# Algorithm Overview

### Algorithm Overview

Several algorithms (algo) work together in the Remesh platform in order for us to understand group at conversational speed.

First, our **language embedding** algorithm (character level Deep Neural Network) computes a dense embedding vector for each response as it comes in. These vectors are then used to compute similarity between responses.

Next, our **dynamic sampling** algo chooses pairs of responses for participants to vote on which maximize information gained per vote. It adapts in real time throughout the voting process as it learns.

Then, our **ranking **algo estimates the mean (~popularity) & variance (~consensus^-1) of participants distribution of opinion about each response.

Lastly, our **representation** algo identifies a small subset of responses which are the most popular versions of the various concepts found in the full responses corpus.