There are many different ways to use our platform. That said, successful research and applications of our models generally follow four steps: exploration, prediction, improvement, and testing.
Researchers and developers generally begin by exploring patterns in their data.
- Are there apparent differences across participants or users in a study?
- Do patterns in expression vary systematically over time?
- Are there different patterns in expression associated with different stages of research or different product experiences?
A great way to evaluate and start building on our APIs is to use them to predict metrics that you already know are important.
- Are key outcomes like mental health or customer satisfaction better predicted by language and expression than by language alone?
- If patterns in expression predict important outcomes, how do these patterns in expression vary over time and reveal critical moments for a user or participant?
The goal is often to use measures of expression to directly improve how the application works.
- Sometimes, being able to predict an important metric is enough to make a decision. For example, if you can predict whether two people will get along based on their expressions and language, then your application can pair them up.
- More formally, you can apply statistics or machine learning to the data you gather to improve how the application works.
- You can incorporate our API outputs into an out-of-the-box large language model, simply by converting them into text (e.g., "The user sounds calm but a little frustrated") and feeding them in as prompts.
- You can use expressions to teach an AI model. For example, if your application involves a large language model, such as an AI tutor, you can use measures of expression that predict student performance and well-being to directly fine-tune the AI to improve over time.
After you've incorporated measures of expression into your application, they can be part of every A/B test you perform. You can now monitor the effects of changes to your application not just on engagement and retention, but also on how much users laugh or sigh in frustration, or show signs of interest or boredom.
As you build expression-related signals, metrics, analyses, models, or feedback into an application, remember to use scientific best practices and follow the ethics guidelines of thehumeinitiative.org.
Updated 3 months ago