Artificial Intelligence (AI) has started to transform our economy and society, more specifically, AI has the potential to make both labor and machines more productive while displacing certain human tasks and simultaneously introducing new tasks into the economy. Using online job postings data, this pa- per proposes novel methodologies to characterize the dynamic evolution of occupational task-share demands across different industries in the U.S. la- bor market and estimates the implied US-$ market values for skills. The paper develops a multi-variate and multi-step long short term memory (LSTM) network architecture to estimate 12-month and 24- month ahead forecasts of task-shares with 10% root mean-squared error. The industry-specific insights on occupation evolution and forecasts on task- shares will facilitate the policy-makers and strategy leaders’ decision-making to transform the current workforce for the future.
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