Tag: Machine Learning
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MLPerf Training v5.1: A New Benchmark Milestone for AI Training Performance
Overview: MLPerf Training v5.1 demonstrates rapid progress The MLCommons organization has released the MLPerf® Training v5.1 benchmark results, underscoring the rapid evolution of the AI hardware and software ecosystem. The latest edition, built to stress test real-world model training workloads, shows notable gains across a range of accelerators, systems, and software stacks. For developers, data…
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Development and external validation of a machine learning-based model for predicting heart failure risk in Chinese adults with type 2 diabetes
Introduction Type 2 diabetes mellitus (T2DM) is a growing public health concern in China, affecting an estimated 11.2% of the adult population and acting as an independent risk factor for heart failure (HF). The close link between T2DM and HF has driven interest in data-driven approaches that can identify high-risk patients early. This article summarizes…
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Developing and Validating a Machine Learning Model to Predict Heart Failure Risk in Chinese Adults with Type 2 Diabetes
Overview Type 2 diabetes mellitus (T2DM) remains a major public health challenge in China, affecting a substantial portion of the adult population and acting as an independent risk factor for heart failure (HF). Recent work in this field focuses on leveraging machine learning (ML) to improve risk stratification and guide early interventions. This article summarizes…
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A Machine Learning Model to Predict Heart Failure Risk in Type 2 Diabetes: Development and External Validation
Overview Type 2 diabetes mellitus (T2DM) significantly increases the risk of heart failure (HF), posing a major challenge for clinicians managing cardiovascular health in diabetic patients. In China, where T2DM affects a substantial share of the adult population, accurate risk stratification is essential to guide preventive strategies and optimize resource allocation. Recent research has focused…
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Meet the Chinese Startup Using AI—and a Small Army of Workers—to Train Robots
In the World of Industrial Robotics, a New Model Emerges Across China’s sprawling tech hubs, a discreet blue-collar workforce collaborates with cutting-edge artificial intelligence to teach robots how to perform complex tasks. A rising Chinese startup is redefining industrial automation by combining AI-driven training with a lean, hands-on workforce. The model is simple in concept…
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Inside the race to train AI robots how to act human in the real world
Introduction: The shift from screens to sidewalks Artificial intelligence has long proven its prowess in the digital sphere. From mastering complex games to parsing vast swaths of online information, AI now faces a different challenge: acting human in the real world. A growing global effort is training robots to move, interpret, and respond as humans…
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AI-Driven BIGE Model: Reducing Injury Risk and Boosting Training for Athletes
Innovating Injury Prevention with Generative AI and Biomechanics In a pioneering effort at UC San Diego, researchers have unveiled BIGE — Biomechanics-informed GenAI for Exercise Science — a generative AI model that promises to transform how athletes train, rehabilitate, and prevent injuries. By fusing advanced machine learning with realistic biomechanical constraints, BIGE aims to deliver…
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New Training Method Enables AI to Localise Personalised Objects Across Scenes
Overview: A step forward in personalised object localisation Researchers at MIT and the MIT-IBM Watson AI Lab have devised a training regime that lets generative vision-language models (VLMs) pinpoint personalised objects in novel scenes. This advances the field beyond generic category recognition to reliably locating a specific item—like a beloved pet or a unique chair—across…
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New method helps AI models locate personalised objects in scenes
Overview Researchers from MIT and the MIT-IBM Watson AI Lab have unveiled a training technique that enables generative vision-language models (VLMs) to pinpoint personalised objects across different scenes. Previously, these models excelled at recognizing general object categories like dogs or chairs but struggled to identify a specific instance—such as a particular cat—in varied environments. The…
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Training AI to Identify Ancient Artists: Griffith’s Digital Archaeology Approach to Finger Fluting
Introduction: A new frontier in understanding ancient hands Researchers at Griffith are exploring whether modern artificial intelligence can help identify the gender of ancient cave artists by studying finger flutings—one of the oldest forms of rock art. Finger flutings are marks formed when a finger strokes a soft mineral film called moonmilk on cave walls.…