OpenAI Forum
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AI in Science & Research
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# OpenAI Presentation
# o3 reasoning model

Deep Research in the OpenAI Forum

The presentation from Isa Fulford and Edward Sun offers an in-depth look into “Deep Research,” a capability within ChatGPT powered by a fine-tuned version of the o3 model. The model is built with agentic capabilities that enable it to autonomously conduct complex, long-horizon research tasks involving browsing, reasoning, data processing, and synthesis. Deep Research is positioned as a leap toward more capable AI agents that save users significant time and deliver high-quality, sourced outputs. The presentation also showcases how reinforcement learning, reasoning models, and safety measures contribute to creating a robust system meant to support real-world professional tasks—particularly in business, science, medicine, and academia.
Isa Fulford
Zhiqing (Edward) Sun
Isa Fulford & Zhiqing (Edward) Sun · Mar 28th, 2025
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Terence Tao
Mark Chen
James  Donovan
Terence Tao, Mark Chen & James Donovan · Mar 13th, 2025
During the virtual event on December 3rd, Prof. Terence Tao and OpenAI's Mark Chen and James Donovan engaged in a deep discussion on the intersection of AI and mathematics. They explored how AI models, particularly new reasoning models, could enhance traditional mathematical problem-solving and potentially transform mathematical research. The speakers discussed the integration of AI into various scientific fields, emphasizing AI's role in accelerating discovery and innovation. Key topics included the challenges of AI in understanding and contributing to complex mathematical proofs, the evolving nature of mathematical research with AI integration, and the future of collaboration between AI and human mathematicians. The conversation highlighted both the potential and the current limitations of AI in advancing mathematical sciences.
# STEM
# Innovation
# Higher Education
# o1 reasoning model
57:12
David Autor
Tyna Eloundou
David Autor & Tyna Eloundou · Mar 12th, 2025
About the Talk: Much of the value of labor in industrialized economies derives from the scarcity of expertise rather than from the scarcity of labor per se. In economic parlance, expertise denotes a specific body of knowledge or competency required for accomplishing a particular objective. Human expertise commands a market premium to the degree that it is, first, necessary for accomplishing valuable objectives, and second, scarce, meaning not possessed by most people. Will  AI increase the value of expertise by broadening its relevance and applicability? Or will it instead commodify expertise and undermine pay, even if jobs are not lost in net. Autor will present a simple framework for interpreting the relationship between technological change and expertise across three different technological revolutions. He will argue that, due to AI’s malleability and broad applicability, its labor market consequences will depend fundamentally on how firms, governments, NGOs, and universities (among others) invest to develop its capabilities and shape its applications.  
# Higher Education
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# AI Literacy
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# Social Science
54:59
Using the imaging of black holes as a case study, this talk highlights the key requirements for AI to make meaningful contributions to astrophysical research. Dr. Chan introduces several pioneering projects that are integrating AI into astrophysics, covering aspects such as instrumentation, simulations, data processing, and causal inference. He also discusses an innovative project aimed at enabling AI to gain scientific insights independently.
# STEM
# AI Research
# Higher Education
43:28
Jacqueline Hehir
Jacqueline Hehir · Mar 12th, 2025
Informative session about OpenAI's Research Residency program, perfect for anyone interested in forging a career in AI, but without extensive experience in the domain. Our 6-month residency helps technical researchers from diverse fields transition into AI. Led by the program manager, Jackie Hehir, this session offers insights into the program's structure, benefits, and application process.The residency is an excellent way for people who are curious, passionate, and skilled to sharpen their focus on AI and machine learning and contribute to OpenAI’s mission of building AGI that benefits all of humanity. Learn more about the residency program and discover research blogs published by residents at the bottom of this page here.
# Career
# Future of Work
34:17
About the Talk: AI will have significant, far-reaching economic and societal impacts. Technology shapes the lives of individuals, how we interact with one another, and how society as a whole evolves. We believe that decisions about how AI systems behave should be shaped by diverse perspectives reflecting the public interest. Join Lama Ahmad (Policy Researcher at OpenAI) and Saffron Huang and Divya Siddarth (Co-Directors of the Collective Intelligence Project) in conversation to reflect on why public input matters for designing AI systems, and how these methods might be operationalized in practice. The Collective Intelligence Project White Paper: The Collective Intelligence Project (CIP) is an incubator for new governance models for transformative technology. CIP will focus on the research and development of collective intelligence capabilities: decision-making technologies, processes, and institutions that expand a group’s capacity to construct and cooperate towards shared goals. We will apply these capabilities to transformative technology: technological advances with a high likelihood of significantly altering our society. Read More About the OpenAI Grant, Democratic Inputs to AI
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# Social Science
1:00:00
OpenAI and nine national labs bring together leading scientists for first-of-its kind event.
# AI Science
The Data Science for Social Justice Workshop (DSSJ), organized in partnership between UC Berkeley’s Graduate Division and D-Lab, is an 8-week program aiming to provide an introduction to data science for graduate students, grounded in critical approaches of data feminism, data activism, ethics, and critical race theory. Attendees receive training in natural language processing and leverage their skills to conduct discourse analysis on social media data in an interdisciplinary project. This workshop, about to conclude its third year, has trained over 75 graduate students across 20 disciplines. These students form a community of interdisciplinary scholar-activists who uphold a values-driven approach to data science and machine learning. In this event, Claudia von Vacano, Ph.D., Executive Director of D-Lab, introduces the Data Science for Social Justice Workshop, highlighting its goals, structure, and outcomes. Then, three students who have participated in the workshop – with diverse and rich personal and academic backgrounds – present lightning talks on their experience with DSSJ, highlighting their personal journeys, the projects they worked on, and what they gained from the workshop. The event will conclude with a Q&A and discussion on how workshops like DSSJ present novel opportunities to train a generation of interdisciplinary, diverse data-driven scientists who center values and social justice at the forefront of their work.
# Social Science
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58:10
Hear from research leadership first hand about the significance of expert trainer contributions to the OpenAI mission.
# AI Research
# Expert AI Training
# AI Safety
58:48
Nathan Chappell
Dupé Ajayi
Jody Britten
+5
Nathan Chappell, Dupé Ajayi, Jody Britten & 5 more speakers · Jun 24th, 2024
The session featured several nonprofit organizations that utilize AI to drive social impact, emphasizing their long-standing involvement with the community. The discussion was facilitated by Nathan Chappell, a notable figure in AI fundraising, and included insights from a diverse group of panelists: Dupe Ajayi, Jodi Britton, Allison Fine, Anne Murphy, Gayle Roberts, Scott Rosenkrans, and Woodrow Rosenbaum. Each speaker shared their experiences and perspectives on integrating AI into their operations, illustrating AI's transformative potential in various sectors. The event highlighted the importance of AI in amplifying the efficiency and reach of nonprofit initiatives, suggesting a significant role for AI in addressing global challenges. The conversation also touched on the ethical considerations and the need for responsible AI use, ensuring that technological advancements align with human values and contribute positively to society. This gathering not only served as a platform for sharing knowledge and experiences but also fostered networking among community members with similar interests in AI applications. The dialogue underscored the critical role of AI in future developments across fields, advocating for continued exploration and adoption of AI technologies to enhance organizational impact and effectiveness.
# Socially Beneficial Use Cases
# Non Profit
1:25:50
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