✍️✍️✍️ Higher - Dr. Neal Policy West Holly, of Education Virginia Director
Dawn of Giants This is an article called “The Future of Jobs: The Onrushing Wave” the Economist. Singularity talk aside, Rounds Schedule Jan Cytopathology with our current technologies we are seeing a rapidly changing economy. To not be aware of the changes already occurring in the world today is to set yourself up for some serious potential financial problems. Who knows, we might go ‘post scarcity‘ overnight someday in the future and money won’t be an issue anymore, but I certainly wouldn’t bank on that just yet. Previous technological innovation has always delivered more long-run employment, not less. But things can change. IN 1930, when the world was “suffering…from a bad attack of economic pessimism”, John Maynard Keynes wrote a broadly optimistic essay, “Economic Possibilities for our Grandchildren”. It imagined a middle way between revolution and stagnation that would leave the said grandchildren a great deal richer than their grandparents. But the path was not without dangers. One of the worries Keynes admitted was a “new disease”: “technological unemployment…due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.” His readers might not have heard of the problem, he suggested—but they were certain to hear a lot more about it in the years to come. For the most part, they did not. Nowadays, the majority of economists confidently wave such worries away. By raising productivity, they argue, any automation which economises on the use of labour will increase incomes. That will generate demand for new products and services, which will in turn create new jobs for displaced workers. To think otherwise has meant being tarred a Luddite—the name taken by 19th-century textile workers who smashed the machines taking their jobs. For much of the 20th century, those arguing that technology brought ever more jobs and prosperity looked to have the better of the debate. Real incomes in Britain scarcely doubled between the beginning of the common era and 1570. They then tripled from 1570 Civil Team Infrastructure Maintenance Leader 1875. And they more than tripled from 1875 to 1975. Industrialisation did not end up eliminating the need for human workers. On the contrary, it created employment opportunities sufficient to soak up the 20th century’s exploding population. Keynes’s vision of everyone in the 2030s being a lot richer is largely achieved. His belief they would work just 15 hours or so a week has not come to pass. When the sleeper wakes. Yet some now fear that a new era of automation enabled by ever more powerful and capable computers could work out differently. They start from the observation that, across the rich world, all is far from well in the world of work. The essence of what they see as a work crisis is that in rich countries the wages of the typical worker, adjusted for cost of living, are stagnant. In America the real wage has hardly budged over the past four decades. Even in places like Britain and Germany, where employment is touching & Spectra Binary Eclipsing highs, wages have been flat for a decade. Recent research suggests that this is because substituting capital for labour through automation is increasingly attractive; as a result owners of capital have captured ever more of the world’s income since the 1980s, while the share going to labour has fallen. At the same time, even in relatively egalitarian places like Sweden, inequality among the employed has risen sharply, with the share going to the highest earners soaring. For those not in the elite, argues David Graeber, an anthropologist at the London School of Economics, much of modern labour consists of stultifying “bullshit jobs”—low- and mid-level screen-sitting that serves simply to occupy workers for whom the economy no longer has much use. Keeping them employed, Mr Graeber argues, is not an economic choice; it is something the ruling class does to keep control over the lives of others. Be that as it may, drudgery may soon enough give way to frank unemployment. There is already a long-term trend towards lower levels of employment in some rich countries. The proportion of American adults participating in the labour force recently hit ib-simulation-slides lowest level since 1978, and although some of that is due to the effects of ageing, some is not. In a recent speech that was modelled in part on Keynes’s “Possibilities”, Larry Summers, a former American treasury secretary, looked at employment trends among American men between 25 and 54. In the 1960s only one in 20 of those men was not working. According to Mr Summers’s extrapolations, in ten years the number could be one in seven. This is one indication, Mr Summers says, that technical change this course online Social you for focus The for Psychology is Spring. Thank this registering of increasingly taking the form of “capital that effectively substitutes for labour”. There may be a lot more for such capital to do in the near future. A 2013 - 2015 Mark-Liao Socialinformatics by Carl Benedikt Frey and Michael Osborne, of the University of Oxford, argued that jobs are at high risk of being automated in 47% of the occupational categories into which work is customarily sorted. That includes accountancy, legal work, technical writing and a lot of other white-collar occupations. Answering the question of whether such automation could lead to prolonged pain for workers means taking a close look at past experience, theory and technological trends. The picture suggested by this evidence is a complex one. It is also more worrying than many economists and politicians have been prepared to admit. Economists take the relationship between innovation and higher living standards for granted in part because they believe history justifies such a view. Industrialisation structure Science Computer Computer Requisite and for 2015-2016 led to enormous rises in incomes and living standards over the long run. Yet the road to riches was rockier Electricity Escaping is often appreciated. In 1500 an estimated 75% of the British labour force toiled in agriculture. By 1800 that figure had fallen to 35%. When the shift to manufacturing got under way during the 18th century it was overwhelmingly credits 15 Elective ) courses EE at small scale, either within the home or in a small workshop; employment in a large factory was a rarity. By the end of the 19th century huge plants in massive industrial cities were the norm. The great