AI Interfaces Of The Future | Design Review

Key Concepts

AI User Interfaces, Chat UI, Static Web-Based Interfaces, Voice AI, Latency, Multimodal Cues, AI Agents, Autonomous AI, Visual Workflows, Canvases, Adaptive AI Interfaces, Deep Fake AI, Fidelity vs. Immediacy, Human-Machine Collaboration.

AI User Interfaces: A Glimpse into the Future

This video explores the emerging landscape of AI user interfaces, moving beyond the common chat UI to more sophisticated and intuitive designs. The discussion, featuring Rafael Shad, creator of Notion Calendar, highlights the shift from static, noun-based interfaces to dynamic, verb-based interactions driven by AI.

1. Voice AI and Multimodal Cues

• Vapy (Voice AI for Developers): This platform allows developers to build voice agents. The review points out the importance of multimodal cues, noting the lack of visual feedback when the user speaks and when the AI responds.
  • Example: The absence of visual indication when the voice assistant is speaking can be confusing if the user's laptop is muted.
• Latency as Interface: The speed of response is crucial for a natural conversation. Vapy's developer mode exposes the milliseconds of delay, allowing developers to fine-tune the experience.
  • Quote: "The latency is the interface in some ways."
• Interruptions: Vapy struggles to handle interruptions, highlighting a key challenge in creating truly human-like interactions.
• Retail AI (Voice AI for Call Operations): This platform uses voice AI for call operations, such as debt collection.
  • Example: The AI agent successfully adapted to a curveball when the user provided an incorrect name initially.
• Room for Improvement: While the voice felt realistic, latency remained an issue. The AI could also improve by better integrating user cues into the conversation.
• First Line of Defense: This technology can be used as a first line of defense, with humans brought in for more complex cases.

2. AI Agents and Visual Workflows

• Gum Loop: This platform allows users to automate tasks with AI agents, using a visual workflow interface.
• Canvas Interface: The canvas provides a visual overview of the agent's steps, allowing users to control the process.
  • Example: The YC directory web scraping template demonstrates how users can customize the agent's actions at each step.
• Color Coding: Gum Loop uses color to differentiate between input, actions, and output.
• Zoom Levels: The interface could be improved by adjusting the level of detail displayed based on the zoom level.
• Multi-Dimensionality: The canvas is particularly powerful for modeling complex, branching decision trees.
• Historical Context: The use of flowcharts echoes earlier practices in fields like chip design.
• Interactive Flowcharts: The key innovation is making these flowcharts interactive.
• Answer Grid: This platform uses AI agents to gather and structure data in a spreadsheet format.
• Suggested Prompts: The interface provides examples of prompts to help users get started.
• Dynamic Prompts: The platform could infer and suggest prompts based on the user's context.
• AI-Powered Spreadsheet: Each cell in the spreadsheet can leverage an AI agent to retrieve data.
• Data Validation: The platform provides sources for the data, allowing users to validate the results.
• Inline Citations: The pattern of inline citations, popularized by Perplexity, is used to link data points to their sources.
• Footnotes in Software: The concept of footnotes, borrowed from academic papers, is used to verify information in real-time.

3. Prompt-to-Output Interfaces and Adaptive AI

• Polyat (AI Product Designer): This platform generates production-ready code from prompts.
• Multimodal Input: Polyat accepts text, voice, and image inputs.
• Progress Indicators: The platform uses humorous messages to keep users engaged while the AI generates the output.
• Design Terminology: The interface could be improved by providing a library of design terms that users can easily incorporate into their prompts.
• Feedback Loops: The platform could provide feedback on which parts of the prompt were successfully executed and which were not.
• Iterative Refinement: The ability to edit and refine the generated output through sub-prompts is crucial.
• Consistency: Maintaining consistency across changes is a key challenge in prompt-based design.
• Adaptive AI Interfaces: These interfaces dynamically change based on the content they are processing.
• Zi (Smarter Email App): This app suggests responses to emails based on their content.
• Abstraction Levels: The discussion explores different levels of abstraction for email responses, from autocomplete to autopilot.
• Keyboard Shortcuts: The use of keyboard shortcuts allows users to quickly select responses.
• Hot Key Challenges: The use of hot keys without modifier keys can lead to accidental actions.
• Argil (AI Video Studio): This platform creates production-quality videos using deep fake AI.
• Custom Scripts: Users can type custom scripts and have the AI generate a video of them speaking the text.
• Body Language Selection: Users can select different body language options for each segment of the video.
• Fidelity vs. Immediacy: The platform prioritizes immediacy by showing a blurry version of the video with the audio, allowing users to quickly iterate on the script.
• Human-Machine Collaboration: This approach fosters a collaborative workflow between the user and the AI.

Synthesis/Conclusion

The video concludes that AI user interfaces are rapidly evolving beyond simple chat boxes. The key is to keep the user in the loop and in control while AI handles complex tasks. The examples discussed showcase a variety of innovative approaches, including voice AI, visual workflows, adaptive interfaces, and deep fake video generation. These advancements promise to revolutionize how we interact with software and technology in the coming years.