trentmkelly/LessWrong-43k huggingface dataset:
Hammertime Final Exam: Pledges, Activation Energy and Evaluating Productivity
by Towards_Keeperhood — 2021-06-20T23:59:25.797Z

This is my Hammertime Final Exam.

Instrumental Rationality Technique: Pledges

Pledges is a technique for setting deadlines that you actually meet.

In March (2021), my flatmate and I decided that we want to do a startup project together. We did not have much time back then and therefore pledged to have decided on an idea and pitched the idea to 3 people until the 19th of April. If we would not fulfill that pledge, we would need to go into the crowded pedestrian zone in Heidelberg with signs "We are incompetent".

We did everything the last week, but we definitely would not have done anything at all if we had not had the pledge.

We then renewed the pledge to set a date where we finish the beta version, with something even more embarrassing on the line, and it keeps us motivated.

When setting a pledge, make your goal specific and set a deadline. You might want to add sub-deadlines for sub-tasks, so you don't procrastinate everything until the deadline.

When choosing a penalty, do something that you really don't want to do. For me, embarrassing stuff works quite well, because my System 1 does not want to be embarrassed.

It is crucial that you tell other people of the pledge, to make yourself more responsible so that others can urge you to do the embarrassing action when you have not succeeded.

For other a bit smaller tasks (e.g. drawing an important decision or doing anything that you would like to procrastinate), pledging something is still powerful, though here I rather use smaller penalties. I sometimes write something like the following message to a friend:

"I pledge that I will do [*the task*] until [*the deadline*]. Otherwise, I [*will endorse the penalty I set myself*]."

For small tasks, I often use penalties like "don't watch Youtube for the next 3 days" or "don't listen to music for the next 2 days", but you can experiment and see what works best for you.

You can also try beeminder, but I think for me it may be less effective, though I must say I have not tested it yet.

To motivate yourself, even more, you can reward yourself after you have completed the task.

Rationality Framework: Activation energy

Activation energy is the term I use to describe that you often only need to get started with a task and then pretty much automatically continue on.

TAPs

One kind of using the principle of activation energy is using TAPs, especially the kind of trigger-action-sequences where ...
Why did the UK switch to a 12 week dosing schedule for COVID-19 vaccines?
by Eli Tyre — 2021-06-20T21:57:04.078Z

On December 30, 2020 Boris Johnson gave a speech in which he announced that the UK would follow a COVID-19 vaccination dosing schedule with a twelve week gap between first and second doses, instead of the 3 weeks that is standard in the US (and the rest of the world? [edit: Orual reports that Canada also used a dosing schedule that prioritized first doses]).

Verbatim:

And there is one important development that’s helping us to accelerate our vaccination programme across the whole of the UK:

We’ve had new advice from the Joint Committee on Vaccination and Immunisation that the first dose can protect people against the worst effects of this virus because the benefits kick in after two or three weeks.

And so from now on we will give a first dose to as many vulnerable people as possible, with the second dose to follow twelve weeks later.

And what that means is we can vaccinate and protect many more people in the coming weeks.

As near as I can tell, this is a straightforwardly good policy in terms of public health, because it increases society's total heard resistance to the virus faster. But it also entails deviating somewhat from the specific conditions of the trials in which the vaccines were tested. If it turns out to be a bad idea somehow, you can't fall back on saying that this plan was standard practice, and you couldn't have done any better." So in some sense, it's a risk.

I would really like to know what causal factors were upstream of this decision. Why did this happen in the UK, but not in the US or elsewhere?

Are there specific individuals, in the limelight or behind the scenes that pushed for this outcome? Is the difference that, in the UK, someone was in the right place at the right time?

Is this a cultural thing? Does the UK government feel more comfortable choosing policies that make analytic sense, but which don't have the social stamp of approval of "The Science"?

