Le projet THINK

Automatic Speech Recognition (ASR) uses AI technology to convert spoken language to readable text. This technology has grown exponentially over the last decade and ASR systems are commonly used in voice assistants like Siri, Alexa and transcription services.

Intel® Liftoff member PixelML is reshaping content creation with agentic workflows. Their AI-powered platform bridges human-AI collaboration gaps, transforming the content supply chain. Keep reading to learn more – or tune into the Intel on AI podcast. 

Learn about and leverage Intel® AI Developer Resources at Univ. of Michigan’s MHacks 2024 hackathon.

Check out the Intel® Liftoff fintech startups shaking up the industry with fresh ideas, smart solutions, and a focus on doing good while staying ahead of the game.

This post will walk you through a workshop that provides hands-on experience with the Stable Diffusion model to create images based on a text prompt.

The latest episode of the « Code Together » podcast centers on the progress and challenges that were overcome in porting scientific applications to the Aurora Supercomputer, a cutting-edge exascale system at Argonne National Laboratory.

The Code Together podcast, hosted by Tony Mongkolsmai, explores key developments in technology, through in-depth conversations with experts. In this latest episode, Tony is joined by Ralph De Wargny, Director of the Intel® Liftoff for Startups program and he shares his hands-on experience in supporting AI startups.

In today’s fast-paced digital landscape, businesses are increasingly moving beyond centralized public clouds to embrace distributed cloud models, blending hybrid, multi-cloud, and edge solutions. This shift offers not only flexibility and scalability but also meets critical demands for data residency, latency, and security.

Aleph Innovations, a US-based AI startup, leverages AI and machine learning to enhance healthcare. By granting medical professionals full access to cutting-edge AI, they boost diagnostic and administrative capabilities, saving time, money, and improving patient outcomes.

Learn about the critical aspects of deploying LLMs using open models, ensuring data privacy, and maintaining high accuracy.