Generative AI
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Mark Russinovich | Generative AI in the Cloud: Inside Microsoft AI Innovation
In this seminar, Mark Russinovich, Azure CTO and Technical Fellow, gave an under-the-hood look at Microsoft’s AI architecture, including the large-scale supercomputers that train foundational models and the infrastructure that efficiently serves small and large pretrained and fine-tuned models.
In this seminar, Mark Russinovich, Azure CTO and Technical Fellow, gave an under-the-hood look at Microsoft’s AI architecture, including the large-scale supercomputers that train foundational models and the infrastructure that efficiently serves small and large pretrained and fine-tuned models.
HAI Policy Fellow Riana Pfefferkorn speaks about a new law in effect, criminalizing the creation, distribution, and possession of AI-generated child sexual abuse material.
HAI Policy Fellow Riana Pfefferkorn speaks about a new law in effect, criminalizing the creation, distribution, and possession of AI-generated child sexual abuse material.
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.
Large Language Models Just Want To Be Liked
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When LLMs take surveys on personality traits, they, like people, exhibit a desire to appear likable.
When LLMs take surveys on personality traits, they, like people, exhibit a desire to appear likable.
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‘.
All Work Published on Generative AI
Mark Russinovich | Generative AI in the Cloud: Inside Microsoft AI Innovation
In this seminar, Mark Russinovich, Azure CTO and Technical Fellow, gave an under-the-hood look at Microsoft’s AI architecture, including the large-scale supercomputers that train foundational models and the infrastructure that efficiently serves small and large pretrained and fine-tuned models.
Mark Russinovich | Generative AI in the Cloud: Inside Microsoft AI Innovation
In this seminar, Mark Russinovich, Azure CTO and Technical Fellow, gave an under-the-hood look at Microsoft’s AI architecture, including the large-scale supercomputers that train foundational models and the infrastructure that efficiently serves small and large pretrained and fine-tuned models.
HAI Policy Fellow Riana Pfefferkorn speaks about a new law in effect, criminalizing the creation, distribution, and possession of AI-generated child sexual abuse material.
HAI Policy Fellow Riana Pfefferkorn speaks about a new law in effect, criminalizing the creation, distribution, and possession of AI-generated child sexual abuse material.
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.
Large Language Models Just Want To Be Liked
When LLMs take surveys on personality traits, they, like people, exhibit a desire to appear likable.
When LLMs take surveys on personality traits, they, like people, exhibit a desire to appear likable.
-as-a-glowing-digital-entity-on-a-computer-screen.-the-llm-is-surrounded-by-a-few-heart-copy.jpg&w=3840&q=75)
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‘.