Talk to Books
Once the AI has learned from that data, it is then able to predict how likely one statement would follow another as a response. In these demos, the AI is simply considering what you type to be an opening statement and looking across a pool of many possible responses to find the ones that would most likely follow.
The technique we’re using to teach computers language is called machine learning. Google’s Machine Learning Glossary defines machine learning as:
“…a program or system that builds (trains) a predictive model from input data.”
What does that mean for us?
Input data: The input data is a billion pairs of statements, where the second statement is a response to the first one.
Predicting: We are predicting the response to a question or a statement. After seeing all those pairs of sentences and responses, the AI learns to identify what a good response might look like.
Model: The trained system that is used for making predictions. After training, our model is able to pick the most likely response from a pool of options.
In this application, there are two models at work. One model, a generative model, was trained on classic poetry in order to learn how to create novel verses in the style of our cadre of poets. The other model, which uses the same semantic understanding technology as our other applications, was trained to semantically understand which verses best follow the previously written line of verse.
Enjoy trying to write poetry with different poets. Each poet has their own individual style. You will probably find most enjoyment when you treat the poets as partners, going back and forth with your own verses and their suggestions. Feel free to write your own verses, use their suggestions, or use the suggestions even as inspirations as you write your own.
Please keep in mind that Verse by Verse is an experiment in how to use machine learning as a creative partner. You may encounter some issues and oddities with the response it generates. If you encounter bugs, please use the feedback tool which you’ll find in the upper right hand corner (an exclamation point in a word bubble).
Try our sample queries to get a feel for how Talk to Books works. Then play around with your own ideas. Use it to explore topics you are interested in. Part of the fun is coming up with queries that help you discover interesting perspectives and books you may want to read.
Talk to Books is more of a creative tool than a way to find specific answers. In this experiment, we don’t take into account whether the book is authoritative or on-topic. The model just looks at how well each sentence pairs up with your query. Sometimes it finds responses that miss the mark or are taken completely out of context.
If Talk to Books isn’t finding responses you like, you may get better results by using different words or simply more words. It often does better with full sentences rather than just keywords or short phrases.
In Semantris’ Arcade, when the AI sorts the list, the most related words are moved to the bottom. In the example above, you can see that it thinks that the word “Moon” is a better conversational response to “Sun” than “Teacher”.
Semantris is similar to other word association games where a person gives clues to help their teammate guess the correct words. However, in Semantris, you give your hints to an AI. Because the AI can sometimes have quirky responses, you’ll need to experiment with different types of clues to learn how this AI thinks and to earn the highest scores. Try playing with slang, technical terms, pop culture references, synonyms, antonyms, and even full sentences.
