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What Can Machines Learn, and What Does It Mean for Occupations and the Economy?
| Content Provider | Semantic Scholar |
|---|---|
| Author | Brynjolfsson, Erik Mitchell, Tom Michael Rock, Daniel L. |
| Copyright Year | 2018 |
| Abstract | Advances in machine learning (ML) are poised to transform numerous occupations and industries. This raises the question of which tasks will be most affected by ML. We apply the rubric evaluating task potential for ML in Brynjolfsson and Mitchell (2017) to build measures of "Suitability for Machine Learning" (SML) and apply it to 18,156 tasks in O*NET. We find that (i) ML affects different occupations than earlier automation waves; (ii) most occupations include at least some SML tasks; (iii) few occupations are fully automatable using ML; and (iv) realizing the potential of ML usually requires redesign of job task content. |
| File Format | PDF HTM / HTML |
| DOI | 10.1257/pandp.20181019 |
| Alternate Webpage(s) | http://ide.mit.edu/sites/default/files/publications/2018-08-MITIDE-researchbrief-Erikb.final_.pdf |
| Alternate Webpage(s) | http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/pubs/AEA2018-WhatCanMachinesLearn.pdf |
| Alternate Webpage(s) | http://ide.mit.edu/sites/default/files/publications/pandp.20181019.pdf |
| Alternate Webpage(s) | https://doi.org/10.1257/pandp.20181019 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
