Jan 16, 2018   |   Azadeh Yazdan

The Future of Retail is All About Artificial Intelligence

Artificial Intelligence is an engine that is poised to drive the future of retail to all-new destinations. We live in an era where a tremendous amount of data is being generated online and offline. However, access to larger datasets doesn’t lead to improved business results. The key to success is the ability to extract meaning…

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Jun 20, 2016   |   Scott Clark

Much Deeper, Much Faster: Deep Neural Network Optimization with SigOpt and Nervana Cloud

By: Scott Clark, Ian Dewancker, and Sathish Nagappan Tools like neon, Caffe, Theano, and TensorFlow make it easier than ever to build custom neural networks and to reproduce groundbreaking research. Current advancements in cloud-based platforms like the Nervana Cloud enable practitioners to seamlessly build, train, and deploy these powerful methods. Finding the best configurations of these deep nets and efficiently tuning their parameters, however, remains…

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#Intel DL Cloud & Systems

Jan 06, 2017   |   Yinyin Liu

Building Skip-Thought Vectors for Document Understanding

The idea of converting natural language processing (NLP) into a problem of vector space mathematics using deep learning models has been around since 2013. A word vector, from word2vec [1], uses a string of numbers to represent a word’s meaning as it relates to other words, or its context, through training. From a word vector,…

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#Model Zoo #NLP

Dec 08, 2016   |   Anthony Ndirango

End-to-end speech recognition with neon

By: Anthony Ndirango and Tyler Lee Speech is an intrinsically temporal signal. The information-bearing elements present in speech evolve over a multitude of timescales. The fine changes in air pressure at rates of hundreds to thousands of hertz convey information about the speakers, their location, and help us separate them from a noisy world. Slower changes in…

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#Model Zoo #Speech Recognition

Mar 26, 2018   |   David Austin

A Titanic Win at Kaggle’s Iceberg Classifier Challenge

Recently, my teammate Weimin Wang and I competed in Kaggle’s Statoil/C-CORE Iceberg Classifier Challenge. The competition challenged participants to classify images acquired from C-band radar and was the most participated in image classification competition that Kaggle has ever hosted—so I’m very excited to announce that we won 1st place out of 3,343 teams! Now we’d…

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#Image Classification

Dec 04, 2017   |   Jessica Rosenthal

Intel AI Showcased at Neural Information Processing Systems (NIPS)

Today marks the beginning of the thirty-first annual conference on Neural Information Processing Systems (NIPS 2017), an interdisciplinary conference that brings together researchers in all aspects of neural and statistical information processing and computation, and their applications. The Intel AI team will be presenting publications and posters along with numerous workshops throughout the week.  …

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Machine Learning

Oct 31, 2017   |   Shashi Jain, Katie Fritsch

A Summer of Space Exploration with Intel and NASA

This summer, Intel has been collaborating with the NASA Frontier Development Lab (FDL) , an AI R&D accelerator targeting knowledge gaps useful to the space program. The NASA FDL, hosted at the SETI Institute, was established to apply AI to five specific challenges in areas relevant to the space program: Planetary Defense (defending the Earth…

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Dec 29, 2016   |   Jennifer Myers

neon v1.8.0 released!

Highlights from this release include:  * Skip Thought Vectors example * Dilated convolution support * Nesterov Accelerated Gradient option to SGD optimizer * MultiMetric class to allow wrapping Metric classes * Support for serializing and deserializing encoder-decoder models * Allow specifying the number of time steps to evaluate during beam search * A new community-contributed Docker image…

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#Release Notes

Dec 22, 2015   |   Tambet Matiisen

Guest Post (Part I): Demystifying Deep Reinforcement Learning

Two years ago, a small company in London called DeepMind uploaded their pioneering paper “Playing Atari with Deep Reinforcement Learning” to Arxiv. In this paper they demonstrated how a computer learned to play Atari 2600 video games by observing just the screen pixels and receiving a reward when the game score increased. The result was remarkable,…

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#neon #Reinforcement Learning