Training a single generic model for solving arbitrary datasets is always a dream for ML researchers, especially in the era of foundation models. While such dreams have been realized in perception domains like images or natural languages, whether they can be reproduced in reasoning domains (like graphs) remains an open challenge.
-
-
Articles récents
- Low-Power AI: Driving the Next Era of Efficient Intelligence
- Specialized Cognitive Experts Emerge in Large AI Reasoning Models
- Evaluating Trustworthiness of Explanations in Agentic AI Systems
- Unlocking AI Development with Windows* ML: Intel and Microsoft’s Strategic Partnership
- Multi-Modal Brand Agent: Connecting Visual Logos to Business Intelligence
-
Neural networks news
Intel NN News
- Low-Power AI: Driving the Next Era of Efficient Intelligence
Falcons.AI’s 4MB neural network mimics the brain to cut power use by 10x, delivering accurate […]
- Specialized Cognitive Experts Emerge in Large AI Reasoning Models
Intel researchers found that DeepSeek-R1 demonstrates greater semantic specialization in expert […]
- Evaluating Trustworthiness of Explanations in Agentic AI Systems
Intel Labs research published at the ACM CHI 2025 Human-Centered Explainable Workshop found that […]
- Low-Power AI: Driving the Next Era of Efficient Intelligence
-