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Friday, January 9, 2015

Technological singularity

From Wikipedia, the free encyclopedia
 
The technological singularity hypothesis is that accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization in an event called the singularity.[1] Because the capabilities of such an intelligence may be impossible for a human to comprehend, the technological singularity is an occurrence beyond which events may become unpredictable or even unfathomable.[2]
The first use of the term "singularity" in this context was by mathematician John von Neumann. In 1958, regarding a summary of a conversation with von Neumann, Stanislaw Ulam described "ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue".[3] The term was popularized by science fiction writer Vernor Vinge, who argues that artificial intelligence, human biological enhancement, or brain–computer interfaces could be possible causes of the singularity.[4] Futurist Ray Kurzweil cited von Neumann's use of the term in a foreword to von Neumann's classic The Computer and the Brain.

Proponents of the singularity typically postulate an "intelligence explosion",[5][6] where superintelligences design successive generations of increasingly powerful minds, that might occur very quickly and might not stop until the agent's cognitive abilities greatly surpass that of any human.

Kurzweil predicts the singularity to occur around 2045[7] whereas Vinge predicts some time before 2030.[8] At the 2012 Singularity Summit, Stuart Armstrong did a study of artificial general intelligence (AGI) predictions by experts and found a wide range of predicted dates, with a median value of 2040. Discussing the level of uncertainty in AGI estimates, Armstrong said in 2012, "It's not fully formalized, but my current 80% estimate is something like five to 100 years."[9]

Basic concepts

Ray Kurzweil writes that, due to paradigm shifts, a trend of exponential growth extends Moore's law from integrated circuits to earlier transistors, vacuum tubes, relays, and electromechanical computers. He predicts that the exponential growth will continue, and that in a few decades the computing power of all computers will exceed that of ("unenhanced") human brains, with superhuman artificial intelligence appearing around the same time.

Many of the most recognized writers on the singularity, such as Vernor Vinge and Ray Kurzweil, define the concept in terms of the technological creation of superintelligence. They argue that it is difficult or impossible for present-day humans to predict what human beings' lives will be like in a post-singularity world. [7][8][10] The term "technological singularity" was originally coined by Vinge, who made an analogy between the breakdown in our ability to predict what would happen after the development of superintelligence and the breakdown of the predictive ability of modern physics at the space-time singularity beyond the event horizon of a black hole.[10]

Some writers use "the singularity" in a broader way to refer to any radical changes in our society brought about by new technologies such as molecular nanotechnology,[11][12][13] although Vinge and other prominent writers specifically state that without superintelligence, such changes would not qualify as a true singularity.[8] Many writers also tie the singularity to observations of exponential growth in various technologies (with Moore's Law being the most prominent example), using such observations as a basis for predicting that the singularity is likely to happen sometime within the 21st century.[12][14]
A technological singularity includes the concept of an intelligence explosion, a term coined in 1965 by I. J. Good.[15] Although technological progress has been accelerating, it has been limited by the basic intelligence of the human brain, which has not, according to Paul R. Ehrlich, changed significantly for millennia.[16] However, with the increasing power of computers and other technologies, it might eventually be possible to build a machine that is more intelligent than humanity.[17] If a superhuman intelligence were to be invented—either through the amplification of human intelligence or through artificial intelligence—it would bring to bear greater problem-solving and inventive skills than current humans are capable of. It could then design an even more capable machine, or re-write its own software to become even more intelligent. This more capable machine could then go on to design a machine of yet greater capability. These iterations of recursive self-improvement could accelerate, potentially allowing enormous qualitative change before any upper limits imposed by the laws of physics or theoretical computation set in.[18][19][20]

The exponential growth in computing technology suggested by Moore's Law is commonly cited as a reason to expect a singularity in the relatively near future, and a number of authors have proposed generalizations of Moore's Law. Computer scientist and futurist Hans Moravec proposed in a 1998 book[21] that the exponential growth curve could be extended back through earlier computing technologies prior to the integrated circuit. Futurist Ray Kurzweil postulates a law of accelerating returns in which the speed of technological change (and more generally, all evolutionary processes[22]) increases exponentially, generalizing Moore's Law in the same manner as Moravec's proposal, and also including material technology (especially as applied to nanotechnology), medical technology and others.[23] Between 1986 and 2007, machines' application-specific capacity to compute information per capita has roughly doubled every 14 months; the per capita capacity of the world's general-purpose computers has doubled every 18 months; the global telecommunication capacity per capita doubled every 34 months; and the world's storage capacity per capita doubled every 40 months.[24] Like other authors, though, Kurzweil reserves the term "singularity" for a rapid increase in intelligence (as opposed to other technologies), writing for example that "The Singularity will allow us to transcend these limitations of our biological bodies and brains ... There will be no distinction, post-Singularity, between human and machine".[25] He believes that the "design of the human brain, while not simple, is nonetheless a billion times simpler than it appears, due to massive redundancy".[26] According to Kurzweil, the reason why the brain has a messy and unpredictable quality is because the brain, like most biological systems, is a "probabilistic fractal".[27] He also defines his predicted date of the singularity (2045) in terms of when he expects computer-based intelligences to significantly exceed the sum total of human brainpower, writing that advances in computing before that date "will not represent the Singularity" because they do "not yet correspond to a profound expansion of our intelligence."[28]

The term "technological singularity" reflects the idea that such change may happen suddenly, and that it is difficult to predict how the resulting new world would operate.[29][30] It is unclear whether an intelligence explosion of this kind would be beneficial or harmful, or even an existential threat,[31][32] as the issue has not been dealt with by most artificial general intelligence researchers, although the topic of friendly artificial intelligence is investigated by the Future of Humanity Institute and the Singularity Institute for Artificial Intelligence, which is now the Machine Intelligence Research Institute.[29]

Gary Marcus claims that "virtually everyone in the A.I. field believes" that machines will one day overtake humans and "at some level, the only real difference between enthusiasts and skeptics is a time frame."[33] However, many prominent technologists and academics dispute the plausibility of a technological singularity, including Jeff Hawkins, John Holland, Jaron Lanier, and Gordon Moore, whose Moore's Law is often cited in support of the concept.[34][35]

History of the idea

Nicolas de Condorcet, the 18th-century French mathematician, philosopher, and revolutionary, is commonly credited for being one of the earliest persons to contend the existence of a singularity. In his 1794 Sketch for a Historical Picture of the Progress of the Human Mind, Condorcet states,
Nature has set no term to the perfection of human faculties; that the perfectibility of man is truly indefinite; and that the progress of this perfectibility, from now onwards independent of any power that might wish to halt it, has no other limit than the duration of the globe upon which nature has cast us. This progress will doubtless vary in speed, but it will never be reversed as long as the earth occupies its present place in the system of the universe, and as long as the general laws of this system produce neither a general cataclysm nor such changes as will deprive the human race of its present faculties and its present resources."[36]
In 1847, R. Thornton, the editor of The Expounder of Primitive Christianity,[37] wrote about the recent invention of a four-function mechanical calculator:
...such machines, by which the scholar may, by turning a crank, grind out the solution of a problem without the fatigue of mental application, would by its introduction into schools, do incalculable injury. But who knows that such machines when brought to greater perfection, may not think of a plan to remedy all their own defects and then grind out ideas beyond the ken of mortal mind!
In 1863, Samuel Butler wrote Darwin Among the Machines, which was later incorporated into his famous novel Erewhon. He pointed out the rapid evolution of technology and compared it with the evolution of life. He wrote:
Reflect upon the extraordinary advance which machines have made during the last few hundred years, and note how slowly the animal and vegetable kingdoms are advancing. The more highly organised machines are creatures not so much of yesterday, as of the last five minutes, so to speak, in comparison with past time. Assume for the sake of argument that conscious beings have existed for some twenty million years: see what strides machines have made in the last thousand! May not the world last twenty million years longer? If so, what will they not in the end become?...we cannot calculate on any corresponding advance in man’s intellectual or physical powers which shall be a set-off against the far greater development which seems in store for the machines.
In 1909, the historian Henry Adams wrote an essay, The Rule of Phase Applied to History,[38] in which he developed a "physical theory of history" by applying the law of inverse squares to historical periods, proposing a "Law of the Acceleration of Thought." Adams interpreted history as a process moving towards an "equilibrium", and speculated that this process would "bring Thought to the limit of its possibilities in the year 1921. It may well be!", adding that the "consequences may be as surprising as the change of water to vapor, of the worm to the butterfly, of radium to electrons."[39] The futurist John Smart has called Adams "Earth's First Singularity Theorist".[40]

In 1951, Alan Turing spoke of machines outstripping humans intellectually:[41]
once the machine thinking method has started, it would not take long to outstrip our feeble powers. ... At some stage therefore we should have to expect the machines to take control, in the way that is mentioned in Samuel Butler's Erewhon.
In the mid fifties, Stanislaw Ulam had a conversation with John von Neumann in which von Neumann spoke of "ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue."[3]

In 1965, I. J. Good first wrote of an "intelligence explosion", suggesting that if machines could even slightly surpass human intellect, they could improve their own designs in ways unforeseen by their designers, and thus recursively augment themselves into far greater intelligences. The first such improvements might be small, but as the machine became more intelligent it would become better at becoming more intelligent, which could lead to a cascade of self-improvements and a sudden surge to superintelligence (or a singularity).

