Numenta / Sparse Distributed Representations

My guest this time is Francisco Webber, founder and General Manager of artificial intelligence startup Cortical.io. Francisco presented at the O’Reilly AI conference on an approach to natural language understanding based on semantic representations of speech. His talk was called “AI is not a matter of strength but of intelligence.” My conversation with Francisco was a bit technical and abstract, but also super interesting.

The show notes can be found at https://twimlai.com/talk/10.

Subscribe!
iTunes ➙ https://itunes.apple.com/us/podcast/this-week-in-machine-learning/id1116303051?mt=2
Soundcloud ➙ https://soundcloud.com/twiml
Google Play ➙ http://bit.ly/2lrWlJZ
Stitcher ➙ http://www.stitcher.com/s?fid=92079&refid=stpr
RSS ➙ https://twimlai.com/feed

Lets Connect!
Twimlai.com ➙ https://twimlai.com/contact
Twitter ➙ https://twitter.com/twimlai
Facebook ➙ https://Facebook.com/Twimlai
Medium ➙ https://medium.com/this-week-in-machine-learning-ai

Using SDR sets and unions to identify SDRs that have been seen in the past.

Help me decide what episode to do next, Encoders or Spatial Pooling! Comment below or vote here: https://discourse.numenta.org/t/htm-school-episode-4-sdr-sets-unions/455

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this episode of HTM School, we talk about SDR overlap sets and subsampling.

Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory: http://arxiv.org/abs/1503.07469
SDR Visualizations: https://github.com/nupic-community/sdr-viz

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this episode of HTM School, we formally introduce the Sparse Distributed Representation (SDR).

Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory: http://arxiv.org/abs/1503.07469
SDR Visualizations: https://github.com/nupic-community/sdr-viz

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

Let's start at the very beginning! HTMs rely heavily on bit arrays, so here are the basics.

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

"Sparse Distributed Representations: Our Brain's Data Structure"

Subutai Ahmad, VP Research, Numenta

Numenta Workshop Oct 2014 Redwood City CA

In this screencast, Jeff Hawkins narrates the presentation he gave at a workshop called "From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications." The workshop was held May 7-11, 2012 at the University of California, Berkeley.

Slides: http://www.numenta.com/htm-overview/05-08-2012-Berkeley.pdf

Abstract:
Sparse distributed representations appear to be the means by which brains encode information. They have several advantageous properties including the ability to encode semantic meaning. We have created a distributed memory system for learning sequences of sparse distribute representations. In addition we have created a means of encoding structured and unstructured data into sparse distributed representations. The resulting memory system learns in an on-line fashion making it suitable for high velocity data streams. We are currently applying it to commercially valuable data streams for prediction, classification, and anomaly detection In this talk I will describe this distributed memory system and illustrate how it can be used to build models and make predictions from data streams.

Live video recording of this presentation: http://www.youtube.com/watch?v=nfUT3UbYhjM

General information can be found at https://www.numenta.com, and technical details can be found in the CLA white paper at https://www.numenta.com/faq.html#cla_paper.