trentmkelly/LessWrong-43k huggingface dataset:
Coping and Cultures
by Spiracular — 2020-05-06T22:49:13.428Z

Coping and Cultures

(or, Acquiring Coping Mechanisms from the Culture of a Field of Study)

One of the useful pieces of implicit knowledge that college or mentorship gives people are mental adaptations that make the stressors particular to a field of study more tolerable. These often go unnoticed, and seem undervalued as a result.

Some adaptations like this seem to become a part of academic cultures, and get osmosed by most students. Others might only have living cultures in industry, with academics carrying a reputation for burnout compared to individuals who learn on the job.

I expect self-taught individuals to be more likely to be missing some of these. Their absence may predispose them to burnout, by getting drawn into neurotic and/or unsustainable attractors when doing professional-grade work in that field.

I feel like this might be partially remedied if people knew to look for this while learning a new field, which is a lot of my motivation to post this.

This mostly focuses on stress and burn-out, but it warrants a broad trigger-warning for body-horror in the biology examples section. Feel free to skip the examples altogether, and to jump to the follow-up questions for any reason.

(Note: I took a stab at this, but this document is still alive. Both this post, and the thought behind it, still feels pretty rough. I am moderately likely to revise it, probably adding to it considerably, after posting.)

Examples

Dark Humored Biologists

In biology, particularly in certain subfields (ex: entomology, medicine, pathology), almost everyone makes jokes about dark things and has learned to take a certain joy in what most would find distasteful or macabre. Anyone who has heard of medical acronyms like GOMER probably has some inkling of what this looks like.

A minority of people even seem to have recontextualized a subset of skin-crawling feelings that most would translate as disgust, and instead perceive them as interest, attention, or excitement.* And we need this to be true of some people, or the world probably wouldn't have enough morticians to go around.

One straightforward example: if you are studying or working in surgery, you expect to be exposed to blood and gore on a regular basis. At a bare minimum, you need to be able to reliably cope with blood. If you have any intention of becoming a really good surgeon you must not only keep your cool, but also adopt an atti

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Does isolating the high-risk suppress COVID-19?
by Fahad Alkhater — 2020-05-06T21:18:40.078Z

We have 1-2 years before COVID-19 lockdowns are lifted permanently.

Until then, government's globally have adopted indiscriminate social distancing measures.

However, +99% of COVID-19 deaths are from those with pre-existing conditions.

If we targeted and isolated these high-risk individuals, would we be able to significantly reduce deaths, and secure hospital systems?

Would this be feasible with a government support program to enable high-risk to stay home, or move to care facilities, to protect them from the virus?
Covid-19: New York’s Antibody Tests 2
by Zvi — 2020-05-06T17:40:01.215Z

Previously: On New York’s Antibody Tests

New York continues to be the only place even trying to do antibody tests in a way that involves releasing information on the population.

What do we now know? How should we update? What comes next?

As a reminder, the method of data collection seems to be to set up in a randomly selected set of geographically distributed grocery stores, with volunteers who are then told their status afterwards.

Here are the third wave slides.

Cuomo expressed confusion that these numbers, in some places, were moving in the ‘wrong’ direction. For any given area or group, infection numbers, and thus the percentage of the population with antibodies, must (of course) go up over time rather than down. Any numbers that declined from 4/22 to 4/27, or from 4/27 t o 5/2, represent some sort of contradiction or error.

The type of contradictions/errors we see, and their magnitude, tells us a lot about how this data was gathered, and how those errors and contradictions came to be. They also tell us, in some ways, better information than if we had only the average of the results from all three days.

We still do have a big mystery on our hands. The numbers dropped a lot when they should have increased. It’s not easy to explain it away.

