<> NYTimes, March 11th 2007
Published in The Edge
By Kevin Kelly
“A particularly fruitful way to look at the history of science is to study how science itself has changed over time, with an eye to what that trajectory might suggest about the future. Kelly chronicled a sequence of new recursive devices in science…
2000 BC — First text indexes
200 BC — Cataloged library (at Alexandria)
1000 AD — Collaborative encyclopedia
1590 — Controlled experiment (Sir Francis Bacon)
1600 — Laboratory
1609 — Telescopes and microscopes
1650 — Society of experts
1665 — Repeatability (Robert Boyle)
1665 — Scholarly journals
1675 — Peer review
1687 — Hypothesis/prediction (Isaac Newton)
1920 — Falsifiability (Karl Popper)
1926 — Randomized design (Ronald Fisher)
1937 — Controlled placebo
1946 — Computer simulation
1950 — Double blind experiment
1962 — Study of scientific method (Thomas Kuhn)
Projecting forward, Kelly had five things to say about the next 100 years in science…
1) There will be more change in the next 50 years of science than in the last 400 years.
2) This will be a century of biology. It is the domain with the most scientists, the most new results, the most economic value, the most ethical importance, and the most to learn.
3) Computers will keep leading to new ways of science. Information is growing by 66% per year while physical production grows by only 7% per year. The data volume is growing to such levels of “zillionics” that we can expect science to compile vast combinatorial libraries, to run combinatorial sweeps through possibility space (as Stephen Wolfram has done with cellular automata), and to run multiple competing hypotheses in a matrix. Deep realtime simulations and hypothesis search will drive data collection in the real world.
4) New ways of knowing will emerge. “Wikiscience” is leading to perpetually refined papers with a thousand authors. Distributed instrumentation and experiment, thanks to miniscule transaction cost, will yield smart-mob, hive-mind science operating “fast, cheap, & out of control.” Negative results will have positive value (there is already a “Journal of Negative Results in Biomedicine”). Triple-blind experiments will emerge through massive non-invasive statistical data collection— no one, not the subjects or the experimenters, will realize an experiment was going on until later. (In the Q&A, one questioner predicted the coming of the zero-author paper, generated wholly by computers.)
5) Science will create new levels of meaning. The Internet already is made of one quintillion transistors, a trillion links, a million emails per second, 20 exabytes of memory. It is approaching the level of the human brain and is doubling every year, while the brain is not. It is all becoming effectively one machine. And we are the machine”
From American Scientist
The instructions that accompany Sudoku often reassure the number-shy solver that “No mathematics is required.” What this really means is that no arithmetic is required. You don’t have to add up columns of figures; you don’t even have to count. As a matter of fact, the symbols in the grid need not be numbers at all; letters or colors or fruits would do as well. In this sense it’s true that solving the puzzle is not a test of skill in arithmetic. On the other hand, if we look into Sudoku a little more deeply, we may well find some mathematical ideas lurking in the background.
La pregunta para el 2006 en Edge:
“The history of science is replete with discoveries that were considered socially, morally, or emotionally dangerous in their time; the Copernican and Darwinian revolutions are the most obvious. What is your dangerous idea? An idea you think about (not necessarily one you originated) that is dangerous not because it is assumed to be false, but because it might be true”
Excelente articulo de Albert-László Barabási aparecido en revista Science (38) 2005
Ver articulo: “Network Theory – the Emergence of the Creative Enterprise“
Ver tambien el articulo: “
Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance“
By Matt Nesvisky, NBER
Innovative thinkers are innovating later than they used to. While conventional wisdom holds that creative thinkers do their best work when they are young, a study by NBER researcher Benjamin Jones shows that over the past century the average age at which individuals produce notable inventions and ideas has increased steadily.
In Age and Great Invention , Jones considers data on Nobel Prize winners in Physics, Chemistry, Medicine, and Economics over the past 100 years, and on outstanding technological innovations over the same period. For comparative purposes, Jones also considers the ages of track and field record-setters and ball players who have received Most Valuable Player awards.
The data on the innovators reveal three initial characteristics. First, there is large variation in age: 42 percent of innovations came about when their creators were in their 30s, while 40 percent occurred when the inventors were in their 40s, and 14 percent appeared when the inventors were over 50. Second, there were no great achievements produced by innovators before the age of 19, and only 7 percent were produced by innovators at or before the age of 26 (Einstein’s age when he performed his prize winning work). Third, the age distributions for the Nobel Prize winners and the technologists are nearly identical.
The most striking finding, however, is that the age distribution shifts over time, with the mean age of great achievement rising by five or six years per century. This parallels another study showing a similar upward trend in the age of persons receiving their first patents (NBER Working Paper No. 11360). Jones finds the trends among great innovators are significant and robust, even after controlling for nationality and field of study. Indeed, these controls strengthen the age trend, causing it to rise to about eight years over the course of the twentieth century. This suggests a compositional shift in great innovation towards fields and countries that favor the young.
One possible explanation for this age shift is a decline in the productivity of younger innovators in favor of older innovators. It may well be that the younger innovators are devoting themselves to an increasing amount of education and training. Or, it may be that the productivity of older innovators is increasing in relative terms simply because innovators are living longer. If we accept that raw ability declines over the life cycle while experience increases, then the shift in the distribution may indicate the rising importance of experience over ability. Alternatively, improved health care may spell increased ability and effort at later ages.
However, Jones urges caution in interpreting these distributional shifts. The shifts, he suggests, may reflect a simple demographic effect. If the population of innovators is getting older, then the older innovators will be more likely to produce substantial innovations even if the relationship between age and innovative potential is fixed. That is to say, the greater the ratio of 50-year-old innovators to 25-year-old innovators, the more likely the Nobel Prize-winning work or the groundbreaking technological development is to come from one of the 50-year-olds. Such demographic effects may be important, because life expectancy and the average age of the population have risen substantially throughout the twentieth century.
By subjecting these various hypotheses to econometric analysis, Jones concludes that the upward trend for productive innovators does not merely reflect the aging population, but in fact is a result of a substantial decline in the innovative output of younger individuals. Meanwhile, there appears to be no relative increase in the innovation potential of those beyond middle age. Other things equal, the less time innovators spend successfully innovating, the smaller will be their lifetime output. In fact, estimates point to a 30 percent decline in life-cycle innovation potential over the twentieth century.
Jones notes that, unlike athletes, who do not require increased training demands over time, innovators appear to spend increasingly significant portions of their early years in education – a kind of human capital acquisition that might well explain the age trends in his study. Because the rules and requirements of their fields of endeavor remain fixed, athletes are not obliged to increase their human capital; accordingly, the data show no distributional shift in the ages of top athletes over the years. But thinkers must increasingly invest in acquiring intellectual capital, and the accumulation of knowledge – the rising distance to the frontier – can explain increased educational attainment.
Jones notes that economists have not focused much on the human capital investments of innovators. Because innovators customarily devote their youngest and perhaps brightest years to acquiring their education, understanding the tradeoffs at the beginning of the life-cycle may be of primary importance for understanding the ultimate output of these individuals – and for understanding why great innovation is steadily declining among younger thinkers.