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By Adam Pearce, Asma Ghandeharioun, Nada Hussein, Nithum Thain, Martin Wattenberg and Lucas Dixon In 2021, researchers made a striking discovery while training a series of tiny models on toy tasks . They found a set of models that suddenly flipped from memorizing their training data to correctly generalizing on unseen inputs after training for much longer. This phenomenon – where generalization se
Big ideas in machine learning, simply explained The rapidly increasing usage of machine learning raises complicated questions: How can we tell if models are fair? Why do models make the predictions that they do? What are the privacy implications of feeding enormous amounts of data into models? This ongoing series of interactive essays will walk you through these important concepts.
By asking language models to fill in the blank, we can probe their understanding of the world. Large language models are making it possible for computers to write stories, program a website and turn captions into images. One of the first of these models, BERT, is trained by taking sentences, splitting them into individual words, randomly hiding some of them, and predicting what the hidden words ar
The People + AI Guidebook is a set of methods, best practices and examples for designing with AI. Our recommendations are based on data and insights from over a hundred Googlers, industry experts, and academic research. Start hereTo help you get started with our materials, we’ve created a set of AI design patterns.
We are Designers People + AI Research (PAIR) is a multidisciplinary team at Google that explores the human side of AI by doing fundamental research, building tools, creating design frameworks, and working with diverse communities. We believe that for machine learning to achieve its positive potential, it needs to be participatory, involving the communities it affects and guided by a diverse set of
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