Time Is Marching On

What should we expect to see in relational terms between results on 4/22, 4/27 and 5/2, assuming a short delay between testing and reporting? Five days should allow for some growth in the net number of infections, and in the number of those who have recovered and how have antibodies. As with last time, I’ll assume two weeks from infection to start testing positive on an antibody test. I will use my current best projections of the true infected rate, which can be found on the “Scrapwork” tab of my Covid-19 information spreadsheet, to estimate ratios of total infected. Even if I’m wrong about levels, the ratios shouldn’t be too far off.

My best previous guess for infected in New York State on 4/6, two weeks plus two testing and reporting days before 4/22, is 2.36 million. On 4/11, sixteen days before 4/27, it is 2.86 million. On 4/16, it is 3.25 million. That’s 21% growth followed by 13% growth (or 38% combined growth). That’s a lot. Changing the lag time can cut these numbers a lot. If the results had no delay from infection to reporting a positive result, we’d only expect 11% growth. So perhaps we’re overest...
[AN #98]: Understanding neural net training by seeing which gradients were helpful
by Rohin Shah — 2020-05-06T17:10:02.563Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

HIGHLIGHTS

LCA: Loss Change Allocation for Neural Network Training (Janice Lan et al) (summarized by Robert): This paper introduces the Loss Change Allocation (LCA) method. The method's purpose is to gain insight and understanding into the training process of deep neural networks. The method calculates an allocation of the change in overall loss (on the whole training set) between every parameter at each training iteration, which is iteratively refined until the approximation error is less than 1% overall. This loss change allocation can be either positive or negative; if it's negative, then the parameter is said to have helped training at that iteration, and if it's positive then the parameter hurt training. Given this measurement is per-parameter and per-iteration, it can be aggregated to per-layer LCA, or any other summation over parameters and training iterations.

The authors use the method to gain a number of insights into the training process of several small neural networks (trained on MNIST and CIFAR-10).

First, they validate that learning is very noisy, with on average only half of the parameters helping at each iteration. The distribution is heavier-tailed than a normal distribution, and is fairly symmetrical. However, parameters tend to alternate between helping and hurting, and each parameter only tends to help approximately 50% of the time.

Second, they look at the LCA aggregated per-layer, summed over the entire training process, and show that in the CIFAR ResNet model the first and last layers hurt overall (i.e. have positive LCA). In an attempt to remedy this and understand the causes, the authors try freezing these layers, or reducing their learning rate. The first layer can't be fixed (freezing makes it's LCA 0, but later layers' LCA is increased in turn so the overall final loss stays the same). However, for the last layer, freezing or reducing the learning rate increases the overall performance of the network, as the last layer's LCA is decreased more than all the other layer's LCAs are increased. The...
Introduction to forecasting worksheet 
by edoarad — 2020-05-06T13:54:12.542Z
Link: https://www.foretold.io/c/a30a859c-2131-4703-9368-2f7c7372fdd4/n/48f6e50a-68d9-4fe6-a8f2-27d5a627dc66

I've written this interactive notebook in Foretold prediction platform. It is meant to be completely beginner friendly and takes about 2 hours to go through. I've used it as the basis for a workshop, and the accompanying slides can be found at the bottom of the notebook.

From the notebook:

In this interactive notebook, our goal is to actively try out forecasting and learn several basic tools. After this, you will be able to more easily use forecasts in your daily life and decision making, understand broadly how forecasters go about predicting stuff, and you should know if this is something you want to dive into deeper and how to go about that. We have 5 sections:

We will start immediately with several examples.
Then go on to understand how probabilities feel like, and how to be more calibrated.
Work on the technique of outside view and inside view reasoning.
Briefly discuss several interesting techniques - research, combining models and changing scope.
Try out some actual forecasts from start to finish!
AudioWorklet Latency: Firefox vs Chrome
by jefftk — 2020-05-06T13:50:02.634Z

Reading the

Firefox 76 Release Notes I noticed that it now supports

AudioWorklet. I tested out my

whistle-controlled bass synthesizer, and
it felt much better than in Chrome, though still not as responsive as
the native version. I decided to take some latency measurements.