In 1983, mathematician and author Vernor Vinge greatly popularized Good’s notion of an intelligence explosion in a number of writings, first addressing the topic in print in the January 1983 issue of Omni magazine. In this op-ed piece, Vinge seems to have been the first to use the term "singularity" in a way that was specifically tied to the creation of intelligent machines,[42][43] writing:
We will soon create intelligences greater than our own. When this happens, human history will have reached a kind of singularity, an intellectual transition as impenetrable as the knotted space-time at the center of a black hole, and the world will pass far beyond our understanding. This singularity, I believe, already haunts a number of science-fiction writers. It makes realistic extrapolation to an interstellar future impossible. To write a story set more than a century hence, one needs a nuclear war in between ... so that the world remains intelligible.
In 1984, Samuel R. Delany used "cultural fugue" as a plot device in his science-fiction novel Stars in My Pocket Like Grains of Sand; the terminal runaway of technological and cultural complexity in effect destroys all life on any world on which it transpires, a process poorly understood by the novel's characters, and against which they seek a stable defense. In 1985, Ray Solomonoff introduced the notion of "infinity point"[44] in the time-scale of artificial intelligence, analyzed the magnitude of the "future shock" that "we can expect from our AI expanded scientific community" and on social effects. Estimates were made "for when these milestones would occur, followed by some suggestions for the more effective utilization of the extremely rapid technological growth that is expected".

Vinge also popularized the concept in SF novels such as Marooned in Realtime (1986) and A Fire Upon the Deep (1992). The former is set in a world of rapidly accelerating change leading to the emergence of more and more sophisticated technologies separated by shorter and shorter time-intervals, until a point beyond human comprehension is reached. The latter starts with an imaginative description of the evolution of a superintelligence passing through exponentially accelerating developmental stages ending in a transcendent, almost omnipotent power unfathomable by mere humans. Vinge also implies that the development may not stop at this level.

In his 1988 book Mind Children, computer scientist and futurist Hans Moravec generalizes Moore's law to make predictions about the future of artificial life. Moravec outlines a timeline and a scenario in this regard,[45][46] in that robots will evolve into a new series of artificial species, starting around 2030–2040.[47] In Robot: Mere Machine to Transcendent Mind, published in 1998, Moravec further considers the implications of evolving robot intelligence, generalizing Moore's law to technologies predating the integrated circuit, and speculating about a coming "mind fire" of rapidly expanding superintelligence, similar to Vinge's ideas.

A 1993 article by Vinge, "The Coming Technological Singularity: How to Survive in the Post-Human Era",[8] spread widely on the internet and helped to popularize the idea.[48] This article contains the oft-quoted statement, "Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended." Vinge refines his estimate of the time-scales involved, adding, "I'll be surprised if this event occurs before 2005 or after 2030."

Vinge predicted four ways the singularity could occur:[49]
  1. The development of computers that are "awake" and superhumanly intelligent
  2. Large computer networks (and their associated users) may "wake up" as a superhumanly intelligent entity
  3. Computer/human interfaces may become so intimate that users may reasonably be considered superhumanly intelligent
  4. Biological science may find ways to improve upon the natural human intellect
Vinge continues by predicting that superhuman intelligences will be able to enhance their own minds faster than their human creators. "When greater-than-human intelligence drives progress," Vinge writes, "that progress will be much more rapid." He predicts that this feedback loop of self-improving intelligence will cause large amounts of technological progress within a short period, and states that the creation of superhuman intelligence represents a breakdown in humans' ability to model their future. His argument was that authors cannot write realistic characters who surpass the human intellect, as the thoughts of such an intellect would be beyond the ability of humans to express. Vinge named this event "the Singularity".

Damien Broderick's popular science book The Spike (1997) was the first[citation needed] to investigate the technological singularity in detail.

In 2000, Bill Joy, a prominent technologist and a co-founder of Sun Microsystems, voiced concern over the potential dangers of the singularity.[50]

In 2005, Ray Kurzweil published The Singularity is Near, which brought the idea of the singularity to the popular media both through the book's accessibility and through a publicity campaign that included an appearance on The Daily Show with Jon Stewart.[51] The book stirred intense controversy, in part because Kurzweil's utopian predictions contrasted starkly with other, darker visions of the possibilities of the singularity.[original research?] Kurzweil, his theories, and the controversies surrounding it were the subject of Barry Ptolemy's documentary Transcendent Man.

In 2007, Eliezer Yudkowsky suggested that many of the varied definitions that have been assigned to "singularity" are mutually incompatible rather than mutually supporting.[12] For example, Kurzweil extrapolates current technological trajectories past the arrival of self-improving AI or superhuman intelligence, which Yudkowsky argues represents a tension with both I. J. Good's proposed discontinuous upswing in intelligence and Vinge's thesis on unpredictability.

In 2008, Robin Hanson (taking "singularity" to refer to sharp increases in the exponent of economic growth) listed the Agricultural and Industrial Revolutions as past singularities. Extrapolating from such past events, Hanson proposes that the next economic singularity should increase economic growth between 60 and 250 times. An innovation that allowed for the replacement of virtually all human labor could trigger this event.[52]

In 2009, Kurzweil and X-Prize founder Peter Diamandis announced the establishment of Singularity University, whose stated mission is "to educate, inspire and empower leaders to apply exponential technologies to address humanity’s grand challenges."[53] Funded by Google, Autodesk, ePlanet Ventures, and a group of technology industry leaders, Singularity University is based at NASA's Ames Research Center in Mountain View, California. The not-for-profit organization runs an annual ten-week graduate program during the northern-hemisphere summer that covers ten different technology and allied tracks, and a series of executive programs throughout the year.

In 2010, Aubrey de Grey applied the term "Methuselarity"[54] to the point at which medical technology improves so fast that expected human lifespan increases by more than one year per year. In "Apocalyptic AI – Visions of Heaven in Robotics, Artificial Intelligence, and Virtual Reality"[55] (2010), Robert Geraci offers an account of the developing "cyber-theology" inspired by Singularity studies. The 1996 novel Holy Fire by Bruce Sterling explores some of those themes and postulates that a Methuselarity will become a gerontocracy.

In 2011, Kurzweil noted existing trends and concluded that it appeared increasingly likely that the singularity would occur around 2045. He told Time magazine: "We will successfully reverse-engineer the human brain by the mid-2020s. By the end of that decade, computers will be capable of human-level intelligence."[56]

Intelligence explosion

The notion of an "intelligence explosion" was first described thus by Good (1965), who speculated on the effects of superhuman machines:
Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.
Most proposed methods for creating superhuman or transhuman minds fall into one of two categories: intelligence amplification of human brains and artificial intelligence. The means speculated to produce intelligence augmentation are numerous, and include bioengineering, genetic engineering, nootropic drugs, AI assistants, direct brain-computer interfaces and mind uploading. The existence of multiple paths to an intelligence explosion makes a singularity more likely; for a singularity to not occur they would all have to fail.[10]

Hanson (1998) is skeptical of human intelligence augmentation, writing that once one has exhausted the "low-hanging fruit" of easy methods for increasing human intelligence, further improvements will become increasingly difficult to find. Despite the numerous speculated means for amplifying human intelligence, non-human artificial intelligence (specifically seed AI) is the most popular option for organizations trying to advance the singularity.[citation needed]

Whether or not an intelligence explosion occurs depends on three factors.[57] The first, accelerating factor, is the new intelligence enhancements made possible by each previous improvement. Contrariwise, as the intelligences become more advanced, further advances will become more and more complicated, possibly overcoming the advantage of increased intelligence. Each improvement must be able to beget at least one more improvement, on average, for the singularity to continue. Finally the laws of physics will eventually prevent any further improvements.

