Writerflint Philosophy

What?

Writerflint uses writing and Machine Learning to facilitate Knowledge Discovery in complex domains. It lets researchers iterate writings easily, get and filter input from others, organise related writings, find and filter existing resources and discover relevant existing or missing information.

Why?

Writing has been used throughout history for knowledge dissemination and also as a potent tool for knowledge exploration and discovery on complex topics. The exploration and discovery process are often hidden in the final product. This discovery process involves repeatedly exploring, refining, reorganising, researching and incorporating feedback. These are currently disparate tasks and the burden of assimilating their outputs rests on the writer. We see these tasks as inextricably intertwined and aim to make them seamless.

The internet and other technological and societal advances mean that there is far more information and knowledge available to more people than has ever been at any time in history. In complex domains, there is too much information and it is rapidly expanding. And more domains are becoming complex. This is a problem for persons who need to explore and understand these domains. But progress in the application of Machine Learning to real-world problems offers hope in finding, filtering, ranking and connecting the information and knowledge available from multiple sources.

How?

Those who need to explore and understand complex domains often write as part of the process. Finding, filtering, ranking and synthesizing the relevant existing information are additional processes which feed into the next iteration of their thoughts and writing. This process, individually or collaboratively, iteratively builds up knowledge and understanding of a topic and drives accurate actions.
Writerflint facilitates this process by providing a writing environment which provides: connecting related writings, sharing writings for editing and feedback, and one-click iteration. Finding, filtering, and ranking relevant existing information happens automatically through a Natural Language Processing and Machine Learning engine which uses the writings to provide resource suggestions, and suggest possibly relevant related knowledge and their connection to the written content. Suggestions evolve as writing content changes.

When?

Now in alpha for biomedical and general domains. Sign in or sign up to join the journey.