nlu podcast

machine learning

nlp highlights playlist

"Machine learning is to big data as human learning is to life experience," says Christopher Nguyen, the co-founder and CEO of big data intelligence company Adatao. Sure, but then, what IS big data? (especially as it's become a buzzword that captures so many things)... 

On this episode of the a16z Podcast, Nguyen puts on his former computer science professor hat to describe 'big data' in relation to 'machine learning' -- as well as what comes next with 'deep learning'. Finally, the former Google exec shares how Hadoop and Spark evolved from the efforts of companies dealing with massive amounts of real-time information; what we need to make machine learning a property of every application (why would we even want to?); and how we can make all this intelligence accessible to everyone.

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a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning

The digital universe is growing at a massive rate, and much of it is unstructured data in the form of images. In fact, in 2015 alone, consumers took 1 trillion pictures and uploaded them to social media. All this visual data provides organizations more opportunities than ever to transform their operations and deliver superior customer experiences at scale. However, without a way to quickly and expertly analyze the content of images, organizations could be left in the dark. In this podcast, Alyssa Simpson, Watson Senior Offering Manager, discusses why computer vision is important in the digital era and how the Watson Visual Recognition cognitive API is helping companies like OmniEarth, Inc. turn image insights into value. Learn how industries as diverse as manufacturing to marketing can benefit from computer vision as well as how you can get started today.
Podcast series: https://soundcloud.com/ibm-watson
Episode guide: http://www.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=LBO12349USEN

Watson Innovations - How Computer Vision Is Creating Business Opportunities

In this episode we briefly say what we're up to with the podcast.  No technical content, just a description of what each episode will look like, and why we're doing this.

00 - Intro to the podcast

https://www.semanticscholar.org/paper/A-Comparative-Study-of-Word-Embeddings-for-Reading-Dhingra-Liu/3ec37205c9201fc891ab51da200e361fdc34bfb3

01 - A Comparative Study of Word Embeddings for Reading Comprehension

https://www.semanticscholar.org/paper/Bidirectional-Attention-Flow-for-Machine-Seo-Kembhavi/007ab5528b3bd310a80d553cccad4b78dc496b02

02 - Bidirectional Attention Flow for Machine Comprehension

https://www.semanticscholar.org/paper/FastQA-A-Simple-and-Efficient-Neural-Architecture-Weissenborn-Wiese/7c1576b96a1e246d77b30f7b80cec63be96fa698

03 - FastQA: A Simple and Efficient Neural Architecture for Question Answering

An interview with Chris Dyer.

https://www.semanticscholar.org/paper/Recurrent-Neural-Network-Grammars-Dyer-Kuncoro/1594d954abc650bce2db445c52a76e49655efb0c

04 - Recurrent Neural Network Grammars, with Chris Dyer

**The podcast is currently on hiatus. For more active NLP content, check out the Holistic Intelligence Podcast linked below.**

Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. All views expressed belong to the hosts/guests, and do not represent their employers.

00 - Intro to the podcast

NLP Highlights