Blog

Jul 20, 2018   |   Peter Tang

Toward Higher-Order Training: A Progressive Batching L-BFGS Method

Stochastic Gradient Descent and its variants, referred here collectively as SGD, have been the de facto methods in training neural networks. These methods aim to minimize a network-specific loss function F(x) whose lower values correspond to better-trained versions of the neural network in question. To find a minimal point x*, SGD relies solely on knowing the…

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#Research #Technical Blog

Jul 13, 2018   |   Michal Karzynski, Adam Rogowiec, Tomasz Socha, Leona Cook

Adaptable Deep Learning Solutions with nGraph™ Compiler and ONNX*

Artificial intelligence methods and deep learning techniques based on neural networks continue to gain adoption in more industries. As neural networks’ architectures grow in complexity, they gain new capabilities, and the number of possible solutions for which they may be used also grows at an increasing rate.  With so many constantly-changing variables at play, finding…

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#Technical Blog

Jul 05, 2018   |   Anna Bethke

Using Artificial Intelligence for Crop Production

What is the Greenhouse Challenge? Recently, the “Deep Greens” team, comprised of Intel AI data scientists and horticultural experts from the Universidad Nacional Autónoma de México (UNAM), competed in, and won, a 24-hour hackathon for the chance to win one of 5 slots to grow cucumbers in an autonomous greenhouse later this year. The competition…

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#Solutions

Jun 21, 2018   |   Neta Zmora, Guy Jacob, Gal Novik

Compressing Deep Learning Models with Neural Network Distiller

Deep Learning (DL) and Artificial Intelligence (AI) are quickly becoming ubiquitous. Naveen Rao, Intel's Artificial Intelligence Products Group's GM, recently stated that "there is a vast explosion of [AI] applications," and Andrew Ng calls AI “the new electricity”. Deep learning applications already exist in the cloud, home, car, our mobile devices, and various embedded IoT…

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#Technical Blog

Jun 19, 2018   |   Oren Pereg, Moshe Wasserblat, Amit Yaccobi, Sapir Tsabari, Daniel Korat, Peter Izsak, Alon Eirew

Revolutionizing Personal Assistant Through Understanding Actionable Requests in Human-to-Human Interactions

Intelligent Personal Assistant Apps Intelligent Personal Assistant Applications (IPAA) are increasingly in use and are becoming essential parts of many people’s lives. IPAAs are designed to help humans with day to day tasks, queries, and actions such as initiating a call to someone or evoking a reminder to bring something somewhere. Human-to-Human Personal Assistant Usages…

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#Solutions

Jun 14, 2018   |   Jack Dashwood

Intel® Movidius™ Neural Compute Stick: One Year On

In the summer of 2017, I was involved in the type of project that very few get to work on during their careers: the launch of a new category of devices. While new product launches happen all the time, it’s rare to witness—let alone help launch—an entirely new type of product. The device in question…

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#Technology

Jun 14, 2018   |   Mahmoud Abuzaina, Mohammad Ashraf Bhuiyan, Wei Wang

Using Intel® Xeon® for Multi-node Scaling of TensorFlow* with Horovod*

TensorFlow* is one of the leading deep learning and machine learning frameworks today. Earlier in 2017, Intel worked with Google* to incorporate optimizations for Intel® Xeon® processor-based platforms using Intel® Math Kernel Library (Intel® MKL) [1].  Optimizations such as these with multiple popular frameworks have led to orders of magnitude improvement in performance. Intel has…

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#Technical Blog

Jun 08, 2018   |   Intel AI

Intel AI Research at CVPR 2018

The 2018 Conference of Computer Vision and Pattern Recognition (CVPR) takes place June 18th-22nd in Salt Lake City, Utah, USA. CVPR is known as the premier annual computer vision event consisting of poster sessions, co-located workshops, and tutorials. Intel’s presence at CVPR consists of 12 accepted papers/poster sessions, one competition, one Intel AI sponsored Doctoral Consortium, two…

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#Events #Research

Jun 07, 2018   |   Dina Suehiro

MLT: The Keras of Kubernetes*

Running distributed machine learning workloads has been a hot topic lately.  Intel has shared documents walk through the process of using Kubeflow* to run distributed TensorFlow* jobs with Kubernetes, as well as a blog on using the Volume Controller for Kubernetes (KVC) for data management on clusters, and a blog describing a real-world use case…

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#Technical Blog #Tutorial

Jun 05, 2018   |   Fiaz Mohamed

Warner Bros. Taps Intel AI to Connect Content and Audience

Consider the following problem. You’re looking for new approaches to marketing a beloved 90s sitcom, which has already been enormously successful in syndication and home video sales. The show produced hundreds of half-hour episodes—more than 80 hours of video altogether. How do you determine which scenes will best resonate with your target audiences? How could…

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#Technology