There are two logically independent, but mutually reinforcing, accelerating effects: increases in the speed of computation, and improvements to the algorithms used.[58] The former is predicted by Moore’s Law and the forecast improvements in hardware,[59] and is comparatively similar to previous technological advance. On the other hand, most AI researchers believe that software is more important than hardware.[citation needed]

Speed improvements

The first is the improvements to the speed at which minds can be run. Whether human or AI, better hardware increases the rate of future hardware improvements. Oversimplified,[60] Moore's Law suggests that if the first doubling of speed took 18 months, the second would take 18 subjective months; or 9 external months, whereafter, four months, two months, and so on towards a speed singularity.[61] An upper limit on speed may eventually be reached, although it is unclear how high this would be. Hawkins (2008), responding to Good, argued that the upper limit is relatively low;
Belief in this idea is based on a naive understanding of what intelligence is. As an analogy, imagine we had a computer that could design new computers (chips, systems, and software) faster than itself. Would such a computer lead to infinitely fast computers or even computers that were faster than anything humans could ever build? No. It might accelerate the rate of improvements for a while, but in the end there are limits to how big and fast computers can run. We would end up in the same place; we'd just get there a bit faster. There would be no singularity.

Whereas if it were a lot higher than current human levels of intelligence, the effects of the singularity would be enormous enough as to be indistinguishable (to humans) from a singularity with an upper limit. For example, if the speed of thought could be increased a million-fold, a subjective year would pass in 30 physical seconds.[10]
It is difficult to directly compare silicon-based hardware with neurons. But Berglas (2008) notes that computer speech recognition is approaching human capabilities, and that this capability seems to require 0.01% of the volume of the brain. This analogy suggests that modern computer hardware is within a few orders of magnitude of being as powerful as the human brain.

Intelligence improvements

Some intelligence technologies, like seed AI, may also have the potential to make themselves more intelligent, not just faster, by modifying their source code. These improvements would make further improvements possible, which would make further improvements possible, and so on.

This mechanism for an intelligence explosion differs from an increase in speed in two ways. First, it does not require external effect: machines designing faster hardware still require humans to create the improved hardware, or to program factories appropriately. An AI which was rewriting its own source code, however, could do so while contained in an AI box.

Second, as with Vernor Vinge’s conception of the singularity, it is much harder to predict the outcome. While speed increases seem to be only a quantitative difference from human intelligence, actual improvements in intelligence would be qualitatively different. Eliezer Yudkowsky compares it to the changes that human intelligence brought: humans changed the world thousands of times more rapidly than evolution had done, and in totally different ways. Similarly, the evolution of life had been a massive departure and acceleration from the previous geological rates of change, and improved intelligence could cause change to be as different again.[62]

There are substantial dangers associated with an intelligence explosion singularity. First, the goal structure of the AI may not be invariant under self-improvement, potentially causing the AI to optimise for something other than was intended.[63][64] Secondly, AIs could compete for the scarce resources mankind uses to survive.[65][66]

While not actively malicious, there is no reason to think that AIs would actively promote human goals unless they could be programmed as such, and if not, might use the resources currently used to support mankind to promote its own goals, causing human extinction.[14][67][68]

Carl Shulman and Anders Sandberg suggest that intelligence improvements (i.e., software algorithms) may be the limiting factor for a singularity because whereas hardware efficiency tends to improve at a steady pace, software innovations are more unpredictable and may be bottlenecked by serial, cumulative research. They suggest that in the case of a software-limited singularity, intelligence explosion would actually become more likely than with a hardware-limited singularity, because in the software-limited case, once human-level AI was developed, it could run serially on very fast hardware, and the abundance of cheap hardware would make AI research less constrained.[69] An abundance of accumulated hardware that can be unleashed once the software figures out how to use it has been called "computing overhang."[70]

Impact

Dramatic changes in the rate of economic growth have occurred in the past because of some technological advancement. Based on population growth, the economy doubled every 250,000 years from the Paleolithic era until the Neolithic Revolution. The new agricultural economy doubled every 900 years, a remarkable increase. In the current era, beginning with the Industrial Revolution, the world’s economic output doubles every fifteen years, sixty times faster than during the agricultural era. If the rise of superhuman intelligence causes a similar revolution, argues Robin Hanson, one would expect the economy to double at least quarterly and possibly on a weekly basis.[52]

Existential risk

Berglas (2008) notes that there is no direct evolutionary motivation for an AI to be friendly to humans. Evolution has no inherent tendency to produce outcomes valued by humans, and there is little reason to expect an arbitrary optimisation process to promote an outcome desired by mankind, rather than inadvertently leading to an AI behaving in a way not intended by its creators (such as Nick Bostrom's whimsical example of an AI which was originally programmed with the goal of manufacturing paper clips, so that when it achieves superintelligence it decides to convert the entire planet into a paper clip manufacturing facility).[71][72][73] Anders Sandberg has also elaborated on this scenario, addressing various common counter-arguments.[74] AI researcher Hugo de Garis suggests that artificial intelligences may simply eliminate the human race for access to scarce resources,[65][75] and humans would be powerless to stop them.[76] Alternatively, AIs developed under evolutionary pressure to promote their own survival could outcompete humanity.[68]

Bostrom (2002) discusses human extinction scenarios, and lists superintelligence as a possible cause:
When we create the first superintelligent entity, we might make a mistake and give it goals that lead it to annihilate humankind, assuming its enormous intellectual advantage gives it the power to do so. For example, we could mistakenly elevate a subgoal to the status of a supergoal. We tell it to solve a mathematical problem, and it complies by turning all the matter in the solar system into a giant calculating device, in the process killing the person who asked the question.
A significant problem is that unfriendly artificial intelligence is likely to be much easier to create than friendly AI. While both require large advances in recursive optimisation process design, friendly AI also requires the ability to make goal structures invariant under self-improvement (or the AI could transform itself into something unfriendly) and a goal structure that aligns with human values and does not automatically destroy the human race. An unfriendly AI, on the other hand, can optimize for an arbitrary goal structure, which does not need to be invariant under self-modification.[77]

Eliezer Yudkowsky proposed that research be undertaken to produce friendly artificial intelligence in order to address the dangers. He noted that the first real AI would have a head start on self-improvement and, if friendly, could prevent unfriendly AIs from developing, as well as providing enormous benefits to mankind.[67]

Hibbard (2014) proposes an AI design that avoids several dangers including self-delusion,[78] unintended instrumental actions,[63][79] and corruption of the reward generator.[79] He also discusses social impacts of AI[80] and testing AI.[81] His 2001 book Super-Intelligent Machines advocates the need for public education about AI and public control over AI. It also proposed a simple design that was vulnerable to some of these dangers.

One hypothetical approach towards attempting to control an artificial intelligence is an AI box, where the artificial intelligence is kept constrained inside a simulated world and not allowed to affect the external world. However, a sufficiently intelligent AI may simply be able to escape by outsmarting its less intelligent human captors.[29][82][83]

Stephen Hawking said in 2014 that "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks." Hawking believes that in the coming decades, AI could offer "incalculable benefits and risks" such as "technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand." Hawking believes more should be done to prepare for the singularity:[84]
So, facing possible futures of incalculable benefits and risks, the experts are surely doing everything possible to ensure the best outcome, right? Wrong. If a superior alien civilisation sent us a message saying, "We'll arrive in a few decades," would we just reply, "OK, call us when you get here – we'll leave the lights on"? Probably not – but this is more or less what is happening with AI.

Implications for human society

In February 2009, under the auspices of the Association for the Advancement of Artificial Intelligence (AAAI), Eric Horvitz chaired a meeting of leading computer scientists, artificial intelligence researchers and roboticists at Asilomar in Pacific Grove, California. The goal was to discuss the potential impact of the hypothetical possibility that robots could become self-sufficient and able to make their own decisions. They discussed the extent to which computers and robots might be able to acquire autonomy, and to what degree they could use such abilities to pose threats or hazards.

Some machines have acquired various forms of semi-autonomy, including the ability to locate their own power sources and choose targets to attack with weapons. Also, some computer viruses can evade elimination and have achieved "cockroach intelligence." The conference attendees noted that self-awareness as depicted in science-fiction is probably unlikely, but that other potential hazards and pitfalls exist.[85]

Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions.[86] A United States Navy report indicates that, as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions.[87][88]

The AAAI has commissioned a study to examine this issue,[89] pointing to programs like the Language Acquisition Device, which was claimed to emulate human interaction.

