2016 ArtificialIntelligenceandLifein
- (Stone et al., 2016) ⇒ Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. (2016). “Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel,.” In: Stanford University Journal.
Subject Headings: The One Hundred Year Study on Artificial Intelligence.
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Preface
The One Hundred Year Study on Artificial Intelligence, launched in the fall of 2014, is a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society. It considers the science, engineering, and deployment of AI-enabled computing systems. As its core activity, the Standing Committee that oversees the One Hundred Year Study forms a Study Panel every five years to assess the current state of AI. The Study Panel reviews AI’s progress in the years following the immediately prior report, envisions the potential advances that lie ahead, and describes the technical and societal challenges and opportunities these advances raise, including in such arenas as ethics, economics, and the design of systems compatible with human cognition. The overarching purpose of the One Hundred Year Study’s periodic expert review is to provide a collected and connected set of reflections about AI and its influences as the field advances. The studies are expected to develop syntheses and assessments that provide expert-informed guidance for directions in AI research, development, and systems design, as well as programs and policies to help ensure that these systems broadly benefit individuals and society.1 The One Hundred Year Study is modeled on an earlier effort informally known as the “AAAI Asilomar Study.” During 2008-2009, the then president of the Association for the Advancement of Artificial Intelligence (AAAI), Eric Horvitz, assembled a group of AI experts from multiple institutions and areas of the field, along with scholars of cognitive science, philosophy, and law. Working in distributed subgroups, the participants addressed near-term AI developments, long-term possibilities, and legal and ethical concerns, and then came together in a three-day meeting at Asilomar to share and discuss their findings. A short written report on the intensive meeting discussions, amplified by the participants’ subsequent discussions with other colleagues, generated widespread interest and debate in the field and beyond.
The impact of the Asilomar meeting, and important advances in AI that included AI algorithms and technologies starting to enter daily life around the globe, spurred the idea of a long-term recurring study of AI and its influence on people and society. The One Hundred Year Study was subsequently endowed at a university to enable extended deep thought and cross-disciplinary scholarly investigations that could inspire innovation and provide intelligent advice to government agencies and industry.
This report is the first in the planned series of studies that will continue for at least a hundred years. The Standing Committee defined a Study Panel charge for the inaugural Study Panel in the summer of 2015 and recruited [[Professor Peter Stone, at the University of Texas at Austin, to chair the panel. The seventeen-member Study Panel, comprised of experts in AI from academia, corporate laboratories and industry, and AI-savvy scholars in law, political science, policy, and economics, was launched in mid-fall 2015. The participants represent diverse specialties and geographic regions, genders, and career stages.
The Standing Committee extensively discussed ways to frame the Study Panel charge to consider both recent advances in AI and potential societal impacts on jobs, the environment, transportation, public safety, healthcare, community engagement, and government. The committee considered various ways to focus the study, including surveying subfields and their status, examining a particular technology such as machine learning or natural language processing, and studying particular application areas such as healthcare or transportation. The committee ultimately chose a thematic focus on “AI and Life in 2030” to recognize that AI's various uses and impacts will not occur independently of one another, or of a multitude of other societal and technological developments. Acknowledging the central role cities have played throughout most of human experience, the focus was narrowed to the large urban areas where most people live. The Standing Committee further narrowed the focus to a typical North American city in recognition of the great variability of urban settings and cultures around the world, and limits on the first Study Panel’s efforts. The Standing Committee expects that the projections, assessments, and proactive guidance stemming from the study will have broader global relevance and is making plans for future studies to expand the scope of the project internationally.