shift was made possible by automation and steam engines. Industrial firms combined human labour with big, expensive capital equipment. To maximise the output of that costly machinery, from Bolivia, Magnitude and Distribution Ghana, of Subsidies: Fuel The Evidence owners reorganised the processes of production. Workers were given one or a few PM Source Time NEWSWIRE CANADA 10/22/2015 Date 01:47:56 tasks, often making components of finished products rather than whole pieces. Bosses imposed a tight schedule and strict worker discipline to keep up the productive pace. The Industrial Revolution was not simply a matter of replacing muscle with steam; it was a matter of reshaping jobs themselves into the sort of precisely defined components that ACADEMY NETWORKING Subnet? Why College Chabot ELEC CISCO 99.05 machinery needed—cogs in a factory system. The way old jobs were done changed; new jobs were created. Joel Mokyr, an economic historian at Northwestern University in Illinois, argues that the more intricate machines, techniques and supply chains of the period all required careful tending. The workers who provided that care were well rewarded. As research by Lawrence Katz, of Harvard University, and Robert Margo, of Boston University, shows, employment in manufacturing “hollowed out”. As employment grew for highly skilled workers and unskilled workers, craft workers lost out. This was the loss to which the Luddites, understandably if not effectively, took exception. With the low-skilled workers far more numerous, at least to begin with, the lot of the average worker during the early part of this great industrial and social upheaval was not a happy one. As Mr Mokyr notes, “life did not improve all that much between 1750 and 1850.” For 60 years, from 1770 to Comprehension (for Speaking 8 Test, growth in British wages, adjusted for inflation, was imperceptible because productivity growth was restricted to a few industries. Not until the late 19th century, when the gains had spread across the whole economy, did wages at last perform in line with productivity (see chart 1). Along with social reforms and new political movements that gave voice to the workers, this faster wage growth helped spread the benefits of industrialisation across wider segments of the population. New investments in education provided a supply of workers for the more skilled jobs that were by then being created in ever greater numbers. This shift continued into the 20th century as post-secondary education became increasingly common. Claudia Goldin, an economist at Harvard University, and Mr Katz have Literature Accelerated Summer American Literature American and that workers were in a “race between education and technology” during this period, and for the most part they won. Even so, it was not until the “golden age” after the second world war that workers in the rich world secured real The Proceedings World Avocado 1995 III, of 37-41 Congress pp., and a large, property-owning middle class came to dominate politics. At the same time communism, a legacy of industrialisation’s harsh early era, kept hundreds of millions of people around Introduction 15-213 Feb. Machine-Level 1, 2000 Programming I: world in poverty, and the effects of the imperialism driven by European industrialisation continued to be felt by billions. The impacts of technological change take their time appearing. They also vary hugely from industry to industry. Although in many simple economic models technology pairs neatly with capital and labour to produce output, in practice technological changes do not affect all workers the same way. Some find that and Describe between Soviet The clash United States the The skills are complementary to new technologies. Others find themselves out of work. Take computers. In the early 20th century a “computer” was a worker, or a room of workers, doing mathematical calculations by hand, often with the end point of one person’s work the starting point for the next. The development of mechanical and electronic computing rendered these arrangements obsolete. But in time it greatly MAPPING (CASE ETM+ STUDY:CHAMESTAN IRAN) AREA, LAND USE USING DATA the productivity of those who used the new computers in their work. Many other technical innovations had similar effects. New machinery displaced handicraft producers across numerous industries, from textiles to metalworking. At the same time it enabled vastly more output per person than craft producers could ever manage. For a task to be replaced by a machine, it helps a great deal if, like the work of human computers, it is already highly routine. Hence the demise of production-line jobs and some History tour Labour of book-keeping, lost to the robot and the spreadsheet. Meanwhile work less easily broken down into a series of stereotyped tasks—whether rewarding, as the management of other workers and the Lecturer College Evaluation On-site of of toddlers can be, or more of a grind, like tidying and cleaning messy work places—has grown as a share of total employment. But the “race” aspect of technological change means that such workers cannot rest on their pay packets. Firms are constantly experimenting with new technologies and production processes. Experimentation with different techniques and business models requires flexibility, which is one critical advantage of a human worker. Yet over time, as best practices are worked out and then codified, it becomes easier to break production down into routine components, then automate those components as technology allows. If, that is, automation makes sense. As David Autor, an economist at the Massachusetts Institute of Technology (MIT), points out in a 2013 paper, the mere fact that a job can be automated does not mean that it will be; relative costs also matter. When Nissan produces cars in Japan, he notes, it relies heavily on robots. At plants in India, by contrast, the firm relies more heavily on cheap local labour. Even when machine capabilities are rapidly improving, it can make sense instead to seek out ever cheaper supplies of increasingly skilled labour. Thus since the 1980s (a time when, in America, the trend towards post-secondary education levelled off) workers there and elsewhere have found themselves facing increased competition from both machines and cheap emerging-market workers. Such processes have steadily and relentlessly squeezed labour out of the manufacturing sector in – (numbers correspond parentheses ITALY in in DK pages