trentmkelly/LessWrong-43k huggingface dataset:
Solipsism is Underrated
by scasper — 2020-03-28T18:09:45.295Z

Consider two simple, non-exhaustive theories.

Theory 1, Strong Materialism: The only ontological primitive is matter. This is to say that there is nothing but physical things, and everything that happens can be reduced down to the material. Minds and consciousness must then be some emergent phenomenon based on matter. For example, some people who take this view speculate that consciousness may be “what an algorithm implemented on a physical machine (like your brain) feels like from the inside.”

Theory 2, Strong Solipsism: The only ontological primitive is my own mind. This is to say that there is nothing (or at least nothing worth believing in) but mental phenomena. Someone who takes this view might say that they have no good reason to believe that their experiences have a physical antecedent, and everything that exists is just what’s going on for them mentally. People with this view might point out that perceptions of things outside themselves don’t actually indicate those things — just the perceptions themselves.

There are also dualistic theories which assert the existence of both physical and mental primitives, but I don’t want to focus on them here, and they’re not too popular among the LessWrong crowd anyway. There are a series of problems with different forms of dualism involving the prior complexity of believing in multiple ontological primitives as well some curious means by which the physical and mental interact. It's generally agreed that dualistic theories would force us to believe in more unlikely phenomena than either materialism or solipsism and aren’t comparatively plausible.

My impression is that most people who I’ve listened to or read from who have discussed these ideas (including Brian Tomasik and Eliezer Yudkowsky), seem to prefer materialism. There are two good reasons people have for this. First, even though when I perceive something, it does not with certainty imply the existence of that thing (only of that perception), it is perfectly consistent with the existence of that thing. Second, I observe many people other than myself, and their behavior and feelings seem to be a lot like my own. Given both of these observations, the likelihood in Bayes rule for materialism is relatively high, while for solipsism, it’s not.

I think that these two points are strong reasons to...
Bogus Exam Questions
by Donald Hobson — 2020-03-28T12:56:40.407Z

Take a look at this multiple choice question from the Open University BSc on psychology. Can you tell what is wrong with it. This is sampled from a set of 15 questions, some of the others aren't this bad, some are guessing the teachers password. One of the questions requires that you know that statistically significant means p<0.05 and can look at a table of p values and say which numbers are less than 0.05.

An educational psychologist believes that an individual's reading speed (in words per minute) and general comprehension abilities (scored out of 10) may be related to how well they do on a timed exam. To study the relative importance of these variables and the association between them, she runs a multiple regression and produces the following predictive model for exam score:

Exam Score =7.78 + (0.05*Reading Speed)+(5.77*Comprehension)

What exam score would you expect students to achieve if they have a reading speed of 120 w.p.m and a comprehension score of 5?
Select one:
o 4726
o 55.67
o 66.44
o 42.63

What level of understanding is necessary to fully understand the question?

From a mathematicians perspective, Multiple linear regression is an algorithm based on variational calculus (Optimizing over a space of functions), to select the function that maximizes the probability density that a model consisting of a linear function from the vector space R3 to R plus a Gaussian random variable (with arbitrary standard deviation, that term happens to cancel out) assigns to the observed data. This involves function spaces, matrix inversions, multivariate integrals ect. To understand the mathematical structure that leads up to multivariate regression, you need the idea of jacobians and vector calculus just to integrate your e−12x2 to find the normalizing constant on your Gaussian random variables. You need continuus probability spaces, but probability isn't defined over arbitrary sets of real numbers, so you need sigma algebras, which brings you to set theory. You need to prove that the integrals exist, that the optimum slope exists and is unique, ect.

In short you need to understand a lot of maths to deeply understand what is going on.

How much understanding do you need to get the answer right. Suppose you have basic English comprehension, but all you know about the symbols "385+*)" is that they are some mysterious code that you can t...
What software projects would be helpful for different groups?
by davelaing — 2020-03-28T07:50:19.686Z

There are various meetups around the world that work on altruistic software development. Random Hacks of Kindness is at the top of my mind at the moment.

The main site appears to be down at the moment, but the Australian and Canadian sites are still up, and the Australian one is asking for projects which help with the COVID-19 response.

That got me wondering. What software projects would be high leverage and not currently saturated? Which of them are amenable to being worked on by groups of developers with mixed skills and backgrounds?

This can probably be broken down further into software for different groups. Healthcare workers probably have different needs in this time than people who are struggling to make the case for working from home. My gut feeling is that efforts that helps with social support and mental health support will also have high value over time.
mind viruses about body viruses
by nostalgebraist — 2020-03-28T04:20:02.674Z

I was going to write this as a Slate Star Codex comment, but I’m going to make it a tumblr post tagging @slatestarscratchpad instead, since experience suggests it’s likely to be more widely and carefully read in this form. (Crossposting to LW too, so you may be reading this there, possibly with mangled formatting.)

The idea frontier

I am getting more and more concerned about the “information epidemiology” of the public conversation about Covid-19.

Here are some distinctive features I see in the public conversation:

1. Information intake must be triaged.

There is a very large amount of new publicly available information every day. There are no slow news days. “Keeping up with the story” in the way one would keep up with an evolving news story would be a full-time job.

Many of us do not have time to do this, and I imagine many of those who do have time cannot tolerate the experience in practice. In fact, there can be a tradeoff between one’s level of personal involvement in the crisis and one’s ability to “follow it” as a news story.

(I work for a telemedicine company, and after a day of dealing with the ever-changing impacts of Covid-19 on my work, I have relatively little patience left to read about its ever-changing impacts on absolutely everything else. That’s just me, though, and I realize some people’s mental bandwidth does not work like this.)

