AI material that learns behaviors and adapts to changing conditions

Just like a pianist who learns to play their instrument without looking at the keys or a basketball player who puts in countless hours to throw a seemingly effortless jump shot, UCLA mechanical engineers have designed a new class of material that can learn behaviors over time and develop a “muscle memory” of its own, …

The Calibration Generalization Gap

This paper was accepted at the Workshop on Distribution-Free Uncertainty Quantification at ICML 2022. Calibration is a fundamental property of a good predictive model: it requires that the model predicts correctly in proportion to its confidence. Modern neural networks, however, provide no strong guarantees on their calibration— and can be either poorly calibrated or well-calibrated …

Orchestrating Data/ML Workflows at Scale With Netflix Maestro

by Jun He, Akash Dwivedi, Natallia Dzenisenka, Snehal Chennuru, Praneeth Yenugutala, Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations. A large number of batch workflows run daily to serve various business needs. …

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Learning from CEOs: Collaboration and connectivity are keys to navigating sustainability

It’s the most frequently identified challenge CEOs expect to face over the next two to three years. It’s more vexing than regulation, cyber risk, and even supply chain disruptions. It’s sustainability, reveals IBM’s Institute for Business Value 2022 CEO Study “Own your impact: Practical pathways to transformational sustainability”. As pressures from a broad set of …

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Table Tennis: A Research Platform for Agile Robotics

Posted by Avi Singh, Research Scientist, and Laura Graesser, Research Engineer, Robotics at Google Robot learning has been applied to a wide range of challenging real world tasks, including dexterous manipulation, legged locomotion, and grasping. It is less common to see robot learning applied to dynamic, high-acceleration tasks requiring tight-loop human-robot interactions, such as table …

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Train a time series forecasting model faster with Amazon SageMaker Canvas Quick build

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how …

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Use Amazon SageMaker Canvas for exploratory data analysis

Exploratory data analysis (EDA) is a common task performed by business analysts to discover patterns, understand relationships, validate assumptions, and identify anomalies in their data. In machine learning (ML), it’s important to first understand the data and its relationships before getting into model building. Traditional ML development cycles can sometimes take months and require advanced …