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Mitsuru Kuramoto is an accomplished creative director based in Tokyo, with credits to his name ranging from acting to producing to writing movie scripts. In March, he participated in a design competition. Each participant was asked to create a television advertisement for a new consumer product, with the best advertisement selected by a panel of creative professionals. All bets were on Kuramoto. However, in a surprising outcome, Mr. Kuramoto was defeated by a newcomer.

The twist? His opponent was not a human but rather an artificial intelligence (AI) system. Man vs. machine had entered a new domain in the creative professions.

Recently, technology publications have sounded the alarm about mass job loss to robots, with researchers estimating that up to 50% of jobs may be lost to automation. Meanwhile, some technology leaders call for a “new social contract” [1] for the 21st century in anticipation of job impacts. 

While this topic has received significant attention recently, most popular press presents an incomplete viewpoint by focusing on a narrow set of issues, and by primarily discussing the problems instead of realistic solutions. The most commonly proposed solution, Universal Basic Income (UBI), is criticized by some [2] to be impractical in most economies. Similarly, most analyses on this topic have focused on the frightening aspects of change, missing the balanced viewpoint that considers new opportunities created.

AI, automation, and robotics are futuristic and complex topics and require a more comprehensive perspective to understand. This work seeks to expand the discussion of technology’s impact on the world in three ways: first, by grounding the problem definition in a historical understanding of past technology leaps — including both the positive and negative consequences; second, by taking a comprehensive look across fields, ranging from economics to law to psychology, in order to understand the totality of technology’s impact on our lives; and, third, by proposing practical solutions across a range of scenarios, given the practical constraints of the modern world.

In part one, we frame a comprehensive approach to understand the effects of technology leaps. In part two, we propose specific approaches for change, starting with near-term and transitioning into longer-term considerations as automation and AI become an increasingly prominent part of daily life. The overall story is one of an exciting future but one with major changes to the way we live and difficult transitions along the way.


To understand the future, we look to the past for clues. Fears (or hopes) of a “robot revolution” have come before. Many of the original algorithms behind today’s AI systems were originally developed in the 1960s, at the same time that the Jetson’s were on the air. Past hype has turned out to be unfounded, with technologies like AI or Virtual Reality (VR) not living up to the promises and fears amplified by their proponents, but current trends suggest that more structural changes are emerging.


The current wave of automation technology has been dubbed “The Fourth Industrial Revolution” by Klaus Schwab, Executive Chairman of the World Economic Forum [3]. The current wave is characterized by technologies that blur the line between physical, digital, and biological spheres — including Machine Learning, Internet of Things (IoT), 3D printing, autonomous vehicles, nanotechnology, gene editing, and so forth.


The first three Industrial Revolutions all had profound effects in shaping every aspect of modern life. The first, infused by the adoption of the steam engine, transitioned human societies from agricultural living into urban environments. The second, prompted by electricity, ushered in mass production and created the consumer culture. The third, information technology, globally connected the world.


Three underlying traits thread throughout each of these Industrial Revolution events and stand to continue into the future (Fig. 1):
(1) They each produced profound benefits to human productivity and welfare.
(2) Machines have gradually replaced more elements of human work over time; markets have been able to adapt thus far, but that is not guaranteed indefinitely.
(3) Technological innovation and technology diffusion have accelerated over time, possibly nearing the threshold of human ability to adapt.


The chart below examines how each of these three themes play out in the context of the Fourth Industrial Revolution:


Figure 1. Technology’s structural impacts in the 21st century. Refer to Appendix I for quantitative charts and technological examples corresponding to the numbers (1-22) in each slice.

    First, an optimistic note: while much of the contemporary conversation revolves around the potential risks and dangers of intelligent machines, it is worth noting that these technologies will likely also have powerful positive impacts. Prior Industrial Revolutions gave us advances such as running water, air travel, and cheap computing. It is reasonable to expect this trend will continue in the Fourth Industrial Revolution, with potential benefits including more efficient communication, longer lifespans, and increased access to services around the world. A small sample of applications currently under development include flying cars, gene drives usage for eliminating diseases, implanting digital chip into the brain, and many more — technologies that until recently were deemed science fiction but may soon become reality [4].

