Vitalik Buterin
@vitalik.eth
Deep funding combines two ideas: 1. Value as a graph: instead of asking "how much did X contribute to humanity?", ask "how much of the credit for Y belongs to X?" 2. Distilled human judgement: an open market of AIs fills in all the weights, human jury randomly spot-checks them https://x.com/TheDevanshMehta/status/1867600164502089925 (1) has been a growing paradigm recently, for good reason. Contribution is hard to measure in the abstract: if you ask people how much they would pay to save N birds, they answer $80 for N=2000 and N=200000. "Local" questions like "is A or B more valuable to C?" are much more tractable. (2) is based on ideas in my info finance post: https://vitalik.eth.limo/general/2024/11/09/infofinance.html#info-finance-for-distilled-human-judgement . Anyone can use any methods (eg. AI) to suggest weights for *all* edges, a human jury does detailed analysis on a random subset. The submissions that are most compatible with the jury answers decide the final output. https://deepfunding.org/
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AJ
@awedjob.eth
I especially like this localized concept: You answer much more concrete questions like ‘compare the direct impact of A and B on C’.” Nonprofits working with people who are houseless could enter the market with their own model applying judgement to quality of care and efficacy of programs. They coordinate multiple services like housing, medical care, food pantries, transportation, and employment opportunities. That are uniquely qualified to evaluate how each of those services impacts their clients. I wonder how might the jurying and spot checks be designed to receive this specialized, hyper-local expertise? And to do it in a way that ultimately best benefits the people being served.
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