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Contrastive Representation Learning

The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning. Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and ...
Prannay Khosla Wang Isola Salakhutdinov Hinton Jason Wei Ekind Cubuk Lajanugen Logeswaran

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What is my chatbot thinking? Nothing. Here's why the Google sentient bot debate is flawed

The controversial case of a Google engineer who claimed Google's LaMDA chatbot had become sentient re-ignited an old debate. But is sentient even the right word? How can an algorithm be extremely sophisticated, yet have no path towards sentience?
United States Blake Lemoine Ray Kurzweil Attention Neural Networks Washington Post Recurrent Neural Networks

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