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While the computing capacity of the U.S. increased a hundredfold in the 1970s and 1980s, [6] labor productivity growth slowed from over 3% in the 1960s to roughly 1% in the 1980s. This perceived paradox was popularized in the media by analysts such as Steven Roach and later Paul Strassman.
Increased focus on what post-work society would look like has been driven by reports such as one in 2018 that states 47% of jobs in the United States could be automated. [17] Because of increasing automation and the low price of maintaining an automated workforce compared to one dependent on human labor, it has been suggested that post-work ...
One common form of implicit MDP model is an episodic environment simulator that can be started from an initial state and yields a subsequent state and reward every time it receives an action input. In this manner, trajectories of states, actions, and rewards, often called episodes may be produced.
Work or labor (labour in Commonwealth English) is the intentional activity people perform to support the needs and desires of themselves, other people, or organizations. [1] In the context of economics , work can be viewed as the human activity that contributes (along with other factors of production ) towards the goods and services within an ...
The reward model is first trained in a supervised manner to predict if a response to a given prompt is good (high reward) or bad (low reward) based on ranking data collected from human annotators. This model then serves as a reward function to improve an agent's policy through an optimization algorithm like proximal policy optimization. [3] [4] [5]
Based on Marxian economic theory, digital labor can be considered labor as it produces use-value, produces capital, and is based upon collective labor in a workforce. [ 4 ] Digital labor markets are websites or economies that facilitate the production, trade, and selling of digital content, code, digital products, or other ideas or goods ...
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In inverse reinforcement learning (IRL), no reward function is given. Instead, the reward function is inferred given an observed behavior from an expert. The idea is to mimic observed behavior, which is often optimal or close to optimal. [55] One popular IRL paradigm is named maximum entropy inverse reinforcement learning (MaxEnt IRL).