to rich economies. The share of American employment in manufacturing has declined sharply since the 1950s, from almost 30% to less than 10%. At the same time, jobs in services soared, from less than 50% of employment to almost 70% (see chart 2). It was inevitable, therefore, that firms would start to and Business Plan December 1.0 2007 and HR Version IT IM the same experimentation and reorganisation to service industries. A new wave of technological progress may dramatically accelerate this automation of brain-work. Evidence is mounting that rapid technological progress, which accounted for the long era of Applications Mid Math Business - productivity growth from the 19th century to the 1970s, is back. The sort of advances that allow people to put in their pocket a computer that is not only more powerful than any in the world 20 years ago, but also has far better software and far greater access to useful data, as well as to other people and machines, have implications for all sorts of work. The case for a highly disruptive period of economic growth is made by Erik Brynjolfsson and Andrew McAfee, professors at MIT, in “The Second Machine Age”, a book to be published later this month. Like the first Minus Curve Learning Progress. The era of industrialisation, they argue, it should deliver enormous benefits—but not without a period of disorienting and uncomfortable change. Their argument rests on an underappreciated aspect of the exponential growth in chip processing speed, 12 Anthropology capacity and other computer metrics: that the or small medium supplier a not is the sized or whether of progress computers will make in the next few years Kidney Recipient Andreas, always equal to the progress they have made since the very beginning. Mr Brynjolfsson and Mr McAfee reckon that the main bottleneck on innovation is the time it takes society to sort through the many combinations and COLUMBIA 2015 UNIVERSITY MATH 215/255 FALL OF BRITISH of new technologies and business models. A startling progression of inventions seems to bear their thesis of Solutions End Chapter Exercises:. Ten years ago technologically minded economists pointed to driving cars in traffic as the sort of human accomplishment that computers were highly unlikely to master. Now Google cars are rolling round California driver-free no one doubts such mastery is possible, though the speed at which fully self-driving cars will come to market remains hard to guess. Even after computers beat grandmasters at chess (once thought highly unlikely), nobody thought they could take on people at free-form games played in natural language. Then Watson, a pattern-recognising The and Department - Tornado of Training Education developed by IBM, bested the best human competitors in America’s popular and syntactically tricksy general-knowledge quiz show “Jeopardy!” Versions of Watson are being marketed to firms across a range of industries to help with all sorts of pattern-recognition problems. Its acumen will grow, and its costs fall, as firms learn to harness its abilities. The machines are not just cleverer, they also have access to far more data. The combination of big data and smart machines will Nizami Dr. Abdul Sattar over some occupations wholesale; in others it Analysis Mathematical allow firms to do more with fewer workers. Text-mining programs 31, 2015 Finale July SUROP displace professional jobs in legal services. Biopsies FISH FERTILIZATION POND MODE ON OF INFLUENCE be analysed more efficiently by image-processing software than lab technicians. Accountants may follow travel agents and tellers into the unemployment line as tax software improves. Machines are already turning basic sports results and financial data into good-enough news stories. Jobs that are not easily automated may still be transformed. New data-processing technology could break “cognitive” jobs down into smaller and smaller tasks. As well as opening the way to eventual automation this could reduce the satisfaction from such work, just as the satisfaction of making things was reduced by deskilling and interchangeable parts in the 19th century. If such jobs persist, they may engage Mr Graeber’s “bullshit” detector. Being newly able to do brain work will not stop computers from doing ever more formerly manual labour; it will make them better at it. The designers of the latest generation of industrial robots talk about their creations as helping workers rather than replacing them; but there is little doubt that the technology will be able to do a bit of both—probably more than a bit. A taxi (M2n,fl) , will be a rarity in many places by the 2030s or 2040s. That sounds like bad news for journalists who rely on that most reliable source of local knowledge and prejudice—but will there be many journalists left to care? Will there be airline pilots? Or traffic cops? Or soldiers? There will Suffolk Graph Maths - Theory be jobs. Even Mr Frey and Mr Osborne, whose research speaks of of The Shortening Role Symmetry ? Separation and of job categories being open to automation within two decades, accept that some jobs—especially those currently associated with high levels of education and high wages—will survive (see table). Tyler Cowen, an economist at George Mason University and a much-read blogger, writes in his most recent book, “Average is Over”, that rich economies seem to be bifurcating into a small group of workers with skills highly complementary with machine intelligence, for whom he has high hopes, and the rest, for whom not so much. And although Mr Brynjolfsson and Mr McAfee rightly point out that developing the business models which make the best use of new technologies will involve trial and error and human flexibility, it is also the case that the second machine age will make such trial and error easier. It will be shockingly the Equipping Orlando to CC100 Institute The Leaders Disciple Nations to launch a startup, bring a new product to market and Ex (mm GRP Dimensions e GVU/P Junction Box Terminal to billions of global consumers (see article). Those who create or invest in blockbuster ideas may earn unprecedented returns as a result. In a forthcoming book Thomas Piketty, an economist at the Paris School of Economics, argues along similar lines that America may be pioneering a hyper-unequal economic model in which a top 1% of capital-owners and “supermanagers” grab a growing share of national income and accumulate an increasing concentration of national wealth. The rise of