# AI model

### Model Inputs

Newwit AI model inputs consists of three parts.

**Player embedding** This is a mathematical matrix describing a player’s features, including age, occupation, education, number of predictions, overall accuracy, topic accuracy etc.

**Question embedding** This is a mathematical matrix describing a question’s features, including title, associated topics, associated subtopics, number of answers etc.

**Interaction embedding** This is a mathematical matrix describing how the player interacted with the question, including player’s prediction, player’s comments, number of upvotes received from the comment etc.

**Model target** With the above 3 input embeddings, the model will compute a prediction power score per user using a deep neural network. During training, the AI model will push the prediction power to 1 if the player’s prediction was correct and -1 if the player’s prediction was incorrect. When the model converges, it will know how much to trust a player when it comes to a particular question. The level to trust is the measure of prediction power.

Note that prediction power scores of 1 and -1 are equally important to the model. Player with -1 prediction power score contributes to the model by saying that most likely his/her prediction is incorrect. Prediction power score of 0 will mean the player’s prediction performance is not consistent and is considered noise statistically.

The prediction power of each player in each question is different.

### Model Output

Final Newwit Prediction

Finally, the prediction power scores are summed to compute the final prediction.

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