Piyush Arora
Piyush Arora is a Staff UX Designer at Google, where he focuses on the intersection of Human-Computer Interaction (HCI) and Machine Intelligence. His work spans foundational AI and productivity platforms, including Google Workspace, Vertex AI, Gemini Enterprise, and Colab. He is dedicated to ensuring that generative models and automated systems are designed with transparency, user agency, and ethical standards at their core.
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Responsive user interfaces enable dynamically adjusting user interfaces based on device-specific aspects such as screen size, aspect ratio, display resolution, etc. However, traditional responsive design fails to account for different types of constraints of a user and task criticality of the task being performed via the UI. Misalignment between the UI design, user context and task criticality can lead to user error. This disclosure describes techniques, implemented with user permission, for dynamically modifying the layout, information density, and/or interactive physics of a user interface based on a dual-factor analysis of user cognitive state and task criticality. The user's cognitive state can be inferred from behavioral telematics. Task criticality can be inferred from semantic analysis. The information density and other parameters of a user interface are automatically adjusted based on such analyses. Such adjustments include applying or relaxing restrictions on interactivity and adjusting visual prominence of various UI elements to adjust the information density of the user interface. The adjustments can also include adjusting friction as appropriate, hiding certain aspects of the user interface, or other types of adjustments.
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The field of Human-Computer Interaction is approaching a critical inflection point, moving beyond the era of static, deterministic systems into a new age of self-evolving systems. We introduce the concept of Adaptive generative interfaces that move beyond static artifacts to autonomously expand their own feature sets at runtime. Rather than relying on fixed layouts, these systems utilize generative methods to morph and grow in real-time based on a user’s immediate intent. The system operates through three core mechanisms: Directed synthesis (generating new features from direct commands), Inferred synthesis (generating new features for unmet needs via inferred commands), and Real-time adaptation (dynamically restructuring the interface's visual and functional properties at runtime). To empirically validate this paradigm, we executed a within-subject (repeated measures) comparative study (N=72) utilizing 'Penny,' a digital banking prototype. The experimental design employed a counterbalanced Latin Square approach to mitigate order effects, such as learning bias and fatigue, while comparing Deterministic interfaces baseline against an Adaptive generative interfaces. Participant performance was verified through objective screen-capture evidence, with perceived usability quantified using the industry-standard System Usability Scale (SUS). The results demonstrated a profound shift in user experience: the Adaptive generative version achieved a System Usability Scale (SUS) score of 84.38 ('Excellent'), significantly outperforming the Deterministic version’s score of 53.96 ('Poor'). With a statistically significant mean difference of 30.42 points (p < 0.0001) and a large effect size (d=1.04), these findings confirm that reducing 'navigation tax' through adaptive generative interfaces directly correlates with a substantial increase in perceived usability. We conclude that deterministic interfaces are no longer sufficient to manage the complexity of modern workflows. The future of software lies not in a fixed set of pre-shipped features, but in dynamic capability sets that grow, adapt, and restructure themselves in real-time to meet the specific intent of the user. This paradigm shift necessitates a fundamental transformation in product development, requiring designers to transcend traditional, linear workflows and evolve into 'System Builders'—architects of the design principles and rules that facilitate this new age of self-evolving software.
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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.
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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.
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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.
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