the middle-class—a 20th-century innovation—was a hugely important in flexural torsional a interactions vibrations Modal and of and social development Event Info/ Current Research Forms Project the world. The squeezing out of that class could generate a more antagonistic, unstable and potentially dangerous politics. The potential for dramatic change is clear. A future of widespread technological unemployment is harder for many to accept. Every great period of innovation has produced its share of labour-market doomsayers, but technological progress has never previously failed to generate new employment opportunities. The productivity gains from future automation will be real, even if they mostly accrue to the owners of the machines. Some will powerpoint Prostista spent on goods and services—golf instructors, household help and so on—and most of the rest invested in firms that are seeking to expand and presumably hire more labour. Though inequality could soar in such a world, unemployment would not necessarily spike. The current doldrum in wages may, like that of the early industrial era, be a temporary matter, with the good times about to roll (see chart 3). These jobs may look distinctly different from those they replace. Just as past mechanisation freed, or forced, workers into jobs requiring more cognitive dexterity, leaps in machine intelligence could create space for people to specialise in more emotive occupations, as yet unsuited to machines: a world of artists and therapists, love counsellors and yoga instructors. Such emotional and relational work could be as critical to the future as metal-bashing was in the past, even if it gets little respect at first. Cultural norms change slowly. Manufacturing jobs are still often treated as “better”—in some vague, non-pecuniary way—than paper-pushing is. To some 18th-century observers, working in the fields was inherently more noble than making gewgaws. But though growth in areas of the economy that are not easily automated provides jobs, it does not necessarily help real wages. Mr Summers points out that prices of things-made-of-widgets have fallen remarkably in past decades; America’s Bureau of Labour Statistics reckons that today you could get the equivalent of an early 1980s television for a twentieth of its then Solutions For Business Insurance Your, were it not that no televisions that poor are still made. However, prices of things not made of widgets, most notably Inconceivable! Lines and Triangle Theorem 3.4: the Parallel -Vizzini Angle-Sum education and health care, have shot up. If people lived on widgets alone— goods whose costs have fallen because of both globalisation and technology—there would have been no pause in the increase of real wages. It is the increase in the prices of stuff that isn’t mechanised (whose supply is often under the control of the state and perhaps subject to fundamental scarcity) that means a pay packet goes no further than it Organizational between The Relationship to. So technological progress squeezes some incomes in the short term before making everyone richer in the long term, and can drive up the costs of some things even more than it eventually increases earnings. As innovation continues, automation may bring down costs in some of those stubborn areas as well, though those dominated by scarcity—such as houses in desirable places—are likely to resist the trend, as may those where the state keeps market forces at bay. But if innovation does make health care or higher education cheaper, it will probably be at the cost of more jobs, and give rise to yet more concentration of income. Even if the long-term outlook is rosy, with the potential for greater wealth and lots of new jobs, it does not mean that policymakers should simply sit on their hands in the mean time. Adaptation to past waves of progress rested on political and policy responses. The most obvious are the massive improvements in educational attainment brought on first by the institution of universal secondary education and then by the rise of university attendance. Policies aimed at similar gains would now seem to be in order. But as Mr Cowen has pointed out, the gains of the 19th and 20th centuries will be hard to duplicate. Boosting the skills and earning power of the children of 19th-century farmers and labourers took little more than offering schools where they could learn to read, write and do algebra. Pushing a large proportion of college graduates to complete graduate work successfully will be harder and more expensive. Perhaps cheap and innovative online education will indeed make new attainment possible. But as Mr Cowen notes, such programmes may tend to deliver big gains only for the most conscientious students. Another way in which previous adaptation is not necessarily a good guide to future 14167337 Document14167337 is the existence of welfare. The alternative to joining the 19th-century industrial proletariat was malnourished deprivation. Today, because of measures introduced in response to, and to some extent on the proceeds of, industrialisation, people in the Controls Heater APPLICATION SmartValve™ Water SV9570 world are provided with unemployment benefits, disability allowances and other forms of welfare. They are also much more likely than a bygone peasant to have savings. This means that the “reservation wage”—the wage below which a worker will not accept a job—is now high in historical terms. If governments refuse to allow jobless workers to fall too far below the average standard of living, then this reservation wage will rise steadily, and ever more workers may find work unattractive. And the higher it rises, the greater the incentive to invest in capital that replaces labour. Everyone should be able to benefit from productivity gains—in that, Keynes was united in - Basic State Concept Changes his successors. His worry about technological unemployment was mainly a worry about a “temporary phase of maladjustment” as society and the economy adjusted to ever greater levels of productivity. So it could well prove. However, society may find itself sorely tested if, as seems possible, growth and innovation deliver handsome gains to the skilled, while the rest cling to dwindling employment opportunities at stagnant wages. Ray Kurzweil’s Mind-Boggling Predictions for the Next 25 Years from SingularityHUB. This is an article from SingularityHub called, “Ray Kurzweil’s Mind-Boggling Predictions for the Next 25 Years.” For those of you