trentmkelly/LessWrong-43k huggingface dataset:
Scoring 2020 U.S. Presidential Election Predictions
by Zack_M_Davis — 2020-11-08T02:28:29.234Z
Link: http://zackmdavis.net/blog/2020/11/scoring-2020-us-presidential-election-predictions/

I was curious to see how various prognosticators—specifically, FiveThirtyEight and The Economist's models, and the PredictIt prediction markets—did on predicting the state-by-state (plus the District of Columbia) results of the recent U.S. presidential election.

Mathematical Sidebar

There are various ways to evaluate probabilistic predictions, but my favorite is to use the logarithmic score: the logarithm of the probability assigned to the right answer. (Logs of probabilities are negative numbers, but negating everything to get positive numbers doesn't change anything interesting—you just minimize instead of maximizing—such that I tend to mentally conflate the log and the negative-log.)

The reason I think the logarithmic score is best is because it has one essential property, one cool property, and a meaningful interpretation.

The essential property is that it incentivizes you to report your actual probabilities: if something actually happens 80% of the time, you get the best expected score by giving it probability 0.8.

The cool property is that it doesn't matter how you chop up your observations: because conjunction of probabilities is multiplication and the logarithm maps multiplication to addition, we get the same score whether we consider "A&B" as one event, or separately add up the scores of "A" and "B, given A".

(Although as David Schneider-Joseph and Oscar Cunningham pointed out in response to the originally published version of this post, my calculations later in this post add up scores of state-by-state predictions as if the state results were independent events, but this independence assumption is not realistic. Sorry.)

The interpretation is that the logarithmic score represents the length of the message you would need to encode the actual outcome using a code optimized for your model. (Er, the negation of the score represents the length—there's that conflation.)

Methodology

So, the election results aren't really final until all the states certify their results—or perhaps, when the Electoral College meets. The Trump campaign has filed lawsuits disputing results in a number of states, and at time of writing, ABC News's map has no call for Alaska, Arizona, North Carolina, and Georgia. But for the purposes of this blog post, I'm just going to go with the current colors on Politico's map, and assume that Biden wins Arizona and Georgia and that Trump win...
Sunday 8th 2pm PT: Scott Garrabrant's Office Hours / Cartesian Frames
by Raemon — 2020-11-08T02:03:53.338Z

Sunday Nov 8th at 2pm, Scott Garrabrant will be in his Walled Garden office at 2pm to host discussion and questions about Cartesian Frames. There won't be a formal lecture, but stop by to chat, ask questions or explore related ideas.

You can find him in the University section of the Garden, also accessible via the Tardis in the central area.

http://garden.lesswrong.com?code=PZbv&event=scott-garrabrant-office-hours

You can read more about Cartesian Frames here:

https://www.lesswrong.com/s/2A7rrZ4ySx6R8mfoT

His office looks like this:
Frequentist practice incorporates prior information all the time 
by Maxwell Peterson — 2020-11-07T20:43:30.781Z

I thought that the frequentist view was that you should not incorporate prior information. But looking around, this doesn't seem true in practice. It's common with the data scientists and analysts I've worked with to view probability fundamentally in terms of frequencies. But they also consider things like model-choice to be super important. For example, a good data scientist (frequentist or not) will check data for linearity, and if it is linear, they will model it with a linear or logistic regression. Choosing a linear model constrains the outcomes and relationships you can find to linear ones. If the data is non-linear, they've commited to a model that won't realize it. In this way, the statement "this relationship is linear" expresses strong prior information! Arguments over model choice can be viewed as arguments over prior distributions, and arguments over model choice abound among practitioners with frequentist views.
I'm not the first to notice this, and it might even be common knowledge: From Geweke,

Understanding Non-Bayesians

:

If a frequentist uses the asymptotic approximation in a given sample, stating only the weak assumptions, he or she is implicitly ruling out those parts of the parameter space in which the asymptotic approximation, in this sample size, and with these conditioning variables, is inaccurate. These implicit assumptions are Bayesian in the sense that they invoke the researcher’s pre-sample, or prior, beliefs about which parameter values or models are likely.