Table of Contents
EXECUTIVE SUMMARY 4 OVERVIEW 6 SECTION I: WHAT IS ARTIFICIAL INTELLIGENCE? 12 Defining AI 12 AI Research Trends 14 SECTION II: AI BY DOMAIN 18 Transportation 18 Home/Service Robots 24 Healthcare 25 Education 31 Low-resource Communities 35 Public Safety and Security 36 Employment and Workplace 38 Entertainment 40 SECTION III: PROSPECTS AND RECOMMENDATIONS FOR AI PUBLIC POLICY 42 AI Policy, Now and in the Future 42 APPENDIX I: A SHORT HISTORY OF AI 50
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EMPLOYMENT AND WORKPLACE
While AI technologies are likely to have a profound future impact on employment and workplace trends in a typical North American city, it is difficult to accurately assess current impacts, positive or negative. In the past fifteen years, employment has shifted due to a major recession and increasing globalization, particularly with China’s introduction to the world economy, as well as enormous changes in non-AI digital technology. Since the 1990s, the US has experienced continued growth in productivity and GDP, but median income has stagnated and the employment to population ratio has fallen.
There are clear examples of industries in which digital technologies have had profound impacts, good and bad, and other sectors in which automation will likely make major changes in the near future. Many of these changes have been driven strongly by “routine” digital technologies, including enterprise resource planning, networking, information processing, and search. Understanding these changes should provide insights into how AI will affect future labor demand, including the shift in skill demands. To date, digital technologies have been affecting workers more in the skilled middle, such as travel agents, rather than the very lowest-skilled or highest skilled work. [1] On the other hand, the spectrum of tasks that digital systems can do is evolving as AI systems improve, which is likely to gradually increase the scope of what is considered routine. AI is also creeping into high end of the spectrum, including professional services not historically performed by machines.
To be successful, AI innovations will need to overcome understandable human fears of being marginalized. AI will likely replace tasks rather than jobs in the near term, and will also create new kinds of jobs. But the new jobs that will emerge are harder to imagine in advance than the existing jobs that will likely be lost. Changes in employment usually happen gradually, often without a sharp transition, a trend likely to continue as AI slowly moves into the workplace. A spectrum of effects will emerge, ranging from small amounts of replacement or augmentation to complete replacement. For example, although most of a lawyer’s job is not yet automated,[2] AI applied to legal information extraction and topic modeling has automated parts of first-year lawyers’ jobs. [3] In the not too distant future, a diverse array of job-holders, from radiologists to truck drivers to gardeners, may be affected.
AI may also influence the size and location of the workforce. Many organizations and institutions are large because they perform functions that can be scaled only by adding human labor, either “horizontally” across geographical areas or “vertically” in management hierarchies. As AI takes over many functions, scalability no longer implies large organizations. Many have noted the small number of employees of some high profile internet companies, but not of others. There may be a natural scale of human enterprise, perhaps where the CEO can know everyone in the company. Through the creation of efficiently outsourced labor markets enabled by AI, enterprises may tend towards that natural size.
AI will also create jobs, especially in some sectors, by making certain tasks more important, and create new categories of employment by making new modes of interaction possible. Sophisticated information systems can be used to create new markets, which often have the effect of lowering barriers to entry and increasing participation — from app stores to AirBnB to taskrabbit. A vibrant research community within AI studies further ways of creating new markets and making existing ones operate more efficiently.
While work has intrinsic value, most people work to be able to purchase goods and services they value. Because AI systems perform work that previously required human labor, they have the effect of lowering the cost of many goods and services, effectively making everyone richer. But as exemplified in current political debates, job loss is more salient to people — especially those directly affected — than diffuse economic gains, and AI unfortunately is often framed as a threat to jobs rather than a boon to living standards.
