Exploring Deep Research: Building a Personal Web Research Agent at Google
Join Arouch, Product Manager at Google, and Ukun, Software Engineer at Google, as they introduce Gemini Deep Research, a personal research agent that can browse the web to build comprehensive reports on your behalf.
- 1. Arouch and Ukun, from Google, discuss Deep Research on Gemini, a personal research agent that browses the web to build reports.
- 2. The motivation behind Deep Research is helping people get smart fast, as research and learning queries are top uses for Gemini.
- 3. Current chatbots often provide blueprints for answers instead of comprehensive responses, leading to user dissatisfaction.
- 4. Deep Research aims to remove compute and latency constraints, providing detailed and thorough answers within a 5-minute timeframe.
- 5. Several product challenges were faced when building Deep Research:
- * Transforming an inherently synchronous feature (chatbot) into asynchronous experiences
- * Setting user expectations for different types of queries
- * Handling long outputs in a chat experience
- 6. User Interface (UX) improvements include:
- * Presenting research plans in cards, allowing users to edit and engage with the plan
- * Showing websites browsed by Deep Research in real-time
- * Creating an artifact that users can question while reading the material
- 7. Trust and ethical considerations are addressed by showing all sources read and used in reports.
- 8. Four challenges faced when building a research agent:
- * The long-running nature of tasks
- * Models must plan iteratively, spending time and compute effectively
- * Managing context while interacting with a noisy environment (the web)
- * Building robust state management solutions to handle intermediate failures
- 9. Cross-platform enablement allows users to register research tasks, receive notifications, and access reports across devices.
- 10. Models need to reason about parallel vs sequential sub-problems and ground information found during planning.
- 11. Planning must address partial information, resolve disambiguity, weave together information from different sources, and perform entity resolution.
- 12. A robust browsing mechanism is essential for navigating the web during research tasks.
- 13. Context size management is crucial as research tasks often involve follow-ups and multiple queries.
- 14. Balancing model context with user needs requires careful design decisions and trade-offs.
- 15. Deep Research received positive reception since its release in December, with users comparing it to a McKinsey analyst's work.
- 16. Future directions for research agents include expertise, domain-specific knowledge, personalized information presentation, and combining web research with coding, data science, and video generatio
- 17. The name "Deep Dive" was considered but discarded in favor of Deep Research before launching the feature.
- 18. Deep Research is a text-in, text-out system that retrieves open web information for users.
- 19. Expertise development, personalized information presentation, and combining abilities like coding and data science are key areas for future research agent advancements.
- 20. The potential impact of research agents on various domains and industries is significant, with the potential to transform professional services, sciences, finance, and more.
Source: AI Engineer via YouTube
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