And if it is cultural, what is the difference? Do UK politicians reason from physical models of the virus more than US politicians? Does the UK government expect that if they're attacked for a decision like this, that voters (or someone else?) will back them up, while that's not the expectation in the US? Does the UK government feel like it has credibility, and therefore can make strong, reasoned, recommendations, while the US feels like it doesn't have the slack to do that? [1]

Insofar as ...
Irrational Modesty
by Tomás B. — 2021-06-20T19:38:25.320Z

At an online meetup I attended, I talked to a fellow who was working on some software that he thought would be useful for aggregating information for Effective Altruists. He said he would like to work on it full time this summer rather than get an internship - but that this was not financially feasible.

His idea seemed not-obviously stupid and I suggested he apply for a LTFF or EAIF grant. He said he did not want to as he was worried the money could go to better use.

I asked him if he thought the grant-makers were in a better position than him to decide if his project would be an efficient use of their capital. He said he did. I then asked him if he thought his idea was so likely to be a waste of time that even reading his proposal would be net negative in expectation; he said, "No".

He is now working on his proposal.

Here we have an example of someone who is likely much smarter than me but was unable to think clearly due to irrational modesty. I am worried this may be true of some extremely capable people, and how tragic it would be if this is the case.

The vast majority lack the requisite ability to do alignment research. I have a good model of my talents and capabilities, and I am certain I do not. In truth, it is not even close. Those who are capable, confident in their abilities, and motivated to work on this problem do not need a peptalk. But the possibility that there is a class of highly-talented would-be-motivated people who lack confidence in their abilities still haunts me.

I am reminded of this humorous exchange:

Though quentin's blindness to his own intelligence is humorous, it is a mistake and a rather large one. Irrational modesty is every bit as bad as overconfidence, possibly more so.

In the event anyone reading this has objective, reliable external metrics of extremely-high ability yet despite this feels unworthy of exploring the possibility that they can contribute directly to research, my advice is to act as if these external metrics correctly assess your ability until you have thoroughly proven to yourself otherwise.

There is no virtue in clutching Kryptonite. I advise you to drop it and see how far you can fly.
Arguments against constructivism (in education)?
by Viliam — 2021-06-20T13:49:01.090Z

Seems to me that constructivist education is already "in the water supply" of Less Wrong. My tweet-length definition of constructivism would be "teaching students so that they construct mental gears-level models of the subject, as opposed to just memorizing the teachers' passwords"; and I assume most people here would agree that this is the right thing to do.

My question is how much this is a fair characterization - or perhaps it is abstracting away some very important stuff - and what are the best arguments against constructivism. Because, per Litany of Tarski, if constructivism is wrong, I want to believe it is wrong; although frankly I do not expect it to be fundamentally wrong, more like constructivists having some typical blind spots, in which case I want to know where they are.

The motivation for the question is to help a (constructivist) teacher do their job well, or to help a teacher unfamiliar with constructivism learn to do their job better. Therefore if the objection is e.g. that Piaget was historically wrong about some technical detail, the importance of this objection depends on how likely today someone is to make a mistake in designing their lessons because of that fact.

Here are my attempts to steelman the opposition to constructivism:

Excesses done in the name of constructivism, such as spending four years letting kids discover how addition of natural numbers works. (Either because according to the extreme interpretation, any guidance from the teacher would be a sin against the child's independent construction of mental models; or because there is always one more "weird trick" that according to internet some natives used to add two numbers, and because having more models is always better, it is absolutely necessary to also teach kids this trick. As a result, 10 years old kids know dozen different ways how to add 15+8, each of those takes them five minutes, they will make a mistake in half of them, and they haven't heard about multiplication yet.) These are rare extremes, and are not what a typical constructivist believes, but in a debate with an opponent of constructivism expect to get an example or two.
Trying to find a perfect model to teach may lead to unnecessary abstraction introduced too soon, and then having kids (and their parents) stumble over the things that were supposed to help them. For example, we all know that base-10 is arbitr

...
Maybe Antivirals aren’t a Useful Priority for Pandemics?
by Davidmanheim — 2021-06-20T10:04:08.425Z

PLEASE KEEP COMMENTS GENERALLY ON THE TOPIC OF ANTIVIRALS.
Epistemic status: Building a better gears-level understanding of why antivirals don’t work very well, explaining why portfolio construction for technology isn’t the same as investing in markets, then speculating on implications.
Note: Crossposted to the EA Forum
The public conversation around COVID-19 response, especially pre-vaccine, prominently featured the idea that there are treatments which we just need to find. The claim is that if we found good, broad-spectrum antivirals, we could treat COVID.

