Networks and often is used in object detection and image processing. The Python-based Amazon DeepLens project, for example, uses neural networks to learn and make predictions through computer vision projects, tutorials, and hands-on exploration using physical devices. AWS DeepLens enables the running of deep learning models locally on a camera to both analyze and take action based on what it sees.

Python for data analysis and number crunching is ideal as the majority of libraries involved in machine learning and data science have a Python interface. Outside of data science, Python-based websites are handling thousands of requests each minute.

Free, easy, and open source, Python has made its way into some of the most complex technologies, and is used by industry giants, developers, data scientists, ML engineers, and beginners.

It simplifies complexities. For example, when number crunching, Python is adept at very fast, complex math. Python’s simple syntax can be used to implement such basic computations as the addition of two strings, all the way to building a ML model with its inherent complexities.

Users write less code. Python supports predefined packages, eliminating coding of a ton of algorithms. And for the code that is needed, understanding, changing, copying, and sharing code is easy. With Python there is an efficient exchange of algorithms, ideas, and tools between users.

Python offers an option to choose either OOP or scripting, and it runs on multiple platforms including Windows, MacOS, Linux, and Unix. Porting code from one platform to another is accomplished with such packages as PyInstaller.

When you use Python, you’re far from alone. Python has a vast number of communities, groups, and forums where programmers assist each other with solving issues. Many resources are available, for beginners and pros alike. There are also hundreds of prebuilt libraries to implement ML and DL algorithms. Installing packages usually takes just a single command.

A great choice of libraries is one of the main reasons Python is the most popular programming language used for AI. Python library modules contain base-level items, eliminating the need to code them each time. For example, NumPy helps manage multidimensional arrays efficiently and has a great random function. It also contains tools for integrating C/C++ and Fortran code, while Pandas helps handle two-dimensional data tables in Python. Although SQL also does this, Pandas is a lot easier to execute in code.

Matplotlib, although not the easiest to use, will save developers approximately six code blocks for basic line charts and scatter plots that can be used over and over again. The creation of charts, histograms, and plots for better data comprehension, effective presentation and visualization can be done very quickly using Matplotlib.

Scikit-Learn is the first library any Python programmer should use for AI /ML. It handles regression methods, classification methods, and clustering, model validation and model selection, and makes dimensionality reduction and feature extraction a snap. Moving to the more advanced, Scipy (not the Scipy stack) provides core mathematical methods for such complex machine-learning processes as integration, interpolation, and optimization. Scikit-Learn relies heavily on the Scipy Library, so move it to the top of the list.

Python is ideal for prototyping because it allows for writing and testing just a few lines of code without compiling or using Makefiles. It is high level, so low-level details like memory management aren’t a concern. There are many easy and well-documented Python modules. It runs on most platforms. The language is so popular, and users so plentiful, that there are likely several people within a group that can read someone’s code and cooperate.

There is no shortage of Python tools, including:

We’ve discussed Python’s many strengths, especially for number crunching in the fields of data science and scripting. Although the 30-year-old language has done extremely well competitively, it suffers in performance, app packaging, and project management. Python’s downsides include:

• It is a high-level language so that it is further away from hardware than a lower-level language, which makes it slower.
• It is not ideal for mobile development.
• It uses flexible data types, precluding its use for memory-intensive tasks.
• High memory consumption.
• Its database access layer is primitive. It is not a good choice for enterprises requiring smooth interaction of complex legacy data.
• Because Python is dynamically typed, it requires more testing and has errors that only show up at runtime.

Getting started with Python is quite easy, but still requires time. For example, one learning resource to join is PythonistaCafe, a peer-to-peer learning community. There are a plethora of exercises, events, and meetups specifically for Python.

Once you’ve made the decision to download Python, there are some open source projects to test out. Here is a short list:

• OpenCog is putting effort into creating the Artificial General Intelligence (AGI) with human capacities. It is used at the SingularityNET project and the Hanson Robotics project, delivering intelligence to Sophia, and other robots.
• Institute for Artificial Intelligence part of the Faculty of Computer Science at the University of Bremen, conducts research on AI and holds workshops and events that move the AI technologies forward. They are committed to education and to supporting existing AI-powered projects and companies.
• Zulip is dubbed the “world’s most productive team chat,” allowing for the processing of thousands of real-time messages daily. Zulip offers clear organization, asynchronous communication, and other great advantages that are useful for teams.
• Magenta is a Python library and research project. Its goal is to enable the creation of music and art using AI. It can create images, songs, and even generate drawings, enabling artists to explore new ways of creating.
• MailPile is an innovative email client focusing on safe and private communication. Its fast, has no ads, and comes with a powerful search function, privacy, and encryption.

The best way to learn Python is by doing. Performing exercises will only take you so far, so roll up your sleeves and dig in.

If you’re a developer, Python is easy enough to learn and it should be in your toolbox. Keep an open mind and try it. Study Python. Look under the hood and experience exactly why it is used by so many. It will help you become an even better developer.