A Introduction
“The End of Theory: The Data Deluge Makes the Scientific Method Obsolete” claimed Wired Magazine Editor Chris Anderson,1 and he went on to explain the Big Data Mindset that is bringing about the end.
We are now in the Petabyte (1015 bytes) Age, with the growing mass of data of all kinds creating bigger and bigger headaches for data collection, storage, and management2 as petabytes are buried under exabytes (1018) and zetta-bytes (1021) of data. For Anderson, the technological hurdles, though truly daunting, are not the most arresting features of the Age. He doubts that we'll be able break the mass of information into chunks small enough to be meaningful and argues that already so much exists that we cannot conceptualize and process it. According to Anderson, we need to “give up the tether of data as something that can be visualized in its totality” Hypotheses and theories will be passe, no longer required or even possible. We will have to develop a new “mindset” and “view data mathematically first and establish a context for it later.”One vision is that the future of science will be shaped by the rise of a Big Data Mindset. A diametrically opposed vision is that Big Data plus artificial intelligence (AI) will enable machines to generate and test hypotheses about highly complex data without human help. In the second scenario, logic and the quest for understanding remain paramount scientific objectives, but “Robot Scientists”3 gradually assume greater autonomy. In this chapter, I want try for a snapshot of the Big Data phenomenon as it promises—or threatens—to revolutionize scientific thinking and practice. An image of anything moving as fast as the Big Data Revolution is, is like a picture taken from a speeding train, blurred and outdated by the time you look at it. The best we can hope for is a sense of its likely impact on hypothesis-based scientific reasoning.
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