But there is no guarantee that the thing we’re looking for exists. We might be searching up and down the street, under the lamps and elsewhere, for keys that are figments of our collective hopes and imaginations. There are words that describe things that do not exist. Like unicorns. Or antigravity. Fortunately, antivirals definitely exist. They just aren’t what I assumed when I was looking into the issue. And investing in them seems like a less promising avenue than I assumed.

NOTE: I am not arguing against any investment in antivirals. I’m discussing the relative promise and synergies or anti-synergies of the approaches for fighting a pandemic.

Antibiotics versus Antivirals

Antibiotics, i.e. antibacterial drugs, are definitely a thing. Actually, around a dozen things. They prevent basic things that bacteria need to do, such as

forming cell walls

,

using a specific protein synthesis pathway

, or

unwinding their DNA to replicate

. Thankfully, some of the things bacteria need to do are unique to bacteria - their cell walls are different from animal cell walls, so interrupting formation of bacterial cell walls doesn’t kill human cells. Other things are the same. Protein synthesis and DNA unwinding are critical for human cells, but they happen inside of the cell, so we can use

drugs that human cells keep out, but bacterial cells don’t

. Those drugs are a bit more toxic, but if you need to kill off bacteria, sometimes it’s worth it.

Viruses are different. They use our cells to replicate, so they don’t do many things which human cells don’t. There just aren’t as many targets - and interfering with the ones that exist are more likely to hurt the human hosts.

We want to find a useful antiviral, but we have good reasons to think that safe ones might not exist.

To be clear, we know of drugs that are effe...
10 Deadly Viruses And Bacteria Created In Labs - 6 SARS 2.0
by ChristianKl — 2021-06-19T22:29:59.999Z
Link: https://listverse.com/2019/01/23/10-deadly-viruses-and-bacteria-created-in-labs/

The post is written in January 23, 2019 about 10 Deadly Viruses created in labs:

6 SARS 2.0

Severe acute respiratory syndrome (SARS) is a lethal virus. More than 700 people were killed during a SARS epidemic that infected 8,000 people in 29 countries between 2002 and 2003. Now, scientists have made it deadlier.

The new mutant SARS virus was created by a group of researchers led by Dr. Ralph Baric of the University of North Carolina. They call it SARS 2.0. The researchers developed the virus by adding some protein to the naturally occurring SARS. SARS 2.0 is immune to vaccines and treatments used to cure the naturally occurring SARS virus.[5]

The team said that the research was necessary because the natural SARS virus could mutate and become immune to our vaccines. By creating a deadlier and mutated virus, we could develop stronger vaccines that will save us from a more lethal SARS epidemic. That is, if the natural SARS ever mutates.

However, other scientists are concerned because the SARS 2.0 that is supposed to save us from a deadly SARS epidemic could start that epidemic if it ever escapes from the lab.

It seems like listverse outperformed most of the EA ThinkTanks on the question of thinking about pandemics pre-COVID.
Visualizing in 5 dimensions
by TekhneMakre — 2021-06-19T18:15:18.160Z

Here's a tool for visualizing some 5 dimensional things. Poetically, the idea is to fold multiple "time dimensions" into the one actual time dimension using a sort of lexicographic ordering. More literally, we play a short 3d movie (each instant is a 3d scene) over and over, varying something about it across each playthrough; each short playthrough traverses the fourth dimension once, and the variation across playthroughs is the traversal in the fifth dimension.

