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Articles récents
- Intel® Xeon® Processors: The Most Preferred CPU for AI Host Nodes
- Building AI With Empathy: Sorenson’s Mission for Accessibility
- Multi-node deployments using Intel® AI for Enterprise RAG
- Connected Data is the Future: How Neo4j Is Enabling the Next Generation of AI
- Orchestrating AI for Real Business Value: Google Cloud’s Approach to Scalable Intelligence
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Neural networks news
Intel NN News
- Intel® Xeon® Processors: The Most Preferred CPU for AI Host Nodes
Today’s AI workloads are not purely offloaded to GPU accelerators. Host CPUs such as the Intel® […]
- Multi-node deployments using Intel® AI for Enterprise RAG
As enterprises scale generative AI across diverse infrastructures, Intel® AI for Enterprise RAG […]
- Building AI With Empathy: Sorenson’s Mission for Accessibility
For Sorenson Senior Director of AI Mariam Rahmani, the future of AI isn’t about building the […]
- Intel® Xeon® Processors: The Most Preferred CPU for AI Host Nodes
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Archives mensuelles : août 2022
No One Rung to Rule Them All: Addressing Scale and Expediency in Knowledge-Based AI
Published October 26, 2021 Image adapted from Elentaris Photo / Shutterstock.com Gadi Singer is Vice President and Director of Emergent AI Research at Intel Labs leading the development of the third wave of AI capabilities. Cognitive AI, hierarchy of knowledge, … Continuer la lecture
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Intel® Labs’ Graph Neural Networks Research Featured as Groundbreaking Work in AI
Research finds that reversible Graph Neural Networks (RevGNNs) significantly outperform existing methods on multiple datasets and improve large models’ memory efficiency for AI applications.
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Sentence Transformer Fine-Tuning (SetFit)
In this work, we demonstrate Sentence Transformer Fine-tuning (SetFit), a simple and efficient alternative for few-shot text classification. The method is based on fine-tuning a Sentence Transformer with task-specific data and can easily be implemented with the sentence-transformers library.
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LM!=KM: Five Reasons Why Language Models Fail to Support Knowledge Model Requirements of Next-Gen AI
This post will discuss the five capabilities that make up an advanced KM and how these areas cannot be easily addressed by LMs in their present form. These capabilities are Scalability, Fidelity, Adaptability, Richness and Explainability.
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Intel Labs Enables AI Innovation with Hardware-Aware Automated Machine-Learning Tools
Intel Labs’ hardware-aware AI-based automation tools (AutoX) enable rapid adoption of AI models for different deployments to address productivity challenges.
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Democratizing AI by Delivering Hardware Performance and Developer Productivity At Scale
In his keynote presentation from the oneAPI DevSummit for AI 2022, Wei Li, VP/GM AI & Analytics at Intel, is joined by Jean-Luc Chatelain, CTO of Accenture Applied Intelligence to launch new AI reference kits for industry-specific use cases.
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Hybrid AI Inferencing managed with Microsoft Azure Arc-Enabled Kubernetes
Azure Arc-Enabled Kubernetes enables centralized management of heterogenous and geographically separate Kubernetes clusters from Azure public cloud.
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Python* Data Science at Scale
Learn about how Intel has been working to improve performance of popular Python* libraries
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