E.T. "thousand-year-old vampire" Jaynes, in the preface of his book on probability, basically says that frequentist methods do incorporate known information, but do it in an ad-hoc, lossy way (bold added by me):

In addition, frequentist methods provide no technical means to eliminate nuisance parameters or to take prior information into account, no way even to use all the information in the data when sufficient or ancillary statistics do not exist. Lacking the necessary theoretical principles, they force one to “choose a statistic” from intuition rather than from probability theory, and then to invent ad hoc devices (such as unbiased estimators, confidence intervals, tail-area significance tests) not contained in the rules of probability theory. Each of these is usable within a small domain for which it was invented but, as Cox’s theorems guarantee, such arbitrary devices always g

...
Three Open Problems in Aging
by johnswentworth — 2020-11-07T19:39:07.352Z

Aging, like many parts of biology, has a surplus of excellent experimentalists but a shortage of good modeling/analysis/theory. LessWrong and the adjacent community have a surplus of people with great modelling/analysis skills, many with an outsider’s interest in aging, so this post outlines a few problems which could fit that skillset. I expect that all of these could be solved to a reasonable degree of certainty with careful modelling and some beyond-high-school-but-not-revolutionary statistics, using already-existing data. For a publication-quality example of the sort of project I have in mind, check out the analysis of Burd et al’s data toward the beginning of

this paper

, or the modelling in

this paper

. (I'd also be happy even with something more back-of-the-envelope than these, as long as the approximations made sense and the predictions/assumptions were checked via multiple channels.)

Note that these are all open to the best of my knowledge. I’d be quite surprised to find existing analyses of the sort I have in mind, since it's not the sort of work academic biology usually incentivizes, but it’s not out of the question.

Atherosclerosis: How Much Oxidation?

For a couple decades now, it’s looked like oxidation of lipids (aka fats) is a mediating upstream cause of atherosclerosis - i.e. whatever the root cause of aging is, its effect on atherosclerosis is mostly mediated by lipid oxidation. There’s also been some

mathematical

modelling

of the lipid oxidation -> atherosclerotic plaque formation process.

Obvious next question: what causes the rate of lipid oxidation to increase with age? Obvious hypothesis: reactive oxygen species (ROS), e.g. superoxide and

fenton reaction

products. ROS play major roles in multiple theories of aging and have confirmed roles in many aging processes, so they’re a natural culprit. But which specific ROS are involved here, and what upstream change causes an increase in concentration of those particular ROS?

I’d like to see someone use models like those linked above to estimate how much lipid oxidation needs to increase to account for age-related development of plaques, then do some back-of-the-envelope calculations on how much we’d expect from specific sources/theories, and compare the two.

For example, one model might be that senescent cells spew ROS, so the age-related increase in senescent cells causes the increase in lipid oxi...
Can we prime people to take risk?
by tonyoconnor — 2020-11-07T17:17:19.922Z

Cross-posted from The Metasophist.

A key contributor to stagnation could be a growing reluctance to take risk. All creation involves risk, because when embarking on the journey you rarely know the result. As Paul Graham said, you might fear creating something "lame".
As society develops, it could become less attractive to take risk. For example, if you are musically inclined, learning and performing the works of Beethoven, Mozart, Chopin, and Wagner may deliver more instant rewards then creating your own musical forms. There is a large audience for the renowned, little for the unknown. In addition, someone like J.S. Bach could focus more on writing music, because he didn't have any of those four composers to listen to, and couldn't be paid for performing their works.
Similarly, as wealth grows, more people are employed to manage it. In a society with little wealth, financiers are few as there are fewer assets to trade. The mathematicians and engineers who become quants in our current age would actually have done maths and engineering in a poorer one.
These are two particular instances of a general problem whereby established paths are less risky and therefore more attractive than novel ones.
But without risk, no one will come up with new paradigms of knowledge and organisation. We need people to take risk to ward off stagnation. How can we encourage people to do so?
A potential answer to this question emerged in a conversation between Jim Rutt and Joscha Bach. Rutt cited a psychological experiment (Dutton and Aron, 1974) whereby those who just walked across a dangerous bridge were more likely to arrange a date with a member of the opposite sex immediately afterwards than those who walked over a safer bridge. The implication in the discussion is that doing something risky raises your willingness to take more risk.
In fact, Dutton and Arron state that this behaviour probably arises from what is known as the "Misattribution of Arousal". Essentially, going over a dangerous bridge induces physiological arousal, which the participant mistakes for romantic arousal. According to Allen et al., this effect even holds when someone is aware that the main source of arousal is non-romantic in nature.
Music can also induce physiological arousal: another study finds that women, but not men, were more likely to rate a man as attractive after listening to music. Interestingly, the st...
Somerville Mask Usage II
by jefftk — 2020-11-07T17:00:02.552Z

A little over four months ago I

collected some data on how often
people were wearing masks. Somerville still requires masks, cases
are up, there is now a

state-wide
order, and I again found myself with some time during which I
could take some notes. These numbers are not completely comparable:
last time I tracked on a Sunday, this time a Saturday; last time I
counted from 9:41am to 10:21am, this time 9:22am to 9:54am. Still,
these should be pretty similar samples of behavior here.