I connected the computer to an external speaker and microphone, set
the browser to run a simple AudioWorklet-based
echo, and set my phone to record. I made a sharp tapping sound,
and looked at the recording to see the delay from when the original
sound is recorded to when the echo plays it back.

I tested Chrome and Firefox. I would have liked to compare to Safari,
since Apple has historically taken audio latency very seriously, but
they haven't implemented the AudioWorklet spec yet. I also tested
Reaper and PortAudio [1] for comparison.

Here's what this [EDIT: initially, see update at the end where everything gets faster] looked like with Reaper, Firefox, and Chrome all set
to echo:

(mp3)

I made twelve clicks in a row, and saw Reaper at 15ms, Firefox at
62ms, and Chrome at 124ms. I learned which echo was which by turning
off each in turn.

You would expect that latency would be completely consistent, because
you can't change the length of the delay without getting ugly
artifacts, but in Chrome it was not consistent. Earlier I made a much
longer recording (mp3) and taking
the two sections where you can clearly hear Chrome echos (mp3, mp3) I get
78ms, 78ms, 77ms, 94ms, 94ms, 93ms, 93ms, 93ms and then later 88ms,
88ms, 88ms, 88ms, 88ms, 93ms, 109ms, 109ms, 114ms, 114ms, 120ms,
147ms, 152ms, 152ms, 152ms, 152ms, 152ms, 152ms, 152ms, 152ms, 152ms,
163ms, 153ms, 153ms. Since one of the
projects I'd like to do with browser audio requires completely
consistent latency, I'm not sure it would work at all in Chrome.

PortAudio compared very similarly to Firefox, about 3ms faster. I'm
not sure what Reaper is doing to get much lower latency than PortAudio
and Firefox? If I was going to look into this further my next step
would be to log which system calls they're making and try to figure
out what they're doing differently.

Overall, I'm very impressed with Firefox's AudioWorklet
implementation's performance, and I hope Firefox pushes Chrome to do
better as well.

(Disclosure: I work for Google, which makes Chrome. I don't work on
the browser, though, and...
Assessing Kurzweil predictions about 2019: the results
by Stuart_Armstrong — 2020-05-06T13:36:18.788Z

EDIT: Mean and standard deviation of individidual predictions can be found here.

Thanks to all my brave assessors, I now have the data about Kurzweil's 1999 predictions about 2019.

This was a follow up to a previous assessment about his predictions about 2009, which showed a mixed bag. Roughly evenly divided between right and wrong, which I found pretty good for ten-year predictions:

So, did more time allow for trends to overcome noise or more ways to go wrong? Pause for a moment to calibrate your expectations.

Methods and thanks

So, for the 2019 predictions, I divided them into 105 separate statements, did a call for volunteers, with instructions here; the main relevant point being that I wanted their assessment for 2019, not for the (possibly transient) current situation. I got 46 volunteers with valid email addresses, of which 34 returned their predictions. So many thanks, in reverse alphabetical order, to Zvi Mowshowitz, Zhengdong Wang, Yann Riviere, Uriel Fiori, orthonormal, Nuño Sempere, Nathan Armishaw, Koen Holtman, Keller Scholl, Jaime Sevilla, Gareth McCaughan, Eli Rose and Dillon Plunkett, Daniel Kokotajlo, Anna Gardiner... and others who have chosen to remain anonymous.

The results

Enough background; what did the assessors find? Well, of the 34 assessors, 24 went the whole hog and did all 105 predictions; on average, 91 predictions were assessed by each person, a total of 3078 individual assessments[1].

So, did more time allow for more perspective or more ways to go wrong? Well, Kurzweil's predictions for 2019 were considerably worse than those for 2009, with more than half strongly wrong:

Interesting details

The (anonymised) data can be found here[2], and I encourage people to download and assess it themselves. But some interesting results stood out to me:

Predictor agreement

Taking a single prediction, for instance the first one:

1: Computers are now largely invisible. They are embedded everywhere--in walls, tables, chairs, desks, clothing, jewelry, and bodies.