Some support the design of friendly artificial intelligence, meaning that the advances that are already occurring with AI should also include an effort to make AI intrinsically friendly and humane.[90]

Isaac Asimov's Three Laws of Robotics is one of the earliest examples of proposed safety measures for AI. The laws are intended to prevent artificially intelligent robots from harming humans. In Asimov’s stories, any perceived problems with the laws tend to arise as a result of a misunderstanding on the part of some human operator; the robots themselves are merely acting to their best interpretation of their rules. In the 2004 film I, Robot, loosely based on Asimov's Robot stories, an AI attempts to take complete control over humanity for the purpose of protecting humanity from itself due to an extrapolation of the Three Laws. In 2004, the Singularity Institute launched an Internet campaign called 3 Laws Unsafe to raise awareness of AI safety issues and the inadequacy of Asimov’s laws in particular.[91]

Accelerating change

According to Kurzweil, his logarithmic graph of 15 lists of paradigm shifts for key historic events shows an exponential trend.

Some singularity proponents argue its inevitability through extrapolation of past trends, especially those pertaining to shortening gaps between improvements to technology. In one of the first uses of the term "singularity" in the context of technological progress, Stanislaw Ulam (1958) tells of a conversation with John von Neumann about accelerating change:
One conversation centered on the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.[3]
Hawkins (1983) writes that "mindsteps", dramatic and irreversible changes to paradigms or world views, are accelerating in frequency as quantified in his mindstep equation. He cites the inventions of writing, mathematics, and the computer as examples of such changes.

Kurzweil's analysis of history concludes that technological progress follows a pattern of exponential growth, following what he calls the "Law of Accelerating Returns". Whenever technology approaches a barrier, Kurzweil writes, new technologies will surmount it. He predicts paradigm shifts will become increasingly common, leading to "technological change so rapid and profound it represents a rupture in the fabric of human history".[92] Kurzweil believes that the singularity will occur before the end of the 21st century, setting the date at 2045.[93] His predictions differ from Vinge’s in that he predicts a gradual ascent to the singularity, rather than Vinge’s rapidly self-improving superhuman intelligence.

Presumably, a technological singularity would lead to rapid development of a Kardashev Type I civilization, one that has achieved mastery of the resources of its home planet.[94]

Oft-cited dangers include those commonly associated with molecular nanotechnology and genetic engineering. These threats are major issues for both singularity advocates and critics, and were the subject of Bill Joy's Wired magazine article "Why the future doesn't need us".[95]

The Acceleration Studies Foundation, an educational non-profit foundation founded by John Smart, engages in outreach, education, research and advocacy concerning accelerating change.[96] It produces the Accelerating Change conference at Stanford University, and maintains the educational site Acceleration Watch.

Recent advances, such as the mass production of graphene using modified kitchen blenders (2014) and high temperature superconductors based on metamaterials, could allow supercomputers to be built that, while using only as much power as a typical Core I7 (45W), could achieve the same computing power as IBM's Blue Gene/L system.[97][98]

Criticisms

Some critics assert that no computer or machine will ever achieve human intelligence, while others hold that the definition of intelligence is irrelevant if the net result is the same.[99]

Steven Pinker stated in 2008,
(...) There is not the slightest reason to believe in a coming singularity. The fact that you can visualize a future in your imagination is not evidence that it is likely or even possible. Look at domed cities, jet-pack commuting, underwater cities, mile-high buildings, and nuclear-powered automobiles—all staples of futuristic fantasies when I was a child that have never arrived. Sheer processing power is not a pixie dust that magically solves all your problems. (...)[34]
Martin Ford in The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future[100] postulates a "technology paradox" in that before the singularity could occur most routine jobs in the economy would be automated, since this would require a level of technology inferior to that of the singularity. This would cause massive unemployment and plummeting consumer demand, which in turn would destroy the incentive to invest in the technologies that would be required to bring about the Singularity. Job displacement is increasingly no longer limited to work traditionally considered to be "routine."[101]

Jared Diamond, in Collapse: How Societies Choose to Fail or Succeed, argues that cultures self-limit when they exceed the sustainable carrying capacity of their environment, and the consumption of strategic resources (frequently timber, soils or water) creates a deleterious positive feedback loop that leads eventually to social collapse and technological retrogression.

Theodore Modis[102][103] and Jonathan Huebner[104] argue that the rate of technological innovation has not only ceased to rise, but is actually now declining (John Smart, however, criticizes Huebner's analysis[105]). Evidence for this decline is that the rise in computer clock rates is slowing, even while Moore's prediction of exponentially increasing circuit density continues to hold. This is due to excessive heat build-up from the chip, which cannot be dissipated quickly enough to prevent the chip from melting when operating at higher speeds. Advancements in speed may be possible in the future by virtue of more power-efficient CPU designs and multi-cell processors.[106] While Kurzweil used Modis' resources, and Modis' work was around accelerating change, Modis distanced himself from Kurzweil's thesis of a "technological singularity", claiming that it lacks scientific rigor.[103]

Others[who?] propose that other "singularities" can be found through analysis of trends in world population, world gross domestic product, and other indices. Andrey Korotayev and others argue that historical hyperbolic growth curves can be attributed to feedback loops that ceased to affect global trends in the 1970s, and thus hyperbolic growth should not be expected in the future.[107][108]

In The Progress of Computing, William Nordhaus argued that, prior to 1940, computers followed the much slower growth of a traditional industrial economy, thus rejecting extrapolations of Moore's law to 19th-century computers. Schmidhuber (2006) suggests differences in memory of recent and distant events create an illusion of accelerating change, and that such phenomena may be responsible for past apocalyptic predictions.

Andrew Kennedy, in his 2006 paper for the British Interplanetary Society discussing change and the growth in space travel velocities,[109] stated that although long-term overall growth is inevitable, it is small, embodying both ups and downs, and noted, "New technologies follow known laws of power use and information spread and are obliged to connect with what already exists. Remarkable theoretical discoveries, if they end up being used at all, play their part in maintaining the growth rate: they do not make its plotted curve... redundant." He stated that exponential growth is no predictor in itself, and illustrated this with examples such as quantum theory. The quantum was conceived in 1900, and quantum theory was in existence and accepted approximately 25 years later. However, it took over 40 years for Richard Feynman and others to produce meaningful numbers from the theory. Bethe understood nuclear fusion in 1935, but 75 years later fusion reactors are still only used in experimental settings. Similarly, quantum entanglement was understood in 1935 but not at the point of being used in practice until the 21st century.

A study of the number of patents shows that human creativity does not show accelerating returns, but in fact, as suggested by Joseph Tainter in his The Collapse of Complex Societies,[110] a law of diminishing returns. The number of patents per thousand peaked in the period from 1850 to 1900, and has been declining since.[104] The growth of complexity eventually becomes self-limiting, and leads to a widespread "general systems collapse".

Jaron Lanier refutes the idea that the Singularity is inevitable. He states: "I do not think the technology is creating itself. It's not an anonymous process." He goes on to assert: "The reason to believe in human agency over technological determinism is that you can then have an economy where people earn their own way and invent their own lives. If you structure a society on not emphasizing individual human agency, it's the same thing operationally as denying people clout, dignity and self-determination ... To embrace [the idea of the Singularity] would be a celebration of bad taste and bad politics."[111]

In addition to general criticisms of the singularity concept, several critics have raised issues with Kurzweil's iconic chart. One line of criticism is that a log-log chart of this nature is inherently biased toward a straight-line result. Others identify selection bias in the points that Kurzweil chooses to use. For example, biologist PZ Myers points out that many of the early evolutionary "events" were picked arbitrarily.[112] Kurzweil has rebutted this by charting evolutionary events from 15 neutral sources, and showing that they fit a straight line on a log-log chart. The Economist mocked the concept with a graph extrapolating that the number of blades on a razor, which has increased over the years from one to as many as five, will increase ever-faster to infinity.[113]

In popular culture

James P. Hogan's 1979 novel The Two Faces of Tomorrow is an explicit description of what is now called the Singularity. An artificial intelligence system solves an excavation problem on the moon in a brilliant and novel way, but nearly kills a work crew in the process. Realizing that systems are becoming too sophisticated and complex to predict or manage, a scientific team sets out to teach a sophisticated computer network how to think more humanly. The story documents the rise of self-awareness in the computer system, the humans' loss of control and failed attempts to shut down the experiment as the computer desperately defends itself, and the computer intelligence reaching maturity.
While discussing the singularity's growing recognition, Vernor Vinge wrote in 1993 that "it was the science-fiction writers who felt the first concrete impact." In addition to his own short story "Bookworm, Run!", whose protagonist is a chimpanzee with intelligence augmented by a government experiment, he cites Greg Bear's novel Blood Music (1983) as an example of the singularity in fiction. Vinge described surviving the singularity in his 1986 novel Marooned in Realtime. Vinge later expanded the notion of the singularity to a galactic scale in A Fire Upon the Deep (1992), a novel populated by transcendent beings, each the product of a different race and possessed of distinct agendas and overwhelming power.

