May 23, 2018   |   Yinyin Liu, Harini Eavani, Zach Dwiel

Applying Deep Learning to Genomics Analysis

Synthetic Genomics, Incorporated (SGI) is a synthetic biology company that aims to bring genomic-driven solutions to market. They design and build biological systems and conduct interdisciplinary research by combining biology and engineering to address global sustainability problems SGI asked for Intel’s help to conduct a deep learning proof of concept that would automatically tag a…

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#Research #Technology

May 23, 2018   |   Yinyin Liu, Moshe Wasserblat

Introducing NLP Architect by Intel AI Lab

Many advances in Natural Language Processing (NLP) and Natural Language Understanding (NLU) in recent years have been driven by advancements in the field of deep learning with more powerful compute resources, greater access to useful data sets, and advances in neural network topologies and training paradigms. At Intel AI Lab, our team of NLP researchers…

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#Research #Technical Blog

Apr 12, 2018   |   Yinyin Liu

Deep Learning Foundations to Enable Natural Language Processing Solutions

Natural language processing (NLP) is one of the most familiar AI capabilities, having become ubiquitous through consumer digital assistants and chatbots as well as commercial applications like textual analysis of financial or legal records. Intel technology is enabling a variety of NLP applications through the advancement of hardware and software capabilities for deep learning and…

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#Research #Technology

May 17, 2017   |   Yinyin Liu

Partnership on AI

At Nervana and now at Intel, our data scientists work directly with domain experts to solve real-world problems using AI across a broad set of industries including agriculture, healthcare, automotive, energy, and finance. We spend our time building connections and applying deep learning to address each use case, and we are finding that the problem…

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#News

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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#neon #Research

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