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Found 10 papers, 2 papers with code
Responsible Reporting for Frontier AI Development
Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems.
Visibility into AI Agents
Increased delegation of commercial, scientific, governmental, and personal activities to AI agents -- systems capable of pursuing complex goals with limited supervision -- may exacerbate existing societal risks and introduce new risks.
The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
We provide a data-driven survey of the role of the compute divide in shaping machine learning research.
Compute at Scale: A Broad Investigation into the Data Center Industry
This report characterizes the data center industry and its importance for AI development.
International Governance of Civilian AI: A Jurisdictional Certification Approach
As international actors reach consensus on risks of and minimum standards for advanced AI, a jurisdictional certification regime could mitigate a broad range of potential harms, including threats to public safety.
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
We analyze the growth of dataset sizes used in machine learning for natural language processing and computer vision, and extrapolate these using two methods; using the historical growth rate and estimating the compute-optimal dataset size for future predicted compute budgets.
Red-Teaming the Stable Diffusion Safety Filter
We then reverse-engineer the filter and find that while it aims to prevent sexual content, it ignores violence, gore, and other similarly disturbing content.
Machine Learning Model Sizes and the Parameter Gap
From 1950 to 2018, model size in language models increased steadily by seven orders of magnitude.
Compute Trends Across Three Eras of Machine Learning
Since the advent of Deep Learning in the early 2010s, the scaling of training compute has accelerated, doubling approximately every 6 months.
Measuring what Really Matters: Optimizing Neural Networks for TinyML
With the surge of inexpensive computational and memory resources, neural networks (NNs) have experienced an unprecedented growth in architectural and computational complexity.
