Piyush Arora
Staff UX Designer, Google.
Authored Publications
Sort By
Preview abstract
Configuring and optimizing artificial intelligence (AI) models for specific tasks can be a complex process that may involve deep technical expertise and iterative manual experimentation. This disclosure describes a system, which may be implemented as a self-improving AI agent, that can assist in automating this process by interpreting a user's intent to recommend a suitable AI model and configuration. The agent can analyze available models and propose settings, for example, system instructions, grounding data, and operational parameters. The system can also perform background evaluations to provide empirical data and a rationale for its recommendation. A human-in-the-loop component may be included to present proposed optimizations to a user for approval before the optimizations are implemented. This approach can aid in streamlining AI model optimization for a broader range of users, help align model behavior with user goals, and reduce the likelihood of unintended outcomes through controlled, human-validated improvements.
View details
Preview abstract
Artificial intelligence (AI) models that depend on context from external data sources may produce outdated responses as underlying information, such as database schemas or web content, changes over time. The described system compartmentalizes AI model context into static and dynamic components. A static context may comprise fixed rules, safety policies, and format requirements. A dynamic context can be populated by a user-provided script configured to run at a specified frequency. This script can fetch, structure, and cache data from external sources, for instance APIs or databases, using stored credentials. This method allows AI models to operate with more current information from external environments, which can help outputs remain synchronized and adhere to predefined operational policies, potentially reducing the need for manual context updates.
View details
Preview abstract
Aspects of the disclosed technology include computer-implemented systems and methods for integrating machine-learned generative models with code editing tools. A code editor is configured to execute computer-executable code within code cells of a code editor interface including a first interface portion and a second interface portion. The interface portion is configured to receive user input for defining and editing a set of code cells within the first interface portion. Each code cell of the set of code cells is independently executable by the code editor application. The second interface portion is configured to receive user input for defining and submitting user queries to a machine-learned generative model. The code editor is configured to modify at least one code cell of the set of cells based at least in part on an output of the machine-learned generative model in response to a user query.
View details
