Open Philanthropy is a grantmaking organization that publishes research on global health, criminal justice reform, and risks from advanced AI.
Commenters point to it as a resource on effective giving and criminal justice reform, and discuss its heavy funding of AI risk work.
Fair enough. I guess I'll engage with this silliness once. Let's start with the basics. There's this vague, handwaving notion of "intelligence." In the community pontificating about this it seems to be operationalized as a combination of manipulating abstractions and extracting new, higher level entities from lower levels of abstraction. For the latter, the only AI work to date that I am aware of on the latter is Drescher's 'Made-up Minds'. Multi-layered neural nets end up with interior points that map to some sort of features, but there is a big jump from that to a system being able to develop an abstraction and then operate in terms of it. For the former, computers are very good at it because humans built them as tools to extend the human ability to do it, just like we built hammers because our hands are kind of a lousy tool to hit things with. There is an illusion of sudden, dramatic progress on this kind of intelligence because curves with lots of parameters (neural nets, SVMs, etc.) can fit all kinds of things if you can get the fit to converge. The advances in machine learning have been tricks to get fits to converge and ways of mapping data into spaces where we know how to get fits to converge. This isn't a denigration of the field. It's given us enormous capabilities that we didn't have before, but we need to be clear about what it is before we start extrapolating: curve fitting. Machine learning today lets us fit curves in order to engineer task specific algorithms.…
You say that as if people had been saying "10 years away" for ages, but I don't think that's true at all.
There's some information about historical predictions at https://www.openphilanthropy.org/research/what-should-we-learn-from-past-ai-forecasts/ (written in 2016) from which (I am including the spreadsheet found at footnote 27) these are some I-hope-representative data points, with predictions from actual AI researchers, popularizers, pundits, and SF authors:
1960: Herbert Simon predicts machines can do all (intellectual) work humans can "within 20 years".
1961: Marvin Minsky says "within our lifetimes, machines may surpass us"; he was 33 at the time, suggesting a not-very-confident timescale of say 40 years.
1962: I J Good predicts something at or above human level circa 1978.
1963: John McCarthy allegedly hopes for "a fully-intelligent machine" within a decade.
1970: I J Good predicts 1994 +- 10 years.
1972: a survey of 67 computer scientists found 27% saying <= 20 years, 32% saying 20-50 years, and 42% saying > 50 years.
1977-8: McCarthy says things like "4 to 400 years" and "5 to 500 years".
1988: Hans Moravec predicts human-level intelligence in 40 years.
1993: Vernor Vinge predicts better-than-human intelligence in the range 2005..2030.
1999: Eliezer Yudkowsky predicts intelligence explosion circa 2020.
2001: Ben Goertzel predicts "during the next 100 years or so".
2001: Arthur C Clarke predicts human-level intelligence circa 2020.
2006: Douglas…
Here are some EA highlights:
* Founding and scaling GiveWell, synthesizing and analyzing global poverty research to make recommendations. This has helped guide about $1B+ in really valuable work, the largest portion in anti-malarial bednets. https://www.givewell.org/about/impact
* Convincing Dustin Moskovitz and Cari Tuna to allocate their giving along these lines, building Open Philanthropy which has made a lot of grants that look really valuable https://www.openphilanthropy.org/grants (this is in addition to the ones where they granted at GiveWell's recommendation -- don't want to double count).
* Michael Kremer won a Nobel prize in econ (shared with Esther Duflo and Abhijit Banerjee who I'd describe as EA-sympathetic but probably don't consider themselves EAs?) for global poverty research.
* Built an "AI Safety" subfield of computer science, with the 2016 paper "Concrete problems in AI safety" having 1.8k citations.
* Founding and scaling 80,000 Hours, helping people figure out how to use their careers to do more good. This includes looking into potential career opportunities, writing up public advice, and 1:1 advising, and 1k+ people have cited them as part of why they changed what they were working on. https://80000hours.org
(Not especially representative; there's been thousands of people working along these lines for years and I'm sure I'm missing a lot of valuable work.)
