Sciences (Social, Health, Biological, Physical)
AI is accelerating discovery in the sciences and fostering interdisciplinary breakthroughs.
Data Feminism for AI
Featuring "Data Feminism" author and Emory University Prof. Lauren Klein, followed by a panel with L. Klein, C. Sabatti, and A. Daub.
Featuring "Data Feminism" author and Emory University Prof. Lauren Klein, followed by a panel with L. Klein, C. Sabatti, and A. Daub.
Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life and run experiments in a fraction of the time it would take a traditional lab.
Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life and run experiments in a fraction of the time it would take a traditional lab.
Environmental, social, and governance risks pose a threat to economies and human well-being around the world. However, we have the power to build a sustainable planet. Recent developments in AI are helping us see issues that were hard to identify before. As machine vision helps us see our world, we are able to detect issues, track them, and create targeted interventions. In this brief, we examine innovations by Stanford researchers that use AI and ML techniques to shift our world from one that depletes resources to one that preserves them for the future. For example, we can now track methane emissions across our energy and food systems, opening an avenue for policy formation and enforcement through near real-time tracing. AI enables knowledge-to-action and will play a key role in measuring and effectively achieving environmental, social, and governance goals.
Environmental, social, and governance risks pose a threat to economies and human well-being around the world. However, we have the power to build a sustainable planet. Recent developments in AI are helping us see issues that were hard to identify before. As machine vision helps us see our world, we are able to detect issues, track them, and create targeted interventions. In this brief, we examine innovations by Stanford researchers that use AI and ML techniques to shift our world from one that depletes resources to one that preserves them for the future. For example, we can now track methane emissions across our energy and food systems, opening an avenue for policy formation and enforcement through near real-time tracing. AI enables knowledge-to-action and will play a key role in measuring and effectively achieving environmental, social, and governance goals.
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.
Bridging the Farm: AI for Science at SLAC and Stanford
This workshop will highlight the significant impact of AI applications in the Department of Energy (DOE) science by showcasing SLAC's research program, which includes national-scale science facilities such as particle accelerators, x-ray lasers, and the Rubin Observatory.
This workshop will highlight the significant impact of AI applications in the Department of Energy (DOE) science by showcasing SLAC's research program, which includes national-scale science facilities such as particle accelerators, x-ray lasers, and the Rubin Observatory.
All Work Published on Sciences (Social, Health, Biological, Physical)
Data Feminism for AI
Featuring "Data Feminism" author and Emory University Prof. Lauren Klein, followed by a panel with L. Klein, C. Sabatti, and A. Daub.
Data Feminism for AI
Featuring "Data Feminism" author and Emory University Prof. Lauren Klein, followed by a panel with L. Klein, C. Sabatti, and A. Daub.
Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life and run experiments in a fraction of the time it would take a traditional lab.
Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life and run experiments in a fraction of the time it would take a traditional lab.
Environmental, social, and governance risks pose a threat to economies and human well-being around the world. However, we have the power to build a sustainable planet. Recent developments in AI are helping us see issues that were hard to identify before. As machine vision helps us see our world, we are able to detect issues, track them, and create targeted interventions. In this brief, we examine innovations by Stanford researchers that use AI and ML techniques to shift our world from one that depletes resources to one that preserves them for the future. For example, we can now track methane emissions across our energy and food systems, opening an avenue for policy formation and enforcement through near real-time tracing. AI enables knowledge-to-action and will play a key role in measuring and effectively achieving environmental, social, and governance goals.
Environmental, social, and governance risks pose a threat to economies and human well-being around the world. However, we have the power to build a sustainable planet. Recent developments in AI are helping us see issues that were hard to identify before. As machine vision helps us see our world, we are able to detect issues, track them, and create targeted interventions. In this brief, we examine innovations by Stanford researchers that use AI and ML techniques to shift our world from one that depletes resources to one that preserves them for the future. For example, we can now track methane emissions across our energy and food systems, opening an avenue for policy formation and enforcement through near real-time tracing. AI enables knowledge-to-action and will play a key role in measuring and effectively achieving environmental, social, and governance goals.
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.
Bridging the Farm: AI for Science at SLAC and Stanford
This workshop will highlight the significant impact of AI applications in the Department of Energy (DOE) science by showcasing SLAC's research program, which includes national-scale science facilities such as particle accelerators, x-ray lasers, and the Rubin Observatory.
This workshop will highlight the significant impact of AI applications in the Department of Energy (DOE) science by showcasing SLAC's research program, which includes national-scale science facilities such as particle accelerators, x-ray lasers, and the Rubin Observatory.