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Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.
The Center for Research on Foundation Models (CRFM), a new initiative of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), invites you to the Workshop on Foundation Models from August 23-24, 2021. By foundation model (e.g. BERT, GPT-3, DALL-E), we mean a single model that is trained on raw data, potentially across multiple modalities, which can be usefully adapted to a wide range of tasks. These models have demonstrated clear potential, which we see as the beginnings of a sweeping paradigm shift in AI. They represent a dramatic increase in capability in terms of accuracy, generation quality, and extrapolation to new tasks, but they also pose clear risks such as use for widespread disinformation, potential exacerbation of historical inequities, and problematic centralization of power.
Given their anticipated impact, we invite you to join us at this workshop, where scholars reflecting a diverse array of perspectives, disciplinary backgrounds (e.g. social science, economics, computer science, law, philosophy, information science) and sectors (academia and industry) will convene to provide vital expertise on the many dimensions of foundation models. Broadly, we will address the opportunities, challenges, limitations, and societal impact of foundation models. Given that future AI systems will likely rely heavily on foundation models, it is imperative that we, as a community, come together to develop more rigorous principles for foundation models and guidance for their responsible development and deployment.
Specific points of emphasis include:
What applications and communities might benefit the most from foundation models and what are some of the unique application-specific obstacles?
How do we characterize and mitigate the disparate, and likely inequitable, effects of foundation models?
How do multimodal methods and grounding impact conversations around meaning and semantics in foundation models?
When foundation models are used in applications that cause harm, how do we handle matters of responsibility, accountability, and recourse?
What should be the professional norms and ethical and legal considerations around the release and deployment of foundation models?
How should various groups (e.g. academia, industry, government), given their complementary strengths, productively collaborate on developing foundation models?
Given foundation models must be adapted for specific tasks, how do we evaluate them in ways that capture the needs of diverse stakeholders?
Foundation models generally coincide with the centralization of power: how do we reason about this centralization, and its potential harms, and build ecosystems that better distribute the benefits of foundation models?
Data plays a central role in foundation models: how do we think about data sourcing, selection, documentation, and how do we build principles to guide how data shapes foundation models?
The scale of foundation models complicates principled scientific study: how do we build foundation models in a sound manner given the potential inability to run comprehensive experiments, and how do we reaffirm our commitments to open and reproducible science in spite of this scale?