This Fine Guidance Sensor test image was acquired in parallel with NIRCam imaging of the star HD147980 over a period of eight days at the beginning of May. This engineering image represents a total of 32 hours of exposure time at several overlapping pointings of the Guider 2 channel. The observations were not optimized for detection of faint objects, but nevertheless the image captures extremely faint objects and is, for now, the deepest image of the infrared sky. The unfiltered wavelength response of the guider, from 0.6 to 5 micrometers, helps provide this extreme sensitivity. The image is mono-chromatic and is displayed in false color with white-yellow-orange-red representing the progression from brightest to dimmest. The bright star (at 9.3 magnitude) on the right hand edge is 2MASS 16235798+2826079. There are only a handful of stars in this image – distinguished by their diffraction spikes. The rest of the objects are thousands of faint galaxies, some in the nearby universe, but many, many more in the distant universe. Credit: NASA, CSA, and FGS team.
Paradoxically, even though we produce more scientific output than ever before – each year, researchers around the world publish millions of academic papers – the pace of scientific discovery is slowing down.
There are several factors behind this general slow-down of scientific advancement, but the most important factor is the simple maturation of any field.
As time goes on fields of science become more mature and sophisticated. This is a good thing, as we take small threads of newfound knowledge and develop them into full-fledged theories of the workings of the universe. But this process ironically slows the pace of future discoveries in that same field.
This is because our questions are becoming more sophisticated, more targeted. In any field of science, the pace of discovery is quite rapid, as individual researchers are capable of making amazing breakthroughs with just their minds or a few simple laboratory experiments and observations. But once those easy questions are answered, all that are left are the hard ones; the problems that require collaborations of humans working together and pooling their resources, the ones that require massive investments in time or money, the ones that require intense effort, years of investigation, to chip away at some small corner of the overall problem.
For example, consider cosmology. A century ago, barely anybody was concerned with the nature and fate of the universe. Even after Hubble’s discovery of an expanding universe, cosmology was considered a niche subject. But its small set of practitioners were able to make astounding leaps, cementing the Big Bang’s place as the key theory of the history of the universe. Today, advancement in science is slowing, with teams of hundreds spending millions of dollars to develop a single survey.
Cosmology is not alone.
Imagine fields of science like a growing soap bubble. The volume of the bubble is knowledge we have already acquired, and the edge of the bubble represents the frontiers of that knowledge. At first the bubble is small, with both a small volume and small surface. When we learn new knowledge about the surface, we expand that surface area, and the volume correspondingly grows.
When the bubble is small, it doesn’t take much to radically increase its volume – even the work of one human is enough to double or triple our total human knowledge of a subject. But as the bubble expands, the volume becomes much bigger than its surface. New knowledge, pushing at the boundaries, only supplies proportionally less new information. Progress becomes harder and harder, and advancement slows down – sometimes grinding to a halt.
This isn’t necessarily a bad thing. Fields of science emerge, grow rapidly, and mature. We can still learn new things in any field, but this general tendency means that we shouldn’t expect rapid leaps and bounds. We just have to manage our expectations.
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