Featured
AI & Social Impact: Exploring the Role of AI in the Non Profit Sector
Nathan Chappell, Dupé Ajayi, Jody Britten & 5 more speakers
On Trust: Backdoor Vulnerabilities and their Mitigation with Turing Recipient, Shafi Goldwasser
Shafi Goldwasser
Practices for Governing Agentic Systems
Yonadav Shavit
All Content
Popular topics
# Democratic Inputs to AI
# Ethical AI
# AI Governance
# Policy Research
Claudia von Vacano · Aug 30th, 2024
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.
+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.
Shafi Goldwasser · May 3rd, 2024
Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate "backdoor key", the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.
Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, our construction can produce a classifier that is indistinguishable from an "adversarially robust" classifier, but where every input has an adversarial.
Can backdoors be mitigated even if not detectable? Shafi will discuss a few approaches toward mitigation.
This talk is based largely on collaborations with Kim, Shafer, Neekon, Vaikuntanathan, and Zamir.
Yonadav Shavit · Apr 26th, 2024
Yonadav presents his research Practices for Governing Agentic AI Systems.
Teddy Lee, Kevin Feng & Andrew Konya · Apr 22nd, 2024
As AI gets more advanced and widely used, it is essential to involve the public in deciding how AI should behave in order to better align our models to the values of humanity. Last May, we announced the Democratic Inputs to AI grant program. We partnered with 10 teams out of nearly 1000 applicants to design, build, and test ideas that use democratic methods to decide the rules that govern AI systems. Throughout, the teams tackled challenges like recruiting diverse participants across the digital divide, producing a coherent output that represents diverse viewpoints, and designing processes with sufficient transparency to be trusted by the public. At OpenAI, we’re building on this momentum by designing an end-to-end process for collecting inputs from external stakeholders and using those inputs to train and shape the behavior of our models. Our goal is to design systems that incorporate public inputs to steer powerful AI models while addressing the above challenges. To help ensure that we continue to make progress on this research, we have formed a “Collective Alignment” team.
Lama Ahmad · Mar 8th, 2024
Collin Burns & Pavel Izmailov · Feb 26th, 2024
Collin Burns and Pavel Izmailov present their research, Weak-to-Strong Generalization
Anton Maximov · Feb 13th, 2024
In our conversation, we explored the fundamental principles of organization and function of biological neural networks. Anton Maximov provided an overview of imaging studies that have revealed the remarkable diversity of neurons in the brain and the complexity of their connections. His presentation began with the pioneering work of Santiago Ramón y Cajal and extended to contemporary research that integrates advanced imaging technologies with artificial intelligence. He discussed discoveries from his laboratory at Scripps, unveiling surprising new mechanisms by which neural circuits in the brain are reorganized during memory encoding. His presentation was engaging, with vibrant videos and images to showcase his findings.
Daniel Miessler & Joel Parish · Jan 30th, 2024
In this talk, Meissler shared his philosophy for integrating AI into all facets of life. He highlighted a framework built for leveraging custom prompts as APIs, as well as demonstrated several specific use cases that the speaker hopes will resonate with OpenAI Forum members and translate across disciplines and professional domains.
Sam Altman & David Kirtley · Nov 29th, 2023
Earlier this year, Sam Altman, CEO and Co-Founder of OpenAI and David Kirtley, CEO and Founder of Helion convened at the OpenAI office among a small group of OpenAI Forum members to discuss the future of energy. This is the recording of their discussion.
Popular