Visual grounding
Using the source input as ground truth will help trust the system and makes it easy to interpret its process and what might have gone wrong.
When checking data, I want to be able to see how the system arrived at its answer, so I can trust the data and identify any potential errors in the process.
- AI Transparency and Explainability: Make AI systems transparent and understandable by explaining how and why decisions are made.
- Multimodal Context: In this example we used the context of an image of a receipt, but it can also include other modalities, such as audio.
More of the Witlist
Provide relatable and engaging translations for people with varying levels of expertise, experience and ways of thinking.
Ordering content along different interpretable dimensions, like style or similarity, makes it navigable on x and y axes facilitating exploration and discovery of relationships between the data.
Textual information often misses intuitive cues for understanding relationships between ideas. AI can clarify these connections, making complex information easier to grasp quickly.
Embedding models can rank data based on semantic meaning, evaluating each individual segment on a spectrum to show its relevance throughout the artifact.
An intelligent assistant that analyzes emails to identify questions and feedback requests, providing pre-generated response options and converting them into complete and contextually appropriate replies.
Proactive agents can autonomously initiate conversations and actions based on previous interactions and context providing timely and relevant assistance.