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Articles récents
- Powering Agentic AI with CPUs: LangChain, MCP, and vLLM on Google Cloud
- Building a Sovereign GenAI Stack for the United Nations with Intel and OPEA
- Accelerating vLLM Inference: Intel® Xeon® 6 Processor Advantage over AMD EPYC
- KVCrush: Rethinking KV Cache Alternative Representation for Faster LLM Inference
- Scaling AI with Confidence: Lenovo’s Approach to Responsible and Practical Adoption
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Neural networks news
Intel NN News
- Accelerating vLLM Inference: Intel® Xeon® 6 Processor Advantage over AMD EPYC
The vLLM (Virtualized Large Language Model) framework, optimized for CPU inference, is emerging as […]
- Building a Sovereign GenAI Stack for the United Nations with Intel and OPEA
The United Nations (UN) has taken a bold step toward digital sovereignty by developing an […]
- KVCrush: Rethinking KV Cache Alternative Representation for Faster LLM Inference
Developed by Intel, KVCrush can improve LLM inference throughput up to 4x with less than 1% […]
- Accelerating vLLM Inference: Intel® Xeon® 6 Processor Advantage over AMD EPYC
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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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