The researchers working at the MIT “Computer Science and Artificial Intelligence Laboratory” or abbreviated simply as CSAIL are all set to make human intuition out of the analysis of big data equation by enabling computers to choose from the set of features that are put into use in order to identify patterns in the data that may be considered to be predictive. This is dubbed as the “Data Science Machine” and as things have progressed so far the software prototype has managed to beat 615 of 908 competing teams vying for the same ability across no less than three competitions of data science.
Big Data may be considered as a complex and huge ecosystem that combines innovative processes from fields as diverse as storage, data analysis, curation, networking as well as search in addition to other functions and processes. As things stand much of analysis of big data is already algorithmic and automated but at the end of the day it is business users and data scientists who are needed in order to determine the particular dataset and analysis features which are required for visualization in the end and take action on the communicated data.
To put it simply at the end of the whole process humans are needed in order to make choices about data point combinations to chart out the relevant information.
The Data Science Machine is intended to naturally complement human intelligence and to make the most of the Big Data that is available for us waiting to be used.
The analysis of Big Data and Engineering of Features
As mentioned earlier actionable information lies at the hands of the big data scientist who is writing the code for analysis. It is this code that guides the analysis of the big data engine. In essence the advancement made by the MIT researchers is that not only does it serve to provide answers to questions regarding the data but also suggests additional questions accordingly.
This may be put into varied uses like to estimate the capacity of wind farms to generate power or making predictions about students who are likely to drop out of online courses.
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