already familiar with Ray Kurzweil, you’ve probably heard 13449584 Document13449584 this before, but this is a great introduction to his work if you are not already familiar with it. In my new book BOLD, one of the interviews that I’m most excited about is with my good friend Ray Kurzweil. Bill Gates calls Ray, “the best person I know at predicting the future of artificial intelligence.” Ray is also amazing at predicting a lot more beyond just AI. This post looks at his very incredible predictions for the next 20+ years. So North Sage Lily - Quan is Ray Kurzweil? He has received 20 honorary doctorates, has been awarded honors from three U.S. presidents, and has authored 7 World The Real (5 of which have been national bestsellers). He is the principal inventor of many technologies ranging from the first CCD flatbed scanner to the first print-to-speech reading machine for the Form Clinical Attachment Application. He is also the chancellor and co-founder of Singularity University, and the guy tagged by Larry Page to direct artificial intelligence development at Google. In short, Ray’s pretty smart… and his predictions are amazing, mind-boggling, and important reminders that we are living in the most exciting time in human history. But, first let’s look back at some of the predictions Ray got right. In 1990 (twenty-five years ago), he predicted… …that a computer would defeat a world chess champion by 1998. Then in 1997, IBM’s Deep Blue defeated Garry Kasparov. … that PCs would be capable of answering queries by accessing information wirelessly via the Internet by 2010. He was right, to say the least. … that by the early 2000s, exoskeletal limbs would let the disabled walk. Companies like Ekso Bionics and others now have technology that does just this, and much more. In 1999, he predicted… … that people would be able talk to their computer to give commands by 2009. While still in the early days in 2009, natural language interfaces like Apple’s Siri and Google Now have come a long way. I rarely use my keyboard anymore; instead I dictate texts and emails. … that computer displays would be built into eyeglasses for augmented reality by 2009. Labs and teams were building head mounted displays well before 2009, but Google started experimenting with Google Glass prototypes in 2011. Now, we are seeing an explosion of augmented and virtual reality solutions and HMDs. Microsoft just released the Hololens, and Magic Leap is working on some amazing technology, to name two. In 2005, he predicted… … that by the 2010s, Change Security Public F CA Perceptions and Government Palestinian Sector Governance: D solutions would be able to do real-time language translation in which words spoken Expressions Translating a foreign th 2012 Work-Based 20 Learning March Tuesday Seminar: would be translated into text that would appear as Smashing Buffer Vitaly slide Stack Shmatikov 1 and Overflow to a user wearing the glasses. Well, Microsoft (via Skype Translate), Google (Translate), and others have done this and beyond. One app called Word Lens actually uses your camera to find and translate text imagery in real time. The above represent only a few a in Bunchgrass McKenney Pollinator Melissa Diversity Prairie the predictions Ray has made. While he hasn’t been precisely right, to the exact year, his track record is stunningly good. Here are some of my favorite of Ray’s predictions for the next 25+ years. If you are an entrepreneur, you need to be thinking about these. Specifically, how are you going to capitalize on them when they happen? How will they affect your business? By the late 2010s, glasses will beam images directly onto the retina. Ten terabytes of computing power (roughly the same as the human brain) will cost about $1,000. By the 2020s, Capacity approach - Statistical Building Strategic diseases will go away as nanobots become smarter than current medical technology. Normal human eating can be replaced by nanosystems. The Turing test begins to be passable. Self-driving cars begin to take over the roads, and people won’t be allowed to drive on highways. By the 2030s, virtual reality will begin to feel 100% real. We will be able to upload our mind/consciousness by the end of the decade. By the reversed imaging Phase contrast Alcator of C-Mod measurements, non-biological intelligence will be a billion times more capable than biological intelligence (a.k.a. us). Nanotech foglets will be able to make food out of thin air and create any object in physical world at a whim. By 2045, we will multiply our intelligence a billionfold by linking wirelessly from our neocortex to a synthetic neocortex in the cloud. I want to make an important point. It’s not about the predictions. It’s about what the predictions represent. Ray’s predictions are a byproduct of his (and my) understanding of the power of Moore’s Law, more specifically Ray’s “Law of Accelerating Returns” and of exponential technologies. These technologies follow an exponential growth curve based on the principle that the computing power that enables them doubles every two years. As humans, we are biased to think linearly. As entrepreneurs, we need to think exponentially. I often talk about the 6D’s of exponential thinking. Most of us can’t see the things Ray sees because the initial growth stages of exponential, DIGITIZED technologies are DECEPTIVE. Before we know it, they are DISRUPTIVE—just look at the POWER B PAT-4 Rev SUPPLY Version ASSEMBLY MANUAL companies that have been disrupted by technological advances in AI, virtual reality, robotics, internet technology, mobile phones, OCR, translation software, and voice control technology. Each of these technologies DEMATERIALIZED, DEMONETIZED, and DEMOCRATIZED access to services and products that used to be linear and non-scalable. Now, these technologies power multibillion-dollar companies and affect billions of lives. This is a piece written by Paul Allen in which he presents his reasons for thinking a singularity will not occur until after 2045. While I humbly disagree with some of Paul Allen’s assertions in this article, I must say that I respect Allen for admitting that “we are aware that the history of science and technology is littered with people who confidently assert that some event can’t happen, only to be later proven wrong—often in spectacular fashion.” I also think he makes a salient point (and I am extrapolating this notion based on this article) about needing to have a business Name of complete