Then we can compute the standard deviation of the predictors' answer for that prediction. This gives an impression of how much disagreement there was between predictors; in this case, it was 0.84.

Perfect agreement would be a standard deviation of 0; maximum disagreement (half find "1", half find "5") would be a standard deviation of 2. Perfect spread - equal numbers of 1s, 2s, 3s, 4s, and

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How to quantify uncertainty about a probability estimate?
by mic — 2020-05-06T12:06:08.217Z

How does one express how confident or uncertain about a probability estimate one is, in numeric terms?

Consider some different situations where you might have 50% confidence in a "yes" answer to a question:

Will this atom of hydrogen have decayed after its half-life?
After this fair coin is flipped, will it land heads?
After this biased coin is flipped, will it land heads?
You didn't quite hear some question and all you know is that it's a yes/no question and has an objective answer. Is the answer “yes”? You don't really know whether in general, “yes” answers are more likely to be correct than "no" answers.
Will Biden win the 2020 election?

These are all things where you might assign a 50% probability, but you'd probably be more confident in some of your answers than others, and that confidence depends on your knowledge in the area. How do we express this difference in confidence?

Here are some possible ways I've thought of:

Using fuzzy words like “I feel pretty confident in this 50% prediction”.
Completely avoiding assigning numerical probabilities, in an attempt to prevent people from taking the probabilities more seriously than they should.
Writing a range of probabilities, such as “5–10%”. It's not clear what a range of probabilities actually means. If that is an x% confidence bound, what would a confidence bound of probabilities even mean?
Drawing a probability density graph. A wider spread might indicate more uncertainty. When we have only have two outcomes like “yes” and “no”, for example, however, we might use a workaround such as “What probability would you assign to tribbles [1] being sentient after 1000 more hours of research?” (elaborated on in this comment by NunoSempere).
Using a theory of imprecise probabilities (https://plato.stanford.edu/entries/imprecise-probabilities/). I'd like a theory that boils down to regular probability theory.
Describing the amount of evidence it would take for you to change your mind. It'd be hard to use this to compare different problems.
Maths writer/cowritter needed: how you can't distinguish early exponential from early sigmoid
by Stuart_Armstrong — 2020-05-06T09:41:49.370Z

It's well known in FHI and similar circles, that it's impossible to distinguish an exponential (growth going up wildly) from a sigmoid/logistic curve (exponential growth until a turning point - an S shape) - until well after the turning point.

Which means we can't effectively predict that turning point. And so can't distinguish when a sigmoid will have a turning point, even when we know it must have one.

But this doesn't seem to exist in the statistics literature; and it would be very useful to have such a paper or textbook to point to.

We don't have time to write a full paper ourselves, but is there someone on this list with statistical experience who would like to write or co-write such a paper?

Since this result is important and as yet unpublished, it's plausible that such a publication may get an extremely high number of citations.

Cheers!
How can nonprofits gain the advantages of the for-profit model?
by jasoncrawford — 2020-05-06T01:15:18.507Z
Link: https://rootsofprogress.org/how-nonprofits-can-gain-for-profit-advantages

In my last post I described the advantages of for-profit models over nonprofit models, including scalable revenue, incentives and metrics to drive effectiveness and efficiency, and incentives to fund high-risk, high-reward experiments.

