May 1, 2013
Everyone is concerned with progress. During my career in industrial research, I was required to write monthly and annual progress reports on my research. I always thought that these were just filed away, somewhere, and they were never read. The important milestone for some administrator was putting a check mark after my name on some sheet to signify that a report had been submitted.
Eventually, such reports migrated to a database application, and from that point I was even more convinced that whatever I typed into the text boxes was never read. I wrote about this in a previous article (Untouched by Human Hands, January 25, 2011).
I had the occasion to work with a respected scientist who had emigrated to our laboratory from another company (colloquially called "Big Blue," but best left unnamed). His former employer also required his writing an annual report, and the form for this contained the question, "What did you accomplish last year?" As a true experimentalist, he decided to test the system; so, he wrote, "Absolutely nothing." He never got any feedback on that answer.
At some level, progress is important, since it translates into money. Using the old analogy, if you're still making buggy whips, your company will lose business to those that progressed into the supply of accessories for newer vehicles. That's why the better companies still have R&D laboratories.
As individual companies progress, they carry their industry with them. One of the best equalizers, as Silicon Valley has found (and has been called to task for illegally trying to stop), is the roving employee who quits one company and brings his general experience to another without compromising any trade secrets. Experience applies to industrial processes, also, giving rise to experience curve effects.
In general, every time the cumulative volume of a good or service doubles, cost falls by a constant percentage. This cost advantage generally arises from the fact that it takes less time to perform a task after the task has been done many times. One example of this was presented in a 1936 study by T.P. Wright that showed that the required labor to produce an aircraft decreased by 10-15 percent when the quantity produced doubled.[1-2]
Another consequence of experience is an improvement in the quality or performance of an item. George Dyson, in his book, "Turing’s Cathedral," gives the following example from vacuum tube days. The simplest active device available for logic operation at that time was the triode; so, the 6J6, a dual triode in a miniature glass envelope (see figure), was an obvious choice for early computers. What was fortunate was the fact that this tube was produced in large quantities during World War II, and it was found to be more reliable than other tubes especially crafted for high reliability.
The best known experience effect is Moore's Law, conceived by Intel co-founder, Gordon E. Moore, in 1965. Moore's original prediction was a doubling of the number of transistors in the highest density integrated circuits every year. The law is now showing a two-year doubling period, but it's often been called a self-fulfilling prophesy, since integrated circuit manufacturers are using the law as their technology roadmap. In 1965, there were barely a thousand transistors on a chip; now, there's billions.
|Data on the 6J6A miniature dual-triode vacuum tube, an improved version of the 6J6. (Scan from author's copy of the RCA Receiving Tube Manual, Radio Corporation of America, November, 1966).|
Moore's Law applies generally to many industrial processes, with the exponent for the rate of improvement depending on the technology. The change rate varies greatly over a range of technologies.
So, we have two basic laws that describe technology trends - Wright's Law, that cost decreases as a function of cumulative production; and, Moore's law, that technologies improve exponentially with time (see the figure below for examples). A research team of members from the Santa Fe Institute, Oxford University, St. John's College (Santa Fe, New Mexico) and the Massachusetts Institute of Technology, has analyzed the compatibility of these two laws in an open access, peer-reviewed article in PLoS One.[5-7]
As can be expected, such an analysis is a lot of work. The study authors assembled data for cost and production for sixty-two industries, including beer, the aluminum from which beer cans are made, and advanced electronic products such as computers, solar cells, aircraft and automobiles. Source material was only considered relevant when there were at least a decade's worth of data for calculating metrics for both production rate and some measure of improvement.
They used these data to check the validity of industrial models proposed in the scientific literature, what they term, "hindcasting," and they found the most valid model to be one proposed by Devendra Sahal in 1979 (see figure). The study authors conclude that this result can aid in "assessments of candidate technologies and policies for climate change mitigation."[PLoS] Says study coauthor, Jessika Trancik of MIT,
The line specifies a two year doubling.
My first personal computers used the 8080 and Z80 microprocessors, which are among the highlighted chips.
(Graph by W.G. Simon (simplified), via Wikimedia Commons.)
"Information technologies improve the fastest... but you also see the sustained exponential improvement in many energy technologies. Photovoltaics improve very quickly. One of our main interests is in examining the data to gain insight into how we can accelerate the improvement of technology."
This research was funded by the National Science Foundation, with additional funding provided by The Boeing Company.
- T. P. Wright. "Factors Affecting the Cost of Airplanes", Journal of the Aeronautical Sciences (Institute of the Aeronautical Sciences), Vol. 3, No. 4 (1936), pp. 122-128.
- Robert J. Banis, Learning Curves (A summary of Wright's 1936 paper), University of Missouri-St. Louis Web Site.
- George Dyson, "Turing's Cathedral: The Origins of the Digital Universe," Vintage (December 11, 2012), 464 pages. ISBN-13: 978-1400075997 (via Amazon).
- Gwern Branwen, "Slowing Moore's Law: Why You Might Want To and How You Would Do It," March 16, 2012 - 03 April 3, 2013.
- David L. Chandler, "How to predict the progress of technology," MIT Press Release, March 6, 2013.
- Béla Nagy, J. Doyne Farmer, Quan M. Bui and Jessika E. Trancik, "Statistical Basis for Predicting Technological Progress," PLoS ONE, vol. 8, no. 2 (February 28, 2013), Document No. e52669, doi:10.1371/journal.pone.0052669.
- Béla Nagy, J. Doyne Farmer, Jessika E. Trancik and Quan Minh Bui, "Testing Laws of Technological Progress," (An early PDF draft of the above paper), Santa Fe Institute Web Site, September 2, 2010.
- Devendra Sahal, "A theory of progress functions," AIIE Transactions, vol. 11, no. 1 (1979), pp. 23–29.
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Linked Keywords: Idea of Progress; progress; industrial research; research; milestone; business administration; administrator; check mark; database application; scientist; emigrated; laboratory; Big Blue; experimentalist; feedback; analogy; buggy whip; research and development; R&D; lobbying; lobbyist; Finland; Finnish; horse-pulling contest; Wikimedia Commons; Silicon Valley; trade secret; industrial process; experience curve effect; good; service; employment; labor; aircraft; quality; performance; George Dyson; vacuum tube; boolean algebra; logic operation; triode; miniature tube; miniature glass envelope; computer; World War II; RCA; Radio Corporation of America; Moore's Law; Intel; Gordon E. Moore; transistor; very-large-scale integration; highest density integrated circuits; self-fulfilling prophesy; semiconductor device fabrication; integrated circuit manufacturer; technology roadmap; 8080; Z80; microprocessor; technology; Santa Fe Institute; University of Oxford; Oxford University; St. John's College (Santa Fe, New Mexico); Massachusetts Institute of Technology; open-access journal; peer-review; PLoS One; dynamic random-access memory; DRAM; Creative Commons Attribution License; beer; aluminum; beverage can; beer can; electronics; electronic product; solar cell; automobile; computer simulation; model; scientific literature; Jessika Trancik; National Science Foundation; The Boeing Company.
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