Mar 06, 2018   |   Kyle Ambert, Deepthi Karkada, Krishna Sumanth Muppalla, Kushal Datta

Teaching Machines to do Image Classification in Health and Life Sciences: Intel® Xeon® Scalable Processors in Lab Coats

Introduction At Intel, we’re quite interested in how systems can be made smarter to solve meaningful tasks relevant to healthcare providers and patients today. The Intel and MobileODT Cervical Cancer Screening Kaggle competition, for example, challenged data scientists to train our respective computational systems to assist with the identification of early-stage cervical cancer in medical…

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#Solutions #Technology

Mar 06, 2018   |   Hema Chamraj

Powering Precision Medicine with Artificial Intelligence

Precision medicine is one of the most exciting and encouraging advances in healthcare today. It is moving us from one-size-fits-all healthcare to personalized, data-driven treatment that enables more efficient spending and improved patient outcomes. As defined by the National Institute of Health (NIH), precision medicine is an emerging approach for disease treatment and prevention that…

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Mar 01, 2018   |   Nagib Hakim

Solving Out of This World Challenges with NASA FDL

For most people, “outside the box” doesn’t mean “out of this world”. However, NASA and Intel data scientists are using out-of-the-box thinking to jointly tackle extraterrestrial problems. Space exploration requires new ways of thinking; even simple tasks can present a dizzying array of challenges. That’s why NASA developed their Frontier Development Lab (FDL), an AI…

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Feb 28, 2018   |   Azadeh Yazdan

Taking Telecom to New Heights with Artificial Intelligence

As the annual Mobile World Congress kicks off this week in Barcelona, event attendees can expect to hear a lot of talk about artificial intelligence. In fact, “Applied AI” is one of the eight themes for this year’s conference – with the intention of helping the industry cut through the complexity of artificial intelligence (AI).…

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Feb 15, 2018   |   Jason Knight

The Importance of Systems in Machine Learning

Imagine starting a modern software project today with the following constraints: No use of existing packages, libraries, or package managers* No use of continuous integration or continuous deployment (CI/CD) No use of network protocols above raw TCP/IP (unless you write it yourself) No unit tests Existing code has little chance of working as-is for your…

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#Technical Blog

Feb 13, 2018   |   Intel AI

Intel AI Research at SysML

SysML is a new conference targeting research at the intersection of systems and machine learning. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learning systems, as well as developing novel learning methods and theory tailored to practical machine learning workflows. Find these Intel AI Research…

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#Events #Research

Jan 31, 2018   |   Andres Rodriguez

Lowering Numerical Precision to Increase Deep Learning Performance

Deep learning training and inference are poised to be computational heavyweights of the coming decades. For example, training an image classifier can require 1018 single-precision operations[1]. This demand has made the acceleration of deep learning computations an important area of research for both Intel and the artificial intelligence community at large. An approach we are…

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Jan 24, 2018   |   Peng Zhang, Wei Wang, Baojun Liu, Jayaram Bobba

neon™ 2.6.0: Inference Optimizations for Single Shot MultiBox Detector on Intel® Xeon® Processor Architectures

We are excited to release the neon™ 2.6.0 framework, which features improvements for CPU inference path on a VGG-16 based Single Shot multibox Detector (SSD) neural network. These updates, along with the training optimizations released in neon 2.5.0, show that neon is gaining significant boosts in both training and inference performance.  (Granular configuration details, as well…

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#Release Notes

Jan 24, 2018   |   Vikram Saletore

Accelerating Deep Learning Training and Inference with System Level Optimizations

Training deep Convolutional Neural Networks (CNNs) is a demanding undertaking. Popular CNN examples such as ResNet-50*, GoogLeNet-v1*, Inception-3*, and others require the execution of hundreds of compute-intensive functions for each of hundreds of thousands of iterations. Intel's optimizations for popular deep learning frameworks have significantly increased processor-level performance, but there is even more we can do. In…

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Jan 23, 2018   |   Mattson Thieme, Tony Reina

Biomedical Image Segmentation with U-Net

Upsampling versus Transposed Convolution We’ve recently applied the U-Net architecture to segment brain tumors from raw MRI scans (Figure 1). With relatively little data we are able to train a U-Net model to accurately predict where tumors exist. The Dice coefficient (the standard metric for the BraTS dataset used in the study) for our model…

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#Solutions #Technology