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Technological Forecasting & Social Change xxx (xxxx) xxx–xxx
Contents lists available at ScienceDirect
Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore
Is the force awakening? Ulrich A.K. Betz Merck KGaA, Frankfurter Straße 250, Postcode F127/003, D-64293 Darmstadt, Germany
Keywords: Science Technology Future foresight Humanity Innovation Progress
Scientific and technological progress over the centuries was very strong in some fields, much weaker in others and even virtually absent in a few so far. For the future, there are a series of areas where new breakthroughs can be expected to occur. However, there is some evidence that such breakthroughs seem to be increasingly difficult to achieve. The paper reviews these discussions, maps scientific and technological progress over the centuries and presents new ideas on how to foster and accelerate scientific and technological advancement.
Since Johannes Kepler with “Somnium” (Kepler, 1634) wrote the world's first science fiction novel (encompassing a travel to the moon), science fiction has remained a source of inspiration for scientists and the public, as convincingly demonstrated for example with the Star Wars movie “The Force Awakens” which broke box-office records all over the world (http://www.boxofficemojo.com/alltime/). Experiencing the impressive visualization of advanced technologies leaves us wondering whether mankind will ever be able to achieve such technological progress. Beyond science fiction, there are predictions made in the frame of serious future foresight activities. Throughout the entire 20th century, the year 2000 often served as a time on which such predictions for advancement were projected, as for example nicely summarized by Davis (2012). Retrospectively, these predictions now seem over-optimistic and have largely not been fulfilled, progress unfortunately having been much slower than initially anticipated (Humphrey, 1967; Kahn and Wiener, 1967). Predictions for example included: Elimination of bacterial and viral diseases, large-scale ocean farming, weather control, establishment of space colonies (e.g. the ringshaped “Taurus” was envisioned as a colony that could house 10,000 people for the purpose of mining ore from the moon) etc. If you would transfer someone from 1967 to 2017 and tell him this is the future, most likely he would be very disappointed. Looking out of the window, the world today looks quite similar to how it looked in the 60s. At first sight, it seems that not much has changed except the design of cars and the smartphones in our hands. How can it be that technological progress overall was much slower than predicted? In fact, efforts to look back on what has been achieved so far and what can be expected from the future have triggered an intense debate on whether technological progress is overall accelerating as usually claimed or whether it might even have decelerated in recent decades. Key examples of proponents for the optimistic position are Vinge
(1993), Kurzweil (2006) and Erik Brynjolfsson/Andrew McAfee (Brynjolfsson and McAfee, 2011, 2014), mostly based on already achieved and projected future breakthroughs in IT and communication technologies - enabling cognitive computing, big data analysis and artificial intelligence which are supposed to catalyze progress in diverse areas (Chen and Butte, 2015). In fact, the rise of computers has become publicly apparent with key events such as the victory of Deep Blue over the chess world champion Gary Kasparov (Weber, 1997) under official tournament conditions in 1997 or the winning of the quiz jeopardy! by the IBM Watson system in 2011 (Markoff, 2011). Machine learning algorithms have already reached an impressive level of sophistication and Google's Deep Q-Network (DQN) has been able to master a diverse range of Atari 2600 games superior to a professional human game tester (Mnih et al., 2015). The fact that Google Deep Mind was able to beat Lee Sedol, one of the world's top players in 2016, was another key milestone, as this game due to its high number of variations is exponentially more complex than chess and requires a certain degree of intuition (Gibney, 2016). The list of achievements was just recently topped by Deep Stack, an algorithm for imperfect-information settings, which was able to defeat with statistical significance professional poker players (Moravcik et al., 2017). Even the occurrence of an event called technological singularity is projected, the generation of artificial intelligence capable of recursive self-improvement whereby smart machines would design successive generations of increasingly powerful machines, creating intelligence far exceeding human intellectual capability (Kurzweil, 2006; Vinge, 1993), basically the last invention humanity would ever have to make. Kurzweil has more generally summarized this as the law of accelerating returns and predicted that paradigm shifts have and will continue to become increasingly common, leading to “technological change so rapid and profound it represents a rupture in the fabric of human history” (Kurzweil, 2011).
