Key concept: Setting mental models can help users understand how to interact with products that adapt over time. This chapter covers:
Identifying existing mental models
Onboarding in stages
Planning for co-learning
Accounting for user expectations of human-like interaction
Key concept: To build effective mental models of AI-powered products, consider what you want people to know about your product before their first use, how to explain its features, and when it will need feedback from them to improve.
Dan Russell is a Senior Research Scientist focusing on search quality and user happiness at Google. He emphasizes the importance of education and teaching users how to effectively search. His top time-saving tip is waking up early at 4am to complete his work before others start their day. Dan uses a combination of digital and paper tools to manage his tasks, including a physical to-do list synced with his online calendar and reminders. Besides his phone and computer, he highly appreciates his durable Maglite flashlight. He enjoys reading extensively across various fields, taking detailed notes that help him retain information. Dan prefers quiet surroundings while working and rarely listens to music. But he likes listening to podcasts for mindless tasks. He identifies himself as an introvert but adjusts his behavior to fit extroverted roles required by his job. Sleep is crucial to him, though he occasionally takes weekend naps if needed. He tries to avoid technology that may lead to high data fees while traveling internationally. Advice played a significant role in shaping Dan's perspective; he learned to listen to guidance given by others after receiving helpful feedback earlier in his career.
Lastly, he maintains three different sets of notes – worklog, journal, and summary – to keep track of his activities, thoughts, and insights.
- Search engines sometimes provide incorrect results due to misinterpretations of the query by the user, highlighting the need for careful evaluation of search results.
- Modifying queries and pursuing multiple avenues of research concurrently improves the effectiveness of searching.
- Advanced search techniques such as site:, filetype:, and double-quoting phrases enhance accuracy.
- Critical thinking skills are essential when analyzing search results to avoid accepting the first answer seen without questioning its validity.
Syllabus Outline
Outline of the course - HCIAI - Zürich Fall 2023
Course Introduction: Course mechanics, intro to the topic
Designing AI with the Mind in Mind
Fairness, Accountability, Transparency, and Ethics in AI
Building AI with Humans in the Loop
Natural Language Interfaces
Data Visualization for understanding fairness and bias
Rethinking the AI-UX Boundary for Designing Human-AI Experiences
AI, Art, Music & Sound Synthesis
Image Generative AI
Self-Driving Vehicles
Humans and Robots
Sentience? Consciousness? Coming to grips with these questions
Building AI systems for sensemaking
Final Presentations part 1 - FINAL PROJECT due today. Video
Final Presentations part 2 - FINAL PROJECT (second half) Video
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- This blog discusses using Large Language Models (LLMs) such as Bard and ChatGPT4 to summarize lengthy texts.
- The author compares the performance of these LLMs on summarizing texts, particularly focusing on the classic gothic tale, Frankenstein by Mary Shelley, and Chapter 10 of their book, The Joy of Search.
- While both Bard and ChatGPT4 show promise in creating decent summaries, there are notable differences between the two, with ChatGPT4 being more adept at handling larger amounts of information.
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