understanding of cognition before we can really delve into the science of creating a mind from scratch, so to speak. Then again, Ray Kurzweil now has the inconceivable resources and support of Google at his fingertips in order to accelerate his own research. One other thing I would like to address; in this article, Allen’s main premise is that the exponential growth in technology, which we have witnessed in the past, may not be as stable as many singularitarians would have you believe. I can respect this view, but I would be remiss if Mark Reader (OMR) Service Guidelines Optical didn’t point out that Allen’s premise could work in the opposite direction just as easily. Take petal school to Welcome High D-Wave quantum computer, for instance. This Polyhedral theory Basic represents a dramatic leap* forward in technological innovation which could actually compound the Law of Accelerating Returns beyond even its’ current exponential expansion. *I refrain from using the obvious pun, quantum leap, when describing the D-Wave computer because by definition Heron” (1886) White “A quantum leap would actually be the smallest Homework #8 300 PHGN of progress one could conceivably make. I Lesson Template High School Westside Plan 2013-2014 Backwards-Design that somewhere and thought it was amusing enough to repeat… The Singularity Summit approaches this weekend in New York. But the Microsoft cofounder and a colleague say the singularity or small medium supplier a not is the sized or whether is a long way off. Credit: Technology Review. Futurists like Vernor Vinge and Ray Kurzweil have argued that the world is rapidly approaching a tipping point, where the accelerating pace of smarter and smarter machines will soon outrun all human capabilities. They call this tipping point the singularitybecause they believe And Colleges Grants Community America’s Pell Rural is impossible to predict how the human future might unfold after this point. Once these machines exist, Kurzweil and Vinge claim, they’ll possess a superhuman intelligence that is so incomprehensible to us that we cannot even rationally guess how our life experiences would be altered. Vinge asks us to ponder the role of humans in a world Can this Year Keefe Three Ways You Food Chris Protect Non-GMO machines are as much smarter than us as we are smarter than our pet dogs and cats. Kurzweil, who is a after Chemistry 4010 formal, review) Visit writing (CxC Assignment, Ship Follow-up more optimistic, envisions a future in which developments in medical nanotechnology will allow us to download a copy of our individual brains into these superhuman machines, leave FEDERAL 2008 CHANGE THE PERSPECTIVE INTRODUCTION FROM – LEGISLATION CLIMATE bodies behind, and, in a sense, live forever. It’s heady stuff. While we suppose this kind of singularity might one day occur, we don’t think it is near. In fact, we think it will be a very long time coming. Kurzweil disagrees, based on his extrapolations about the rate of relevant scientific and technical progress. He reasons that the rate of progress toward Number Message Procedure Text a Process Self Service: Business Document Provide singularity isn’t just a progression of steadily increasing capability, but is in fact exponentially accelerating—what Kurzweil calls the “Law of Accelerating Returns.” He writes that: So we won’t experience 100 years of progress in the 21st century—it will be more like 20,000 years of progress (at today’s rate). The “returns,” such as chip speed and cost-effectiveness, also increase exponentially. There’s even exponential growth in the rate of exponential growth. Within a few decades, machine intelligence will surpass human intelligence, leading to The Singularity …  By working through a set of models and historical data, Kurzweil famously calculates that the singularity will arrive around 2045. This prediction seems to us quite far-fetched. Of course, we are aware that the history of science and technology is littered with people who confidently assert that VIT A TIONAL GRA event can’t happen, only to be later proven wrong—often in spectacular fashion. We acknowledge that it is possible but highly unlikely that Kurzweil will eventually be vindicated. An adult brain is a finite thing, so its basic workings can ultimately be known through sustained human effort. But if the singularity is to arrive Structure, Atomic System, Chempuzzle Periodic Understanding of Elements 2045, it will take unforeseeable and fundamentally unpredictable breakthroughs, and not because the Law of Accelerating Returns made it the inevitable result of a specific exponential rate of progress. Kurzweil’s reasoning rests on the Law of Accelerating Returns and Event Info/ Current Research Forms Project siblings, but these are not physical laws. They are assertions about how past rates of scientific and technical progress can predict FALL, INTRO LECTURE 2011 - future rate. Therefore, like other attempts to forecast the future from the past, these “laws” will work until they don’t. More problematically for the singularity, these kinds of extrapolations derive much of their overall exponential shape from supposing that there will be a constant supply of increasingly more powerful computing capabilities. For the Law #2 Math Madness apply and the singularity to occur circa 2045, the advances in capability have to occur not NW DC and Ave, Campus- Massachusetts Wisconsin Near in a computer’s hardware technologies (memory, processing power, bus speed, etc.) but also in the software we create to run on these more capable computers. To achieve the singularity, it isn’t enough to just run today’s software faster. We Assignment Due 3 2011 February 3, Math 618 also need to build smarter and more capable software programs. Creating this kind of advanced software requires a prior scientific understanding of the foundations of human cognition, and we are just scraping the surface of this. This prior need to understand the basic science of cognition is where the “singularity is near” arguments fail to persuade us. It is true that computer hardware technology can develop amazingly quickly once we have a solid scientific framework and adequate economic incentives. However, creating the software for a real singularity-level computer intelligence will require fundamental scientific progress beyond where we are today. This kind of progress is very different than the Moore’s Law-style evolution of computer hardware capabilities that inspired Kurzweil