Discussing the risks and benefits of open AI models, including biosecurity threats and non-consensual imagery. Exploring a risk assessment framework inspired by cybersecurity. Emphasizing the need for common ground in assessing threats posed by AI. Addressing the balance between openness for research and cybersecurity vulnerabilities.
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Quick takeaways
Establishing common ground for assessing AI risks is crucial for mitigating potential harms of open models.
The risks of open model weights, including biosecurity concerns, highlight the need for structured risk assessment frameworks.
Deep dives
The Importance of Open Foundation Models
The podcast episode delves into the societal impact of open foundation models, exploring the motivations behind a recent paper's analysis. By focusing on the risks and benefits of openness in AI, the researchers aimed to create a framework that clarifies misconceptions and fosters constructive debate about the impacts of open models. Emphasizing the necessity for a common ground among experts from various sectors, the paper highlights the significance of addressing risks collectively and mitigating potential harms of open foundation models.
Diverse Authorship and Collaborative Efforts
The episode discusses the collaborative efforts that led to the paper, involving a diverse range of authors with expertise in the societal impact of openness on society. Originating from a workshop that brought together industry, academia, and civil society perspectives, the paper aimed to establish common ground and substantiated evidence to guide discussions on the impacts of open models. By including policy makers in the author list, the research underscores the importance of informing policy conversations regarding the societal impacts of foundation models.
Defining Open Foundation Models and Risks
The exploration delves into the contentious definition of open foundation models, emphasizing the distinction from open-source models based on freely available weights. The discussion centers on the risks associated with model weights alone, citing concerns about biosecurity and the ease of harm when these models are openly accessible. By analyzing the irreversibility of releasing model weights, the researchers underscore the need to assess and address the risks associated with open foundation models.
Assessing Marginal Risks and Providing Framework for Analysis
The podcast episode details the significance of assessing marginal risks associated with open foundation models by comparing them to existing technologies and closed models. The episode introduces a structured risk assessment framework inspired by cybersecurity threat modeling to identify and analyze the risks posed by open models. By focusing on threat identification, existing risks, defenses, and ease of defense, the framework provides a systematic approach to evaluating the potential risks of open foundation models in various domains like cybersecurity and disinformation.
Today we’re joined by Sayash Kapoor, a Ph.D. student in the Department of Computer Science at Princeton University. Sayash walks us through his paper: "On the Societal Impact of Open Foundation Models.” We dig into the controversy around AI safety, the risks and benefits of releasing open model weights, and how we can establish common ground for assessing the threats posed by AI. We discuss the application of the framework presented in the paper to specific risks, such as the biosecurity risk of open LLMs, as well as the growing problem of "Non Consensual Intimate Imagery" using open diffusion models.
The complete show notes for this episode can be found at twimlai.com/go/675.
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