Having recently written about scientific civilization through the lens of comments by Jacob Bronowski and Susanne Langer, I have been doing more research on the idea of scientific civilization for further posts in the series.
Having recently written about scientific civilization through the lens of comments by Jacob Bronowski and Susanne Langer, I have been doing more research on the idea of scientific civilization for further posts in the series. This has brought additional material to my attention, but it has also raised questions. Why focus on scientific civilization? Does scientific civilization have a special place in the future of civilization, or ought it to have a special place in the future of civilization?
In particular, what relationship does scientific civilization have to other forms of post-agricultural civilization, or what we might also call modern civilization? One can find “industrial civilization,” “technological civilization,” and “scientific civilization” used synonymously, which raises the question as to whether these ideas are subtly distinct or not. Is there a reason to distinguish between industrial civilization, technological civilization, and scientific civilization, or should we regard them as different names for the same thing?
One way to distinguish these three formations of modernity, and yet show them in relation to each other, is by way of what I call the STEM cycle, which is a tightly-coupled loop of scientific research, technological applications, and industrial engineering which characterizes civilization today. A STEM cycle has long been present in civilization, but in the past the STEM cycle was loosely-coupled, often with generations passing between each stage in the cycle. The combined effect of the scientific revolution and the industrial revolution served to transform the loosely-coupled STEM cycle of agricultural civilizations (which make intensive use of specialized agricultural technologies, though often in a highly traditional context that discourages innovation) into the tightly-coupled STEM cycle of modern civilization.
There are, of course, many technologies that came about not because of science, but through mere tinkering. It seems that James Watt’s steam engine was the iterated result of the tinkering of many men over a long period of time, so that the central exhibit of the industrial revolution seems to defy my characterization of technology. If one wanted to take the time to carefully select one’s examples, one could assemble a history of technology that almost entirely excluded the contribution of science. I concede this point, but at the same time, I could write a history of technology that was entirely based upon technologies that emerged as a direct result of the dispassionate pursuit of scientific knowledge.
Selective histories aside, all of the most difficult and demanding technologies — nuclear energy, spacecraft, computing, DNA therapies in medicine, and so on — are the result of extensive scientific research, including pure science (Rutherford was doing pure science, but his pure science ultimately made nuclear technologies possible) performed with little or no interest in practical application. This also appears that it will hold good in the future, as the role of tinkering decreases and the role of scientific rigor in the advancement of technology increases. There are thresholds beyond which tinkering cannot pass.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Understand how data changes in a fast growing company makes working with data challenging. In the last article, we looked at how users view data and the challenges they face while using data.
Intro to Data Engineering for Data Scientists: An overview of data infrastructure which is frequently asked during interviews
🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...
Understanding how users view data and their pain points when using data. In this article, I would like to share some of the things that I have learnt while managing terabytes of data in a fintech company.