I counted 130 people, but last time I didn't count babies and this
time I did, by accident, which means that my number for children is no
good. Looking just at adults, I counted 109 (vs 165) people of which
80% (87, +6pp) were masked, 9% (10, +4pp) had their nose exposed, 6%
(7, -9pp) had their mask removed, and 5% (5, -1%) had no mask:

It looks like masking rates are up! Additionally, some people with
masks on their chin would pull them up over their nose and mouth when
passing people, but I counted everyone based on their masking status
as they passed a particular mostly dead tree in front of me:

As before, women were less likely to be masked, but both women and men
were more likely to be masked than in June:

Outdoors is still relatively safe, even without masks, since
ventilation is so rapid, but the bike path is relatively crowded as
outdoor places go. I'd be more interested in a comparison of grocery
store numbers, but I haven't been inside a store since March and so am
not in a good position to check.

Comment via: facebook
How can I bet on short timelines?
by Daniel Kokotajlo — 2020-11-07T12:44:20.360Z

I currently have something like 50% chance that the point of no return will happen by 2030. Moreover, it seems to me that there's a wager for short timelines, i.e. you should act as if short timelines scenarios are more likely than they really are, because you have more influence over them. I think that I am currently taking short timelines scenarios much more seriously than most people, even most people in the AI safety community. I suppose this is mostly due to having higher credence in them, but maybe there are other factors as well.

Anyhow, I wonder if there are ways for me to usefully bet on this difference.

Money is only valuable to me prior to the point of no return, so the value to me of a bet that pays off after that point is reached is approximately zero. In fact it's not just money that has this property. This means that no matter how good the odds are that you offer me, and even if you pay up front, I'm better off just taking out a low-interest loan instead. Besides, I don't need money right now anyway, at least to continue my research activities. I'd only be able to achieve significant amounts of extra good if I had quite a lot more money.

What do I need right now? I guess I need knowledge and help. I'd love to have a better sense of what the world will be like and what needs to be done to save it. And I'd love to have more people doing what needs to be done.

Can I buy these things with money? I don't think so... As the linked post argues, knowledge isn't something you can buy, in general. On some topics it is, but not all, and in particular not on the topic of what needs to be done to save the world. As for help, I've heard from various other people that hiring is net-negative unless the person you hire is both really capable and really aligned with your goals. But IDK.

There are plenty of people who are really capable and really aligned with my goals. Some of them are already helping, i.e. already doing what needs to be done. But most aren't. I think this is mostly because they disagree about what needs to be done, and I think that is largely because their timelines are longer than mine. So, maybe we can arrange some sort of bet... for example, maybe I could approach people who are capable and aligned but have longer timelines, and say: "If you agree to act as if my timelines are correc...
Why philosophy of science?
by Richard_Ngo — 2020-11-07T11:10:02.273Z

During my last few years working as an AI researcher, I increasingly came to appreciate the distinction between what makes science successful and what makes scientists successful. Science works because it has distinct standards for what types of evidence it accepts, with empirical data strongly prioritised. But scientists spend a lot of their time following hunches which they may not even be able to articulate clearly, let alone in rigorous scientific terms - and throughout the history of science, this has often paid off. In other words, the types of evidence which are most useful in choosing which hypotheses to prioritise can differ greatly from the types of evidence which are typically associated with science. In particular, I’ll highlight two ways in which this happens.

First is scientists thinking in terms of concepts which fall outside the dominant paradigm of their science. That might be because those concepts are too broad, or too philosophical, or too interdisciplinary. For example, machine learning researchers are often inspired by analogies to evolution, or beliefs about human cognition, or issues in philosophy of language - which are all very hard to explore deeply in a conventional machine learning paper! Often such ideas are mentioned briefly in papers, perhaps in the motivation section - but there’s not the freedom to analyse them with the level of detail and rigour that is required for making progress on tricky conceptual questions.
Secondly, scientists often have strong visions for what their field could achieve, and long-term aspirations for their research. These ideas may make a big difference to what subfields or problems those researchers focus on. In the case of AI, some researchers aim to automate a wide range of tasks, or to understand intelligence, or to build safe AGI. Again, though, these aren’t ideas which the institutions and processes of the field of AI are able to thoroughly discuss and evaluate - instead, they are shared and developed primarily in informal ways.
Now, I’m not advocating for these ideas to be treated the same as existing scientific research - I think norms about empiricism are very important to science’s success. But the current situation is far from ideal. As one example, Rich Sutton’s essay on the bitter lesson in AI was published on his blog, and then sparked a fragmented discussion on other blogs and personal fa...
the scaling “inconsistency”: openAI’s new insight
by nostalgebraist — 2020-11-07T07:40:06.548Z

I’ve now read the new OpenAI scaling laws paper. Also, yesterday I attended a fun and informative lecture/discussion with one of the authors.