But not everything can be for-profit. How can nonprofit organizations get some of these advantages? Here are five ideas:

Nonprofits that generate revenue primarily through products & services, rather than through charitable donations, gain some of the advantages of for-profits: they survive only to the extent that they can deliver a product to the market that people are willing to buy, out-compete alternatives, and keep their costs below their prices. To the extent that a nonprofit’s paid services are subsidized by donations (as is the case with universities, museums, and opera houses, among others), this requirement is weakened but not destroyed.
In the case of charity, I wonder if the most effective form of it is simply giving money directly to beneficiaries, with the goods and services themselves provided by for-profit businesses. This would seem to let free-market capitalism work to the maximum extent. There is some research to support this idea.
If enough people promote the idea of donating based primarily on demonstrated impact, the world might slowly shift towards more strategic nonprofits driven by output metrics and other clear indication of delivered benefits. For instance, when reporting on a contribution from a major donor, the news media could focus more on the impact or potential impact of the contribution, rather than the amount of money given or the percent of their wealth that represents.
To drive innovation, perhaps we should be putting more of our resources into prizes or mechanisms like advance market commitments, rather than grants. Tyler Cowen summarizes: “The case for prizes is stronger when you don’t know who is likely to make the breakthrough, you value the final output more than the process, there is an urgency to solutions (talent development is too slow), success is relatively easy to define, and efforts and investments are likely to be undercompensated.” It seems to me that most of those conditions apply to a lot of breakthrough scientific and technological R&D. Indeed, one of the earliest and most famous prizes, the Longitude Prize, had exactly the effect of uncoveri

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Modeling naturalized decision problems in linear logic
by jessicata — 2020-05-06T00:15:15.400Z
Link: https://unstableontology.com/2020/05/06/modeling-naturalized-decision-problems-in-linear-logic/

The following is a model of a simple decision problem (namely, the 5 and 10 problem) in linear logic. Basic familiarity with linear logic is assumed (enough to know what it means to say linear logic is a resource logic), although knowing all the operators isn't necessary.

The 5 and 10 problem is, simply, a choice between taking a 5 dollar bill and a 10 dollar bill, with the 10 dollar bill valued more highly.

While the problem itself is trivial, the main theoretical issue is in modeling counterfactuals. If you took the 10 dollar bill, what would have happened if you had taken the 5 dollar bill? If your source code is fixed, then there isn't a logically coherent possible world where you took the 5 dollar bill.

I became interested in using linear logic to model decision problems due to noticing a structural similarity between linear logic and the real world, namely irreversibility. A vending machine may, in linear logic, be represented as a proposition "$1 → CandyBar", encoding the fact that $1 may be exchanged for a candy bar, being consumed in the process. Since the $1 is consumed, the operation is irreversible. Additionally, there may be multiple options offered, e.g. "$1 → Gumball", such that only one option may be taken. (Note that I am using "→" as notation for linear implication.)

This is a good fit for real-world decision problems, where e.g. taking the $10 bill precludes also taking the $5 bill. Modeling decision problems using linear logic may, then, yield insights regarding the sense in which counterfactuals do or don't exist.

First try: just the decision problem

As a first try, let's simply try to translate the logic of the 5 and 10 situation into linear logic. We assume logical atoms named "Start", "End", "$5", and "$10". Respectively, these represent: the state of being at the start of the problem, the state of being at the end of the problem, having $5, and having $10.

To represent that we have the option of taking either bill, we assume the following implications:

TakeFive : Start → End ⊗ $5

TakeTen : Start → End ⊗ $10

The "⊗" operator can be read as "and" in the sense of "I have a book and some cheese on the table"; it combines multiple resources into a single linear proposition.

So, the above implications state that it is possible, starting from the start state, to end up in the end state, yielding $5 if you took the five dollar bill, and $10 if you to

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The ways of knowing
by aelwood — 2020-05-05T20:35:11.445Z
Link: https://pursuingreality.com/2020/04/18/the-ways-of-knowing/

When trying to understand the nature of reality, we have to grapple with the question of how it is that we can know something of it. As we are limited to view the universe through a human lens, it is not clear how our mental constructs correspond to reality. To explore this, we should look at our lens in detail, refine its shape, polish out any scratches and understand its limitations.

In this article I want to introduce and explore the implications of the two fundamental ways in which we understand the world, through our intuition and models.