Semantic highlights
Embedding models can rank data based on semantic meaning, evaluating each individual segment on a spectrum to show its relevance throughout the artifact.
When scanning large amount of text, I want to be able to rank data based on meaning, so I can understand the significance of selected concepts easily.
- The flow of language: When we are able to rank content on specific parameters, we also make the flow of the language visible. This makes it easier to scan large amounts of text for the information you are searching for.
More of the Witlist
Provide relatable and engaging translations for people with varying levels of expertise, experience and ways of thinking.
When an observation is added to the context from an implicit action and a prediction is made, users should be able to easily evaluate and dismiss it.
Embedding models can rank content along virtually any dimension. This capability provides significant value by enabling users to explore and analyze the embeddings to create a spectrum of any features.
AI can enhance live chat streams by analyzing real-time data, identifying trends, and driving interactive elements like voting to boost audience engagement.
Use a spatial dimension to explore and manipulate language. By pulling text around on a map, you can play with different features in a playful and meaningful way.
Generating multiple outputs and iteratively using selected ones as new inputs helps people uncover ideas and solutions, even without clear direction.