As someone in the "malaria nets wing", I think you're directionally correct, but overstating things.
> it's quite obvious that the opinionated people who control the (huge!) purse-strings are gradually gaining organizational control of the movement
This is to some extent true of Open Philanthropy, although the effect looks larger than it is because they're consciously committed to not throwing all of their resources behind whatever they think is the best option. I'm not a fan in principle, but it's not insane. See https://www.openphilanthropy.org/research/worldview-diversification/ for their take.
GiveWell remains firmly on the global health side, and I don't see that changing. Here's the first my-screen-worth of organizations they've funded in the last year, with approximate numbers:
- 87 million to the Malaria Consortium
- 77 million to Hellen Keller International (Vitamin A supplementation)
- 42 million to New Incentives (infant vaccination)
- 17 million to Sightsavers (parasitic worms)
- 8 million to PATH (malaria)
- 6 million to Nutrition International (Vitamin A)
- 5 million to IRD Global (healthcare infrastructure)
- 5 million to Miracle Feet (clubfoot)
- 7 million to Deworm the World (parasitic worms)
- 2 million to RICE (postnatal care)
From https://airtable.com/appGuFtOIb1eodoBu/shr1EzngorAlEzziP/tblG72bMUu36lrWsr/viwJScgH8B6uRwJ8C?blocks=hide
Dear Rich Pineapple:
Congrats on your huge BTC surplus!
To make the biggest difference, consider looking into Effective Altruism[1] (an idea, not a charity) if you haven't already. Their motto is “using reason and evidence to do the most good”.
Perhaps do away with the charity proposal form, and instead choose one or several of the most effective charities as ranked by GiveWell.org[2] (who do rigorous effectiveness analysis). OpenPhilanthropy[3] is a charity that takes the EA perspective.
Unfortunately, seemingly worthy charities are often orders of magnitude less effective than the few most effective causes. Charity evaluators like GiveWell help us find the gems that can leverage our money many times more effectively.
For more info about Effective Altruism, listen to Tim Ferriss[4] and Sam Harris[5] interview the co-founder of Effective Altruism, check out this NYT piece[6], or read Doing Good Better[7].
[1] https://www.effectivealtruism.org/
[2] https://www.givewell.org/
[3] https://www.openphilanthropy.org/
[4] http://tim.blog/2015/11/22/will-macaskill/
[5] https://www.samharris.org/podcast/item/being-good-and-doing-good
[6] https://www.nytimes.com/2015/08/16/upshot/effective-altruism-where-charity-and-rationality-meet.html?_r=0
[7] https://www.amazon.com/Doing-Good-Better-Effective-Altruism/dp/1592409660/
1. Glad to hear! I lead our data team, and we've got a bunch of open roles, multiple of which probably match your background:
https://jobs.lever.co/flyzipline/?team=Software%20Engineering
Happy to talk it over if any catch your fancy! My email is matt dot e dot fay at gmail dot com
2. There's certainly fewer, but still plenty in this field! For example, if you check the 80,000 Hours Job Board, both Beyond Meat and Impossible Foods are hiring for software positions:
https://80000hours.org/job-board/factory-farming/
I can also ask around if you'd like.
3. The x-risk work is definitely speculative, by definition honestly. If the first two are stocks and bonds, this is the angel investing of cause areas:
https://www.openphilanthropy.org/blog/hits-based-giving
I've stayed away mostly because I was looking for tangibly impactful work (too removed for me), but there are really smart, kind folks making convincing arguments that this is the best use of your time and/or money. For example:
- https://80000hours.org/articles/future-generations/
- https://www.vox.com/future-perfect/2018/12/21/18126576/ai-artificial-intelligence-machine-learning-safety-alignment
- https://futureoflife.org/2018/09/17/moral-uncertainty-and-the-path-to-ai-alignment-with-william-macaskill