By Kalevi Rantanen
Predictions of innovations, based on scientific and technical expertise, are more reliable than people usually think. Using nine screens, patterns of evolution and other TRIZ tools they can be further improved.
TRIZ, journalism, predictions, futurology, technology studies, technology research
Technology journalism compels writers to realize a mission impossible – to foresee things to come. Technology stories have a special feature: the most important things happen in the future. Readers want to get answers to journalists' questions in another tense: not what happened, where, why, how, and when, but what will happen after ten, twenty or thirty years. I wrote about 150 science and technology stories from 2002 to 2006 – most of the articles are "news from the future."
To forecast not only what happens, but also when it happens seems to be a yet more impossible task. There are literally billions of possibilities to combine materials, production technologies and components. If we add the time, the number of alternative technologies that are possible in theory gets still bigger.
Fortunately, experience has shown that it is possible to make predictions that are accurate "enough." At least one of ten carefully made predictions will be realized, even if we "subtract" innovations that happened but were not predicted (like personal computers). [4, 5, 8]
Japanese researchers evaluated the realization time for predictions. From innovations forecasted in 1971 for 1986-1990 – 20 percent happened; from innovations forecasted for 1996-2000, 9 percent happened.  . If we assume that the number of unforeseen innovations is the same as predicted innovations, the time of innovation can be predicted by an accuracy of five percent looking ahead thirty years.
One example of earlier timelines was provided by British author and inventor Sir Arthur C. Clarke's in a list with 10 predictions for 1985–2030. The list included, for example, a return to the Moon in 1990, commercial fusion power in 2000 and manned light to Mars in 2005. Clarke made one prediction that can be interpreted as realized – a wrist telephone. We can accept that wrist telephones, forecasted for 1990, are the same as mobile phones.
There is time for more of Clarke's predictions to come to pass – space cities in 2010, Mars base 2020 and "manned exploration of the solar system" in 2030.
But even if the mobile telephone remains the single forecast that hits the mark, Clarke's list as a whole is still rather good. The success of mobile phones with related technologies will more than compensate the failure of all other predictions.
Of course it would not be bad to get a bit more accurate predictions. Predictions can be improved and enriched using TRIZ tools. Nine screens (or windows) is a thinking tool developed by Genrich Altshuller and his colleagues [1, 2]. Nine screens – with patterns of evolution – give visionaries two more levels (micro and macro) from which to make predictions as shown in Tables 1 and 2.
|Table 1: Predictions Using One Timeline|
|Past (1960-1970)||Present (2000-2010)||Future (2040-2050)|
|System level||Robot hand|
Ex: Unimation (1962)
|Robot playing soccer|
Ex: The official goal of the RoboCup
|Table 2: Predictions on Three Levels with Nine Screens|
|Past (1960-1970)||Present (2000-2010)||Future (2040-2050)|
|Macro-level||Wire communication||Wireless communication||Smart network|
|System level||Robotic hand||Walking robot||Robot playing soccer|
Studying the "windows" one can see new problems, contradictions and solutions. For example, the robot sometimes should be big to work and it should be small, to move in difficult environments. We can predict that in the future robots will split in parts and then reassemble themselves.
Let's consider another example – discussions about bicycles are usually limited to the vehicle itself. A conventional upright bicycle is improved by the change to a recumbent bicycle. A transformable bicycle can be upright in the city and recumbent in the country. Looking at the environment of the bicycle at the macro-level, there appear additional possibilities – there are roads, the cyclist and the cyclist's clothing, for example. One problem is that there is little room for bicycling in modern cities. One solution? Build tubes for bicycles. Tubes may be expensive. Another solution is the evolution of comfortable all-weather bicycling suits. What about the micro-level? Bicycles, cyclists, roads and the environment will contain more and smarter chips and sensors, networked with each other, to help prevent congestion and collisions.
|Table 3: A Look at Bicycle Timelines at Three Levels|
|Past (1960-1970)||Present (2000-2010)||Future (2040-2050)||Another Future (2040-2050)|
|Macro-level||Tubes for bicycles||All-weather biking suit|
|System level||Conventional up-right bicycles||Recumbent bicyles||Transformable bicycles||Transformable bicycle|
|Micro-level||Chips in bicycle||Chips everywhere|
Another example of nine windows prediction can be found looking at satellite technology. Satellites as individual devices are getting better and smarter, but looking at micro- and macro-levels we can monitor some weaknesses. Perhaps thousands of micro-satellites, networked with each other, will some day fly around the globe. Such a system will be less vulnerable than a single large satellite.
|Table 4: A Look at Satellite Timelines at Three Levels|
|Past (1957)||Present (2000-2010)||Future (2050-2060)|
|Macro-level||Satellite systems||"Smart" satellite network|
|System level||Sputnik||Advanced satellites||"Smart" satellite|
|Micro-level||Myriads of micro-satellites|
To test the reliability of the prediction tools, the following is a timeline that can be checked in coming decades – twenty innovations likely to come to pass from 2010 to 2060, scheduled in ten year increments. (The list is comparable with Clarke's list.) TRIZ tools used for a particular forecast are noted in parentheses. 13 of the following 20 predictions were made with TRIZ, the remaining seven are based on conventional knowledge.
In 2060, this list can be compared with Clarke's list and we will learn how much TRIZ can help improve predictions. You may disagree with some of the predictions on this list. I encourage you to publish your own timetables using this list as a template.
Kalevi Rantanen worked in Finnish youth organizations, primarily on problems of education, in the 1970s. From 1979-1985, he studied in the former USSR and earned his M.Sc in mechanical engineering and was introduced to TRIZ. Rantanen worked in Finnish industry until 1991, while also a TRIZ trainer. Since 1991, he has been an independent entrepreneur and has concentrated on science and technology journalism since 2002. Contact Kalevi Rantanen at kalevi.rantanen (at) kolumbus.fi.