I was motivated to write this story for two reasons.
First, I think that there is a lack of clear visual metaphors, stories, or other easy accessible analogies for concepts in AI and its impacts on society. I am often speaking with intelligent non-technical people - including potential users or "micro-regulators" (e.g., organisational policymaker) of AI tools - who have read about AI in the news but don't have good handles on how to think about these tools and how they interact with existing organisational processes or social understandings.
Second, this specific story was motivated by a discussion with a highly qualified non-technical user of LLMs who expressed skepticism about the capabilities of LLMs (in this case, chatGPT 3.5) because when they prompted the LLM to provide citations for a topic area that the user was an expert in, the research citations provided in the LLM response were wrong or misleading / hallucinations.
One insight that came from our follow-up conversation was that the user were imagining that writing prompts for an LLM to be similar to writing a Google search query. In their understanding, they were requesting a pre-existing record that was stored in the LLM's database, and so for the LLM to respond with an incorrect list of records indicated that the LLM was fundamentally incapable of a 'basic' research task.
I had wondered if it was too hyperbolic to claim that this was an example of proto- or early-PASTA. My earlier draft hedged and said that the next version of these tools would be something like an early PASTA. I would characterise Holden Karnovsky's post introducing PASTA as describing an agentic system that could improve by making copies of itself and improving itself.
However, when he first introduces the idea of 'explosive' scientific and technological advancement, it's through the thought experiment of creating digital people, which mean that many more minds can be allocated to different research problems.
I would argue that using Whisper or GPT-3 in the way I've described in this article is applying a kind of information processing system that in a very limited sense, is similar to allocating another mind to the research problem of capturing and analysing speech & text data - because it essentially replaced me or another researcher doing the task. This is especially the case when chaining tools together with (for now) human supervision. This allows Whisper (language processing module) and GPT-3 with prompting (summarisation and analysis module) to combine for more useful 'mind-replacement' than either alone.
Thanks for the update.
I'd like to recommend that part of the process review for providing travel grant funding includes consideration of the application process timing for CEA-run or supported events. In my experience, key dates in the process (open, consideration/decision, notification of acceptance, notification of travel grant funding) happen much closer to the date of the event than other academic or trade conferences.
For example, in 2022, several Australian EAs I know applied ~90 days in advance of EAG London or EAG SF, but were accepted only around 30-40 days before the event.
A slow application process creates several issues for international attendees:
Providing travel grant funding can help to "smooth over" some of these issues, e.g., by subsidising the increase in flight costs, offsetting the (literal or emotional) costs of navigating / negotiating commitments and needs. It is not a panacea - the application process itself also needs to be reviewed to reduce these issues. If the travel grant funding is significantly reduced but no change is made to the application process, there may be an unintended consequence of fewer international attendees who would otherwise be a good fit for events.
I support a review of travel grant funding processes. I ask that you also consider the application process (especially timing) and its relationship with the travel grant funding process, to improve the experience for international attendees so that the flagship events of EA Global can continue to live up to their name.
Thanks Peter! I appreciate the work you've put in to synthesising a large and growing set of activities.
Nicholas Moes and Caroline Jeanmaire wrote a piece, A Map to Navigate AI Governance, which set out Strategy as 'upstream' of typical governance activities. Michael Aird in a shortform post about x-risk policy 'pipelines' also set (macro)strategy upstream of other policy research, development, and advocacy activities.
One thing that could be interesting to explore is the current and ideal relationships between the work groups you describe here.
For example, in your government analogy, you describe Strategy as the executive branch, and each of the other work groups as agencies, departments, or specific functions (e.g., HR), which would be subordinate.
Does this reflect your thinking as well? Should AI strategy worker / organisations be deferred to by AI governance workers / organisations?
Thanks for the plausible explanation!
Re: adding images to your post, I literally just copy and paste. But you could also read a longer post on how to enable advanced editing features such as tables and images.
Thanks for writing up this work, Zoe. I'm pleased to see a list of explicit recommendations for effective charities to consider in framing their requests for donations.
Selfishly, I'm also pleased that our paper (Saeri et al 2022) turned up in your search!
It's be interesting to understand your motivations for the literature review and what you might do next with these findings / recommendations.
One thing that our paper necessarily didn't do was aggregate from individual studies (it only included systematic reviews and meta-anlayses). So it's interesting to see some of the other effects out there that haven't yet been subject to a review.