Le projet THINK

In the race to operationalize AI, success depends not on flashy pilots, but on turning experimentation into measurable business value. According to David Ellison, Chief Data Scientist and Director of AI Engineering at Lenovo, the most successful AI projects start with clear business outcomes—not models. From cost savings to new revenue streams, the focus is on impact, supported by infrastructure that can scale and systems that users trust.

In this article, we will cover how to deploy high-performance AI inferencing for media data curation and retrieval-augmented generation (RAG) without requiring discrete GPUs.

When it comes to scaling AI, the conversation isn’t only about the cloud—it’s about the edge. According to Matthew Formica, Senior Director and Head of Edge Product Marketing & AI PC/Edge AI Software Developer Relations at Intel, the edge represents one of the company’s fastest-growing opportunities. With more than 200 million processors shipped into edge devices and over 100,000 deployments worldwide, Intel’s edge business is vast, yet often overlooked. The mission now: demonstrate how AI at the edge is quietly shaping everyday life, from retail checkout to robotics-powered manufacturing.

Today’s AI workloads are not purely offloaded to GPU accelerators. Host CPUs such as the Intel® Xeon® 6 processors play a significant role in maximizing the performance of AI-accelerated systems.

For Sorenson Senior Director of AI Mariam Rahmani, the future of AI isn’t about building the flashiest models—it’s about creating solutions that close communication gaps and empower people, especially the Deaf and Hard of Hearing community. With a third of Sorenson’s workforce personally connected to this community, empathy isn’t an afterthought—it’s built into the company’s DNA. 

As enterprises scale generative AI across diverse infrastructures, Intel® AI for Enterprise RAG solution delivers a modular, hardware-aware framework for Retrieval-Augmented Generation (RAG) optimized for Kubernetes and Intel platforms. With intelligent scheduling, NUMA-aware resource isolation, and dynamic scaling, it ensures predictable, performant AI workloads tailored to enterprise needs.

In the evolving landscape of artificial intelligence, connected data is becoming a core competitive advantage.

In the race to operationalize AI, success hinges not on hype, but on clarity, customization, and speed to value. According to Riyaz Habibbhai, Director of Product Marketing at Google Cloud, truly effective AI product marketing focuses on one simple but powerful goal: delivering meaningful outcomes through a clear and compelling story. In today’s noisy tech landscape, simplicity, security, and strategic alignment are table stakes for any enterprise-ready solution. 

Researchers from the University of Texas at Austin and Intel Labs investigated chain-of-thought reasoning structures in large language models to identify and calibrate flawed reasoning pathways

Large Language Models are revolutionizing AI applications; however, slow inference speeds continue to be a significant challenge. Intel researchers, along with industry and university partners, are actively working to address this issue and accelerate the efficiency of LLMs. In a series of blog posts, Intel Researchers introduce several novel works, including a method that accelerates text generation by up to 2.7 times, a method that extends assisted generation to work with a small language model from any model family, and a technique that enables any small “draft” model to accelerate any LLM, regardless of vocabulary differences