This episode explores the development and capabilities of OpenAI's Deep Research, a new agentic product released in February. Against the backdrop of advancements in reinforcement learning algorithms, the creators sought to apply these to everyday user tasks, focusing initially on online browsing and research tasks rather than simple transactional actions like ordering food. More significantly, the discussion highlights the challenges of creating training datasets for such open-ended tasks, requiring novel approaches and human expert input to evaluate model performance. For instance, the development team used tasks like finding all papers co-authored by specific researchers or identifying product recommendations based on Reddit reviews. As the discussion pivoted to practical applications, the interviewee emphasized the importance of read-only tasks for initial safety and the potential for future expansion into action-taking capabilities. In contrast to simpler agent approaches, Deep Research aims for comprehensive information synthesis, crucial for knowledge work and scientific discovery. This ultimately means a shift towards more sophisticated, multi-step reasoning in AI, with implications for how humans interact with and delegate tasks to increasingly capable agents in the future.
