What can machine learning do? Workforce implications

By Erik Brynjolfsson, Tom Michael Mitchell. 2017

929 citations recorded by the source corpus.

Subfields: Organizational Behavior and Human Resource Management, Economics & Operations Research, Social Sciences & Humanities

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Citations in the corpus

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “…and Autor, 2011a) (means may deviate slightly from 0 due to imperfect matching of occupational groups), Suitability for Machine Learning from (Brynjolfsson and Mitchell, 2017; Brynjolfsson et al., 2018, 2023), AI Occupational Exposure from (Felten et al., 2018), and Automation exposure from…”
Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “Indeed, machine learning (ML) will have effects on jobs and the workforce, since parts of many jobs may be “suitable for ML applications” [3].”
Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases (2019, positive): “Prior research also discusses how AI and robots will replace the labor and work force (Brynjolfsson and Mitchell 2017, Lu et al. 2018).”
Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (2020, background): “For instance, some complementary conditions and restructuring of business processes may be necessary to enable the integration of AI in the workplace (Brynjolfsson et al., 2018; Brynjolfsson and Mitchell, 2017).”
Generative artificial intelligence (2023, background): “Machine learning as a subfield of AI deals with the development of algorithms capable to autonomously solve tasks through exposure to data without being explicitly programmed—i.e., learning (Brynjolfsson & Mitchell, 2017).”
What Can Machines Learn, and What Does It Mean for Occupations and the Economy? (2018, methodology): “Furthermore, tasks within jobs typically show considerable variability in SML, while few ( if any jobs can be fully automated using ML. Machine learning technology can transform many jobs in the economy, but full automation will be less significant than the reengineering of processes and the reorgan”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “The following criteria, inspired by [20], offer useful guidelines on the type of engineering tasks that can benefit from the use of machine”
Artificial Intelligence and Management: The Automation–Augmentation Paradox (2020, background): “…one-sided augmentation is likely to cause another “ digital divide ” (Norris, 2001), with social tensions arising between the few who currently have the capabilities and resources for augmentation and those who do not (Brynjolfsson & McAfee, 2014: 134f; see also Brynjolfsson & Mitchell, 2017).”
The Effects of Digital Transformation on Firm Performance: Evidence from China’s Manufacturing Sector (2021, background): “This is not only about the reduction of labor intensity resulting from automation [45], or the support to dynamic decision-making driven by data [46], but about the overall improvement of work efficiency brought about by organizational structure optimization [47].”
Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey (2020, positive): “…owing to a lack of comprehensive data on firms’ adoption and use of such technologies, we are “flying blind into what has been called the Fourth Industrial Revolution” (Brynjolfsson and Mitchell 2017b).2 This data gap hinders evidence-based decision making at all levels of government and society.”
Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “…diagnosis of skin cancer based on dermatological images, which use the results of biopsies, can employ AI-based software with higher diagnostic accuracy than trained physicians (Esteva et al., 2017), though computers and people make different types of mistakes (Brynjolfsson and Mitchell, 2017).”
Personalized machine learning for robot perception of affect and engagement in autism therapy (2018, methodology): “nt advances in ML and data collection, using unobtrusive sensors such as cameras and microphones, and wearable technology for measurement of autonomic physiology, have paved a way for such technology (61), however, little progress has been done so far (62). To this end, we introduced a novel persona”