VNDF importance sampling for an isotropic Smith-GGX distribution

In this blog post, we introduce an optimized importance sampling routine for GGX materials. Such materials are ubiquitous in games and VFX so our optimizations will benefit to a large number of content creators.

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Accelerating PyTorch on Intel with DirectML support

Intel is proud to announce support for PyTorch with DirectML.

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Accelerating Language Models: Intel and Microsoft Collaborate to Bring Efficient LLM Experiences.

Propelling AI workloads with solutions to enable LLMs on a vast range of Intel client platforms

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How Intel® Liftoff AI Startups Are Impacting Europe’s Manufacturing Sector

Explore how Intel® Liftoff for Startups is transforming manufacturing through cutting-edge AI innovation.

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Intel’s Flexible AI App Development: Models, Optimizations, and Runtimes

Developers working on AI applications for Intel platforms have flexibility in their choices to make the development process more streamlined.

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Creative AI Projects Built at Intel-sponsored HackDavis 2024 Hackathon

Highlights from the University of California Davis HackDavis collegiate hackathon

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Federated Graph Neural Networks for Drug Discovery

Drug discovery is becoming slower and more expensive. Eroom’s law –Moore’s Law in reverse – is that the cost of Research and Development (R&D) of all new drugs approved has risen exponentially over the last 60 years. One of the early steps in drug discovery is predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of candidate drug molecules. ADMET prediction is essential for understanding how drugs are absorbed, distributed, metabolized, and eliminated within the body, which is crucial for determining the pharmacokinetics and safety of potential drug candidates.  

Machine/Deep Learning, Graph Neural Networks in particular, have become indispensable to predicting these properties. However, the databases containing the chemical structure of compounds and their corresponding ADMET properties are typically proprietary and a closely guarded secret. Federated Machine learning has shown promising results due to its ability to address challenges related to privacy, data decentralization, and collaboration across multiple institutions to realize more accurate and generalizable models.  

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Intel Presents SYCL implementation of Fully-Fused Multi-Layer Perceptrons for Intel GPUs

Intel is proud to present the first SYCL implementation of fully-fused Multi-Layer Perceptrons applied on Intel GPUs that support Intel Xe Matrix Extensions (XMX) instructions and an open-sourced repository of the implementation

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AI Cloud for Everyone: Nodeshift Leverages the Intel® Tiber™Cloud for Cost-Effective AI Solutions

Nodeshift provides cost-effective AI cloud solutions using Intel® Tiber™ Developer Cloud with support from the Intel Liftoff program, reducing costs up to 80% compared to major providers.

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AI Innovations in Mental Health: Intel® Liftoff Startups Lead the Way

Intel® Liftoff startups are pioneering next-gen solutions for mental health. From optimizing healthcare conversations to emergency diagnostics, these innovators are reshaping mental wellness with genAI. Keep reading to learn more about their journey, and see the future of AI-powered mental health support taking shape.

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