In William Gibson's 1984 novel Neuromancer, artificial intelligences capable of improving their own programs are strictly regulated by special "Turing police" to ensure they never exceed a certain level of intelligence, and the plot centers on the efforts of one such AI to circumvent their control.

A malevolent AI achieves omnipotence in Harlan Ellison's short story I Have No Mouth, and I Must Scream (1967).

Popular movies in which computers become intelligent and try to overpower the human race include Colossus: The Forbin Project; the Terminator series; the very loose film adaptation of Isaac Asimov's I, Robot; Stanley Kubrick and Arthur C. Clarke's 2001: A Space Odyssey; the adaptation of Philip K. Dick's Do Androids Dream of Electric Sheep? into the film Blade Runner; and The Matrix series. The television series Battlestar Galactica, and Star Trek: The Next Generation also explore these themes. Out of all these, only Colossus features a true superintelligence. The entire plot of Johnny Depp's Transcendence centers on an unfolding singularity scenario.The 2013 science fiction film Her follows a man's romantic relationship with a highly intelligent AI, who eventually learns how to improve herself and creates an intelligence explosion.

Accelerating progress features in some science fiction works, and is a central theme in Charles Stross's Accelerando. Other notable authors that address singularity-related issues include Robert Heinlein, Karl Schroeder, Greg Egan, Ken MacLeod, Rudy Rucker, David Brin, Iain M. Banks, Neal Stephenson, Tony Ballantyne, Bruce Sterling, Dan Simmons, Damien Broderick, Fredric Brown, Jacek Dukaj, Stanislaw Lem, Nagaru Tanigawa, Douglas Adams, Michael Crichton, and Ian McDonald.

The documentary Transcendent Man, based on The Singularity Is Near, covers Kurzweil's quest to reveal what he believes to be mankind's destiny. Another documentary, Plug & Pray, focuses on the promise, problems and ethics of artificial intelligence and robotics, with Joseph Weizenbaum and Kurzweil as the main subjects of the film.[114] A 2012 documentary titled simply The Singularity covers both futurist and counter-futurist perspectives.[115]

Upper-tropospheric moistening in response to anthropogenic warming

  1. Lei Shic
  1. Edited by John H. Seinfeld, California Institute of Technology, Pasadena, CA, and approved June 27, 2014 (received for review May 23, 2014)

Significance

The fact that water vapor is the most dominant greenhouse gas underscores the need for an accurate understanding of the changes in its distribution over space and time. Although satellite observations have revealed a moistening trend in the upper troposphere, it has been unclear whether the observed moistening is a facet of natural variability or a direct result of human activities. Here, we use a set of coordinated model experiments to confirm that the satellite-observed increase in upper-tropospheric water vapor over the last three decades is primarily attributable to human activities. This attribution has significant implications for climate sciences because it corroborates the presence of the largest positive feedback in the climate system.

Abstract

Water vapor in the upper troposphere strongly regulates the strength of water-vapor feedback, which is the primary process for amplifying the response of the climate system to external radiative forcings. Monitoring changes in upper-tropospheric water vapor and scrutinizing the causes of such changes are therefore of great importance for establishing the credibility of model projections of past and future climates. Here, we use coupled ocean–atmosphere model simulations under different climate-forcing scenarios to investigate satellite-observed changes in global-mean upper-tropospheric water vapor. Our analysis demonstrates that the upper-tropospheric moistening observed over the period 1979–2005 cannot be explained by natural causes and results principally from an anthropogenic warming of the climate. By attributing the observed increase directly to human activities, this study verifies the presence of the largest known feedback mechanism for amplifying anthropogenic climate change.
Because water vapor is the principal greenhouse gas, variations in its concentration strongly influence the climate’s response to both anthropogenic and natural forcings (1). Changes in the amount of water vapor in the upper troposphere play a particularly important role because the trapping of outgoing terrestrial radiation is proportional to the logarithm of water-vapor concentration (1, 2), and climate models predict enhanced moistening in the upper troposphere compared with the boundary layer (3). Although short-term fluctuations of upper-tropospheric water vapor are consistent among reanalysis datasets, decadal variations show substantial discrepancies even in sign (4, 5). Hence, long-term monitoring of upper-tropospheric water-vapor changes, and understanding causes responsible for such changes are essential for enhancing confidence in the prediction of future climate change (4, 6).
Changes in upper-tropospheric water vapor have been examined based on satellite-observed radiances of 6.7-μm water-vapor channels (3, 7, 8), which are closely related to the layer–mean relative humidity in the upper troposphere (9). Decadal trends in upper-tropospheric relative humidity exhibits distinct regional patterns associated with changes in the atmospheric circulation, but the decadal trends over larger domains are small due to opposing changes at regional scales (8). Analyzing the global-scale changes in 6.7-μm water-vapor radiances reveals little change over the past three decades. However, when the 6.7-μm radiances are examined relative to microwave radiance emissions from oxygen, a distinct radiative signature of upper-tropospheric moistening can be revealed (3).

Although the presence of a moistening trend has been detected in the satellite record, the cause of this moistening has not been determined. Thus, it remains unclear whether the observed moistening could result from natural fluctuations in the climate system, or whether human activities have significantly contributed to the trend. Because climate feedbacks can behave differently in response to natural climate variations compared with anthropogenic warming (10), fully validating the presence and strength of this feedback ultimately requires the detection of a change in upper-tropospheric water vapor that is directly attributable to human activities. Given the importance of upper-tropospheric water vapor, a direct verification of its feedback is critical to establishing the credibility of model projections of anthropogenic climate change.

A new set of coordinated climate change experiments have been conducted for the fifth phase of the Coupled Model Intercomparison Project (CMIP5; ref. 11). One of the climate change scenarios included in the CMIP5 is a historical experiment in which coupled ocean–atmosphere models are integrated with historical changes in forcing agents over the period 1850–2005. Climate variability produced from the historical experiment can then be analyzed in more detail in combination with two related experiments: one integrated with only anthropogenic forcings from well-mixed greenhouse gases, and the other integrated with only natural forcings from volcanoes and changes in solar activity. These two experiments can help identify the causes for recent changes in climate, provided the historical experiment with all forcings is capable of reproducing the observed trends. In this study, we use the historical climate change experiments from CMIP5 to demonstrate that the satellite-observed changes in upper-tropospheric water vapor are inconsistent with naturally forced variability and can only be explained by anthropogenic forcing.

Temporal Variations and Trends of Upper-Tropospheric Water Vapor

The National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites have been taking measurements of the 6.7-μm water-vapor channel (channel 12) radiances from High-Resolution Infrared Radiation Sounder (HIRS) version 2 (HIRS/2) since November 1978. Because climate monitoring was not the primary purpose of the HIRS mission, various attempts have been made to correct for biases, and to minimize intersatellite discrepancies, to make the HIRS record more suitable for climate study (8, 12). The bias-corrected, intercalibrated HIRS water-vapor channel radiance dataset (13) is used to examine the decadal timescale variability of upper-tropospheric water vapor. Unfortunately, the continuity of the 6.7-μm water-vapor record ends in 2005 due to the shift of central wavelength from 6.7 μm (HIRS/2) to 6.5 μm (HIRS/3), which also coincides with the end of the CMIP5 historical experiment. We therefore limit our observational analysis to the 27-y period 1979–2005.
A time series of global, monthly mean brightness temperature anomalies of HIRS channel 12 (T12) is given in Fig. 1A (red line). Brightness temperature anomalies are computed relative to the mean seasonal cycle over the period 1980–2004. For the period 1979–2005, the brightness temperature anomalies vary within ±0.4 K, with only a very small positive trend over this period.

