Moderate a (artificially) Intelligent RepublicanDebate

Inspiration

We have quite an eclectic collection of people seeking to occupy the nation's highest office. Here, artificial intelligence learns about the peculiarities of the Republican candidates.

What it does

A web app that attempts to construct a coherent debate from the predictions of a Recurrent Neural Network trained on political speeches.

How I built it

I trained a Recurrent Neural Network (RNN) with the Long Short-Term Memory (LSTM) architecture on ~1 million characters from political speeches. The RNN has two layers and 128 LSTM internal states, with a total of 256346 parameters to train. LSTM is well-suited to classify and predict texts, as its internal gates can accommodate time lags between characteristic events, whereas the error gradients decay exponentially in a regular convolutional network. The training, cross-validation, and sampling are done using Torch link.

I then used Node.js to build a server to parse and select the next lines from the RNN output, depending on the context of the previous arguments in the debate. I then created a frontend to process user input and interactively display the debate.

Challenges I ran into

Long training times on a CPU as opposed to a GPU.

Accomplishments that I'm proud of

Funny debates that can make sense (when interpreted liberally).

What I learned

More about neural networks and more JavaScript.

What's next for RepublicanDebate

I want to train on a larger dataset for more sensical sentences and coherent debates. Also incorporate the previous arguments in the debate as seed context for the next argument in the RNN.

Built With

• html5
• javascript
• neural-net
• node.js
• python