E-mail address: [email protected] http://dx.doi.org/10.1016/j.techfore.2017.08.006 Received 24 July 2017; Accepted 15 August 2017 0040-1625/ © 2017 The Author. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Please cite this article as: Betz, U.A., Technological Forecasting & Social Change (2017), http://dx.doi.org/10.1016/j.techfore.2017.08.006
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children entering the labor market today (Chetty et al., 2017), largely due to lower GDP growth rates and greater inequality in the distribution of growth (Goldin and Katz, 2008). It has been argued that this slowdown would be the best available evidence that the third industrial revolution (mainly digital, post 1972) was a mere shadow of the second industrial revolution (1875–1972) (Gordon, 2012). Another interesting phenomenon signaling a decrease in dynamism is that the share of startup firms declined from 2001 to 2011, a trend which has continued in recent years (OECD, 2015). The aging of firms at the global frontier was suggested by the OECD to foreshadow a slowdown in the arrival of radical innovations and productivity growth (Andrews et al., 2015). Most indices agree that in most of the world's regions an excess of funds is chasing too few growth investment opportunities and even companies considered to be innovation pioneers are sitting on huge cash piles rather than investing them (Riley, 2015). Against this view it was convincingly argued that the digitalization and IT revolution produces great benefits that are not reflected in an immediate GDP increase (e.g. free access to knowledge and digital assets) such that the GDP may no longer be the right measure of progress (The Economist, 2016). In addition, rather than indicating a slow-down of science and technology, the lack of GDP growth could originate from the fact that humanity on earth is inevitably facing the limits of growth, as first published in the famous Club of Rome report in 1972 (Meadows et al., 1972). Despite a lot of criticism that the first book has received, recent analysis has demonstrated that the essential points of the report are correct (Meadows and Randers, 2004). It could be shown that the “business-as-usual scenario” described in the Limits to Growth report in 1972 unfortunately aligns well with historical data so far. Going on unchanged, this would finally result in collapse of the global economy and environment around 2020 with signs of decline becoming visible earlier (Turner, 2014, Turner and Alexander, 2014). Beyond GDP, looking at the healthcare sector as an example to determine whether technological progress is accelerating or decelerating, it is evident that the most important sources of higher life expectancy in the 20th century were achieved in the first half of that century, when life expectancy rose at twice the rate of the last half (Cutler and Miller, 2005). Just recently, for the first time since 1993, Americans' life expectancy has even decreased (Xu et al., 2016). The fact that life expectancy in developing countries is increasing to levels seen in the Western countries is an argument of improved development and technology distribution but not of top technology advancement. Looking specifically at achieved breakthroughs in science and technology, Dong et al. have found that the last science and technology productivity surge begins around the middle of the 16th century, peaks at the early 20th century but declines since then (Dong et al., 2016). Likewise, the Pentagon physicist Jonathan Huebner (Hübner, 2005) using a list of important technological discovery landmarks, has calculated the global rate of innovation vs. population and has found that the curve peaked around 1870 and has decreased since then. Interestingly, Ray Kurzweil using a similar methodology has reached the opposite conclusion of Huebner: namely that technological progress has been accelerating throughout all of Earth's history, and he predicted that it will continue to do so (Kurzweil, 2006). It is important to note that in such assessments, the level of technology that was already reached in the past and lost later should not be underestimated, such as e.g. indicated in recent discoveries around the ancient Greek Antikythera mechanism which is an analog computer designed by Greek scientists in 205 BCE (Marchant, 2006). A similar Archimedes sphere has been described by Cicero (Marchant, 2015). Another striking example is nanotechnology operated already by the ancient Romans. A historic glass chalice, known as the Lycurgus Cup, appears green when lit from the front and red when lit from behind, a characteristic that puzzled scientists for decades. The mystery wasn't solved until 1990 when researchers discovered that the Roman artisans were nanotechnology pioneers that had impregnated the glass with particles of silver and gold, ground down until they were as small as 50 nm in diameter (Merali, 2013).
He even arrived at the prediction that already the generation of humans living today will live forever (Goldman, 2013). Amazingly, just recently a series of discoveries have brought a breakthrough in anti-aging research (Castellano et al., 2017). On the other end of the spectrum, however, several proponents have argued that technological progress in the past decades has decelerated. This is, for example, claimed by Michael Mandel (Mandell, 2000), Peter Thiel with Gary Kasparow (Thiel and Kasparow, 2011), David Graeber (Thiel and Graeber, 2014), Cowen, 2011a, b, 2016, Gordon, 2012, 2016, Hanlon, 2014, Fredrick Erixon with Bjorn Weigel (Erixon and Weigel, 2016a, b) and summarized by Rotman (2016), Graeber (2012), Karlgaard (2012), Pfeiffer (2016), several articles in The Economist (Economist, 2013a, 2013b; The Economist, 2015), Buchanan, 2015 and Fry, 2016 but heavily contradicted by others such as Gates (2014) or Mokyr (2014) just to mention a few. And the argument for a slowdown at first sight really seems ridiculous, as conventional wisdom is that the world is moving faster and faster and that the pace of innovation is constantly accelerating. Since 2007, when the first i-phone was released, we experience a digital surge that has had a visible impact on the way we live our lives. Driverless cars are arriving on our roads, drones will soon fly over our heads delivering goods, advanced surgery can be done by robots and modern medicine will soon have made significant impact on cancer (Erixon and Weigel, 2016a, b). For the first time in history, more people die today from eating too much than from eating too little; more people die from old age than from infectious diseases; and more people commit suicide than are killed by soldiers, terrorists and criminals combined (Harari, 2015). The argument of an innovation slowdown is mainly based on the thought that the 1870 to 1970 period had experienced a technological revolution - unique in human history in its tremendous impact on daily lives (e.g. electricity, cars, antibiotics, telephone etc.). According to the proponents, such an impact on our lives has not been achieved by the digital technologies since then. Gordon describes in his book, “The rise and fall of American growth” (Gordon, 2016), the century between 1870 and 1970 as a special century, a period of unprecedented economic growth and improvements in health and standard of living. He stated that this economic revolution was unique in human history and by 1970 lives had totally changed in the developed world. The introduction of fundamentally new classes of technology seems rarer now than it was in the past. Information technology has certainly transformed the present day, but railways, telephony, automobiles and the chemical and steel industries each brought transformations as big as anything IT has wrought so far (The Economist, 2015). Indeed, it seems that the genuine progress in IT from the 1970s up to the 2000s has masked the relative stagnation of energy, transportation, space, materials, agriculture and medicine, at least when the advancement factors described above are taken as key performance indicators. Our ability to do basic things such as protect ourselves from earthquakes and hurricanes, to travel and to extend our lifespans is barely increasing. Many technologies that are considered modern are actually already quite old, Augustin Mouchot wrote the first book on solar energy in 1869, John Ericson designed an engine powered by the sun a few years later, Robert Anderson designed the first electric car in 1831. Looking at the pure numbers in a non-biased way it has to be noted that the GDP growth has in fact slowed down in western countries (e.g. 2.82% 1920–1970 and 1.62% 1970–2014 for the U.S.) (Rotman, 2016). Productivity growth actually had slowed down in many OECD countries already before the financial crisis, which only amplified the phenomenon (OECD, 2015). Since the start of the financial crisis none of the Western economies have so far returned to the pre-crises trend of GDP growth (Erixon and Weigel, 2016a, b). The real median wage earned by men in the United States is lower today than it was in 1969 and median household income adjusted for inflation is now lower than it was in 1999 (Cowen, 2016). Rates of absolute upward income mobility, children's prospects of earning more than their parents in the U.S. have fallen sharply from ~90% for children born in 1940 to ~50% for 2
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to the period between the two world wars and 8 to 9% up to 2012 (Bornmann and Mutz, 2014). Not just in relation to scientific papers but also with patents, the past three decades have seen dramatic growth and filings almost tripled between 1985 and 2014. Around 2.68 million patent applications were filed worldwide in 2014 (http://www.wipo. int/pressroom/en/articles/2015/article_0016.html). In addition, the world is producing more PhDs than ever before, China has now overtaken the U.S. to become the world's biggest producer of PhDs (Cyranoski et al., 2011) and it is now estimated there are 7.8 million researchers worldwide (UNESCO, 2015). Last but not least, R & D funding seems not to be the problem either. Worldwide R & D expenditures totaled an estimated $1.4 trillion (current PPP dollars) in 2011. The corresponding estimate for 5 years earlier in 2006 is $1.1 trillion. Ten years earlier, in 2001, it was $750 billion. By these figures, growth in total global R & D has been rapid, averaging 6.4% annually over the 5-year period and 6.7% annually over the 10-year period. Adjusted for inflation in the US total spending on R & D has almost tripled since 1965. Strikingly, however, also here it seems that recently we have moved into a decline. The OECD report published December 2016 (OECD Science, 2016) stated that government-financed R & D has declined (in real purchasing power parity terms) by 2.4% since 2010, when it accounted for 31% of total OECD R & D expenditures, falling to 27% by 2014. In its December report the OECD raised serious concerns about declining public funding for R & D and innovation raising concerns that the situation could deteriorate further with aging societies. In many OECD countries public research funding in 2015 was below the level determined for 2000 (e.g. Australia, Finland, France, UK, Italy, Spain and the U.S.). The report also criticizes a shift from funding of basic research to R & D tax incentives for corporations, leading to less breakthrough and more incremental innovation. For the U.S., the situation has been described in a recent MIT analysis (MIT, 2016). In summary, the question of whether there is an acceleration or a decline in the speed of science and technology advancement depends very much on the specific area under investigation. While there is certainly room for optimism on some breakthrough technology areas such as digitalization and robotics, there are also some signs of a decline in GDP growth and generation of completely new breakthroughs. While the reasons for this are not entirely clear, it is safe to state that some technology areas are progressing much faster than others. If an extraterrestrial civilization would look at us humans from outer space, they would probably be surprised on our progress in some areas and disappointed about our progress in others. Similarly, it could be shown that for the past 100 years information technology has outpaced energy technology in progress rates by a factor of 1.5 to 7 with neither type of technology showing strong saturation effects (Koh and Magee, 2006). Nevertheless, 80-year exponential improvement in information storage by mechanical and electronic means had only recently (~ 1990) surpassed printing on paper as to information storage per unit cost (Koh and Magee, 2008). Let's have a look at what humanity achieved so far in the various technology areas in boosting its natural capabilities.
