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Robust and efficient medical imaging with self-supervision

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Posted by Shekoofeh Azizi, Senior Research Scientist, and Laura Culp, Senior Research Engineer, Google Research Despite recent progress in the field of medical artificial intelligence (AI), most existing models are narrow, single-task systems that require large quantities of labeled data to train. Moreover, these models cannot be easily reused in new clinical contexts as they …

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ML Lifecycle

Deliver your first ML use case in 8–12 weeks

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Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? You’re not alone. Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. This post describes how to implement your first ML use case using Amazon …

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The Future of Intelligent Vehicle Interiors: Building Trust With HMI & AI

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Imagine a future where your vehicle’s interior offers personalized experiences and builds trust through human-machine interfaces (HMI) and AI. In this episode of the NVIDIA AI Podcast, Andreas Binner, chief technology officer at Rightware, delves into this fascinating topic with host Katie Burke Washabaugh. Rightware is a Helsinki-based company at the forefront of developing in-vehicle …

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A power-efficient engine that can disentangle the visual attributes of objects

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Most humans are innately able to identify the individual attributes of sensory stimuli, such as objects they are seeing, sounds they are hearing, and so on. While artificial intelligence (AI) tools have become increasingly better at recognizing specific objects in images or other stimuli, they are often unable to disentangle their individual attributes (e.g., their …

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LayerNAS: Neural architecture search in polynomial complexity

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Posted by Yicheng Fan and Dana Alon, Software Engineers, Google Research Every byte and every operation matters when trying to build a faster model, especially if the model is to run on-device. Neural architecture search (NAS) algorithms design sophisticated model architectures by searching through a larger model-space than what is possible manually. Different NAS algorithms, …

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Run your local machine learning code as Amazon SageMaker Training jobs with minimal code changes

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We recently introduced a new capability in the Amazon SageMaker Python SDK that lets data scientists run their machine learning (ML) code authored in their preferred integrated developer environment (IDE) and notebooks along with the associated runtime dependencies as Amazon SageMaker training jobs with minimal code changes to the experimentation done locally. Data scientists typically …

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Google Cloud Startup Summit introduces benefits for AI startups

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Google Cloud is committed to supporting the growth and advancement of startups, with particular focus on helping startups looking to build and scale. We’re seeing tremendous innovation from startups choosing Google Cloud to advance their generative AI development, thanks to our fully-managed and serverless data, AI, and infrastructure solutions. That’s why today’s Startup Summit, and …

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