While the topic is on my mind, I should probably jot down some of my thoughts.

This post is mostly about what the new paper says about the “inconsistency” brought up in their previous paper.

The new paper has a new argument on this topic, which is intuitive and appealing, and suggests that the current scaling trend will indeed “switch over” soon to a new one where dataset size, not model size, is the active constraint on performance. Most of this post is an attempt to explain and better understand this argument.

——

The new paper is mainly about extending the scaling laws from their earlier paper to new modalities.

In that paper, they found scaling laws for transformers trained autoregressively on text data. The new paper finds the same patterns in the scaling behavior of transformers trained autoregressively on images, math problems, etc.

So the laws aren’t telling us something about the distribution of text data, but about something more fundamental. That’s cool.

They also have a new, very intuitive hypothesis for what’s going on with the “scaling inconsistency” they described in the previous paper – the one I made a big deal about at the time. So that’s the part I’m most excited to discuss.

I’m going to give a long explanation of it, way longer than the relevant part of their paper. Some of this is original to me, all errors are mine, all the usual caveats.

——

1. L(C) and L(D)

To recap: the “inconsistency” is between two scaling laws:

The law for the best you can do, given a fixed compute budget.
This is L(C), sometimes called L(C_min). L is the loss (lower = better), C is your compute budget.
The law for the best you can do, given a fixed dataset size.
This is L(D), where D is the number of examples (say, tokens) in the dataset.

Once you reach a certain level of compute, these two laws contradict each other.

I’ll take some time to unpack that here, as it’s not immediately obvious the two can even be compared to one another – one is a function of compute, the other of data.

2. C sets E, and E bounds D

Budget tradeoffs

Given a compute budget C, you can derive the optimal way to spend it on different things. Roughly, you are trading off between two ways to spend compute:

Use C to buy “N”: Training a bigger model – “N” here is model

...
Nuclear war is unlikely to cause human extinction
by Jeffrey Ladish — 2020-11-07T05:42:24.380Z

A number of people have claimed that a full-scale nuclear war is likely to cause human extinction. I have investigated this issue in depth and concluded that even a full scale nuclear exchange is unlikely (<1%) to cause human extinction.

By a full-scale war, I mean a nuclear exchange between major world powers, such as the US, Russia, and China, using the complete arsenals of each country. The total number of warheads today (14,000) is significantly smaller than during the height of the cold war (70,000). While extinction from nuclear war is unlikely today, it may become more likely if significantly more warheads are deployed or if designs of weapons change significantly.

There are three potential mechanisms of human extinction from nuclear war:

1) Kinetic destruction

2) Radiation

3) Climate alteration

Only 3) is remotely plausible with existing weapons, but let's go through them all.

1) Kinetic destruction

There simply aren't enough nuclear warheads to kill everyone directly with kinetic force, and there likely never will be. There are ~14,000 nuclear weapons in the world, and let’s suppose they have an average yield of something like 1 megaton. This is a conservative guess, the actual average is probably closer to 100 kilotons. With a 1 megaton warhead, you can create a fireball covering 3 km², and a moderate pressure wave that knocks down most residential houses covering 155 km². The former kills nearly everyone and the latter kills a decent percentage of people but not everyone. Let's be conservative and assume the pressure wave kills everyone in its radius. 14,000 * 155 = 2.17 million km². The New York Metro area is 8,683 km². So all the nuclear weapons in the world could destroy about 250 New York Metro areas. This is a lot! But not near enough, even if someone intentionally tried to hit all the populations at once. Total land surface of earth is: 510.1 million km². Urban area, by one estimate, is about 2%, or 10.2 million km.² Since the total possible area destroyed from nuclear weapons is ~2.17 million km² is considerably less than a lower bound on the area of human habitation, 10.2 million km², there should be basically no risk of human extinction from kinetic destruction.

The circle with the white border indicates the zone of moderate blast damage radius (5 psi): 7.03 km (155 km²) from a 1,000 kiloton warhead,

link to nukemap

If you want to check my...