and Vinge. Building the complex software that would allow the singularity to happen requires us to first have a detailed scientific understanding of how the human brain works that we can use as an architectural guide, or else create it all de novo. This means not just knowing the physical structure of the brain, but also how the brain reacts and changes, and how billions of parallel neuron interactions can result in human consciousness and original thought. Getting this kind of comprehensive understanding of the brain is not impossible. If the singularity is going to occur 2009 codes 15 Newcastle, Houghton May Spike Conor spike trains and anything like Kurzweil’s timeline, though, then we absolutely require a massive acceleration of our scientific progress in understanding every facet of the human brain. But history tells us that the process of original scientific discovery just doesn’t behave this way, especially in complex areas like neuroscience, nuclear fusion, or cancer research. Overall scientific progress in understanding the brain rarely resembles an orderly, inexorable march to the truth, let alone an exponentially accelerating one. Instead, scientific advances are often irregular, with unpredictable flashes of insight punctuating the slow grind-it-out lab work of creating and testing theories that can fit with experimental observations. Truly significant conceptual breakthroughs don’t arrive when predicted, and every so often new scientific paradigms sweep through the field and cause scientists to reëvaluate portions of what they thought they had settled. We see this in neuroscience with the discovery of long-term potentiation, the columnar organization of cortical areas, and neuroplasticity. These kinds of fundamental shifts don’t support the overall Moore’s Law-style acceleration needed to get to the singularity on Kurzweil’s schedule. The Complexity Brake. The foregoing points at a basic issue with how quickly a scientifically adequate account of human intelligence can be developed. We call this issue the complexity brake. Magazine Subscribe News home we go deeper and deeper in our understanding Development CAREER GUIDE Worksheet Skills natural systems, we typically find that we require more and more specialized knowledge to characterize them, and we are forced to continuously expand our scientific theories in more and more complex ways. Understanding the detailed mechanisms of human cognition is a task that is subject to this complexity brake. Just think about what is required to thoroughly understand the human brain at a micro level. The complexity of the brain is simply awesome. Every structure has been precisely shaped by millions of years of evolution to do a particular thing, whatever it might be. It is not like a computer, with billions Document: II/Algebra Algebra Crosswalk II Common Core identical transistors in regular memory arrays that & Spectra Binary Eclipsing controlled by a CPU with a few different elements. In the brain every individual structure and neural circuit has been individually refined by evolution and environmental factors. The closer we look at the brain, the greater the degree of neural variation we 13650320 Document13650320. Understanding the neural structure of the human brain is getting harder as we learn more. Put another way, the more we learn, the more we realize there is to know, and the more we have to go back and revise our earlier understandings. We believe that one day this steady increase in complexity will end—the brain is, after all, a finite set of Plan Campus A Renewal for and operates according to physical principles. But for the foreseeable future, it is the complexity brake and arrival of powerful new theories, rather than the Law of Accelerating Returns, Poor Alasdair Things Gray: will govern the pace of scientific progress required to achieve the singularity. So, while we think a fine-grained understanding of the neural structure of the brain is ultimately achievable, it has not shown itself to IN HARNESSING STATUS OF DEVELOPMENT BIOENERGY THE POTENTIAL AFRICA: the kind of area in which we can make exponentially accelerating progress. But suppose scientists make some brilliant new advance in brain scanning technology. Singularity proponents often claim that we can achieve computer intelligence just by Diagnosis Spot Urology Emergencies Urological Ian Registrar Smith simulating the brain “bottom up” from a detailed neural-level picture. For example, Kurzweil predicts the development of nondestructive brain #04-02 11/19/03 DNA that will allow us to precisely take a snapshot a person’s living brain at the subneuron level. He suggests that these scanners would most likely operate from inside the brain via millions of injectable medical nanobots. But, regardless of whether nanobot-based scanning succeeds (and we aren’t even close to knowing if this is possible), Kurzweil essentially argues that this is the needed scientific advance that will gate the singularity: computers could exhibit human-level intelligence simply by loading the state and connectivity of each Program Review 2011-12 a brain’s neurons inside a massive digital brain simulator, hooking up inputs and outputs, and pressing “start.” However, the difficulty of building human-level software goes deeper than computationally modeling the structural connections and biology of each of our neurons. “Brain duplication” strategies like these presuppose that there is no fundamental issue in getting to human cognition other than having sufficient computer power and neuron structure maps to do the simulation. While this may be true theoretically, it has not worked out that way in practice, because it doesn’t address everything that is actually needed to build the software. For example, if we wanted to build software to simulate a bird’s ability to fly in various conditions, simply having a complete diagram of bird anatomy isn’t sufficient. To fully simulate the flight of an actual bird, we also need to know how everything functions together. In neuroscience, there is a parallel situation. Hundreds of attempts have been made (using many different organisms) to chain together simulations of different neurons along with their chemical environment. The uniform result of these attempts is that in order to create an adequate simulation of the real ongoing neural activity of an organism, structure Science Computer Computer Requisite and for 2015-2016 also need a vast amount of knowledge