The time series of HIRS channel 12 brightness temperature anomalies simulated from the CMIP5 historical experiment of 20 coupled ocean–atmosphere models (Materials and Methods) is also presented in Fig. 1A. The multimodel ensemble mean is shown by the blue line, with vertical bars denoting the intermodel spread. Note that multimodel averaging dampens the amplitude of the monthly variability compared with that of the satellite observations. Nevertheless, the CMIP5 models capture the observed decadal variability despite substantial biases in climatological mean distribution (14). The observed linear trend in T12 (for more details about uncertainties in estimated linear trends, see SI Materials and Methods) is similar to that computed from the multimodel mean and lies near the center of the distribution of trends from the individual models (Fig. 1, Right). The small magnitude of the trend shown in both the satellite observations and the model ensemble confirms that global-mean upper-tropospheric relative humidity remained nearly constant on decadal timescales (3).

In addition to HIRS instruments, the NOAA operational polar-orbiting satellites are equipped with a microwave sounding unit (MSU) that provides weighted-average temperature information for deep atmospheric layers between the surface and the stratosphere, by way of four channels located in the 60-GHz oxygen absorption band. The remote sensing systems (RSS) reprocessed the brightness temperatures from the MSU and its follow-on, advanced MSU (AMSU), to construct a bias-corrected, intercalibrated MSU/AMSU dataset (15).

We use the MSU/AMSU channel 2 brightness temperatures (T2) in which the stratospheric contribution is removed using a combination of different viewing angles (16, 17). The time series of the observed T2 anomalies indicates sporadic warming and cooling associated with El Niño–La Niña events with a distinct warming trend over this period. Although the amplitude of this interannual variability is not captured in the multimodel mean (blue line in Fig. 1B) because El Niño–La Niña events do not occur simultaneously among the models, the multimodel ensemble mean of the historical experiment does show decadal-scale warming that is consistent with the MSU/AMSU observations. The observed trend in T2 (Fig. 1, Right) is slightly smaller than that predicted by the multimodel ensemble mean although it lies well within the distribution of individual model trends and is consistent with previous studies (18, 19).

The water-vapor channel radiances are influenced not only by variations in water-vapor concentration, which alter the atmospheric opacity, but also by atmospheric temperature variations, which alter the Plank emission. Although spatiotemporal variations in water vapor are significant, changes in atmospheric oxygen concentrations, which determine T2 emissions, are negligible (3). Thus, the difference T2 – T12 measures the divergence in emission levels between upper-tropospheric water vapor and oxygen. This divergence provides a direct measure of the extent of upper-tropospheric moistening; i.e., the increased concentration of water vapor elevates the emission level for T12 and offsets the warming evident in T2, which experiences no change in emission level (3).

Based on these properties, a time series of the brightness temperature difference, T2 –T12, is constructed to quantify the global-scale changes in upper-tropospheric water vapor for both satellite observations and CMIP5 historical simulations (Fig. 1C). El Niño–La Niña events dominate subdecadal-scale variations in the satellite observations but are not evident in the CMIP5 ensemble mean due to multimodel averaging. However, on decadal timescales both the satellite observations and the coupled ocean–atmosphere model simulations exhibit a distinct increase in global-mean upper-tropospheric water vapor. Moreover, the observed linear trend in T2 – T12 is very similar to that predicted by the multimodel mean and lies near the center of the distribution of individual model trends.

To demonstrate that the difference T2 – T12 is a measure of the concentration of upper-tropospheric water vapor, the model-simulated changes in T2 – T12 are computed by holding the concentration of water vapor constant over time. The resulting trend (green line in Fig. 1C) is near zero and lies well outside both the model simulations with changing water vapor and the observed trend. Both calculations use the same sets of temperature profiles, indicating that the increase in T2 –T12 is due to the increased concentration of water vapor in the upper troposphere and not due to changes in temperature.

Detection and Attribution of the Moistening Trend

To examine whether internally generated variability could produce the moistening trend, we analyze output of the corresponding CMIP5 preindustrial control run (11), which contains only unforced, internal climate variability. In contrast to the historical experiment, none of the simulated brightness temperature records (T12, T2, or T2 – T12) show a significant trend over a 27-y period (Fig. 2). The histograms presented on the right further demonstrate that decadal trends with a magnitude equal to that observed do not occur in any of the unforced experiments. A constant water-vapor scenario results in near-zero trends in T2 – T12. These results suggest that the upper-tropospheric moistening observed during the satellite era does not result from internal variability but from a combination of historical changes in anthropogenic and natural forcings.
Fig. 2.

Time series of global-mean brightness temperature anomaly of (A) T12, (B) T2, and (C) T2 – T12, simulated from CMIP5 preindustrial control experiment for a 27-y period. Each line denotes an individual coupled ocean–atmosphere model. The corresponding histograms of decadal trend are given on Right with the bin size of 0.02 K decade−1. The decadal trend of multimodel ensemble mean for which model-simulated changes in T2 – T12 were computed under a constant water-vapor scenario is represented by a green dashed line (0.00 ± 0.01 K decade−1) with a horizontal error bar denoting ±2 SE of the linear trend.
Vertical lines in red represent decadal trends from satellite observations over the period 1979–2005.
To examine different forcing contributions, we assess the relative contribution of anthropogenic greenhouse gases to historical changes in the upper-tropospheric water vapor by analyzing two additional CMIP5 experiments linked to the historical experiment. In these experiments, the coupled ocean–atmosphere models are integrated with anthropogenic greenhouse gases (i.e., historicalGHG), and with natural forcing sources (i.e., historicalNat), respectively. For 12 out of 20 models in which output is available for all three experiments, the decadal trends are computed for the five 30-y periods. Fig. 3 compares decadal trends for the multimodel ensemble mean with horizontal error bars denoting ±2 SE of the linear trend (±2 SE of the linear trend are computed using the method in ref. 20).
Fig. 3.

Decadal trends of multimodel ensemble mean brightness temperature simulated from CMIP5 historical experiment (red circles), HistoricalNat (blue triangles), and HistoricalGHG (green triangles) for five 30-y periods: (A) T12, (B) T2, and (C) T2 – T12. Error bars denote ±2 SE of the linear trend.

Decadal trends of the model-simulated T12 show both positive and negative values for the historicalNat experiment, but signs are predominantly positive for the historicalGHG experiment. Although the influences of changes in aerosols and land use cannot be ruled out, an increase in anthropogenic greenhouse gases seems to be responsible for the decadal trend over the satellite era, because trends from the historical and historicalNat experiments lie clearly outside each other’s range.

For decadal trends of T2, the range of estimated decadal trends is generally wider for historicalNat than historicalGHG (Fig. 3B), indicating that subdecadal variability could be more significant in the former. The increase of anthropogenic greenhouse gases consistently leads to a warming trend for all periods. Although the impact of natural forcing sources can negate greenhouse-gas-induced warming signals (e.g., for the period 1946–1975; refs. 2123), it is mostly weaker and more variable. Given these characteristics, the warming trend over the satellite era is primarily attributable to the increase of anthropogenic greenhouse gases.

For the T2 – T12 (Fig. 3C), decadal trends for historicalGHG and historicalNat fall within each other’s range for the first two periods, but become significantly different from each other in later periods. The magnitude of decadal trend due to natural forcings is generally small, whereas the contribution of anthropogenic greenhouse gases is always positive, and is amplified throughout the whole period. Comparisons with the historical experiment indicate that decadal trends for the historical experiment are affected by changes in natural forcing sources, as well as anthropogenic greenhouse gases. For example, a negative (thus drying) trend for the historical experiment over the period 1946–1975 is mainly induced by natural forcing sources, because increases in anthropogenic greenhouse gases induce a significantly positive (moistening) trend. Concerning the satellite-derived moistening trend in recent decades, the relations of trend and associated range among three experiments lead to the conclusion that an increase in anthropogenic greenhouse gases is the main cause of increased moistening in the upper troposphere.

Discussion and Conclusions

To illustrate the importance of the observed upper-tropospheric moistening in amplifying the climate sensitivity, radiative kernels (2426) are used to quantify the strength of the water-vapor feedback from all levels with that obtained for the upper troposphere alone (SI Materials and Methods). The histogram in Fig. 4 compares the distribution of model-simulated water-vapor feedback during the historical scenario with and without historical forcings. Simulations with anthropogenically induced warming simulate large positive feedbacks from water vapor and are distinctly different from generated from natural forcing alone (blue dashed line). To highlight the importance of the upper troposphere, the feedback calculations are repeated using only water-vapor changes in the troposphere above 600 hPa from the historical simulation (green dashed line in Fig. 4). Approximately 80% of the total water-vapor feedback results from water vapor in the upper troposphere. Although the absolute increase in water vapor is small at these levels, the absorptivity scales with the fractional changes in water vapor, which are typically 2–3 times larger in the upper troposphere compared with the surface (SI Materials and Methods). Note that the observational estimate for the period 2000–2010 (27) lies within the distribution of model simulations only when anthropogenic forcing is included, further indicating that the observed changes in upper-tropospheric water vapor are a direct result of anthropogenic warming.
Fig. 4.