Let's look deeper in digitalization and IT as the poster child that undoubtedly has progressed tremendously. Indeed, your cell phone has more computing power than all of NASA did when it sent a man to the moon (Kaku, 2011). Gordon E. Moore, the co-founder of Intel and Fairchild Semiconductor, described in a seminal paper in 1965 (Moore, 1965) a doubling every year in the number of components per integrated circuit (with a revised forecast to doubling every two years (Moore, 1975)). This is called Moore's law, which in fact has guaranteed an ever-increasing computation power over the years and has enabled the IT revolution. Even in this textbook example of accelerating innovation, it seems that the speed of advancement is decreasing and Moore's law seems to come to an end (Waltrop, 2016). In addition, the annual reduction in the price of ICT equipment relative to its performance has peaked at minus 15% in 1998, but its rate of decline has steadily diminished until then (Gordon, 2014). If the viewpoint that the speed of innovation is decelerating in the past decades is true, then the question, of course, comes up “how could this be”? In their recent book “The innovation Illusion: Why so little is created by so many working so hard” Erixon and Weigel (2016a, b) exhibit the view that a crisis of capitalism is the underlying reason for a decline in the speed of progress, listing as the four apocalyptic horsemen of decline: gray capitalism (aging populations with a lack of willingness to invest in risky long-term innovation), excessive corporate managerialism (play it safe and avoid all risks attitude), second-generation globalization (ability to expand markets via globalization without the need to innovate) and complex government regulations that have rendered companies moribund and risk-averse. Their conclusion is that R & D basically is still doing well, but that the generated discoveries are not appropriately leveraged towards economic growth via risk-embracing entrepreneurs investing to channel the invention to the market to reach an audience. Along the same line, Jan Vijg has argued that responsible government and industry leaders have begun to refrain from risky bets on exciting new exploits and the time of grand projects, such as the Eisenhower Interstate System, the Moon Landing Program or the development of the internet is behind us. Instead, we mainly pursue incremental improvements of existing technologies, embark in catch-up programs for developing countries and spend huge resources on social programs and military. The consequences are slowing advancement and a decrease of the dramatic progress society has undergone since the industrial revolution now > 200 years ago (Vijg, 2011). It might even be, that the digitalization revolution which often allows for very rapid pay-backs on investments has spoiled investors' appetite for more risky long-term endeavors. Other analysts have looked at the nature of discovery research itself and came to interesting conclusions. Youn et al. (2015), via analyzing the US patent records and associated technology codes since 1790, could demonstrate that the creation of completely new technological capabilities has significantly decelerated and that combinatorial innovation (Kodama, 1992) has become the norm. Things already changed around 1870 when growth in the number of new distinct technology patent codes slowed down. This suggests that invention now proceeds mainly by recombining existing technologies and chimes with the idea that inventions were, in some sense, more fundamental in the past than they are today (The Economist, 2015). It seems that humanity desperately awaits the moment when we break through into some new domain of science, radically different from anything we currently embrace, which opens a new door making a new innovation explosion again possible (Buchanan, 2015). It is clear though that if there is a problem it is a problem of quality but not quantity and these days ~2 million scientific papers are published each year, more than ever before in human history (Ioannidis et al., 2014). Lutz Bornmann and Rüdiger Mutz have identified three growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from < 1% starting in the 17th up to the middle of the 18th century, to 2 to 3% up
1. Population Growth in human population size can be used as a baseline of our advancement and has certainly seen a tremendous increase. Starting from a bottleneck of only 2000–20,000 humans (Ambrose, 1998; Behar et al., 2008) we have now expanded to 7.3 billion (http://www. worldometers.info/world-population/). This would result in a population advancement factor of 4 ∗ 10E5. 2. Transport speed The fastest human footspeed on record is 45.7 km/h seen during a 100 m sprint (average speed between the 60th and the 80th meter) by 3
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6. Energy generation
Usain Bolt (IAAF, 2009). While the fastest transportation speed achieved up to date is 39,897 km/h reached by Apollo 10 CSM upon reentry into earth orbit (Furniss, 2003). This results in a transport advancement factor or 1 ∗ 10E3.
Historically humans were mainly generating energy from burning wood. A typical wood fire generates 16.2 MJ/kg (http://www.worldnuclear.org/info/Nuclear-Fuel-Cycle/Introduction/Energy-for-theWorld—Why-Uranium-/), while a modern nuclear power plant is able to generate 80,620,000 MJ/kg from U-238/Pu-239 (https:// whatisnuclear.com/physics/energy_density_of_nuclear.html). This results in an advancement factor of 5 ∗ 10E6.
3. Calculation speed In general, the speed of supercomputers is measured and benchmarked in FLOPS (Floating point Operations Per Second), currently the Sunway TaihuLight at Wuxi in China is considered the world's fastest computer with 93 PetaFLOPs (93 ∗ 10E15FLOPS) (https://www. top500.org/lists/2016/11/) while the human brain can only make about two conscious calculations per second. This results in an advancement factor of 5 ∗ 10E16.
7. Information storage density Areal density is a measure of the quantity of information bits that can be stored on a given length of track, area of surface, or in a given volume of a computer storage medium. In 2016 Sony has announced a new tape that holds 148 gigabits (Gb) per square inch beating a record set in 2010 more than five times over (http://www.bbc.com/ news/technology-27282732). Interestingly, the data storage capacity, speed and duration of DNA goes far beyond (Extance, 2016). Naturally it can be calculated that on stone or paper 10 bits per square inch can be engraved. (http://www.tecchannel.de/storage/ extra/401897/nanotechnik_ibm_speichert_1_tbit_in/). (http://www.nature.com/nnano/journal/v5/n7/pdf/nnano.2010. 137.pdf). This results in an advancement factor of 1 ∗ 10E10.
