The Open Philanthropy Blog

Open Philanthropy is interested in when AI systems will be able to perform various tasks that humans can perform (“AI timelines”). To inform our thinking, I investigated what evidence the human brain provides about the computational power sufficient to match its capabilities. I consulted with more than 30 experts, and considered four methods of generating estimates, focusing on floating point operations per second (FLOP/s) as a metric of computational power.

The full report on what I learned is here. This blog post is a medium-depth summary of some context, the approach I took, the methods I examined, and the conclusions I reached. The report’s executive summary is a shorter overview.

In brief, I think it more likely than not that 1015 FLOP/s is enough to perform tasks as well as the human brain (given the right software, which may be very hard to create). And I think it unlikely (<10%) that more than 1021 FLOP/s is required.1 But I’m not a neuroscientist, and the science here is very far from settled.2 I offer a few more specific probabilities, keyed to one specific type of brain model, in the report’s appendix.

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In arriving at our funding priorities—including criminal justice reform, farm animal welfare, pandemic preparedness, health-related science, and artificial intelligence safety—Open Philanthropy has pondered profound questions. How much should we care about people who will live far in the future? Or about chickens today? What events could extinguish civilization? Could artificial intelligence (AI) surpass human intelligence?

One strand of analysis that has caught our attention is about the pattern of growth of human society over many millennia, as measured by number of people or value of economic production. Perhaps the mathematical shape of the past tells us about the shape of the future. I dug into that subject. A draft of my technical paper is here. (Comments welcome.) In this post, I’ll explain in less technical language what I learned.

It’s extraordinary that the larger the human economy has become—the more people and the more goods and services they produce—the faster it has grown on average. Now, especially if you’re reading quickly, you might think you know what I mean. And you might be wrong, because I’m not referring to exponential growth. That happens when, for example, the number of people carrying a virus doubles every week. Then the growth rate (100% increase per week) holds fixed. The human economy has grown super-exponentially. The bigger it has gotten, the faster it has doubled, on average. The global economy churned out $74 trillion in goods and services in 2019, twice as much as in 2000.1 Such a quick doubling was unthinkable in the Middle Ages and ancient times. Perhaps our earliest doublings took millennia.

If global economic growth keeps accelerating, the future will differ from the present to a mind-boggling degree. The question is whether there might be some plausibility in such a prospect. That is what motivated my exploration of the mathematical patterns in the human past and how they could carry forward. Having now labored long on the task, I doubt I’ve gained much perspicacity. I did come to appreciate that any system whose rate of growth rises with its size is inherently unstable. The human future might be one of explosion, perhaps an economic upwelling that eclipses the industrial revolution as thoroughly as it eclipsed the agricultural revolution. Or the future could be one of implosion, in which environmental thresholds are crossed or the creative process that drives growth runs amok, as in an AI dystopia. More likely, these impulses will mix.

I now understand more fully a view that shapes the work of Open Philanthropy. The range of possible futures is wide. So it is our task as citizens and funders, at this moment of potential leverage, to lower the odds of bad paths and raise the odds of good ones.

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This post compares our progress with the goals we set forth a year ago, and lays out our plans for the coming year.

In brief:

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Since our last hiring update, we have had a lot of new staff join Open Philanthropy. I’d like to use this post to introduce the new members of our team. We’re excited to have them!

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As part of our work on biosecurity and pandemic preparedness, we have contracted Good Judgment Inc. to expand its efforts to aggregate, publish, and track forecasts about the COVID-19 pandemic, with the hope that these forecasts can help improve planning by health security professionals and the broader public, limit the spread of the virus, and save lives.

The initial set of predictions, available here [update 6/28/2021: the commissioned forecasts have closed and are no longer online], are aggregated from forecasts by professional “Superforecasters,” who qualified by being in the most accurate 1-2% of forecasters from a large-scale, government-funded series of forecasting tournaments that ran from 2011-2015 (see Superforecasting) and, since then, by being in the top handful of forecasters from Good Judgment’s public forecasting platform, Good Judgment Open.

We may commission additional forecasts related to COVID-19 in the coming months, and we welcome suggestions of well-formed questions for which regularly updated forecasts would be especially helpful to public health professionals and the broader public. If you would like to suggest one or more questions for potential forecasting, please fill out this short form, especially if you are a medical or public health professional, and especially if you know how to state the forecasting question(s) precisely enough that it’s clear how to decide later how the question(s) resolved.

We’ve been funding scientific research and policy analysis on biosecurity and pandemic preparedness for several years and are glad to support the work many of our grantees are already doing to respond to this crisis. We’re continuing to support them and are pursuing other opportunities to help mitigate the effects of this pandemic, which we expect to share more about in the future.

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Last year, the year before, the year before that, and the year before that, we published a set of suggestions for individual donors looking for organizations to support. This year, we are repeating the practice and publishing updated suggestions from Open Philanthropy program staff who chose to provide them.