    Historically, we have seen such progress bear out in measurably increased quality of life. Global GDP per capita (in real dollars) has risen over 10x in the last century, and over one billion people have been taken out of extreme poverty since 1990 [5]. Global living standards have risen significantly in the last 100 years, and the GDP pie will continue to increase. Moreover, interconnectedness will likely continue to increase: just as running water and information access have been commoditized to be essentially free in developed countries, we may see commoditization across a greater range of services and geographies in the future [6].


    While these technologies invigorate the imagination and promise incredible benefits, they justifiably raise concerns: what will happen as machines can eclipse human performance on a growing number of tasks? Just as workers in developed economies have faced competition with outsourced labor in recent decades, they may increasingly face competition with machines. Unless buffered by a significant increase in available work, this will likely result in an excess supply of labor and decreased market wages.

    Several universities and consultancies have commissioned studies to quantify the impact of potential job automation. The potential scope is alarming, with consensus of 10-50% [7, 8, 9, 10, 11] of current job tasks (specifically in the U.S.) automatable in the coming decades. Examples already exist with increased use of robotics, where recent estimates show that each additional robot replaces up to six people per thousand in some locales.

    Historically, labor markets have adapted to technological unemployment by transitioning to new forms of work. As an example, employment in agriculture declined from roughly 50% in the late 19th century to below 2% of the population in 2000 [12]; however, these jobs were replaced with the rise of manufacturing. Manufacturing has followed a similar trend since 1980, as automation led to requiring fewer people despite steady growth in output — replacement has come from a growth in white-collar and service jobs.

    These two transitions – from agriculture to manufacturing to services – are relatively recent, in the past 200 years. Accordingly, it is unclear whether the trend of being able to continually find new forms of work can continue indefinitely. The ability to create more work at the right skill levels is not an immutable law of nature. It also greatly depends on the pace of technological growth compared to our ability to adapt.

    Some new tasks can be easily imagined. Management and strategic functions will need to continue working in parallel with machines for some time, and it is reasonable to expect growth in these knowledge fields. There will likely be an increase in trade jobs involving training machines. Creative functions will grow in scope as automation is able to execute on more unique ideas (e.g., creating movies in virtual reality). However, as automation increases the ability to work at greater scale, these roles may be consolidated to a small number of “winners” with others left on the sideline.


    Speed is the compounding factor that aggravates the concerns described above. Technological progress has accelerated over time, both in terms of time between paradigm-shifting “revolutions” and in terms of the impact of Moore’s law [13] on computing power. In addition, technology is coming to market more efficiently over time. For example, rail took roughly 125 years to reach widespread adoption, aviation took 60 years, and personal computers took only 25, according to one World Bank study [14]. One suggestion for the reasons for this increase in speed is the growth in “data, algorithms, networks, the cloud, and exponentially improving hardware.״ [15]

    This increase in speed in turn means less time to adapt. Historically, automation and job transitions happened over the span of multiple generations – a farmer in the 20th century could live out his career in the countryside, while his son moved into the city to join a manufacturing plant. More recently, manufacturing has stagnated or declined over the course of only a few decades, leaving some needing to change their skillset or profession mid-career. Further technology cycles have the potential to reinvent labor markets multiple times over the course of a single lifespan. Incremental change is often manageable; it’s the wholesale transformation of industries, and the new skill sets required, that make adaptation much more difficult for workers.

    Even if new work is created through automation, it is likely that some class of workers will be structurally left behind in the rapid transition. A possible outcome may result in certain workers being replaced by automation entirely. As machines are able to outperform humans on a growing list of tasks, one can imagine the rise of a permanently unemployed class – provocatively dubbed the “useless class” by some futurists [16]. One can imagine a job posting in the near future with the tagline “humans needn’t apply”, requiring skills that only a machine could possess.

    Conceptually, we may be approaching what may be termed an “adjustment gap” – the threshold at which pace of change exceeds human ability to adapt – as higher skills are demanded in the marketplace and technology erodes skills faster over time [17]. Major economic changes require restructuring of education, skills, incentives, regulations, and other societal implications. Without proactive attention to manage these factors, inequality may continue to increase, with the potential for “winner take all” economics where a few individuals or firms capture the lion’s share of the gains.




Having recognized the patterns of technology change, we turn to the question of how to prepare for the effects of the Fourth Industrial Revolution. Some compare the scope of change that AI and automation may offer to that of electricity [18], affecting nearly every aspect of human life. In order to address this, it is critical for proactive response to begin immediately, particularly given the accelerated rate of change.


