When it comes to understanding computing processes, especially in today’s front end and backend development world, most of the times everything revolves heavily around analyzing the algorithmic architecture in tools, applications, or more complex pieces of software.
In fact, a thorough analysis of what concerns the algorithmic side of things within the computing processing industry has led to a common conclusion— algorithmic functions are moving with architectural rendering languages to build much more complex tools.
Let’s analyze some of these.
Algorithmic Retargeting in R and Python
The biggest Python application currently available for the mass market is the one related to front-end tools installed on enterprise sites.
This includes tools related to the web personalization industry, retargeting, remarketing, and Big Data manipulation, which are, in fact, a massive part of this statement.
The way these tools work is by restructuring a catalog onto specific user preferences.
This is done with the combination of Python features and R-rendering algorithms.
After this is done, R algorithms are set up to render automatically the data, via (generally) AngularJS-coded scripts.
In this particular case, R functions are simply acting as a processing functionality.
Which Rendering Languages are Used
The above-mentioned process (gathering via Python, processed in R, and then exported in JS) is pretty common in a variety of architecture and, depending on the usage, the only variable for what concerns which programming languages are used is related to the “export” side of the matter.
C#, on the other hand, is used when the tool (or software) is native and, therefore, the rendering langue used to print the pieces of information must be tailored onto the building architecture.
Why is this Considered AI?
Although for many, the matter could sound a bit dark and complicated, the combination of R algorithms to rendering languages (and computing power in general) could be aggregated within the AI sphere.
This is possible because, technically, those features (data gathering, processing, and printing) are related to AI as a whole.
Artificial pieces of intelligence in 2019 have moved, in fact, to this very matter: fast processing, personalization, and projections tailored onto Big Data, automatically gathered without any human input.
Futuristic projections of AI controlling our lives still live in science fiction and sometimes, given how they’re covered in many technology blogs/newspapers, these statements are extremely downgrading for an industry that is moving massively for what concerns both development and business awareness.