Warmup: visualizing a 3-sphere

As a warmup to the warmup, let's visualize a 2-sphere S², a.k.a. a regular old plumbus sphere (hollow ball). A sphere S² can be seen as bunch of cross-section circles stacked on top of each other. Or, instead of stacking them in space, we can "stack them in time" by playing a 2d movie: blank screen -> point -> rapidly expanding circle -> circle gradually reaching its widest, then reversing (at the "equator" of the sphere) -> circle rapidly shrinking to a point and disappearing.

Now the warmup: how to visualize a 3-sphere S³, which "lives in four dimensions"? A 3d cross-section of S³ is a 2-sphere. Each cross-section S² fits in our 3d world ℝ³, but how to deal with the fourth dimension? We can use time. We play a movie forward. At each moment, we have a 3d scene. The scene starts off as empty space. Then for an instant, there's a single point p. The point immediately becomes a rapidly expanding sphere S² (centered at that p). The sphere expands, slower and slower, until it reaches its widest; then it starts shrinking. It shrinks faster and faster until in an instant, it shrinks to a point and disappears.
As landmarks, say the whole movie starts at time 0 and lasts until time 2, and at its widest the S² has radius 1. Then we have a picture of S³, with radius 1; its center is at point p at time 1; and it has a "south pole" at point p at time 0, with corresponding north pole at p at time 2, and with equator being the sphere at time 1. Exercise: what are the "lines of latitude"? What about "line of longitude"? Instead of putting the south pole at point p at time 0, can you picture the south pole in some other placetime, and visualize the corresponding north pole, equator, and latitude and longitude lines?

Basic example: visualizing a 4-sphere

Can you picture a 4-sphere?

The method I'm describing works like this: each cross section of S⁴ is an S³, with a radi...
Questions about multivitamins, especially manganese
by ryan_greenblatt — 2021-06-19T16:09:21.566Z

In Inadequate Equilibria Eliezer mentions that multivitamins may cause harm via excess manganese

Well, because I looked up previous ketogenic Soylent recipes, and they used standard multivitamin powders containing, e.g., way too much manganese and the wrong form of selenium. (You get all the manganese you need from ordinary drinking water, if it hasn’t been distilled or bottled. Excess amounts may be neurotoxic. One of the leading hypotheses for why multivitamins aren’t found to produce net health improvement, despite having many individual components found to be helpful, is that multivitamins contain 100% of the US RDA of manganese. Similarly, if a multivitamin includes sodium selenite instead of, e.g., se-methyl-selenocysteine, it’s the equivalent of handing you a lump of charcoal and saying, “You’re a carbon-based lifeform; this has carbon in it, right?”)

I started taking multivitamins somewhat recently to get additional iron,
vitamin B12, and vitamin D (I'm vegan). Specifically I am taking
this multivitamin.
I also take creatine. Taking these supplements was
partially informed by this post,
though I have been taking creatine on and off for quite a while.

I picked multivitamins instead of individual supplements because it was cheaper and more convenient. I also thought that covering additional vitamin/minerals would almost certainly be either neutral or slightly positive. However I now place somewhat more probability in the these additional vitamins/minerals being harmful in net (via excess vitamin A, manganese, or similar).
How concerned should I be?

At the moment, I think the additional manganese I am getting from this multivitamin (2.3 mg or 100% DV) is probably not harmful.
Precisely, I estimate that the probability of taking the supplement causing modest negative health effects (for instance, reducing expected IQ by >0.5 points or reducing expected life span by >1 month) is 20%. This is primarily based on noting that many common foods have high manganese content and not seeing any arguments against this dosage of manganese after briefly googling. I have seen arguments against higher dosages of manganese in supplements; this may be what Eliezer is discussing.

More generally beyond just manganese, how should the expected value of the 'unknown unknowns' of taking a multivitamin be estimated? It isn't immediately clear to me what the sign would be....
Parameter counts in Machine Learning
by Jsevillamol — 2021-06-19T16:04:34.733Z

In short: we have compiled information about the date of development and trainable parameter counts of n=139 machine learning systems between 1952 and 2021. This is, as far as we know, the biggest public dataset of its kind. You can access our dataset

here

, and the code to produce an interactive visualization is available

here

.

