W⁴ project was initiated with the report that a lot of companies and users were willing to put on their markets a new race of applications able to handle multiple sources of information or large amount of data at lower costs.

 

It  provides consulting services and promote technology based on a set of breakthroughs in the way digital information can be found or analyzed quickly and with maximized relevancy.

 

Working with W⁴ allows you to have access to unique methods, accelerate cloud computing, get mobile devices new usage, implement new computer chip designs and remove some social networking pains. Technology promoted by W⁴ is “unique” and respects patent, author and commercial rights.

 

Technology is based, but not only, on an Artificial Intelligence (AI) engine whose paradigm is far different from standard statistical and computational approaches.  The core concept is to mimic how a human brain handles signals for automatic recognition of text. A useful set of applications are made accessible to most developers around “Big Data” (indexation, routing, user profiling…), major improvement of existing search systems, disruptive discovery associated to new business models which “act on data”, intelligent browsing.

 

Technology enhances learning of all sort of text based information as a set of prerequisites or monitoring efforts are removed. The neuronal engine learns ex nihilo from the sole texts which are submitted to it without any others resources.

 

The engine is disruptive as it is beyond theories like « multilayer perceptrons », Hopfield model, Kohonen self-organized maps, Bayesian statistics, Markov chains and conditional random fields. It directly simulates the brain area of Wernicke, a small region where recognition, association and similarities occur. The engine is build, but not only, on an associative memory, entirely autonomous, which growth and internal topology depends on the incoming signal. This topology also depends from the learned experience which behaves like an internal stimulus which in return modifies the structure and topology of the associative memory network. Hence, there is no need to create a statistical or mathematical model to process the data. We named this automatic creation of knowledge in the memory, based on the internal coherency and correlation of the signal, mARC (Memory Association by Reinforcements of Contexts).

 

The engine learns language structures with semantic links, either explicit or implicit, through analyzed documents thanks to its intrinsic association capabilities. The direct consequence of this approach is a maximized relevancy to users’ queries. This allows powerful contextual applications.

 

In order to show the unique value of promoted technology, W⁴ makes available a set of tools.

 

These tools show how the technology automatically and incrementally creates, without any external resource (like ontologies, stop lists, specific dictionaries, RDF or OWL vocabulary, manual tagging of large heterogeneous corpus…), semantic associations which allow to categorize corpus per context beyond any keyword approach. The engine can auto-correct and auto-evaluate itself. It is able to extract a context from a semantic unit. In other words it allows polysemy (different usages) and synonymy.

 

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