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Articles récents
- Curious Case of Chain of Thought: Improving CoT Efficiency via Training-Free Steerable Reasoning
- Intel Labs Works with Hugging Face to Deploy Tools for Enhanced LLM Efficiency
- AI’s Next Frontier: Human Collaboration, Data Strategy, and Scale
- Efficient PDF Summarization with CrewAI and Intel® XPU Optimization
- Rethinking AI Infrastructure: How NetApp and Intel Are Unlocking the Future with AIPod Mini
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Neural networks news
Intel NN News
- Curious Case of Chain of Thought: Improving CoT Efficiency via Training-Free Steerable Reasoning
Researchers from the University of Texas at Austin and Intel Labs investigated chain-of-thought […]
- AI’s Next Frontier: Human Collaboration, Data Strategy, and Scale
Ramtin Davanlou, CTO of the Accenture and Intel Partnership, explores what it really takes for […]
- Intel Labs Works with Hugging Face to Deploy Tools for Enhanced LLM Efficiency
Large Language Models are revolutionizing AI applications; however, slow inference speeds continue […]
- Curious Case of Chain of Thought: Improving CoT Efficiency via Training-Free Steerable Reasoning
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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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