4. Communication Naturally humans are able to communicate with ~300 other humans as determined via analysis of hunter-gatherer social networks (Hamilton et al., 2007). With the mobile communication technologies today one person is basically able to reach every other human on the planet. This results in an advancement factor of 2 ∗ 10E7. 5. Life expectancy What is commonly known as ‘average life expectancy’ is technically ‘life expectancy at birth’ (LEB), in other words, it is the average number of years that a newborn baby can expect to live in a given society at a given time. A major determinant of life expectancy at birth is the child mortality rate which, in our ancient past, was extremely high skewing the life expectancy rate dramatically downward. The life expectancy at birth of hunter gatherers is around 30 years (Gurven and Kaplan, 2007), in Japan currently it is about 84 years (http://data.worldbank.org/ indicator/SP.DYN.LE00.IN). The combination of high infant mortality and deaths in young adulthood from accidents, epidemics, plagues, wars, and childbirth, particularly before modern medicine was widely available, has significantly lowered LEB in the past, but for those who survived early hazards, a life expectancy of sixty or seventy would not be uncommon even in ancient times. Taking LEB as a measure we calculate an advancement factor of only 3, if we would look at life expectancy of adults it would be basically zero! Human lifespans have remained constant for almost 2000 years, when Socrates died at the age of 70 around 399 BCE, he did not die of old age but instead by execution. It is ironic that ancient Greeks lived into their 70s and older, while > 2000 years later modern humans aren't living much longer (Radford, 2009). Contrary to common notions, seventy-year-olds weren't considered rare freaks of nature in previous centuries. Galileo Galilei died at seventy-seven, Isaac Newton at eighty-four, Michelangelo at eighty-eight (Harari, 2015). The war against death is likely to be one of the flagship projects in the coming centuries.
8. Food production/acres needed to feed 1 person The 25 top calorie staple crops worldwide according to 2008 FAOSTAT data, required 1 ∗ 10E9 ha to grow. In terms of calories, then, approximately 1 billion ha can feed 10 billion persons, and 1 ha can feed about 10 persons these days (http://www.gardeningplaces.com/ articles/global-food-crisis.htm). In contrast it takes one to ten square miles of land per person to support a hunter gatherer lifestyle (http:// www.trunity.net/sam2/view/article/51cbf44b7896bb431f6af515/). With 1 mile2 = 259 ha this results in an advancement factor of 3 ∗ 10E4. 9. Construction (buildings height) Göbekli Tepe is an archaeological site at the top of a mountain ridge in the Southeastern Anatolia Region of Turkey with a height of ~15 m. The tell includes two phases of ritual use dating back to the 10th – 8th millennium BCE and is considered the oldest building on earth (https://en.wikipedia.org/wiki/G%C3%B6bekli_Tepe). The Jeddah Tower is a skyscraper under construction in Jeddah, Saudi Arabia, if completed in 2020 as planned, the Jeddah Tower will reach unprecedented heights becoming the tallest building in the world, as well as the first structure to reach the one-kilometre-high mark (https://en. wikipedia.org/wiki/Jeddah_Tower). This results in an advancement factor of 70 only. All advancement factors are visualized here (Fig. 1). Fig. 1. Advancement factors of various technology areas as calculated from the most advanced technological status achieved by humanity today vs. capabilities of a hunter gatherer human in pre-historic times.
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the generation of artificial intelligence with the consequence of the emerging so called technological singularity, a computer capable of designing computers better than itself with the expectation that repetitions of this cycle would result in a runaway effect and create intelligence far exceeding human intellectual capacity. The median expectance date for the singularity is 2040. The first use of the term “singularity” in this context was made by Stanislaw Ulam in his 1958 obituary for John von Neumann. The term was later popularized by Vernor Vinge and Ray Kurzweil. Areas closely connected to this field are voice-based computing, augmented reality or brain-electronic-interfaces connecting the artificial with our biological brains. Elon Musk has recently announced that in his opinion meaningful interfaces between the brain and computers were five years away and has heavily invested in the field with the founding of his new company “Neuralink” that starts making invasive devices for treating neurological ailments but finally targets an implant that would let the wearer tap directly into the internet and all of the computational power available there (The Economist, 2017). In the area of computer-brain-interfaces already amazing breakthroughs have been achieved today such as for example the electronic transfer of sensorimotor information between two brains (Pais-Vieira et al., 2013) or the direct thinking of letters into a computer (Herff and Schulz, 2016). Also synthetic biology carries a lot of hope to become the next super-enabling technology (Church, 2014; Waltz, 2015). Via advanced synthetic biology all organisms could become the subject of complete manipulation and engineering, including humans. Imagine a future in which human beings have become immune to all viruses or no longer develop diseases like Alzheimer or back-pain. Bacteria or other artificial organisms could custom-produce all materials, even items, or generate enough electricity or biofuel. Pioneering geneticist George Church and science writer Ed Regis have intriguingly described the potential of this technology (Specter, 2015). Nanotechnology in all its aspects, especially in the most sophisticated form as nano-robotics certainly holds tremendous game changer potential. Intriguing options are described for medicine, such as robots patrolling our bodies (Saadeh and Vyas, 2014), a technology that amazingly has already been partly realized (Hoffman, 2015). The generation of so