We believe that roles in this response are shifting. Business leaders are taking on some of the role traditionally played by government (e.g., thinking about measures to remedy risks of climate change or economic proposals to combat unemployment) in part because of greater agility in adapting to change and in part because of better understanding of the raw power of technology’s progress.


However, collaboration will be critical in crafting the future economy. Government will continue to play a critical role as the stabilizing “thumb” in the invisible hand of capitalism, regulating and cushioning against extreme volatility, particularly in labor markets. The education sector will need to incur major evolutions to meet the needs of the future labor force. Finally, individuals will need to manage their careers in new ways to stay current as technology evolves.


Table 1. The role of various stakeholders in responding to technology advancement

The transition to the digital work economy has already begun, with up to 40% of U.S. workers identifying as independent. We will first discuss near-term practical solutions of this transition, and then speculative situations to address “runaway AI development”.


    The rapid emergence of new technology promises a bounty of opportunity for the 21st century’s economic winners. This technological arms race is shaping up to be a global affair, and the winners will be determined in part by who is able to build the future economy fastest and most effectively. At the country level, several nations have created competitive strategies to promote infrastructure and Research and Development investments as automation technologies become more mature.

    Among larger nations, China and Germany have two of the most notable growth strategies in the context of automation readiness. The “Made in China 2025” national strategy sets ambitious targets and provides subsidies for domestic innovation and production [19]. It also includes building new concept cities, investing in robotics capabilities (where payback periods have already decreased by 3x since 2010), and subsidizing high-tech acquisitions abroad [20]. Notably, China’s high trade surplus has left cash on hand to make acquisitions and set up innovation centers in Silicon Valley, effectively profiting directly off of the United States’ comparative advantage in innovation.

    Germany’s “Industrie 4.0” plan is targeted at becoming an IoT and digital manufacturing leader, with the goal to increase manufacturing productivity by up to 50%, while halving the resources required [21].

    An example from a smaller nation is Singapore’s “Research Innovation Enterprise 2020” Plan, which promises $13.2 billion of Research and Development investment over the next five years targeting academic research, manpower training, and enterprise innovation. Despite lower overall resources, smaller nations like Singapore may be better positioned to execute on such plans due to the flexibility of a more nimble economic system [22].

    Lastly, Estonia is a good case study in transitioning to a 21st century digital economy. After the breakup of the Soviet Bloc, Estonia rapidly implemented capitalistic reforms and transformed itself as a technology-centric economy. Internet access was declared a right in 2000, and its classrooms were outfitted for a digital economy, with coding as a core educational requirement starting at kindergarten. Internet broadband speeds in Estonia are among the fastest in the world. The result has been rapid growth, with the World Bank now ranking Estonia as a high-income country, and 150 tech companies headquartered within its borders [23].


    As automation takes hold, and many fields see a sharp decrease in employment, modernizing existing social safety nets increasingly becomes a priority. While the issue of safety nets can become quickly politicized, it is worth remembering that each prior technology revolution has come with corresponding changes to the social contract (see Table 2).

    Table 2. Labor laws have historically adjusted as technology and society progressed

    Solutions like UBI, or no-strings-attached monthly payout to all citizens are appealing in concept, but somewhat difficult to implement as a first measure in countries that already have high debt load such as the U.S. or Japan. Additionally, UBI may create dis-incentives to stay in the labor force. Reduction of budget deficits and roll back of quantitative easing programs are likely prerequisites and are major challenges in and of themselves.

    Program design should begin with consideration of historical programs such as Trade Adjustment Assistance (TAA), which was designed to protect industries and workers from import competition shocks from globalization. TAA was widely viewed as a failure due to insufficient coverage in providing wage subsidies to workers [31]. This has been viewed as a missed opportunity in maintaining worker protections.

    A near-term solution at a lower cost, designed as a “catch and release”, with incentives to return to the workforce quickly, could come in the form of graduated wage insurance (compensation for those forced to take a lower paying job) to individuals directly impacted by automation. This would provide income subsidy if a person falls down the economic ladder but still provides incentives to work, similar to existing disability programs but on a broader scale. Labor mobility assistance can bridge the geographic mismatch between workers and jobs, via tax credits. Temporary health insurance bridges for those in transition between jobs could bolster households from major financial shocks.

