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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

Face-off Probability is the National Hockey League’s (NHL) first advanced statistic using machine learning (ML) and artificial intelligence. It uses real-time Player and Puck Tracking (PPT) data to show viewers which player is likely to win a face-off before the puck is dropped, and provides broadcasters and viewers the opportunity to dive deeper into the …

Researchers Use AI to Help Earbud Users Mute Background Noise

Thanks to earbuds, people can take calls anywhere, while doing anything. The problem: those on the other end of the call can hear all the background noise, too, whether it’s the roommate’s vacuum cleaner or neighboring conversations at a café. Now, work by a trio of graduate students at the University of Washington, who spent …

AI Esperanto: Large Language Models Read Data With NVIDIA Triton

Julien Salinas wears many hats. He’s an entrepreneur, software developer and, until lately, a volunteer fireman in his mountain village an hour’s drive from Grenoble, a tech hub in southeast France. He’s nurturing a two-year old startup, NLP Cloud, that’s already profitable, employs about a dozen people and serves customers around the globe. It’s one …

AI Governance: Break open the black box

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations. While the promise of AI isn’t guaranteed and doesn’t always come easy, …

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Large Motion Frame Interpolation

Posted by Fitsum Reda and Janne Kontkanen, Google Research Frame interpolation is the process of synthesizing in-between images from a given set of images. The technique is often used for temporal up-sampling to increase the refresh rate of videos or to create slow motion effects. Nowadays, with digital cameras and smartphones, we often take several …

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Redact sensitive data from streaming data in near-real time using Amazon Comprehend and Amazon Kinesis Data Firehose

Near-real-time delivery of data and insights enable businesses to rapidly respond to their customers’ needs. Real-time data can come from a variety of sources, including social media, IoT devices, infrastructure monitoring, call center monitoring, and more. Due to the breadth and depth of data being ingested from multiple sources, businesses look for solutions to protect …

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Reduce cost and development time with Amazon SageMaker Pipelines local mode

Creating robust and reusable machine learning (ML) pipelines can be a complex and time-consuming process. Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. Creating and running a full pipeline during experimentation adds unwanted overhead and cost to the development lifecycle. In this post, we …

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Searidge Technologies Offers a Safety Net for Airports

Planes taxiing for long periods due to ground traffic — or circling the airport while awaiting clearance to land — don’t just make travelers impatient. They burn fuel unnecessarily, harming the environment and adding to airlines’ costs. Searidge Technologies, based in Ottawa, Canada, has created AI-powered software to help the aviation industry avoid such issues, …

Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss

A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm’s privacy loss remain open — even in the seemingly simple setting of smooth convex losses over …