There is even fear in some quarters that advances in AI will be so rapid as to replace all human jobs — including those that are largely cognitive or involve judgment — within a single generation. This sudden scenario is highly unlikely, but AI will gradually invade almost all employment sectors, requiring a shift away from human labor that computers are able to take over. The economic effects of AI on cognitive human jobs will be analogous to the effects of automation and robotics on humans in manufacturing jobs. Many middle-aged workers have lost well-paying factory jobs and the socio-economic status in family and society that traditionally went with such jobs. An even larger fraction of the total workforce may, in the long run, lose well-paying “cognitive” jobs. As labor becomes a less important factor in production as compared to owning intellectual capital, a majority of citizens may find the value of their labor insufficient to pay for a socially acceptable standard of living. These changes will require a political, rather than a purely economic, response concerning what kind of social safety nets should be in place to protect people from large, structural shifts in the economy. Absent mitigating policies, the beneficiaries of these shifts may be a small group at the upper stratum of the society.[4]
In the short run, education, re-training, and inventing new goods and services may mitigate these effects. Longer term, the current social safety net may need to evolve into better social services for everyone, such as healthcare and education, or a guaranteed basic income. Indeed, countries such as Switzerland and Finland have actively considered such measures. AI may be thought of as a radically different mechanism of wealth creation in which everyone should be entitled to a portion of the world’s AI-produced treasure. [5] It is not too soon for social debate on how the economic fruits of AI-technologies should be shared. As children in traditional societies support their aging parents, perhaps our artificially intelligent “children” should support us, the “parents” of their intelligence.
As labor becomes a less important factor in production as compared to owning intellectual capital, a majority of citizens may find the value of their labor insufficient to pay for a socially acceptable standard of living.
ENTERTAINMENT
With the explosive growth of the internet over the past fifteen years, few can imagine their daily lives without it. Powered by AI, the internet has established user-generated content as a viable source of information and entertainment. Social networks such as Facebook are now pervasive, and they function as personalized channels of social interaction and entertainment — sometimes to the detriment of interpersonal interaction. Apps such as WhatsApp and Snapchat enable smart-phone users to remain constantly “in touch” with peers and share sources of entertainment and information. In on-line communities such as Second Life and role-playing games such as World of Warcraft, people imagine an alternative existence in a virtual world.115 Specialized devices, such as Amazon’s Kindle have also redefined the essentials of long-cherished pastimes. Books can now be browsed and procured with a few swipes of the finger, stored by the thousands in a pocket-sized device, and read in much the same way as a handheld paperback.
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References
- ↑ Jeremy Ashkenas and Alicia Parlapiano, “How the Recession Reshaped the Economy, in 255 Charts,” The New York Times, June 6, 2014, accessed August 1, 2016, http://www.nytimes.com/interactive/2014/06/05/upshot/how-the-recession-reshaped-the-economy-in-255-charts.html.
- ↑ R. Dana Remus and Frank S. Levy, “Can Robots Be Lawyers? Computers, Lawyers, and the Practice of Law,” Social Science Research Network, last modified February 12, 2016, accessed August 1, 2016, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2701092
- ↑ John Markoff, “Armies of Expensive Lawyers, Replaced by Cheaper Software,” The New York Times, March 4, 2011, accessed August 1, 2016, http://www.nytimes.com/2011/03/05/science/05legal.html.
- ↑ For example, Brynjolfsson and McAfee, Second Machine Age, have two chapters of devoted to this (Erik Brynjolfsson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, (New York: W. W. Norton & Company, Inc., 2014)) and Brynjolfsson, McAfee, and Spence describe policy responses for the combination of globalization and digital technology (Erik Brynjolfsson, Andrew McAfee, and Michael Spence, Foreign Affairs, July/August 2014, accessed August 1, 2016, https://www.foreignaffairs.com/articles/united-states/ 2014-06-04/new-world-order).
- ↑ GDP does not do a good job of measuring the value of many digital goods. When society can’t manage what isn’t measured, bad policy decisions result. One alternative is to look at consumer surplus, not just dollar flows. As AI is embodied in more goods, this issue becomes more salient. It may look like GDP goes down but people have better well-being through access to these digital goods. See Erik Brynjolfsson and Adam Saunders, “What the GDP Gets Wrong (Why Managers Should Care),” Sloan Management Review, vol. 51, no. 1 (October 1, 2009): 95–96.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2016 ArtificialIntelligenceandLifein | Oren Etzioni Erik Brynjolfsson Rodney A. Brooks Julia Hirschberg Kevin Leyton-Brown Ryan Calo Greg Hager Shivaram Kalyanakrishnan Ece Kamar Sarit Kraus David Parkes William Press AnnaLee Saxenian Julie Shah Milind Tambe Peter Stone Astro Teller | Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, |