This is an online portal with information on donations that were announced publicly (or have been shared with permission) that were of interest to Vipul Naik. The git repository with the code for this portal, as well as all the underlying data, is available on GitHub. All payment amounts are in current United States dollars (USD). The repository of donations is being seeded with an initial collation by Issa Rice as well as continued contributions from him (see his commits and the contract work page listing all financially compensated contributions to the site) but all responsibility for errors and inaccuracies belongs to Vipul Naik. Current data is preliminary and has not been completely vetted and normalized; if sharing a link to this site or any page on this site, please include the caveat that the data is preliminary (if you want to share without including caveats, please check with Vipul Naik). We expect to have completed the first round of development by the end of March 2022. See the about page for more details. Also of interest: pageview data on analytics.vipulnaik.com, tutorial in README, request for feedback to EA Forum.
We do not have any donee information for the donee Robert Miles in our system.
|Cause area||Count||Median||Mean||Minimum||10th percentile||20th percentile||30th percentile||40th percentile||50th percentile||60th percentile||70th percentile||80th percentile||90th percentile||Maximum|
|Effective Altruism Funds: Long-Term Future Fund (filter this donee)||39,000.00||39,000.00|
|Donor||Amount (current USD)||Amount rank (out of 1)||Donation date||Cause area||URL||Influencer||Notes|
|Effective Altruism Funds: Long-Term Future Fund||39,000.00||1||AI safety/content creation/video||https://app.effectivealtruism.org/funds/far-future/payouts/6vDsjtUyDdvBa3sNeoNVvl||Oliver Habryka Alex Zhu Matt Wage Helen Toner Matt Fallshaw||Donation process: Donee submitted grant application through the application form for the April 2019 round of grants from the Long-Term Future Fund, and was selected as a grant recipient (23 out of almost 100 applications were accepted)
Intended use of funds (category): Direct project expenses
Intended use of funds: Grant to create video content on AI alignment. Grantee has a YouTube channel at https://www.youtube.com/channel/UCLB7AzTwc6VFZrBsO2ucBMg (average 20,000 views per video) and also creates videos for the Computerphile channel https://www.youtube.com/watch?v=3TYT1QfdfsM&t=2s (often more than 100,000 views per video)
Donor reason for selecting the donee: Grant investigator and main influencer Oliver Habryka favors the grant for these reasons: (1) Grantee explains AI alignment as primarily a technical problem, not a moral or political problem, (2) Grantee does not politicize AI safety, (3) Grantee's goal is to create interest in these problems from future researchers, and not to simply get as large of an audience as possible. Habryka notes that the grantee is the first skilled person in the X-risk community working full-time on producing video content. "Being the very best we have in this skill area, he is able to help the community in a number of novel ways (for example, he’s already helping existing organizations produce videos about their ideas)." In the previous grant round, the grantee had requested funding for a collaboration with RAISE to produce videos for them, but Habryka felt it was better to fund the grantee directly and allow him to decide which organizations he wanted to help with his videos
Donor reason for donating that amount (rather than a bigger or smaller amount): Likely to be the amount requested by the donee in the application (this is not stated explicitly by either the donor or the donee)
Percentage of total donor spend in the corresponding batch of donations: 4.22%
Donor reason for donating at this time (rather than earlier or later): Timing determined by timing of grant round
Other notes: The grant reasoning is written up by Oliver Habryka and is available at https://forum.effectivealtruism.org/posts/CJJDwgyqT4gXktq6g/long-term-future-fund-april-2019-grant-decisions (GW, IR).