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Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

ML @ CMU

In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. The major components of RELand are illustrated in Fig.

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Hammerspace Unveils the Fastest File System in the World for Training Enterprise AI Models at Scale

insideBIGDATA

Hammerspace, the company orchestrating the Next Data Cycle, unveiled the high-performance NAS architecture needed to address the requirements of broad-based enterprise AI, machine learning and deep learning (AI/ML/DL) initiatives and the widespread rise of GPU computing both on-premises and in the cloud.

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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

To reduce costs while continuing to use the power of AI , many companies have shifted to fine tuning LLMs on their domain-specific data using Parameter-Efficient Fine Tuning (PEFT). Manually managing such complexity can often be counter-productive and take away valuable resources from your businesses AI development.

AWS 110
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How Neurosymbolic AI merges logical reasoning with LLMs

Dataconomy

AI is good at pattern recognition but struggles with reasoning. What if we could combine the best of both worlds the raw processing power of Large Language Models (LLMs) and the structured, rule-based thinking of symbolic AI? Can AI get it right? Meanwhile, human cognition is deeply rooted in logic and coherence. The problem?

AI 121
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FPGA vs. GPU: Which is better for deep learning?

IBM Journey to AI blog

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Deep learning requires a tremendous amount of computing power.

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This AI can predict genetic mutations before they happen

Dataconomy

Thanks to machine learning (ML) and artificial intelligence (AI), it is possible to predict cellular responses and extract meaningful insights without the need for exhaustive laboratory experiments. They introduce PERTURBQA , a benchmark designed to align AI-driven perturbation models with real biological decision-making.

AI 91
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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

Although setting up a processing cluster is an alternative, it introduces its own set of complexities, from data distribution to infrastructure management. We use the purpose-built geospatial container with SageMaker Processing jobs for a simplified, managed experience to create and run a cluster. format("/".join(tile_prefix),

ML 124