Quick takes

TL;DR: Someone should probably write a grant to produce a spreadsheet/dataset of past instances where people claimed a new technology would lead to societal catastrophe, with variables such as “multiple people working on the tech believed it was dangerous.” Slightly longer TL;DR: Some AI risk skeptics are mocking people who believe AI could threaten humanity’s existence, saying that many people in the past predicted doom from some new tech. There is seemingly no dataset which lists and evaluates such past instances of “tech doomers.” It seems somewhat ridiculous* to me that nobody has grant-funded a researcher to put together a dataset with variables such as “multiple people working on the technology thought it could be very bad for society.” *Low confidence: could totally change my mind  ——— I have asked multiple people in the AI safety space if they were aware of any kind of "dataset for past predictions of doom (from new technology)"? There have been some articles and arguments floating around recently such as "Tech Panics, Generative AI, and the Need for Regulatory Caution", in which skeptics say we shouldn't worry about AI x-risk because there are many past cases where people in society made overblown claims that some new technology (e.g., bicycles, electricity) would be disastrous for society. While I think it's right to consider the "outside view" on these kinds of things, I think that most of these claims 1) ignore examples of where there were legitimate reasons to fear the technology (e.g., nuclear weapons, maybe synthetic biology?), and 2) imply the current worries about AI are about as baseless as claims like "electricity will destroy society," whereas I would argue that the claim "AI x-risk is >1%" stands up quite well against most current scrutiny. (These claims also ignore the anthropic argument/survivor bias—that if they ever were right about doom we wouldn't be around to observe it—but this is less important.) I especially would like to see a

The next technological revolution could come this century and could last less than a decade This is a quickly written note that I don't expect to have time to polish. Summary This note aims to bound reasonable priors on the date and duration of the next technological revolution, based primarily on the timings of (i) the rise of homo sapiens; (ii) the Neolithic Revolution; (iii) the Industrial Revolution. In particular, the aim is to determine how sceptical our prior should be that the next technological revolution will take place this century and will occur very quickly. The main finding is that the historical track record is consistent with the next technological revolution taking place this century and taking just a few years. This is important because it partially undermines the claims that (i) the “most important century” hypothesis is overwhelmingly unlikely and (ii) the burden of evidence required to believe otherwise is very high. It also suggests that the historical track record doesn’t rule out a fast take-off. I expect this note not to be particularly surprising to those familiar with existing work on the burden of proof for the most important century hypothesis. I thought this would be a fun little exercise though, and it ended up pointing in a similar direction. Caveats: * This is based on very little data, so we should put much more weight on other evidence than this prior * I don’t think this is problematic for arguing that the burden of evidence required to think a technological revolution this century is likely is not that high * But these priors probably aren’t actually useful for forecasting – they should be washed out by other evidence * My calculations use the non-obvious assumption that the wait times between technological revolutions and the durations of technological revolutions decrease by the same factor for each revolution * It’s reasonable to expect the wait times and durations to decrease, e.g. due to increased populati

For a long time I found this surprisingly nonintuitive, so I made a spreadsheet that did it, which then expanded into some other things. * Spreadsheet here, which has four tabs based on different views on how best to pick the fair place to bet where you and someone else disagree. (The fourth tab I didn't make at all, it was added by someone (Luke Sabor) who was passionate about the standard deviation method!)  * People have different beliefs / intuitions about what's fair! * An alternative to the mean probability would be to use the product of the odds ratios. Then if one person thinks .9 and the other .99, the "fair bet" will have implied probability more than .945. *  The problem with using Geometric mean can be highlighted if player 1 estimates 0.99 and player 2 estimates 0.01. This would actually lead player 2 to contribute ~90% of the bet for an EV of 0.09, while player 1 contributes ~10% for an EV of 0.89. I don't like that bet. In this case, mean prob and Z-score mean both agree at 50% contribution and equal EVs. * "The tradeoff here is that using Mean Prob gives equal expected values (see underlined bit), but I don't feel it accurately reflects "put your money where your mouth is". If you're 100 times more confident than the other player, you should be willing to put up 100 times more money. In the Mean prob case, me being 100 times more confident only leads me to put up 20 times the amount of money, even though expected values are more equal." * Then I ended up making an explainer video because I was excited about it   Other spreadsheets I've seen in the space: * Brier score betting (a fifth way to figure out the correct bet ratio!) * Posterior Forecast Calculator * Inferring Probabilities from PredictIt Prices These three all by William Kiely. Does anyone else know of any? Or want to argue for one method over another?

