aiqbench analysis
This article presents a technical visualization of recent AI safety literature using embedding and clustering methods to map research trends like interpretability. It serves as an analytical tool for researchers seeking to identify gaps and overlaps within the technical AI safety field rather than reporting on new products or policies.
Map of the Shallow Review of Technical AI Safety
Someone recently pointed out that I underestimated the amount of interpretability researchers when I thought about the most important and neglected research directions, so I made a semantic map and clustering of recent AI safety papers to get a better understanding of what AI safety researchers are working on. This map was created by taking the title and abstract of each paper, embedding with BGE-small-v1.5, reducing to 5 dimensions with UMAP, clustering with HBDSCAN, and then reducing to 2 dimensions. The data is from the Shallow Reviews of Technical AI Safety (2025, 2024, 2023). The idea is from this old LessWrong post. Discuss
Someone recently pointed out that I underestimated the amount of interpretability researchers when I thought about the most important and neglected research directions, so I made a semantic map and clustering of recent AI safety papers to get a better understanding of what AI safety researchers are working on.
This map was created by taking the title and abstract of each paper, embedding with BGE-small-v1.5, reducing to 5 dimensions with UMAP, clustering with HBDSCAN, and then reducing to 2 dimensions. The data is from the Shallow Reviews of Technical AI Safety (2025, 2024, 2023). The idea is from this old LessWrong post.
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