No Priors: Artificial Intelligence | Technology | Startups
Inside Deep Research with Isa Fulford: Building the Future of AI Agents
Apr 24, 2025
Isa Fulford, a key figure in deep research at OpenAI, discusses the evolution of AI agents with real-world capabilities. She highlights the importance of using human expert data and how reinforcement learning enhances agent functionality. Isa delves into the challenges of latency, safety, and the balance between functionality and trust as AI systems become more autonomous. She also shares predictions on the future of AI agents, including their potential to complete tasks more efficiently and develop a sense of 'taste' in their operations.
30:45
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Quick takeaways
- The development of Deep Research leverages human expertise to create high-quality data sets essential for effective information synthesis across various domains.
- Future advancements aim to enhance the model's capabilities, fostering user trust and enabling personalized, collaborative interactions in research tasks.
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Origins of Deep Research
The idea for Deep Research emerged from advancements in reinforcement learning algorithms that showed promising results in solving math, science, and coding tasks. The development team, particularly Issa Fulford and his colleague Yash, sought to apply these innovations to everyday research tasks such as online browsing and information synthesis. They aimed to move beyond conventional transactional uses of AI, like ordering food, to focus on tasks useful for knowledge workers who require in-depth research capabilities. This shift reflects a broader goal at OpenAI to create an artificial general intelligence (AGI) capable of making scientific discoveries, emphasizing the need for machines that can effectively synthesize information.
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