We chose to focus on parameter count because previous work indicates that it is an important variable for model performance [1], because it helps as a proxy of model complexity and because it is information usually readily available or easily estimable from descriptions of model architecture.

We hope our work will help AI researchers and forecasters understand one way in which models have become more complex over time, and ground their predictions of how the field will progress in the future. In particular, we hope this will help us tease apart how much of the progress in Machine Learning has been due to algorithmic improvements versus increases in model complexity.

It is hard to draw firm conclusions from our biased and noisy dataset. Nevertheless, our work seems to give weak support to two hypotheses:

There was no discontinuity in any domain in the trend of model size growth in 2011-2012. This suggests that the Deep Learning revolution was not due to an algorithmic improvement, but rather the point where the trend of improvement of Machine Learning methods caught up to the performance of other methods.
In contrast, it seems there has been a discontinuity in model complexity for language models somewhere between 2016-2018. Returns to scale must have increased, and shifted the trajectory of growth from a doubling time of ~1.5 years to a doubling time of between 4 to 8 months.

The structure of this article is as follows. We first describe our dataset. We point out some weaknesses of our dataset. We expand on these and other insights. We raise some open questions. We finally discuss some next steps and invite collaboration.

Model size of popular new Machine Learning systems between 1954 and 2021. Includes n=139 datapoints. See expanded and interactive version of this graph here.

Model size of popular new Machine Learning systems between 2000 and 2021. Includes n=114 datapoints. See expanded and interactive version of this graph here.

Features of the dataset

The dataset spans systems from 1952 to 2020, though we included far more information

...
Up-to-date advice about what to do upon getting COVID?
by DanielFilan — 2021-06-19T02:37:10.940Z

LessWrong has hosted a few posts that at least in part tell people what they should do upon getting COVID. That being said, they don't seem to have been updated since last March, and presumably more must be known now. The topic is newly relevant now that the delta variant is spreading in many locations, with vaccination rates being low in most countries.
Why did no LessWrong discourse on gain of function research develop in 2013/2014?
by ChristianKl — 2021-06-18T23:35:35.918Z

While I wasn't at 80% of a lab leak when Eliezer asseted it a month ago, I'm now at 90%. It will take a while till it filters through society but I feel like we can already look at what we ourselves got wrong.

In 2014, in the LessWrong survey more people considered bioengineered pandemics a global catastrophic risk then AI. At the time there was a public debate about gain of function research. On editoral described the risks of gain of function research as:

Insurers and risk analysts define risk as the product of probability times consequence. Data on the probability of a laboratory-associated infection in U.S. BSL3 labs using select agents show that 4 infections have been observed over <2,044 laboratory-years of observation, indicating at least a 0.2% chance of a laboratory-acquired infection (5) per BSL3 laboratory-year. An alternative data source is from the intramural BSL3 labs at the National Institutes of Allergy and Infectious Diseases (NIAID), which report in a slightly different way: 3 accidental infections in 634,500 person-hours of work between 1982 and 2003, or about 1 accidental infection for every 100 full-time person-years (2,000 h) of work (6).

A simulation model of an accidental infection of a laboratory worker with a transmissible influenza virus strain estimated about a 10 to 20% risk that such an infection would escape control and spread widely (7). Alternative estimates from simple models range from about 5% to 60%. Multiplying the probability of an accidental laboratory-acquired infection per lab-year (0.2%) or full-time worker-year (1%) by the probability that the infection leads to global spread (5% to 60%) provides an estimate that work with a novel, transmissible form of influenza virus carries a risk of between 0.01% and 0.1% per laboratory-year of creating a pandemic, using the select agent data, or between 0.05% and 0.6% per full-time worker-year using the NIAID data.

Even at the lower bar of 0.05% per full-time worker-year it seems crazy that society continued playing Russian Roulette. We could have seen the issue and protested. EA's could have created organizations to fight against gain-of-function research. Why didn't we speak every Petrov day about the necessity to stop gain of function research? Organizations like OpenPhil should go through the 5 Why's and model why they messed this up and didn't fund the cau...