Generative AI
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Hari Subramonyam | Learning by Creating: A Human-Centered Vision for AI in Education
A Stanford HAI workshop brought together experts to develop new evaluation methods that assess AI's hidden capabilities, not just its test-taking performance.
A Stanford HAI workshop brought together experts to develop new evaluation methods that assess AI's hidden capabilities, not just its test-taking performance.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
This brief introduces a framework of eight techniques for approximating political neutrality in AI models.
This brief introduces a framework of eight techniques for approximating political neutrality in AI models.
AI Leaders Discuss How To Foster Responsible Innovation At TIME100 Roundtable In Davos
HAI Senior Fellow Yejin Choi discussed responsible AI model training at Davos, asking, “What if there could be an alternative form of intelligence that really learns … morals, human values from the get-go, as opposed to just training LLMs on the entirety of the internet, which actually includes the worst part of humanity, and then we then try to patch things up by doing ‘alignment’?”
HAI Senior Fellow Yejin Choi discussed responsible AI model training at Davos, asking, “What if there could be an alternative form of intelligence that really learns … morals, human values from the get-go, as opposed to just training LLMs on the entirety of the internet, which actually includes the worst part of humanity, and then we then try to patch things up by doing ‘alignment’?”
All Work Published on Generative AI
Axios chief technology correspondent Ina Fried speaks to HAI Senior Fellow Yejin Choi at Axios House in Davos during the World Economic Forum.
Axios chief technology correspondent Ina Fried speaks to HAI Senior Fellow Yejin Choi at Axios House in Davos during the World Economic Forum.
Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.
Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.
This brief evaluates the impact of authorship labels on the persuasiveness of AI-written policy messages.
This brief evaluates the impact of authorship labels on the persuasiveness of AI-written policy messages.
Percy Liang
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Riana Pfefferkorn, Policy Fellow at HAI, urges immediate Congressional hearings to scope a legal safe harbor for AI-generated child sexual abuse materials following a recent scandal with Grok's newest generative image features.
Riana Pfefferkorn, Policy Fellow at HAI, urges immediate Congressional hearings to scope a legal safe harbor for AI-generated child sexual abuse materials following a recent scandal with Grok's newest generative image features.
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability. To facilitate such research, we introduce pyvene, an open-source Python library that supports customizable interventions on a range of different PyTorch modules. pyvene supports complex intervention schemes with an intuitive configuration format, and its interventions can be static or include trainable parameters. We show how pyvene provides a unified and extensible framework for performing interventions on neural models and sharing the intervened upon models with others. We illustrate the power of the library via interpretability analyses using causal abstraction and knowledge localization. We publish our library through Python Package Index (PyPI) and provide code, documentation, and tutorials at ‘https://github.com/stanfordnlp/pyvene‘.
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability. To facilitate such research, we introduce pyvene, an open-source Python library that supports customizable interventions on a range of different PyTorch modules. pyvene supports complex intervention schemes with an intuitive configuration format, and its interventions can be static or include trainable parameters. We show how pyvene provides a unified and extensible framework for performing interventions on neural models and sharing the intervened upon models with others. We illustrate the power of the library via interpretability analyses using causal abstraction and knowledge localization. We publish our library through Python Package Index (PyPI) and provide code, documentation, and tutorials at ‘https://github.com/stanfordnlp/pyvene‘.