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Announcing the first Machine Unlearning Challenge

Google Research AI blog

Posted by Fabian Pedregosa and Eleni Triantafillou, Research Scientists, Google Deep learning has recently driven tremendous progress in a wide array of applications, ranging from realistic image generation and impressive retrieval systems to language models that can hold human-like conversations.

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Detecting novel systemic biomarkers in external eye photos

Google Research AI blog

The comparison with a clinicodemographic baseline is useful because risk for some diseases could also be assessed using a simple questionnaire , and we seek to understand if the model interpreting images is doing better. due to the multiple comparisons problem ). Top: Sample images scaled to different sizes for this experiment.

Photo 125
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Moving from Red AI to Green AI, Part 1: How to Save the Environment and Reduce Your Hardware Costs

DataRobot

This increase in accuracy is important to make AI applications good enough for production , but there has been an explosion in the size of these models. To illustrate the energy needed in deep learning, let’s make a comparison. To better quantify this, we have developed methods to measure efficiency.

Green 145
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How to Use Psycholinguistics to Communicate Effectively

Forum One

It combines methods and theories of psychology and linguistics to derive a fuller understanding of how the brain processes language. To understand how we are using language, we need to understand psycholinguistics. What is psycholinguistics? Psycholinguistics is a technical-sounding word but not a new practice.

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Making ML models differentially private: Best practices and open challenges

Google Research AI blog

Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in modern applications: from spam filters to recommender systems and virtual assistants. Finally, non-rigorous privacy reporting makes it challenging to compare and choose the best DP methods.

Model 102
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ReAct: Synergizing Reasoning and Acting in Language Models

Google Research AI blog

Posted by Shunyu Yao, Student Researcher, and Yuan Cao, Research Scientist, Google Research, Brain Team Recent advances have expanded the applicability of language models (LM) to downstream tasks. Previous methods prompt language models (LM) to either generate self-conditioned reasoning traces or task-specific actions. Act-only 25.7

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AVFormer: Injecting vision into frozen speech models for zero-shot AV-ASR

Google Research AI blog

Posted by Arsha Nagrani and Paul Hongsuck Seo, Research Scientists, Google Research Automatic speech recognition (ASR) is a well-established technology that is widely adopted for various applications such as conference calls, streamed video transcription and voice commands. Overall architecture and training procedure for AVFormer.

Model 103