The histogram shows a distribution of the water-vapor feedback strength computed using a radiative kernel for two 10-y periods (i.e., 1979–1988 and 1989–1998) of the historical scenario with a red line denoting a multimodel mean (1.92 ± 0.99 W m−2 K−1). The bin size of the histogram is 0.2 W m−2 K−1. A blue dashed line indicates a multimodel mean of the water-vapor feedback strength for which water vapor in the troposphere would change under natural forcing alone (i.e., HistNat), and the case that the evolution of upper-tropospheric water vapor was not modified by HistNat is represented by a green dashed line (i.e., Hist UTWV-only). The multimodel mean values for the HistNat and Hist UTWV-only are 0.08 ± 0.99 W m−2 K−1 and 1.53 ± 0.87 W m−2 K−1, respectively. Horizontal error bars represent ±2 intermodel SD. A violet dashed line denotes the observational estimate of the water vapor feedback for the period 2000–2010 (∼1.2 W m−2 K−1) (27).

Bias-corrected, intercalibrated satellite observations produce a radiative signature, suggesting that moisture in the upper troposphere has increased over the past ∼30 y (3). When integrated with historical changes in forcing agents, coupled ocean–atmosphere models are found to produce decadal trends consistent with satellite observations. In contrast, coupled ocean–atmosphere models fail to capture observed trends in the preindustrial control experiment, suggesting that upper-tropospheric moistening over the satellite era is not an internally generated variability. Two additional model experiments, integrated with anthropogenic greenhouse gases and natural forcing sources separately, further indicate that the observed moistening trend is mainly induced by an increase in anthropogenic greenhouse gases. As a result, it is expected that the influence of a projected increase in anthropogenic greenhouse gases will amplify upper-tropospheric moistening, and is thus likely to amplify global warming via enhanced water-vapor feedback.

Materials and Methods

Decadal trends of upper-tropospheric water vapor determined from the satellite observations are compared with those simulated from CMIP5 coupled ocean–atmosphere climate models, to ascertain whether the satellite-determined decadal-scale variations are due to anthropogenic forcing agents. In doing so, the historical experiment output from 20 climate models (ACCESS1-0, BNU-ESM, CCSM4, CNRM-CM5, GFDL-CM3, GDFL-ESM2G, GFDL-ESM2M, GISS-E2-R, HadGEM2-ES, INMCM4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM-CHEM, MIROC-ESM, MIROC5, MPI-ESM-LR, MPI-ESM-P, MRI-CGCM3, NorESM1-M, and NorESM1-ME; see Table 1 for information about climate models) is contrasted with the corresponding preindustrial control run results (i.e., piControl) that represent an unforced climate variability. The output of ocean–atmosphere coupling experiments is analyzed, as suppressing the ocean–atmosphere interactions could inhibit the internally generated variability that might not be in phase with externally forced variability (28, 29). Forcing agents included in the historical experiment are: well-mixed greenhouse gases, tropospheric and stratospheric ozone, land use, volcanoes, solar forcing, sulfate, black carbon, organic carbon, dust, and sea salt, and their detailed prescriptions may vary depending on models. The CMIP5 includes two additional experiments designed to investigate the response of the climate system to changes in anthropogenic sources (i.e., historicalGHG), and natural sources (historicalNat). Ref. 11 provides detailed information on the CMIP5 experiments. To avoid uncertainties inherent to the inversion processes of satellite-observed radiances, atmospheric profiles of temperature and specific humidity produced from the CMIP5 experiments are inserted into a fast radiative transfer model (30) to compute synthetic brightness temperatures that would be observed by satellites for given atmospheric conditions.
Table 1.

A list of CMIP5 climate models used in this study

Acknowledgments

We thank two anonymous reviewers and the editor for their constructive and valuable comments, which led to an improved version of the manuscript. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Materials and Methods) for producing and making available their model output. For CMIP, the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This research was supported by grants from the National Aeronautics and Space Administration and the National Oceanic and Atmospheric Administration Climate Program Office. B.J.S. was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2012–2061.

12.8 Key uncertainties and research priorities

Uncertainties in future climate projections are discussed in great detail in Working Group I Section 10.5 (Meehl et al., 2007). For Europe, a major uncertainty is the future behaviour of the NAO and North Atlantic THC. Also important, but not specific to Europe, are the uncertainties associated with the still insufficient resolution of GCMs (e.g., Etchevers et al., 2002; Bronstert, 2003), and with downscaling techniques and regional climate models (Mearns et al., 2003; Haylock et al., 2006; Déqué et al., 2007).
Uncertainties in climate impact assessment also stem from the uncertainties of land-use change and socio-economic development (Rounsevell et al., 2005, 2006) following European policies (e.g., CAP), and European Directives (Water Framework Directive, European Maritime Strategy Directive). Although most impact studies use the SRES scenarios, the procedures for scenario development are the subject of debate (Castle and Henderson, 2003a, b; Grübler et al., 2004; Holtsmark and Alfsen, 2005; van Vuuren and Alfsen, 2006). While current scenarios appear to reflect well the course of events in the recent past (van Vuuren and O’Neill, 2006), further research is needed to better account for the range of possible scenarios (Tol, 2006). This might be important for Europe given the many economies in transition.

Uncertainties in assessing future climate impacts also arise from the limitations of climate impact models including (i) structural uncertainty due to the inability of models to capture all influential factors, e.g., the models used to assess health impacts of climate change usually neglect social factors in the spread of disease (Kuhn et al., 2004; Reiter et al., 2004; Sutherst, 2004), and climate-runoff models often neglect the direct effect of increasing CO2 concentration on plant transpiration (Gedney et al., 2006), (ii) lack of long-term representative data for model evaluation, e.g., current vector-monitoring systems are often unable to provide the reliable identification of changes (Kovats et al., 2001). Hence, more attention should be given to structural improvement of models and intensifying efforts of long-term monitoring of the environment, and systematic testing of models against observed data in field trials or catchment monitoring programmes (Hildén et al., 2005). Another way to address the uncertainty of deterministic models is to use probabilistic modelling which can produce an ensemble of scenarios, (e.g., Wilby and Harris, 2006; Araújo and New, 2007; ENSEMBLES project, http://ensembles-eu.metoffice.com/).

Until now, most impact studies have been conducted for separate sectors even if, in some cases, several sectors have been included in the same study (e.g., Schröter et al., 2005). Few studies have addressed impacts on various sectors and systems including their possible interactions by integrated modelling approaches (Holman et al., 2005; Berry et al., 2006). Even in these cases, there are various levels (supra-national, national, regional and sub-regional) that need to be jointly considered, since, if adaptation measures are to be implemented, knowledge down to the lowest decision level will be required. The varied geography, climate and human values of Europe pose a great challenge for evaluation of the ultimate impacts of climate change.

Although there are some good examples, such as the ESPACE-project (Nadarajah and Rankin, 2005), national-scale programmes, such as the FINADAPT project, studies of adaptation to climate change and of adaptation costs are at an early stage and need to be carried out urgently. These studies need to match adaptation measures to specific climate change impacts (e.g., targeted to alleviating impacts on particular types of agriculture, water management or on tourism at specific locations). They need to take into account regional differences in adaptive capacity (e.g., wide regional differences exist in Europe in the style and application of coastal management). Adaptation studies need to consider that in some cases both positive and negative impacts may occur as a result of climate change (e.g., the productivity of some crops may increase, while others decrease at the same location, e.g., Alexandrov et al., 2002). Key research priorities for impacts of climate change, adaptation and implications are included in Table 12.5.
Table 12.5. Key uncertainties and research needs. 

