Archives mensuelles : mai 2022

Women’s History Month: Meet the Women Innovating New Technologies at Intel Labs

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 … Continuer la lecture

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Learning Challenges Multitasking in the Human Brain and Artificial Neural Networks

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

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Understanding of and by Deep Knowledge

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

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Advancing Machine Intelligence: Why Context Is Everything

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

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Using Natural Language in Imitation Learning to Instruct Robots

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.

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Advancing Confidential Computing with Intel’s Project Amber

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, … Continuer la lecture

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Witness the power of Intel® iGPU with Azure IoT Edge for Linux on Windows(EFLOW) & OpenVINO™ Toolkit

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 … Continuer la lecture

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One-Layer Implicit Deep Model Uses Less Memory for Pattern Recognition

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.

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Seat of Knowledge: Information-Centric Classification in AI

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.

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Seat of Knowledge: Information-Centric Classification in AI – Class 2

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

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