Le projet THINK

Women researchers at Intel Labs are working on new technology every day. In honor of Women’s History Month, we would like to highlight their innovative work. Through their contribution of novel ideas, problem-solving skills, and collaborative team efforts, Intel Labs continues to build tomorrow’s technology today.

The ability to learn tasks in a way that multiple ones can be performed simultaneously challenges both people and artificial neural networks.

How knowledge constructs can transform AI from surface correlation to comprehension of the world

This blog will discuss the significance of context in ML, and how late binding context could raise the bar on machine enlightenment.

Our team of researchers from Arizona State University, Intel AI Labs, and Oregon State University used language as a flexible goal specification for imitation learning (IL) in manipulation tasks, providing a communication channel between the human expert and the robot.

In just a few short years, confidential computing has gained wide attention and momentum as a powerful new way to provide end-to-end protection of in-use code and data. Financial services, government, retail, healthcare, cloud service providers (CSPs) and many others, especially in regulated industries, are piloting and adopting this emerging security paradigm. So, too, are a wide variety of companies eager to protect the valuable intellectual property of machine learning and artificial intelligence models running in multi-tenant environments.

In this blog you will learn how to: 

Setup and deploy your application or docker container in Linux VMs using Azure IoT Edge for Linux on Windows (EFLOW) on a windows device  

System Setup for EFLOW 
Install EFLOW 
EFLOW deployment 
Azure IOT Hub Deployment 

Offload your AI workloads by harnessing the power of iGPU 
Get a significant performance boost by using OpenVINO™ toolkit 

Researchers at Carnegie Mellon University and Intel Labs introduced a multiscale deep equilibrium model (MDEQ) that represents an “infinitely” deep network with only a constant memory cost.

In this series of blogs, I offer a different perspective: an information-centric classification with emphasis on the type, structure, and representation of knowledge and its implications for the attributes of the systems that deploy them.

The information-centric classification proposed here includes three key classes of AI systems:  Fully Encapsulated Information, Semi-Structured Adjacent Information, and Deeply Structured Knowledge.