Hi all!  Nice to see that there is now a sub-forum dedicated to Forecasting, this seems like a good place to ask what might be a silly question.   I am doing some work on integrating forecasting with government decision making.  There are several roadblocks to this, but one of them is generating good questions (See Rigor-Relevance trade-off among other things).   One way to avoid this might be to simple ask questions about the targets the government has already set for itself, a lot of these are formulated in a SMART [1] way and are thus pretty forecastable. Forecasts on whether the government will reach its target also seem like they will be immediately actionable for decision makers.  This seemed like a decent strategy to me, but I think I have not seen them mentioned very often. So my question is simple: Is there some sort of major problem here I am overlooking?  The one major problem I could think of is that there might be an incentive for a sort of circular reasoning: If forecasters in aggregate think that the government might not be on its way to achieve a certain target then the gov might announce new policy to remedy the situation. Smart Forecasters might see this coming and start their initial forecast higher.  I think you can balance this by having forecasters forecast on intermediate targets as well.  For example: Most countries have international obligations to reduce their CO2 emissions by X% by 2030, instead of just forecasting the 2030 target you could forecasts on all the intermediate years as well.    1. ^ SMART stands for: Specific, Measurable, Assignable, Realistic, Time-related - See  https://en.wikipedia.org/wiki/SMART_criteria 

This is some advice I wrote about doing back-of-the-envelope calculations (BOTECs) and uncertainty estimation, which are often useful as part of forecasting. This advice isn’t supposed to be a comprehensive guide by any means. The advice originated from specific questions that someone I was mentoring asked me. Note that I’m still fairly inexperienced with forecasting. If you’re someone with experience in forecasting, uncertainty estimation, or BOTECs, I’d love to hear how you would expand or deviate from this advice. 1. How to do uncertainty estimation? 1. A BOTEC is estimating one number from a series of calculations. So I think a good way to estimate uncertainty is to assign credible intervals to each input of the calculation. Then propagate the uncertainty in the inputs through to the output of the calculation.  1. I recommend Squiggle for this (the Python version is https://github.com/rethinkpriorities/squigglepy/). 2. How to assign a credible interval: 1. Normally I choose a 90% interval. This is the default in Squiggle. 2. If you have a lot of data about the thing (say, >10 values), and the sample of data doesn’t seem particularly biased, then it might be reasonable to use the standard deviation of the data. (Measure this in log-space if you have reason to think it’s distributed log-normally - see next point about choosing the distribution.) Then compute the 90% credible interval as +/- 1.645*std, assuming a (log-)normal distribution. 3. How to choose the distribution: 1. It’s usually a choice between log-normal and normal. 2. If the variable seems like the sort of thing that could vary by orders of magnitude, then log-normal is best. Otherwise, normal. 1. You can use the data points you have, or the credible interval you chose, to inform this. 3. When in doubt, I’d say that most of the time (for AI-related BOTECs), log-normal distribution is a good choice. Log-normal is the default distribution

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On January 6, 2022, at 4pm GMT, I am going to host a gather town meetup to go through Scott Alexander's Prediction Competition on Blind Mode which means you only spend max 5 minutes on each question. Because of that, and also possibly because these are the rules (I'm finding out), we likely won't collaborate (though if the rules ok it, maybe we do!), but if you've been wanting to enter and haven't yet made time, come, and we'll set some pomodoros and have a good time! Event link here: https://forum.effectivealtruism.org/events/wENgADx63Cs86b6A2/enter-scott-alexander-s-prediction-competition