Jul 28, 2024

AI Alignment Knowledge Graph

Matin Mahmood, Samuel Ratnam, Sruthi Kuriakose, Pandelis Mouratoglou

🏆 1st place by peer review

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Summary

We present a web based interactive knowledge graph with concise topical summaries in the field of AI alignement

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Cite this work:

@misc {

title={

AI Alignment Knowledge Graph

author={

Matin Mahmood, Samuel Ratnam, Sruthi Kuriakose, Pandelis Mouratoglou

date={

7/28/24

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

Reviewer's Comments

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