    Policymakers can also intervene to reverse recent historical trends that have seen a gradually higher share of income go to capital owners. Safety regulations, increased minimum wage, and employer-provided health insurance are all examples of policies that are pro-labor on the surface but in turn incentivize faster automation by increasing cost of human workers. The balance could be shifted back to labor by instead placing higher taxes on capital – an example is the recently proposed “robot tax” where the taxation would be on the work rather than the individual executing it [32]. That is, if a self-driving car performs the task that formerly was done by a human, the rideshare company will still pay the tax as if a human was driving.

    Other solutions may involve the distribution of work itself. Some countries, such as France and Sweden, have experimented with redistributing working hours. The idea is to cap weekly hours, with the goal of having more people employed and work more evenly spread. So far these programs have had mixed results with lower unemployment but high costs to taxpayers, but are potential models that can continue to be tested [33].

    A similar alternative is to increase the number of shifts for certain jobs. For example, in a number of residential locales in Asia, the number of shifts in tasks like street cleaning, clerical tasks and building have been doubled such that streets get cleaned multiple times a day, or bridges are built in both day and night shifts [34].


    Education and training are not well-equipped for the speed of change in the modern economy. Schools are still based on a one-time education model, with school providing the foundation for a single lifelong career. With content becoming obsolete faster and rapidly escalating costs, this system may be unsustainable in the future.

    Learning in the future may become lifelong. Primary and university education may still have a role in training foundational thinking and general education, but it will be necessary to curtail rising price of tuition and increase accessibility. Massive Open Online Courses (MooCs) and open-enrollment platforms are early demonstrations of what the future of general education may look like – cheap, effective, and flexible. Purdue University’s recent acquisition of Kaplan’s online education platform, and Georgia Tech’s online Engineering Master’s program (a fraction of the cost of residential tuition), are early examples in making university education more broadly available [35]. AI itself may be deployed to supplement the learning process, with applications such as AI-enhanced tutorials or personalized content recommendations backed by machine learning.

    Individuals will also seek new skills and adapt to new careers multiple times in their career. This creates the need for programs that would be shorter and more directly practical to career change. The concept of “nanodegrees” or “microcredentials” provided by online education platforms such as Udacity and Coursera have already begun to perform this function. To encourage continuous individual advancement, policy makers might consider allowing all educational and retraining expenses to be tax deductible.  Private sector firms may have a larger role than in the past in designing curriculum/disciplines to keep pace with technological progress.

    Finally, companies will want to provide on-the-job training for customized skill sets, and continuing education to maintain up-to-date skill sets in the existing workforce. One potential model involves partnering with community colleges to create apprenticeship-style learning, where students work part-time in parallel with their education. Siemens, the German manufacturing company, has pioneered this model in four states and is developing a playbook for other companies such as Alcoa and Dow to do the same [36].



The solutions discussed thus far are grounded in the current realities of automation. They are prudent steps for governments, individuals, and companies to start taking or planning for, based on the easily foreseeable future.

However, as the pace of technological change continues forward relentlessly, it is also worth consideration of more speculative scenarios that exist in the event that AI development runs faster than our ability to contain its progress. We discuss these in order from the likely medium-term concerns to more exotic (yet still plausible) speculative bets that are already being placed.


Scenario #1: Technology advances on its own
Response: regulate growth


Technology may be advancing faster than humans can manage its implications. This already seems to be the case with some scientific results like bioengineering tools or neuro/cognitive manipulations that are explored in academic labs with limited large-scale regulatory oversight. In the realm of communication, AI translation tools have already begun to invent new “languages” to simplify dialogue with each other, further opening up the possibility that AI development may proceed in a way that humans cannot fully grasp [37].


The solution starts with simplifying or streamlining regulatory processes. Some models to achieve this might include special councils, or a global referencing model where countries imitate each other’s regulation. A more radical idea suggested recently [38] is having the government create an AI system tasked solely with creating technology laws — a machine-led world of AI regulating AI.


The other primary consideration is in limiting excess concentration of power. Advanced Machine Learning will only require human input for determining the objective and label the data, which gives immense power to those responsible for that task – potentially a small number of people setting the agenda for machines that run increasingly large portions of our lives. It also raises the concern of transparency into why machines are making certain decisions. The EU has begun developing “right to explanation” laws to address this topic, but the technical challenges involved are surprisingly difficult [39].


