Evolving outputs
Generating multiple outputs and iteratively using selected ones as new inputs helps people uncover ideas and solutions, even without clear direction.
When exploring options, I want multiple outputs to help me refine my preferences and narrow down what I am looking for, so I can achieve more satisfactory outcomes.
- Patterns overtime: Using preferred outputs as new inputs creates a trajectory that an AI model can recognize and leverage, generating increasingly refined and relevant outputs.
- Exploring Suggestions: The process of making suggestions and progressively narrowing down options can lead to more personalized and satisfactory outcomes. Ideal for situations where users may not fully know what they want.
More of the Witlist
Realtime generation allows people to manipulate content instantly, giving them more agency in using generative AI as a tool for exploration.
AI can enhance live chat streams by analyzing real-time data, identifying trends, and driving interactive elements like voting to boost audience engagement.
Embedding models can rank data based on semantic meaning, evaluating each individual segment on a spectrum to show its relevance throughout the artifact.
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.
Empower users to make decisions and give feedback quickly or engage more deeply when needed in natural language.
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.