This strategy differs from standard centralized machine learning methods, which store all local datasets on a single server.

New research describes how two machine-learning tools developed by Johns Hopkins researchers can determine where seizures begin and the success of …

DeepNash uses a novel approach, based on game theory and model-free deep reinforcement learning.

"I'm proud to announce that we have acquired Pulse, an incredible team that has developed some truly novel machine learning approaches to help …

An increasing number of the machine learning (ML) models we build at Apple each year are either partly or fully adopting the Transformer …

… to predict player injuries through machine learning and computer vision. … NFL games using artificial intelligence and related disciplines.

Research Fellow Associate in Machine Learning applied to electronic health records in Research Related with KINGS COLLEGE LONDON.

FREMONT, CA: Researchers are developing solutions to integrate artificial intelligence (AI) in the clinical setting. AI is relevant in medical applications like disease prediction, diagnosis, and prognosis. Data from patients in medical images, texts, and electronic records provide enough information for machine learning (ML) to undertake automation certain functions with accuracy and reliability. Cardiology: Cardiovascular diseases is a major cause of morbidity and mortality, globally. It is an expensive treatment that requires expensive treatments. AI in cardiology improves prediction and diagnosis of cardiac events.

According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and visualizing data (21%). Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. If you prefer a GUI-based and interactive interface, you can use Amazon SageMaker Data Wrangler, with over 300 built in visualizations, analyses, and transformations to efficiently process data backed by Spark without writing a single line of code.

Among the most fundamental trends in the consumer electronics industry today is the proliferation of sensors. For applications such the Internet of Things (IoT), wearables, and AR/VR, sensing has been a core technology. As devices become embedded with more and more sensors, system developers face about how to best handle and fuse that data into a useful format. To address this issue, STMicroelectronics (ST) has recently released a new 6-axis IMU which includes a number of integrated features such as sensor fusion blocks and Machine Learning (ML) cores. In this article, we'll discuss sensor fusion, why it's a challenge, and how ST's new product hopes to enable low-power sensing applications.