called smart dust networks of programmable matter, matter that can take each form via programmable re-assembly of the nano-devices, is another striking example. Smart dust entered the Gartner's hype cycle in 2013 as the most speculative entrant (http:// www.gartner.com/newsroom/id/2575515). Interestingly self-assembling matter has already been realized in the macro-format (Hardesty, 2013). Another area with potential to deliver a significant game changing breakthrough is nuclear and quantum physics with new groundbreaking insights on matter and the universe such as e.g. dark matter, dark energy or the universal theory combining Einstein's theory of relativity with quantum theory (theory of everything) (Weinberg, 1994). There is the hope that nuclear physics will solve the energy problem once and for all with the technology of nuclear fusion becoming reality. There are some encouraging signs that this might actually happen rather sooner than later (Carrington, 2016). Needless to say, solving the energy problem would simultaneously solve many other problems (water, food etc.). Quantum physics could result in further major breakthroughs. Apparently quantum communication has already been achieved just recently (http://physicsworld.com/cws/article/news/ 2017/may/18/particle-free-quantum-communication-is-achieved-inthe-lab and https://phys.org/news/2017-06-atomic-imperfectionsquantum-network-closer.html). Recently it could also be shown that quantum effects are actually affecting living organisms (Ball, 2011). Effects like photosynthesis, some forms of enzymatic catalysis as well as the birds' compass are apparently working with quantum phenomena. Going even further than that, quantum physics has the potential to shatter our entire understanding of reality. This is visible in the so called “Bells Theorem”. In 1935 Einstein, Podolsky, and Rosen
This method brings us to the following rankings of most advanced vs. less advanced technologies: 1) 2) 3) 4) 5) 6) 7) 8) 9)
calculation information storage communication energy generation population food production transportation construction medicine (life expectancy)
Interestingly this list intuitively makes a lot of sense. Highest progress has been achieved in the poster child of technological progress, IT and communication/mobile phones/internet. The technology fields of energy generation and food production show an average advancement (without progress in food production the population growth would not have been possible so the two are certainly interlinked). Interestingly, construction and particularly medicine are apparently dramatically under-developed technologies. Looking at the materials we build our buildings with (often still brick and mortar as in the classic antique or even prehistoric ages) this is pathetic. Also when it comes to life expectancy progress has been close to zero, so despite all the hype about new medicines, progress here, when measured against life expectancy, indeed seems to be very slow. Hopefully the new gene editing and stem cell technologies will bring some progress in this apparently underdeveloped area. Also the fact that transportation is below average seems to correlate well with the poster-child of technological underachievement: space travel and the inability of humanity to colonize space and the dramatically slower progress vs. what was anticipated fifty years ago. Looking into the future of science and technology is always difficult, especially when it comes to the emergence of completely new breakthrough discoveries and technologies. Yet it is exactly these discoveries that, for decades or even centuries, power global progress, triggering so called Kondratieff waves (Kondratieff, 1935) of high sectoral growth, separated by intervals of relatively slow growth until the next breakthrough technology emerges (Devezas et al., 2005; Korotayev et al., 2011). Examples for such past breakthrough discoveries are: The development of the steam engine, emergence of railways, steel and heavy engineering, electricity, the automobile and finally the emergence of telecommunication and IT. At this point in time it is hard to predict what could be the breakthrough that will trigger the next Kondratieff cycle. The question is of outstanding importance, the Obama administration's policy also emphasized a search for the “next trillion dollar trigger”, as IT and the internet were for the Clinton years (Glenn and Gordon, 2007). Past forecasts have never been accurate and while the advent of automobiles, spaceships and robots was widely anticipated, the arrival of x-rays, radio, transistors, lasers, superconductors, nuclear energy, quantum mechanics, communication satellites, fiber-optic technology, gene editing and the entire electronics and digital revolution were all surprises. When Bill Clinton assembled the top minds of the nation to discuss the economy in 1992, “no one mentioned the internet” (Brynjolfsson and McAfee, 2011). Interestingly, and coming back to the area of science fiction, Jules Verne however had correctly predicted many technological advances in his writings, long before they were invented, such as e.g.: electric submarines, helicopters, newscasts, solar sails, lunar modules, videoconferencing, sky-writing or tasers (Kerr, 2015). Likewise, the concept of nanotechnology was envisioned by Richard Feynman during a lecture called “There's plenty of room at the bottom”, delivered to the American Physical Society in Pasadena December 1959 (Feynman, 1959). Looking at the situation today, which areas have the potential to deliver the future breakthroughs? As already mentioned at the beginning of this article, the most frequently cited expected breakthrough is 5
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innovation and pre-competitive consortia, promoting free societies or keeping R & D funding up, as important as they are, in the frame of this article five new proposals are developed and recommended for implementation. The list is by no means meant to be exhaustive but should constitute a contribution of a few stimulating thoughts to an ongoing discussion:
