Title: What Can Machines Learn, and What Does It Mean for Occupations and Industries?

Abstract: This talk will present a preliminary framework and approach for understanding the potential effects of machine learning (ML) on tasks, occupations and industries. Digital technologies have already had a substantial effect on the wages and income. The increased availability of high quality data and rapid advances in ML have the potential to generate even larger effects in the coming decade. The ultimate impact will depend in part on the feasibility, costs and capabilities of ML-based applications for various types tasks and the speed with which they are implemented. Workers, firms and industries with complementary investments (e.g. relevant skills, data, and technologies) are well positioned to benefit, while those whose tasks are easily substituted for by ML will likely face downward pressure on wages and prices. We are developing a taxonomy of tasks most suitable for ML and plan to estimate some implications of our model by analyzing data from a major online resume and job postings marketplace.

Speaker Bio: Erik Brynjolfsson is Director of the MIT Initiative on the Digital Economy, Professor at MIT Sloan School, and Research Associate at NBER. His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. At MIT, he teaches courses on the Economics of Information and the Analytics Lab. Author or co-editor of several books including NYTimes best-seller The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies, Brynjolfsson is editor of SSRN’s Information System Network and has served on the editorial boards of numerous academic journals.