One issue is that good research tools are hard to build, and organizations may be reluctant to share them (especially since making good research tools public-facing is even more effort.). Like, can I go out and buy a subscription to Anthropic's interpretability tools right now? That seems to be the future Toby (whose name, might I add, is highly confusable with Justin Shovelain's) is pushing for.
This is an interesting possibility for a middle ground option between open-sourcing and fully private models. Do you have any estimates of how much it would cost an AI lab to do this, compared to the more straightforward option of open sourcing?
Some initial thoughts:
OpenAI have an API (which was evidently not prohibitively expensive to build). Would they be able to share any information about how long this took to develop, how useful they've found it, improvements for future iterations etc?
The AI developer enforcing the rules by monitoring usage could be labour-intensive to do thoroughly. And it may not be clear whether or not the model is being used for a prohibited application, particularly since those seeking to use it for prohibited reasons are incentivised to hide this.
Do you think it would be possible/valuable to build a common open source system for structured access, with basic features covering common use cases, available for use by any AI lab?
This is a linkpost for: https://www.governance.ai/post/sharing-powerful-ai-models
On the GovAI blog, Toby Shevlane (FHI) argues in favour of labs granting "structured access" to AI models.