published a paper which showed that under certain circumstances quantum mechanics predicted a breakdown of locality “spooky action at a distance”, but doubted this could really happen and thus viewed it as evidence that quantum mechanics was incomplete. Correlations of this sort, however, later were observed experimentally. As Bell proved in 1964, this leaves two options for the nature of reality: The first is that reality is irreducibly random, meaning that there are no hidden variables that “determine the results of individual measurements”. The second option is that reality is ‘non-local’, meaning that “the setting of one measuring device can influence the reading of another instrument, however remote”. In other words, the world is either non-causal or nonlocal, meaning that either causal influences propagate faster than light, or a common-sense notion about what the word “cause” signifies is wrong (Wiseman, 2014; Wiseman, 2015). Then there is the area of parapsychology that has for millennia been part of human mythology but has so far not really come into the reach of science, although amazing scientific literature exists on the apparent reality of psi phenomena such as telepathy, clairvoyance or precognition. For example physiologic reactions to a positive or negative picture can apparently already be detected before the subject has seen the picture, even before the computer has randomly selected the picture to display, a variant of the phenomenon termed precognition, confirmed in a meta-analysis of 90 experiments (Bem et al., 2016). Also a metaanalysis on telepathy apparently came to a positive result (Storm et al., 2010). Then there is an ancient Indian literature that mysteriously accurately predicts the speed of light, which must have been arrived at intuitively, and which seems an extreme coincidence even to those well-versed in modern measurement and probability theory (Kak, 1998) (http://www.science20.com/machines_organizations_and_us_ sociotechnical_systems/fringe_editor%E2%80%99s_dilemma_raises_ questions_about_future_science). Needless to say that these results were heavily challenged by other members of the scientific community. Should it one day be possible to reproducibly and convincingly demonstrate such effects, for example with a particularly gifted individual, we would certainly see a significant paradigm shift in science as the astronomer and author of many popular science books Carl Sagan pointed out: “There are three claims in the extra-sensory-perception field which, in my opinion, deserve serious study: (1) that by thought alone humans can (barely) affect random number generators in computers; (2) that people under mild sensory deprivation can receive thoughts or images “projected” at them; and (3) that young children sometimes report the details of a previous life, which upon checking turn out to be accurate and which they could not have known about in any other way than reincarnation. I pick these claims not because I think they're likely to be valid (I don't), but as examples of contentions that might be true. They have at least some, although still dubious, experimental support” (Sagan, 1997). Overall, it is most amazing to see the progress humanity has made over the millennia to understand the world we are living in and to be able to manipulate it to our advantage. On the other hand there are areas where we are as ignorant as we have ever been and no progress what so ever has been achieved. A strikingly visible example is looking at the quote from Seneca written 2000 years ago: “…whether all matter from which the universe is formed is continuous, without intervals of space, or dispersed as emptiness mixed with solid matter; what kind of abode a god has, looking upon his work in detachment or actively controlling it, whether he encompasses it from without or is implanted in the whole; whether the world is immortal or to be reckoned among perishable things and things born at a certain time” (Seneca, n.d.). In this article we have seen that although technological progress has overall been slower than expected, there are numerous fields that hold extraordinary promise for the future. How can we awaken the “force”? What can be done to boost progress in science, technology and innovation? In terms of new thinking, rather than repeating proposals already extensively made in the past such as increased collaboration, open
1) The new superstars: Scientists and entrepreneurs need to be celebrated as the new global superstars. This needs to be reflected in global competitions, prizes, awards and last but not least salaries. Why should a super star scientist be celebrated less than a super star football player? A series of high impact prizes has recently emerged with cash volumes surpassing the Nobel prize, although their impact is a matter of ongoing debate (Bays et al., 2009; Krauss, 2016). 2) Moonshot academies: Special research investments should be taken in areas with potential to deliver new breakthroughs and enable new moonshots. The list provided above could be used as a first starting point. In the Stokes's scheme this would relate to working primarily in the Pasteur's quadrant (Stokes, 1997), i.e. seeking fundamental understanding of scientific problems which have immediate use for society. This needs to be achieved by special research funding schemes providing long term stable support, as work in the anticipated breakthrough areas will not deliver the constant stream of publications required for current scientific careers. Recently philanthropists have started to provide such funding with key examples such as the $3 bio Chan Zuckerberg Science initiative (Cha, 2016) or the Breakthrough Prize in life sciences, fundamental physics and mathematics with laureates receiving each $3 million, three times the size of the Nobel prize (https://breakthroughprize.org/News/12). 