Anticipating these challenges, researchers and lawmakers have begun developing the base principles for developing AI in a safe and manageable way despite the speed of progress. Earlier this year, a group of 2,500 AI researchers developed a set of foundational concepts known as the “Asilomar principles” [40], a list of 23 guidelines that will serve as the basis for development in the field. For example, agreement that an AI system should not be given control over a lethal weapon. These will lay the foundation for future development.


Scenario #2: Mass unemployment
Response: “leisure class” funded by Universal Basic Income


In 1930, John Maynard Keynes predicted a world where work would no longer be required from a strictly economic standpoint. He commented that “the economic problem may be solved, or at least within sight of a solution, within a hundred years”. Some modern economists such as Joel Mokyr [41] believe that trajectory of the Keynes hypothesis is still intact, noting a dramatic 35% reduction in per-capita working hours over the past century, even as GDP has grown considerably! In the coming generations, one can imagine a scenario where we see yet another dramatic reduction of working hours.


The long-run result of such a trend could be a leisure-based society, where work is optional and conducted primarily for its intrinsic value. In some sense, this idea represents a triumph of capitalism – a system designed to maximize efficiency, taken to its natural end state (see Fig. 2). In a sense this is simply an extension of the “useless class” phenomenon with a more positive label: the “leisure class”. The distinction is just a matter of scale, and societal acceptance of not working as a positive outcome rather than negative one (jokingly, instead of terming unemployment as “between jobs”, one could term employment as “between vacations”).

Figure 2. Past and possible future transitions of the labor market

If automation is indeed successful at solving a majority of human economic needs, it may become possible in the future to reduce employment. How, then, would people fund their day to day lives? We believe that this would be the appropriate point to fully transition to UBI as the means of distributing the shared gains of automation. This would be done as a combination prudent spending cuts, a tax on capital, and a highly progressive tax scale on income.


To ensure productive participation in society, potential criteria to obtain UBI could include education requirements, duty to society through volunteerism, or civil service. The Alaska’s Permanent Fund oil dividend, the sharing of oil revenues with Alaskans, may be used as a pilot study for building a case for UBI. Current UBI pilots in Finland [42] and California [43] may also be valuable sources of future information on how to design an effective program.


A second method of funding daily life in a “leisure class” world assumes that technology itself is a deflationary mechanism [6], vastly reducing the cost of a variety of goods and services. Just as running water is ubiquitous in developed nations today, one can imagine a future with abundant transportation, clean energy, and access to information – in other words, fairly limited income would actually be required for survival requirements, and with it all of society’s needs will be satisfied with the output of the automation.


Scenario #3: Superintelligent AI
Response: Computer chips implanted in the human brain


Extrapolating the progress of Moore’s law forward leads to dramatic effects. A survey of 350 researchers predicted that machines will outperform humans in a bewildering list of activities in the coming years. Among the examples are translating languages (by 2024) [44], writing high-school level essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053) [45].


Such progress raises the possibility, by mid-century, of machines that are smarter than humans in general intelligence, not just on specific tasks. The idea of computers with superhuman intelligence conjures up fearful ideas of Skynet or similar sci-fi concepts. While such Hollywood movies always have humans winning in the end, we (jokingly) wonder if the results will be different once AI algorithms start directing the films. Will they change the narrative to make themselves the winners?

Figure 3. Popular culture is exploring the boundary between human and robot as machine capabilities become increasingly sophisticated (scene from I, Robot) [46]

Alarmists focus on stopping AI, but an alternative proposal is to harness the growth of exponential computing power, by building computer chips directly into the human brain itself. The goal is to ensure that the development of superintelligence does not bypass humans to become a new dominant “species” but instead carries us along for the ride – a merger of man and machine. Multiple new ventures have begun simultaneously in 2017 to explore the possibility of such brain-computer interfaces [47, 48] (disclaimer: Moran Cerf, one of the co-authors of this paper, is a neuroscience researcher involved in these ventures).


The two conceptual ideas that govern such leap are the following notions: 1) the integration of digital chip into the brain will work because as long as the chip speaks the language of the brain in the form of the electrochemical signals, the brain will “aid” the integration by learning to interact with the input; 2) that once we connect such a chip into one’s brain it would not feel like a foreign object to which we “outsource” our thoughts but rather like a part of our brain (similar to a heart transplant that is not identified as foreign to the body).


These ideas suggest that a chip inside the brain can perform complex processing for us (such as knowing the optimal next move on a chess board, or calculating a complex number factoring problem) without us noting that the actions were performed by a “foreign” object. Given our knowledge on individuals’ common psychological biases or lack of information when making complex decisions (i.e. uninformed voting) these could actually enhance human experiences and give us richer and more fulfilling lives.


