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The Clortex Project is building a new kind of software for machine intelligence. With Hawkins Hierarchical Temporal Memory and Cortical Learning Algorithm at its heart, Clortex is a window into the amazing power of the human brain, and a real-world system for using that power to solve your own problems.

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The Clortex Project is building a new kind of software for machine intelligence. With Hawkins Hierarchical Temporal Memory and Cortical Learning Algorithm at its heart, Clortex is a window into the amazing power of the human brain, and a real-world system for using that power to solve your own problems.
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The Clortex Project is building a new kind of software for machine intelligence. With Hawkins Hierarchical Temporal Memory and Cortical Learning Algorithm at its heart, Clortex is a window into the amazing power of the human brain, and a real-world system for using that power to solve your own problems.

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Focus: Sparse Distributed Representations | InBits.com

http://inbits.com/nupic-machine-intelligence/focus-sparse-distributed-representations

Better Living through Thoughtful Technology. Book: Real Machine Intelligence. Focus: Sparse Distributed Representations. Focus: Sparse Distributed Representations. Nov 24 / 2013. Focus: Sparse Distributed Representations. For more). Some of the following is based on material in my upcoming book,. Real Machine Intelligence with NuPIC Using Neuroscience to Build Truly Intelligent Machines. And Chapter 1 Some Context for Machine Intelligence. Basic Properties of SDRs. The most basic properties of SDRs are t...

2

January | 2015 | InBits.com

http://inbits.com/2015/01

Better Living through Thoughtful Technology. Book: Real Machine Intelligence. Focus: Sparse Distributed Representations. Monthly Archives / January 2015. Jan 02 / 2015. Clortex (HTM in Clojure). Self-Stabilisation in Hierarchical Temporal Memory. This post was written in response to Jeff Hawkins’ comments on last week’s article on a new Multilayer Model of Neocortex. But goes into much more detail when describing how a non-sparse output from one sublayer is absorbed and processed by downstream sublayers.

3

Hire Fergal | InBits.com

http://inbits.com/hire-fergal

Better Living through Thoughtful Technology. Book: Real Machine Intelligence. Focus: Sparse Distributed Representations. Nov 13 / 2013. NuPIC – machine intelligence applied to the real world. Now also known as Grok), who are currently developing a server monitoring product for Amazon Web Services servers. For details of the project and what we’re up to, visit Numenta.org. In Vienna, who have built a powerful semantic processing engine (based on Hawkins’ principles) for representing text, and using ...

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Implications of the NuPIC Geospatial Encoder | InBits.com

http://inbits.com/2014/08/implications-of-the-geospatial-encoder

Better Living through Thoughtful Technology. Book: Real Machine Intelligence. Focus: Sparse Distributed Representations. Implications of the NuPIC Geospatial Encoder. Aug 14 / 2014. Implications of the NuPIC Geospatial Encoder. Numenta’s Chetan Surpur recently demoed and explained. And a tutorial by Matt Taylor. Are available on Youtube. First, here is Chetan presenting to, and discussing it with, Numenta people:. And here’s Matt with another excellent hands-on tutorial:. Between 0 and 1, and a. Bits in ...

5

InBits.com

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Better Living through Thoughtful Technology. Book: Real Machine Intelligence. Focus: Sparse Distributed Representations. Nov 13 / 2014. Efficiency of Predicted Sparseness as a Motivating Model for Hierarchical Temporal Memory. Part 1 – Introduction and Description. Which quickly leads to all sorts of unintuitive phenomena like time dilation, length contraction, and so on. This discussion aims to do this for HTM by proposing that its key underlying principle is the efficiency of predicted sparseness.

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Rebel Science News: Why Deep Learning Is a Hindrance to Progress Toward True AI

http://rebelscience.blogspot.com/2015/06/why-deep-learning-is-hindrance-to.html

I rebel, therefore I am. Wednesday, June 3, 2015. Why Deep Learning Is a Hindrance to Progress Toward True AI. Supervised vs Unsupervised Learning. In a recent article titled Emtech Digital: Where Is AI Taking Us? MIT Technology Review editor Will Night. However, [Quoc] Le said that the biggest obstacle to developing more truly intelligent computers is finding a way for them to learn without requiring labeled training data—an approach called “unsupervised learning.”. Because, as Google Brain. Data The st...

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Better Living through Thoughtful Technology. Book: Real Machine Intelligence. Focus: Sparse Distributed Representations. May 21 / 2015. The Brain is a Universal Dynamical Systems Computer – Hierarchical Temporal Memory. Note: This post is a sketch of a paper in progress, due to be completed in May-June 2015.]. Nonlinear Dynamics – an Introduction. Which involves combining mixed feedback loops between sensation and action. Here’s a cute video of such a system:. Dynamical Systems and the Brain. Well, yes a...

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