Sequential training of multiple tasks followed by unsupervised sleep-like replay partially restores performance across all previously learned tasks in neural networks.
Computers and Education: Artificial Intelligence 5, 100165
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The paper argues that preoccupation with the moral status of hypothetical future AI creates an algorithmic blind spot that marginalizes existing algorithmic harms to human populations and calls for re-centering ethics on current institutional accountability.
Survey of Chinese math teacher trainees finds basic AI-TPACK levels, self-efficacy helps, and strong teaching beliefs may hinder progress.
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Not Just After One: Sleep-Inspired Replay Prevents Catastrophic Forgetting After Sequential Tasks
Sequential training of multiple tasks followed by unsupervised sleep-like replay partially restores performance across all previously learned tasks in neural networks.
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The Algorithmic Blind Spot: Bias, Moral Status, and the Future of Robot Rights
The paper argues that preoccupation with the moral status of hypothetical future AI creates an algorithmic blind spot that marginalizes existing algorithmic harms to human populations and calls for re-centering ethics on current institutional accountability.
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