# Universal Encoder

**URL:** <https://discourse.numenta.org/t/universal-encoder/354>\
**Category:** Numenta Theory\
**Tags:** encoders\
**Created:** [May 14, 2016, 11:31am UTC](https://discourse.numenta.org/t/universal-encoder/354 "2016-05-14T11:31:21Z")\
**Posts on this page:** 1\
**Showing post:** 104

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**Author:** ![Paul\_Lamb](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/paul_lamb/32/2725_2.png) [@Paul\_Lamb](https://discourse.numenta.org/u/Paul_Lamb)\
**Post date:** [February 20, 2018, 3:21pm UTC](https://discourse.numenta.org/t/universal-encoder/354/104 "2018-02-20T15:21:56Z")

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> [@Bitking](#):
>
> I don’t seriously think that the Cortical IO system is how the brain does it. It is an abstraction of part of how the brain does things in much the same way that deep learning mimics portions of how the hierarchy of the brain works and enables point neurons to do more than a single layer is capable of. In both cases - mimicking even a small part of the brain’s function turns out to be very powerful.

I’ve written a proof of concept variation on semantic folding to explore the possibility of a universal encoder (after a discussion with @jordan.kay on [this thread](https://discourse.numenta.org/t/can-we-deduce-a-universal-encoder-algorithm-via-the-fundamentals/2080)). My implementation is intended to be a bit more general purpose than [cortical.io](http://cortical.io) implementation, though.

Their implementation, as I understand it, requires a complete data set ahead of time for pre-training (each snipit from Wikipedia is known ahead of time and given a specific coordinate on the semantic map). My implementation instead is trained on the fly, modifying on its encodings of a particular input over time, using the concept of eligibility traces from RL to establish semantics from other nearby inputs.

The system assumes causality is an important element of establishing semantics (the more often two inputs are encountered near to each other, the more overlap they will have in their encoding). So far I have only used it for generating word SDRs, though… next step is to give it a non-language problem to solve, such as the hot gym data.

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