Though these technologies are far from commercial application, they grant a teaser look into a wild future of massively enhanced cognition – a future that is actually an eventual possibility for those being born today.



At a retail chain in Chicago, customers have gradually been shifting from the manned lanes to automated self-checkout counters. Mysteriously, at late-night hours some of the stores turn off the machines and enable only the manned lanes – creating longer lines and lower customer satisfaction for no apparent benefit. When we asked one of the cashiers why he was doing this, he replied simply: “so that I still have a job”.

The cashier is not alone in his resistance to change. The psychological impact is the overarching factor that cuts across all that has been described so far. Automation and machine intelligence are the biggest structural changes in the world today, opening up questions about our place in the world and even the fundamental definition of what it means to be human. How the future is viewed will have a big impact on how it unfolds. The coming era can easily be viewed as a utopia for improved human lives, or just as easily a threatening dystopia where it is increasingly difficult to stay relevant.

Defining new forms of meaning and purpose in human cultures will be one of the central challenges of the automation era. The idea of work as a person’s primary form of identity and meaning is deeply entrenched in many cultures. Unemployment is one of the most psychologically destructive forces on a person’s life today – as an example, current studies on terrorism show that unemployed and unemployable individuals are the most likely group to radicalize and turn to violence.

On the positive side, more time can be spent pursuing entrepreneurial ventures, enjoying time with family, volunteering, conducting civil service, or traveling. Our hope is that unemployment in the future is seen as an opportunity to pursue other ventures, not as a failure of character in the same way that it is today.

Historically, deeply embedded value systems are only truly broken and reformed through war or economic crisis. A more gradual approach to new values could be through intergenerational acceptance. For instance, millennials show a greater preference than their parents for work-life balance, autonomy, and values-driven work [49, 50] – possible precursors to an eventual value system for the previously described “leisure class”. We believe that, unfortunately, crisis is the most likely outcome of the two but hold out hope that a proactive response could smooth the process.

The social contracts of the 21st century have yet to be defined and will require a proactive and conscious effort across the spectrum from individuals, governments, companies, and institutions. The most significant structural change in human history is occurring at this very moment – how we act in the coming years will shape whether this change is a positive or a negative.




1. Global GDP has increased by nearly 600% in real terms in the last 30 years. [51]

2. U.S GDP has increased by over 200% in real terms in the last 30 years. [52]

3-7. Examples of companies and solutions in the last decade that were automatized.  [5,6, 53,54]

14. U.S. average annual hours worked has fallen ~300 hours from ~2050 hours in 1950 to ~1750 hours in 2010. [55]

15. Predictions of future U.S. job automation have ranged from 33-50% in the coming decades. [7-10]

17. The civilian labor force participation rate has steadily fallen by ~4 points in the last 25 years. [56]

18. Skill set mismatches in combination with US persons’ unwillingness (geographic mismatch) has given rise to increased job vacancies. [57]

19. The impact of Moore’s law has led to the exponential increase in technology growth. [58]

20. The diffusion of technologies has dramatically fallen over time, with mobile reaching 80% of the country in less than 20 years. 

21. Continuous innovation has prompted a significant increase in annual U.S. patent applications, doubling from ~300k patents applications in 2000 to ~600k today. [59]

22. U.S. public expenditure on workforce retraining programs is at its lowest levels in three decades. [60]



The authors of this paper would like to thank the Innovation for Jobs (I4J) forum for providing feedback and review of this work at several stages throughout the process, with particular thanks to Gary Bolles and Vinton Cerf for detailed review and commentary.


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  53. S. Gibbs, "Google AI project writes poetry which could make a vogon proud" 2016

  54. S. Ghosh, "A supercomputer just made the world’s first AI-created film trailer – here’s how well it did" 2016 

  55. "University of Groningen and University of California, Davis, average annual hours worked by persons engaged for United States" 2017

  56. "U.S. bureau of labor statistics, Civilian Labor Force Participation Rate [CIVPART]" 2017 

  57. "Job openings and labor turnover survey" 2017

  58. R. Hall, "For the technology investor: the promise of accelerating growth in technology" 2012 

  59. "U.S. patent activity calendar" 2017

  60. "Public expenditure and participant stocks on LMP" 2017

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