The same caveats as in previous years apply:

  • These are reasonably strong options in causes of interest, and shouldn’t be taken as outright recommendations (i.e., it isn’t necessarily the case that the person making the suggestion thinks they’re the best option available across all causes).
  • In many cases, we find a funding gap we’d like to fill, and then we recommend filling the entire funding gap with a single grant. That doesn’t leave much scope for making a suggestion for individuals. The cases listed below, then, are the cases where, for one reason or another, we haven’t decided to recommend filling an organization’s full funding gap, and we believe it could make use of fairly arbitrary amounts of donations from individuals.
  • Our explanations for why these are strong giving opportunities are very brief and informal, and we don’t expect individuals to be persuaded by them unless they put a lot of weight on the judgment of the person making the suggestion.

In addition, we’d add that these recommendations are made by the individual program officers or teams cited, and do not necessarily represent my (Holden’s) personal or Open Phil’s institutional “all things considered” view. Also, I just want to note that per our policy we’re no longer publishing all potentially relevant relationships.

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We believe that every life has equal value — and that philanthropic dollars can go particularly far by helping those who are living in poverty by global standards. This year, we explored the high bar set by the best global health and development interventions. Currently, the best giving opportunities we’ve found in the Global Health and Development focus area are recommended by GiveWell, a nonprofit dedicated to finding outstanding giving opportunities and publishing its full analysis to help donors decide where to give.

GiveWell recently announced its updated list of top charities that focus on programs with a strong track record and excellent cost-effectiveness, can use additional funding to expand their core programs, and are exceptionally transparent. As we have in the past, we asked GiveWell to make a recommendation — both in terms of the total amount donated and in terms of the distribution between recipient charities. GiveWell recommended, and we plan to approve, an allocation of $54.6 million for its top charities in 2019.

For setting the total amount, our methodology was the same as last year’s. In brief, we started from the assumption that 10% of total available capital will eventually go to a “straightforward charity” bucket that is reasonably likely to line up fairly well with GiveWell’s work and recommendations. This 10% allocation includes a fixed percentage of total giving each year of 5% and another flexible bucket of 5%, which can be spent down quickly or slowly, based in part on GiveWell’s expectations of when funds can accomplish the most good. (For more detail, please see our blog post on our 2017 allocation.)

Based on these considerations, GiveWell recommended that Open Philanthropy grant $54.6 million this year, allocated to its top charities as follows:

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We are excited to announce a new co-funding partnership with Ben Delo, co-founder of the cryptocurrency trading platform BitMEX and a recent Giving Pledge signatory. In his Giving Pledge letter, Ben said his ambition is to do the most good possible with his wealth, in particular by funding work to safeguard future generations and protect the long-term prospects of humanity. He explained the reasons for his focus:

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How accurate do long-range (≥10yr) forecasts tend to be, and how much should we rely on them?

As an initial exploration of this question, I sought to study the track record of long-range forecasting exercises from the past. Unfortunately, my key finding so far is that it is difficult to learn much of value from those exercises, for the following reasons:

  1. Long-range forecasts are often stated too imprecisely to be judged for accuracy. [More]
  2. Even if a forecast is stated precisely, it might be difficult to find the information needed to check the forecast for accuracy. [More]
  3. Degrees of confidence for long-range forecasts are rarely quantified. [More]
  4. In most cases, no comparison to a “baseline method” or “null model” is possible, which makes it difficult to assess how easy or difficult the original forecasts were. [More]
  5. Incentives for forecaster accuracy are usually unclear or weak. [More]
  6. Very few studies have been designed so as to allow confident inference about which factors contributed to forecasting accuracy. [More]
  7. It’s difficult to know how comparable past forecasting exercises are to the forecasting we do for grantmaking purposes, e.g. because the forecasts we make are of a different type, and because the forecasting training and methods we use are different. [More]

We plan to continue to make long-range quantified forecasts about our work so that, in the long run, we might learn something about the feasibility of long-range forecasting, at least for our own case. [More]

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Note: This post originally appeared in the monthly farm animal welfare newsletter written by our farm animal welfare team. Sign up here to receive regular email updates with research and insights into a farm animal advocacy research topic. We decided to cross-post this one because we thought it was especially interesting and wanted to make people aware of the newsletter, but note that the newsletter is not thoroughly vetted by other staff and does not necessarily represent consensus views of the Open Philanthropy Project as a whole.

From 2015-17, advocates secured pledges from over 300 US food companies to eliminate battery cages for the more than 240M egg-laying hens in their supply chains, mostly by 2025. (Advocates also secured another 800+ pledges from non-US food companies — the subject of a future newsletter.)

This was a big win for the farm animal movement. Fewer than 50 full-time advocates pushed the $9B US egg industry to commit to eliminate its core business practice — confining hens in tiny cages — at a cost to the industry of $7B-$9.5B. A 2016 Washington Post front-page story declared a “victory for the animal welfare movement”, noting that even egg producers think a “cage-free future is a fait accompli.”

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