Summary of On the Limitations Of Compute Thresholds As a Governance Strategy, by Sara Hooker
On the Limitations of Compute Thresholds as a Governance Strategy
by Sara Hooker
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent paper explores the concept of compute thresholds, a governance tool that raises questions about whether bigger always means better in computer science. The debate centers on how changes in computational power affect the risk profile of AI models. The authors argue that current implementations of compute thresholds are insufficient and may even exacerbate risks due to their reliance on uncertain and rapidly changing relationships between compute and risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This tool, used in initiatives like the White House Executive Orders on AI Safety (EO) and the EU AI Act, is meant to identify more risky systems. However, the paper concludes that this approach oversimplifies the complexity of AI development. The authors recommend a different approach to mitigate risks, acknowledging the need for ongoing research to understand the relationship between compute and risk. |
