Word association game powered by machine learning!
Experiment with different combinations of models and ranking methods to see which works best for your use case!
Two Types of gaming Mood
this app is playing audio
Arcade Mood
Think Fast, Type Fast!
Blocks Mood
Take your time and puzzle it out!
Semantic Experiences
Experiments in understanding language
Introducing the Semantic Reactor
Natural Language Understanding (NLU) makes it possible to communicate with our digital devices with everyday words, phrases, and sentences. It can be used to make customer service bots, digital virtual assistants, search engines, games, game characters, and lots of things we haven’t thought of yet.
To show how easy it can be to use NLU, we’ve created a tool called the Semantic Reactor that uses pre-trained language models, offers different ranking methods, and has a few advanced features.
How to use the Semantic Reactor
The Semantic Reactor lets you sort lines of text in a Google Sheet using a variety of machine-learning models. The models offer ranking methods that determine how the list will be sorted.
With the semantic similarity method, the lines more similar to the input will be ranked higher.
With the input-response method, the lines that are the most appropriate conversational responses are ranked higher.
To use, open a new sheet and write the lines you would like the model to sort in Column A. Row 1 is assumed to be a header and will be ignored by the tool.
Remember to press the “Reload” button anytime you’d like changes in your sheet to be considered by the Semantic Reactor.
Game Making
If you’re interested in making games that take advantage of Semantic ML, you should take a look at Semantris, which uses the same technology to detect word associations. You can experiment with the words it uses in the Semantic Reactor.
You might also be interested in the open-sourced code for Mystery of the Three Bots, a simple game where a player solves a mystery by asking questions of the three AI-powered bots
For Developers
The Semantic Experiences on this site are all based on fully learned end-to-end models that can be used for a wide variety of natural language understanding applications. We’re excited to share these models with the community to see what else can be built with them. We know that what we’re showing here is just the beginning…
Training the Models
The models we’re sharing above are primarily trained on pairs of natural language inputs, together with their responses (or some other semantic relationship such as entailment).
These models are trained using English language examples, but the same approach can and has been used for other languages.
