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The Future of Work and How the Workforce Adapts to Change
An AI Measurement Science (AIMS) Seminar Series
An AI Measurement Science (AIMS) Seminar Series
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
The labor market is changing ever more rapidly, and with that, the pressure to adapt and evolve has increased on everyone, One of the main drivers of change is technological progress, with technologies such as robots, software or AI fundamentally changing how work gets done and by whom. Automation both replaces certain tasks that humans used to do, and more importantly changes the required skills in occupations that are adapting new technologies. This presentation will draw on my research that sheds light on how individual adapt to change on the labor market. I explore how individuals transition between different career pathways and how they react, when faced with negative shocks. Knowing who can adapt to change and who may need help, provides important policy implications for the future of work. The research presented uses data spanning Europe and the US as well as state-of-the art empirical methods, including machine learning.
