ai-one™ provides machine learning APIs to build intelligent machines. The Nathan API is not currently available to outside developers. Further information about ai-one’s products and solutions are available at the Analyst Toolbox.
Our flagship product, Nathan, is an entirely new class of artificial neural network that enables developers to build artificial intelligence into almost any application. The core “brain” is a holosemantic dataspace (HSDS) that detects the contextual meaning of data by detecting patterns. The current version of Nathan learns the meaning of any text or unstructured data.
Unlike other tools, our biologically inspired intelligence™ is modeled on the human brain so it offers the following benefits:
- Easy – Most programmers can learn to use Nathan in less than a few hours. The API provides an easy way to generate dynamic knowledge graphs using keywords and associations.
- Autonomic – Nathan learns from any exposure to data. It can be taught using training sets and/or ontologies OR it can simply learn the inherent semantics from unstructured data. Developers have complete control over how Nathan learns.
- Data agnostic – Nathan can learn the meaning of any data so it works in any language or character set.
- Transparent – Nathan tells you what it has learned by generating a lightweight ontology (LWO). This is a machine generated representation of knowledge that shows how every data element is connected.
The Nathan API powers tools to understand any data in any human language. It can learn both unstructured, semi-structured and fully structured data. Developers have the choice of providing it raw text or JSON data. It works nicely with natural language processing (NLP) tools for those that want to include parts-of-speech, named entity extraction and other NLP language specific functions. The Nathan library includes commands for semantic analysis, associative search, finding similarities, semantic matching, phonetic analysis, intrinsic semantics, semiotic analysis etc. Furthermore, all commands can be combined and developers can define new functions. An example of a content analytics application that uses NathanAPI is BrainDocs.