Syntience Inc is an independent and privately funded Machine Learning
and Artificial Intelligence Research company founded in 2004.

We are focusing on Deep Neural Network based
Natural Language Understanding (NLU).

We have developed a superfast algorithm for learning human languages called Organic Learning (OL).

Under certain constraints we can claim that OL can learn a useful amount of any language on the planet
in five minutes of unsupervised learning from a plaintext 5MB Unicode corpus when running on
an old x86 laptop without using a GPU.

We are productizing this capability as a RESTful cloud server named Understanding Machine One

Below we discuss our offering and some end user oriented applications we could implement
in order to demonstrate UM1 capabilities

The high level results of our 20+ years of research are discussed in our blog and on our video page

Organic Learning

Our language learning algorithm is described in Chapter 8.

It uses a Deep Discrete Neuron Network where
discrete Java Node objects are used as
Epistemologically Adequate pseudo-neurons.
The system starts out empty and adds "Neurons" as required when learning from the given corpus. This is very different from Deep Learning.

Understanding Machine One

UM1, our Understander (our runtime inference engine) is described in Chapter 9 of the blog and is available as a free alpha service in the cloud. You can test the service yourself by downloading our python test code from GitHub


The long term goal of the company is to create a portable personal AI which uses voice for input and output, as seen in the movie "Her" and as discussed in Science Fiction for decades.

Bubble City

In the short term we intend to use our UM1 Understander as the basis for a novel message routing system called Bubble City. It is not a social medium, it is an Idea Router. The current design proposal is available as a PDF.