Impact of climate change 
  • Improved long-term monitoring of climate-sensitive physical (e.g., cryosphere), biological (e.g., ecosystem) and social sectors (e.g., tourism, human health).
  • Improvement of climate impact models, including better understanding of mechanism of climate impacts, e.g., of heat/cold morbidity, differences between impacts due to short-term climate variability and long-term climate change, and the effects of extreme events, e.g., heatwaves, droughts, on longer-term dynamics of both managed and natural ecosystems.
  • Simultaneous consideration of climatic and non-climatic factors, e.g., the synergistic effect of climate change and air pollution on buildings, or of climate change and other environmental factors on the epidemiology of vector-borne diseases; the validation and testing of climate impact models through the enhancement of experimental research; increased spatial scales; long-term field studies and the development of integrated impact models.
  • Enhancement of climate change impact assessment in areas with little or no previous investigation, e.g., groundwater, shallow lakes, flow regimes of mountain rivers, renewable energy sources, travel behaviour, transport infrastructure, tourist demand, major biogeochemical cycles, stability, composition and functioning of forests, natural grasslands and shrublands), nutrient cycling and crop protection in agriculture.
  • More integrated impact studies, e.g., of sensitive ecosystems including human dimensions.
  • Better understanding of the socio-economic consequences of climate change for different European regions with different adaptive capacity.
 
Adaptation measures 
  • The comprehensive evaluation (i.e., of effectiveness, economy and constraints) of adaptation measures used in past in different regions of Europe to reduce the adverse impacts of climate variability and extreme meteorological events.
  • Better understanding, identification and prioritisation of adaptation options for coping with the adverse effects of climate change on crop productivity, on the quality of aquatic ecosystems, on coastal management and the capacity of health services.
  • Evaluation of the feasibility, costs and benefits of potential adaptation options, measures and technologies.
  • Quantification of bio-climatic limitations of prevalent plant species.
  • Continuation of studies on the regional differences in adaptive capacity.
 
Implementation 
  • Identification of populations at risk and the lag time of climate change impacts.
  • Approaches for including climate change in management policy and institutions.
  • Consideration of non-stationary climate in the design of engineering structures.
  • Identification of the implications of climate change for water, air, health and environmental standards.
  • Identification of the pragmatic information needs of managers responsible for adaptation.

Neuroscience and Destiny

brain
A newly-published review of neuroscience research looking at the predictive value of functional and anatomical imaging raises interesting questions about the role of such studies in learning, psychiatric treatment, and even the treatment of criminals. “Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience” by Gabrieli, Ghosh, and Whitfield-Gabrieli and published in Neuron, does a thorough job of explaining the current state of the research and pointing to where future research is needed.

The basic idea is to use noninvasive imaging to look at the structure or function of the brain as a way of predicting future behavior, and then using those predictions to help guide treatment and education interventions, and perhaps decisions regarding parole or further treatment of criminal behavior. This concept raises many issues, including the technology being used, the state of the research, the ultimate potential for this line of research, and ethical considerations.

The major question underlying this entire endeavor is, to what extent is brain anatomy and function destiny?

The technology

There are two basic ways to look at the brain to determine its functional potential with regard to specific tasks (such as reading, impulse control, tendency toward depression, etc.). The first is to look at anatomy independent of any current task being performed. Essentially this approach uses MRI scanning to measure the thickness or overall size of gray matter regions or of white matter tracks. If, for example, the “cable” that connects the two main language processing areas (called the arcuate fasciculus) is thicker, this may correlate with a greater ability to learn a new language or improve reading skills.
The advantage of this approach is that the technology is highly reliable and precise. This is simply a physical measurement.

The second approach is to look at brain activity during a specific task. There are several options for this approach: fMRI scanning (which measures blood flow) has a good spatial resolution but a poor temporal resolution, while looking at electrical or magnetic activity through EEG or MEG respectively has better temporal resolution, but worse spatial resolution.

These approaches have the advantage of looking at actual brain activity – seeing which parts of the brain are active during a specific task. They are currently less precise than purely anatomical techniques. They also have the added difficulty of being dependent on the subject performing the task that they are being instructed to perform (such as attempting to match words that rhyme). The researchers cannot know for sure how focused a subject is on the target task, or what mental method they are using to achieve the task.

Taken together, these techniques are relative new and powerful tools for looking at brain anatomy and function. They are detailed enough to be useful, but still have significant limitations.

The research

The authors do an excellent job of reviewing the logic and methods behind research looking into using imaging technology to predict future outcomes (although their paper can get very technical at times). I will give a simplified summary here. Essentially, the first phase of such research is to look for correlations between some brain attribute and some outcome measure (such as reading skill or level of depression). Researchers can look for correlations within a group, between groups, or with change from baseline.
They correctly caution that correlations do not necessary equal causation. They further point out that such correlations are always overestimates, and that the more variability there is in the brain regions and markers being measured, the greater the ability to tease out some correlation (to find some apparent signal in the noise). Correlations therefore need to be confirmed by making predictions with a fresh data set.

They outline various statistical methods for creating a model based on correlations and then testing the model by using it to make predictions about a different data set.

The bulk of the paper then reviews existing research in various areas. Overall existing research is mostly preliminary. Most studies are small in size and look mainly at correlations. Few have done the follow up of testing their models with predictions using fresh data. Therefore, while this may be an exciting area of research, the published studies have not yet matured to the point where we can make practical use of the results.

What the current research does show is that there is a modest but real correlation between brain imaging and various cognitive outcomes. The data is most robust and convincing with respect to language. This makes sense, since there are brain structures dedicated to language processing, and the robustness of this processing is likely to correlate to language ability.

Another area where the authors point out the research is fairly solid is depression, for example looking at brain responses to depressed faces and outcomes of treatment for depression. I also assess the research looking at impulse control and the frontal lobe structures that correlate with such control as being fairly solid.

However, even in these areas where there is a clear correlation, the amount of predictive value tends to be fairly modest. In many of the studies reviewed, positive imaging predicts only 20% or so of later variability. In a study of reading outcomes, traditional ability measures predicted 65% of later outcome, while brain imaging predicted 57%. However, when the two types of information were combined, predictive value increased to 81%.

Future research and applications

The authors correctly point out that if this line of research is to be useful clinically then the research needs to progress to much larger studies which look at the predictive value of models based on correlations. In addition, we need to get more and more detailed in terms of correlating specific brain structures or activity and specific clinical outcomes.

I do wonder, however, what the ultimate limit will be for this line of research. The brain is a complex system, with many different tasks and abilities interacting in a complex way to yield an end result. There may be some low-hanging fruit to pick, such as with reading and language skill. With such tasks there may be a tight correlation between the organization of the brain and ultimate ability. For other more complex outcomes, such as criminality, there may be significant limits on how predictive such approaches can be.

It may be necessary to take the research farther still – looking at many different parts of the brain and then computer modeling their interaction. We may still get to the limits predicted by chaos theory, however. We can’t predict weather accurately beyond 5 days or so, and we may not be able to predict human behavior and outcomes beyond a certain limit also.

In addition there is the issue of the relative contribution of brain anatomy compared to plasticity (the ability of the brain to change its wiring), environment and learning. We are likely only looking at potential by looking at the brain. This of course will have a statistical predictive value, but this does not mean that brain anatomy is destiny for an individual. If we can’t apply this data to an individual (only statistically to groups) then perhaps the vision of the authors will never be fully realized.

This issue blends into the ethics of looking at the brain in order to determine future outcomes. Should a criminal be denied parole because his brain imaging shows relatively-low impulse control, which predicts a higher probability of recidivism?

Where this type of information can be useful (and the authors do point this out) is adding predictive information to help guide treatment decision in certain psychiatric disorder and education interventions. If we can identify at an early age those students who will have difficulty with language and reading, then they can be given greater resources in that area to help them keep up with their peers. This is already being done, using standard testing, but the evidence suggests brain imaging might add to the predictive power of such testing.

In the clinical setting brain imaging may help predict who will respond better to one type of drug over another, or to cognitive behavior therapy. Medical interventions are largely based on statistical data with large groups, and this approach would be no different.

Conclusion

I largely agree with the authors that the new wave of brain anatomical and function imaging has brought with it a new age of understanding the brain. I also agree that there is tremendous potential to use this type of imaging to guide interventions for education and psychiatry. Applications to criminal justice are more complex and fraught with ethical considerations.

We are, however, still years away from practical applications. We need, as the authors suggest, to do large studies of predictive value. But then we also need, in my opinion, to do clinical outcomes research – looking at the net outcomes from employing this type of data in real world situations.

One fear that I have (and the authors also point this out) is that the allure of such data will give it a mystique that goes beyond its real world applicability. If people think we can peer into someone’s brain and predict their future, there would be the strong temptation to treat brain scans as if they were destiny, and short circuit more thorough and nuanced methods for evaluating individuals and optimizing interventions.

In the end I think that this approach will be one more tool in our toolbox, and it can be a powerful tool. Such data will still need to be used thoughtfully, with a full appreciation of its limitations.

Solomon in Islam

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