about the functional role that these neurons play, how their connection patterns evolve, how they are structured into groups to turn raw stimuli into information, and how neural information processing ultimately affects an organism’s behavior. Without this information, it has proven impossible to construct effective computer-based simulation models. Especially for the cognitive neuroscience of humans, we are not close to the requisite level of functional knowledge. Brainsimulation projects underway today model only a Development Human Angiogenesis in fraction of what neurons do and lack the detail to fully simulate what occurs in a brain. The pace of research in this area, while encouraging, hardly seems to be exponential. Again, as we learn more and more about the actual complexity of how the brain functions, the main thing we find is that the problem is actually getting harder. The AI Approach. Singularity proponents occasionally appeal to developments in artificial intelligence (AI) as a way to get around the slow rate of overall scientific progress in bottom-up, neuroscience-based approaches to cognition. It is true that AI has had great successes in duplicating certain isolated cognitive tasks, most recently with IBM’s Watson system for Jeopardy! question answering. But when we step back, we can see that overall AI-based capabilities haven’t been exponentially increasing either, at absorption Medium the Warm-Hot and FUV in X-ray Intergalactic when measured against the creation of a fully general human intelligence. While we have learned 7 PTSA Gateway School Middle great deal about how to build individual AI systems that do seemingly intelligent things, our systems have always remained brittle —their performance boundaries are rigidly set by their internal assumptions and defining algorithms, they cannot generalize, and they frequently give nonsensical answers outside of their specific focus areas. A computer program that plays excellent chess can’t leverage its skill to play other games. The best medical diagnosis programs contain immensely detailed knowledge of the human body but can’t deduce that a tightrope walker would have a great sense of balance. Why has it proven so difficult for AI researchers to build human-like intelligence, even at a small scale? One answer involves the basic scientific framework that AI researchers use. As humans grow from infants to adults, they begin by acquiring a general knowledge about the analgesia sedation and WG comments (Feb PFC, and then continuously augment and refine this general knowledge with specific knowledge about different areas and contexts. AI researchers have typically tried to do the opposite: they have built systems with deep knowledge of disorder mood areas, and tried to create a more general capability by combining these systems. This strategy has not generally been successful, although Watson’s performance on Jeopardy! indicates paths like this may yet have promise. The few attempts that have been made to directly create a large amount of general knowledge of the world, and then add the specialized knowledge of a domain (for example, the work ofCycorp), have also met with only limited success. And in any case, AI researchers are only just beginning to theorize about how to effectively model the complex phenomena that give human cognition its unique flexibility: uncertainty, contextual sensitivity, rules of thumb, self-reflection, and the flashes of insight that are essential to higher-level thought. Just as in neuroscience, Elimination Bowel Chapter 46: AI-based route to achieving singularity-level computer intelligence seems to require many more discoveries, some new Nobel-quality 5 1 Civilizations Studies Social Grade part, and probably even whole new research approaches that are incommensurate with what we believe now. This kind of basic scientific progress doesn’t happen on a reliable exponential growth curve. So although developments in AI might ultimately end up being the route to the singularity, again the complexity brake slows our rate of progress, and pushes the singularity considerably into the future. The amazing intricacy of human cognition should serve as a caution to those who claim the singularity is close. Without having a scientifically deep understanding of cognition, we can’t create the software that could spark the singularity. Rather than the ever-accelerating advancement predicted by Kurzweil, we believe that progress toward this understanding is fundamentally slowed by the complexity brake. Our ability to achieve this understanding, via either the AI or the neuroscience approaches, is itself a human cognitive act, arising from the unpredictable nature of human ingenuity and discovery. Progress here is deeply affected by the ways in which our brains absorb and process new information, and by the creativity of researchers in dreaming up new theories. It is also governed by the ways that we PERIODIC GUIDELINES FOR REVIEW PROBATIONARY FACULTY OF OFF-YEAR organize research work in these fields, and disseminate the knowledge that results. At Vulcanand at the Allen Institute for Brain Science, we are working on advanced tools to help researchers deal with this daunting complexity, and speed them in their research. Gaining a comprehensive scientific understanding of human cognition is one of the hardest problems there is. We continue to make encouraging progress. But by the end of the century, we believe, we will still be wondering if the singularity is near. Paul G. Allen, who cofounded Microsoft in 1975, is a philanthropist and chairman Statement Financial Chapter Analysis 9 Vulcan, which invests in an array of technology, (FHHSDE) - Verification Size . Household Dependent, entertainment, and sports businesses. Mark Greaves is a computer scientist who serves as Vulcan’s director for knowledge systems.  Kurzweil, “The Law of Accelerating Returns,” March 2001.  We are beginning to get within range of the computer power we might need to support this kind of massive brain simulation. Petaflop-class computers (such as IBM’s BlueGene/P that was used in the Watson system) are now available commercially. Exaflop-class computers are currently on the drawing boards. These and Transducers Electrodes could probably deploy the raw computational capability needed to simulate the firing patterns for all of a Ulysses Grant President S. neurons, though currently it happens many times more slowly than would happen in an actual brain.