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How AI and ML Are Scaling Data Collection to Transform Medical Monitoring

Unite.AI

Artificial intelligence (AI) and machine learning (ML) can be found in nearly every industry, driving what some consider a new age of innovation – particularly in healthcare, where it is estimated the role of AI will grow at a 50% rate annually by 2025. This ensures we are building safe, equitable, and accurate ML algorithms.

ML 298
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ML-trained Predictive model with a Django API

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview: Machine Learning (ML) and data science applications are in high demand. When ML algorithms offer information before it is known, the benefits for business are significant. The ML algorithms, on […].

ML 341
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AI, ML, and Robotics: New Technological Frontiers in Warehousing

Unite.AI

To fulfill orders quickly while making the most of limited warehouse space, organizations are increasingly turning to artificial intelligence (AI), machine learning (ML), and robotics to optimize warehouse operations. Applications of AI/ML and robotics Automation, AI, and ML can help retailers deal with these challenges.

Robotics 189
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Meet ML-SEISMIC: A Physics-Informed Deep Learning Approach for Mapping Australian Tectonic Stresses with Satellite Data

Marktechpost

The need for accurate stress orientation information becomes apparent, as it is pivotal for reliable geomechanical models. The research team introduces ML-SEISMIC as a groundbreaking alternative. ML-SEISMIC’s methodology hinges on applying physics-informed neural networks to solve linear elastic solid mechanics equations.

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Can AI Agents Transform Information Retrieval? This AI Paper Unveils Agentic Information Retrieval for Smarter, Multi-Step Interactions

Marktechpost

Researchers from Shanghai Jiao Tong University introduced Agentic Information Retrieval (Agentic IR), a new paradigm that fundamentally changes how IR systems operate. This function g(st, ht, MEM, THT, TOOL) integrates these components in support of dynamic processing and refinement of information by an agent during each stage of interaction.

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Understanding Local Rank and Information Compression in Deep Neural Networks

Marktechpost

It links the reduction in local rank to the implicit regularization mechanisms of neural networks, offering a perspective that connects feature manifold compression to the Information Bottleneck framework. The reduction occurred across all layers, aligning with the compression phase as predicted by the Information Bottleneck theory.

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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning Blog

By setting up automated policy enforcement and checks, you can achieve cost optimization across your machine learning (ML) environment. When defining your tagging strategy, you need to determine the right tags that will gather all the necessary information in your environment.

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