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As automation increasingly takes its place in industry, especially high risk industry, it is often blamed for causing harm and increasing the chance of human error when failures do occur. I propose that the problem is not the presence of automation, but rather its inappropriate design. The problem is that the operations under normal operating conditions are performed appropriately, but there is inadequate feedback and interaction with the humans who must control the overall conduct of the task. When the situations exceed the capabilities of the automatic equipment, then the inadequate feedback leads to difficulties for the human controllers. The problem, I suggest, is that the automation is at an intermediate level of intelligence, powerful enough to take over control that used to be done by people, but not powerful enough to handle all abnormalities. Moreover, its level of intelligence is insufficient to provide the continual, appropriate feedback that occurs naturally among human operators. This is the source of the current difficulties. To solve this problem, the automation should either be made less intelligent or more so, but the current level is quite inappropriate. The overall message is that it is possible to reduce error through appropriate design considerations. Appropriate design should assume the existence of error, it should continually provide feedback, it should continually interact with operators in an effective manner, and it should allow for the worst situations possible. What is needed is a soft, compliant technology, not a rigid, formal one.

We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment.Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform.This displacement effect tends to reduce the demand for labor and wages.But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks.The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor.These countervailing effects are incomplete.Even when they are strong, automation in-creases output per worker more than wages and reduce the share of labor in national income.The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to increase the labor share to counterbalance the impact of automation.Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting productivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies.

Modeling automation as factor-augmenting technological change has unappealing implications. Instead, modeling it as the process of machines replacing tasks previously performed by labor is both descriptively realistic and leads to distinct and plausible predictions. In contrast to factor-augmenting technological change, the automation of tasks always reduces the labor share and can reduce the equilibrium wage (for realistic parameter values). This approach to automation underscores the role of new tasks, changes in the comparative advantage of labor, the possibility that machines become more productive in automated tasks, and the elasticity of substitution and capital accumulation in the adjustment of the economy.

Abstract We argue theoretically and document empirically that aging leads to greater (industrial) automation, because it creates a shortage of middle-aged workers specializing in manual production tasks. We show that demographic change is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across U.S. commuting zones. We also document more automation innovation in countries undergoing faster aging. Our directed technological change model predicts that the response of automation technologies to aging should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation and that productivity should improve and the labor share should decline relatively in industries that are more amenable to automation. The evidence supports all four of these predictions.

The revolution ushered in by the digital computer in the latter half of this century has transformed many of the characteristics of work, leisure, and travel for most people throughout the world. Even more radical changes are anticipated in the next century as computers increase in power, speed, availability, flexibility, and in that elusive concept known as “intelligence.” Only a neo-Luddite would want to enter the 21st century without the capabilities that the new computer tools provide; and perhaps even a latter-day Thoreau would not wish to trade in his word processor for pen and paper. And, yet, although we have become accustomed to the rise of computers and as consumers demanded that they perform even greater feats, many have felt a sense of unease at the growth of computerization and automation in the workplace and in the home. Although there are several aspects to this disquiet (see Hancock, chap. 22, this volume), there is one overriding concern: Who will watch the computers?

Taking three recent business books on artificial intelligence (AI) as a starting point, we explore the automation and augmentation concepts in the management domain. Whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task. Taking a normative stance, the three books advise organizations to prioritize augmentation, which they relate to superior performance. Using a more comprehensive paradox theory perspective, we argue that, in the management domain, augmentation cannot be neatly separated from automation. These dual AI applications are interdependent across time and space, creating a paradoxical tension. Overemphasizing either augmentation or automation fuels reinforcing cycles with negative organizational and societal outcomes. However, if organizations adopt a broader perspective comprising both automation and augmentation, they could deal with the tension and achieve complementarities that benefit business and society. Drawing on our insights, we conclude that management scholars need to be involved in research on the use of AI in organizations. We also argue that a substantial change is required in how AI research is currently conducted in order to develop meaningful theory and to provide practice with sound advice.

The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.

RETURN TO ISSUEPREVAdvertisementNEXTAUTOMATION IN ANALYTICAL CHEMISTRYCite this: Chem. Eng. News 1967, 45, 38, 28Publication Date (Print):September 4, 1967Publication History Published online6 November 2010Published inissue 4 September 1967https://doi.org/10.1021/cen-v045n038.p028Copyright © 1967 AMERICAN CHEMICAL SOCIETY This publication is free to access through this site. Learn MoreArticle Views76Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (112 KB) SUBJECTS:Analytical chemistry Get e-Alerts

This review examines several recent books that deal with the impact of automation and robotics on the future of jobs. Most books in this genre predict that the current phase of digital technology will create massive job loss in an unprecedented way, that is, that this wave of automation is different from previous waves. Uniquely digital technology is said to automate professional occupations for the first time. This review critically examines these claims, puncturing some of the hyperbole about automation, robotics and Artificial Intelligence. The review argues for a more nuanced analysis of the politics of technology and provides some critical distance on Silicon Valley's futurist discourse. Only by insisting that futures are always social can public bodies, rather than autonomous markets and endogenous technologies, become central to disentangling, debating and delivering those futures.