Le projet THINK

What if a spreadsheet could think, create, and collaborate? Thanks to Intel® Liftoff member Embedded LLM and their innovative platform, JamAI Base, the familiar spreadsheet transforms into an AI powerhouse. From auto-generating content and extracting data from images to building complex workflows—no coding required. Explore how the Generative Table is simplifying AI development for everyone.

Optimizing the PyTorch Frontend to support diverse AI accelerators

CLIP-InterpreT offers a suite of five interpretability analyses to understand the inner workings of Contrastive Language-Image Pretraining (CLIP) vision-language models, which is crucial for responsible artificial intelligence (AI) development.

This release includes support for onboarding new set of optimized applications and software, plus support for multi-tenancy and simplified concurrent onboarding of multiple edge nodes.

Understanding the internal mechanisms of large vision-language models (LVLMs) is a complex task. LVLM-Interpret helps users understand the model’s internal decision-making processes, identifying potential responsible artificial intelligence (AI) issues such as biases or incorrect associations. The tool adapts multiple interpretability methods to LVLMs for interactive analysis, including raw attention, relevancy maps, and causal interpretation.

OpenFL 1.6 Is Released and Federated Learning for Healthcare Is Showcasing Exciting Possibilities

At Intel Labs, we believe that responsible AI begins with ensuring the integrity and transparency of ML systems, from model training to inferencing, making the ability to verify model lineage an essential foundation of ethical ML development.

The Conference on Neural Information Processing Systems (NeurIPS 2024) will run from Tuesday, December 10th, through Sunday, December 15th, at the Vancouver Convention Center in Vancouver, B.C., Canada. This year, Intel presents 36 papers at NeurIPS, including eight at the main conference.

Discover how Ultralytics tackles real-world challenges with customized YOLO models, solving problems from hazardous zone monitoring to parking management.

The Federated Learning (FL) machine learning paradigm addresses model bias through diverse data, while maintaining privacy and security for data owners.