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Amazon and UCLA announce fellowship recipients
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The Science Hub for Humanity and Artificial Intelligence, launched in October 2021 to facilitate collaboration between academic researchers and Amazon ...

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How Prime Video distills time series anomalies into actionable alarms
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Prime Video customers must be able to reliably stream content at all times on any device that supports the Prime Video application, such as mobile phones, ...

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A quick guide to Amazon’s papers at NeurIPS 2022
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The Conference on Neural Information Processing Systems (NeurIPS) remains the highest-profile conference in AI, and as such, it draws paper submissions from ...

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NeurIPS: Why causal-representation learning may be the future of AI
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In a conversation right before the 2021 Conference on Neural Information Processing Systems (NeurIPS), Amazon vice president and distinguished scientist ...

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How a NeurIPS workshop is increasing women’s visibility in AI
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A. I absolutely think so. This is one of the most respected workshops at NeurIPS, because it has been going for a long time and the quality is pretty high. ...

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Amazon SURE program hosts three Amazon Days
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Over the summer, the Amazon Summer Undergraduate Research Experience (SURE), a program focused on increasing diversity in science, technology, math, and ...

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Amanda Prorok: Scaling new frontiers in multi-robotic research
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When Amanda Prorok chose computer science as an elective in high school, its main appeal was that it was objectively evaluated — a solution either works or ...

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Preventing updated NLP models from backsliding on particular tasks
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Machine learning (ML) models need regular updates to improve performance, but retraining a model poses risks, such as the loss of backward compatibility or ...

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reMARS revisited: Frontiers of fair and accessible AI
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In June 2022, Amazon re:MARS, the company’s in-person event that explores advancements and practical applications within machine learning, automation, ...

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In reinforcement learning, slower networks can learn faster
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Reinforcement learning (RL) is an increasingly popular way to model sequential decision-making problems in artificial intelligence. RL agents learn through ...

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