The core idea behind Wolfram Alpha is to collect and curate every bit of objective data; implement all known models, methods, and algorithms; to act as comprehensive source for definitive answers to factual queries based on expert-level knowledge. It serves as a knowledge engine, is an intellectual endeavor with participation from experts across different fields. Beyond Mathematica, what holds the key to it has been A New Kind of Science (NKS), related to algorithms.
Here is a quick grasping of what and how this innovative search engine tries to do for both data and images what’s common for text, to put it in the words of its inventor Dr. Stephen Wolfram:
- Most data generated or collected with considerable effort seldom gets any kind of serious analysis. But that’s not surprising, since doing data science, wrangling code and data has always been tough.
- The tools and methods behind it have gradually evolved, over time. With the release of Wolfram|Alpha Pro, the way data is approached is going to change considerably. The key idea behind it is automation.
- The concept is: One should simply be able to take one’s data in whichever raw form that it arrives, and push it into it. And Wolfram|Alpha Pro should automatically give a well-organized report based on do a whole bunch of analysis about your data. And if that data is not too large, this should take place in a matter of seconds.
- There are all kinds of data sets around: measurements, business reports, and personal analytics being fed into Wolfram|Alpha Pro, showing visualizations and producing analyses that reveal many useful things and insights about the data.
Explaining how it carries vast algorithms and heuristics to try and deduce what the data it’s fed represents. This quickly puts it on track to perceive the kind of visualizations and analyses that it should do. Dr. Stephen Wolfram notes:
“There are many challenges, and we’re still at any early stage in addressing all of them. But with the whole Wolfram|Alpha technology stack, as well as with the underlying Mathematica language, we were able to start from a very strong foundation. And in the course of building Wolfram|Alpha Pro we’ve invented all kinds of new methods.”
For any given data sets that have been input, it usually has a large number of analyses that it can run. But the challenge is to prune, combine as well as organize the results for emphasizing what’s important, and to make them easy to assimilate as possible—adding appropriate textual summaries – rigorous albeit understandable to even non-experts.
Wolfram|Alpha Pro usually gives a summary as its ‘default report’, and have all sorts of buttons/ pulldowns, which allow drill-down to many details or variations. While working with data, it generates the kinds of intricate plots, tables, analyses etc. The key thing is that it gives you something by automatically generating a whole report with carefully chosen entries to understand what’s in your data at a glance.
In the future, it will have plenty other additional analyses, and new types of data with special attributes and characteristics to handle.