3) Information tsunami floodgates: In terms of information and communication overflow, it seems we have reached the limits of what the human brain can reasonably digest. For the first time in history people are bombarded with far more information than they can process, so exposing them to increasing amounts of random pieces of information will not increase their rate of innovation. At the same time the new social media are amplifying our communication frequencies leading to constant disruptions, all resulting in an information overflow (Levitin, 2015). Key inventions which increase the rate of innovation in the future may include technologies that filter and prioritize information appropriately in a personalized manner (intelligent assistants). In the end what we need is a shift from opportunity/information seeking focus to a prioritization focus. 4) Dr. All: In the previous centuries breakthrough innovations have often been produced by so-called gentlemen scientist, individuals freed from the necessities to earn themselves a living, broadly interested in multiple areas of science, combining fields and working inter-disciplinary. With time passing, this way of working has become virtually impossible due to the explosion of knowledge and an increased need for specialization to be able to compete. Larger and larger teams were required for meaningful discoveries and author lists of publications are growing longer and longer (Aboukhalil, 2016). The IT revolution might allow us to reverse this trend and to re-establish the gentleman scientist. Relevant information is broadly available and can be easily and quickly obtained on an as need basis. Furthermore, robotics might soon allow us to automate and conduct experiments in a speedy manner. Maybe the technology-empowered gentlemen scientist can be revived in the future? (Casserly, 2012). 5) Innovation isolators: In the history of humanity increased spreading of information and more intense communication has always led to a jump in innovation output, starting with the formation of cities, the development of writing and the tremendous progress triggered by the development 6
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of the printing press. In that sense the boost in communication made possible with the internet has advanced humanity to a level never seen before. Information is rapidly spreading over the planet; all the knowledge of humanity is available at our fingertips and people can constantly communicate online in a way that has never been possible before. Accordingly, if this theory is right, a new golden age of innovation should follow soon. On the other hand it has been argued that the creation of breakthrough innovation is negatively affected by too much communication (Derex and Boyd, 2016). It could be shown that partially connected groups produce more diverse solutions and more complex solutions than fully connected groups. While in the short run fully connected groups outperformed partially connected groups but then ended up being trapped on local optima. So rather than further increase communication we might have to restrict it. In that sense an unconventional scenario could be to completely information-isolate several larger groups of highly competent scientists (each team comprising ~100 people) for extended periods of time (> 5 years) to work independently on a breakthrough research problem and then to bring them successively in contact. Although this article is focusing on scientific and technological progress it should not go unmentioned that this is only one aspect of many in a more holistic view and beyond science and technology humanity is progressing at various other dimensions such as morality with a decrease in violence (Pinker, 2011), better social organization (Bury, 1920) and increased happiness (Helliwell et al., 2017). Indeed it even seems that for the first time in many decades social change is outpacing technological change (Phillips, 2011). Nevertheless it is fair to say that science and technology is a very powerful force that in the past millennia has enabled humanity to grow in numbers, and advance in capabilities. This force has tremendously changed the shape of the world we live in and promises to continue to do so in the future. The eternal quest for truth, the desire to understand the universe and its governing laws, combined with the ambition to apply the discovered principles for the good of humanity continue to hold big promises for a bright future. The door to the next big thing, the next breakthrough could somewhere on this planet be opened just today, and the “force” be awakened. References Aboukhalil, Robert, 2016. The Rising Trend in Authorship. The Winnower. https:// thewinnower.com/papers/the-rising-trend-in-authorship. Ambrose, Stanley, 1998. Late Pleistocene human population bottlenecks, volcanic winter, and differentiation of modern humans. J. Hum. Evol. 34, 623–651. Andrews, Dan, Criscuolo, Chiara, Gal, Peter, 2015. Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries. https://www.oecd.org/eco/ growth/Frontier-Firms-Technology-Diffusion-and-Public-Policy-Micro-Evidencefrom-OECD-Countries.pdf. Ball, Philipp, 2011. The dawn of quantum biology. Nature 474, 272. Bays, Jonathan, Goland, Tony, Newsum, Joe, 2009. Using Prizes to Spur Innovation. McKinsey & Company (July, 2009). Behar, Doron M., Villems, Richard, Soodyall, Himla, Blue-Smith, Jason, Pereira, Luisa, Metspalu, Ene, Scozzari, Rosaria, Makkan, Heeran, Tzur, Shay, Comas, David, Bertranpetit, Jaume, Quintana-Murci, Lluis, Tyler-Smith, Chris, Spencer Wells, R., Rosset, Saharon, The Genographic Consortium, 2008. The dawn of human matrilineal diversity. Am. J. Hum. Genet. 82, 1130. Bem, Daryl, Tressoldi, Patrizio, Rabeyron, Thomas, Duggan, Michael, 2016. Feeling the Future: A Meta-analysis of 90 Experiments on the Anomalous Anticipation of Random Future Events. F1000Research 4, 1188. Bornmann, Lutz, Mutz, Rüdiger, 2014. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. J. Assoc. Inf. Sci. Technol(www.nber.org/papers/w18315). Brynjolfsson, Erik, McAfee, Andrew, 2011. Race Against The Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Digital Frontier Press (17. October 2011). Brynjolfsson, Erik, McAfee, Andrew, 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company (20. January 2014). Buchanan, Mark, 2015. Innovation slowdown. Nat. Phys. 11, 2. Bury, J.B., 1920. The Idea of Progress – An Inquiry into Its Origin and Growth. MacMillan
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