2. Abstractions are needed, and the relevant abstractions are novel and contested.

Crucial and time-sensitive decisions must be made on the basis of simulations, abstract mental models, and other intellectual tools.

In some sense this is true of everything, but in most cases we have a better sense of how to map the situation onto some large reference class of past intellectual work. When there is an economic downturn, the standard macroeconomic arguments that have existed for many decades pop back up and make the predictable recommendations they always make; even though there is no expert consensus, the two or three common expert stances are already familiar.

With Covid-19, this is not so. All the intervention types currently under discussion would be, in their own ways, unprecedented. As it struggles to follow the raw facts, the general public is also struggling to get its head around terms and concepts like “suppression,” “containment,” “contact tracing,” etc. which were (in the relevant senses) not part of our m...
The case for C19 being widespread
by Hauke Hillebrandt — 2020-03-28T00:07:27.878Z

GDoc version

Epistemic status: very, very uncertain. Please continue following official advice like social distancing etc.

I've seen all the previous discussions (including in this thread and on Tyler Cowen's blog, but remain unconvinced]

--

A preprint from 24/03 by Gupta et al. at Oxford[1] suggests that the current data on C19 is consistent with both:

Few infections, low infectiousness (r0), and high infection fatality rate (IFR)
Widespread actual (asymptomatic) infections, high r0 and low IFR

For instance, the authors suggest that if the IFR is low and C19 is very infectious, it is possible that by 19/03, 36%-68% of the UK population would have already been infected with C19. The ongoing epidemics in the UK and Italy started at least a month before the first reported death.

This study has been criticized, but scientists agree that serological assays will show whether this hypothesis is true.[2], [3]

Here I make the strongest possible case that C19 is widespread, r0 is underestimated and IFR is low.

I base this on the following:

Much C19 transmission might be asymptomatic[4] and presymptomatic.[5],[6]
Tim Spector, professor of genetic epidemiology a King's College London, finds that

10% of 650,000 UK users of their C19 symptom tracker app showed mild symptoms. Thus 6.5m people in UK are infected, not taking into account asymptomatic cases

A preprint from 26/03 by epidemiologists Gutierrez et al.[7], Professor and Chair of Mathematics at University of Texas at San Antonio (

Google Scholar Profile

) suggests

An R0 between 5.5 and 25.4[8], if you account for asymptomatic spread. In this scenario, the peak of symptomatic infections is reached in 36 days with approximately 9.5% of the entire population showing symptoms.
The authors argue that it’s unlikely for a pathogen to blanket the planet in three months with an R0= ~3 and that it has to be more contagious than measles, which has an R0 of 18.

A preprint from 13/03 by Chowell et al., Professor & Chair - Georgia State University School of Public Health suggesting (

GScholar profile first author who has written 2 papers

on the Diamond Princess

,

senior author

quoted in the

NYT

) suggests that

r0=5.20 (95%CrI: 5.04-5.47)
IFR=0.12% (95%CrI: 0.08-0.17%), several orders of magnitude smaller than the crude CFR estimated at 4.19%.[9]
~20% of the all people in Wuhan were infected on Jan 23rd (~2 mi

...
What are the best online tools for meetups and meetings?
by Raemon — 2020-03-27T22:58:01.287Z

tl;dr – If you have tried at least two telepresence tools, explain their relative strengths, and which one you prefer.

A key question for both remote organizations, and apparently all social life now, is "what tools can best keep me connected to people in the way that in-person-meetings used to."

There are plenty of tools available, ranging from videoconferencing (like Skype or Zoom) to video-games (like Minecraft). But it can be hard to evaluate them in isolation.

So instead of posting "Hey, I like X", this thread is modeled after The Best Textbooks on Any Subject – don't just post about a tool you like. Instead, compare that tool to at least one other tool (preferably two), and explain what's superior about your favorite software.
Is donating to AMF and malaria interventions the most cost-effective way to save lives from COVID-19?
by warrenjordan — 2020-03-27T22:25:16.692Z

This is a cross-post from the EA forum and wanted to gather the thoughts of LessWrong. I am new to both communities so I apologize in advance if I overstep any rules. If I have, please let me know!

Looking at WHO's recommendation to maintain malaria interventions and AMF's response to COVID-19, it sounds like reducing mortality of malaria can and will reduce mortality of COVID-19.

This seems to be based on the assumption that if you prevent a malaria patient from having to go to the hospital, then that frees up resources for them to focus on saving a COVID-19 patient.

So if we were to oversimplify this as an example:

Let's say a hospital has 50 beds. There are 50 COVID patients and 50 malaria patients. Thus, you can only save half of them. If we were able to prevent malaria in those 50 patients, then the hospital can allocate all 50 beds to the 50 COVID patients - thus, saving everyone.

Now, I'm very sure there's much more to consider than my oversimplification - so I would love to hear people's thoughts and be proven wrong!

---

EDIT: I reached out to AMF for further clarification and here is their response.

There are several ways in which reducing malaria – both morbidity and mortality – helps reduce the impact of COVID-19.

First, if people are ill with malaria their immune system is less able to cope with the effect of COVID-19 so the impact of COVID-19 can be expected to be greater. This is consistent with those most at risk being people with ‘underlying health issues’. Increased malaria leads to a greater strain on already fragile health systems including taking up more intensive care beds.

Second, if people are ill will malaria, whose symptoms are very similar to those of COVID (high fever), (“….the early symptoms of COVID-19, including fever, myalgia, and fatigue, might be confused with malaria and lead to challenges in early clinical diagnosis.” Source: Lancet article) the identification and management of COVID-19 is made more difficult. There is a greater risk of mis/under-diagnosis (treated as malaria when in fact COVID-19) or over-diagnosis (treated as COVID-19 and perhaps a bed in a hospital allocated, when malaria is the illness), which makes the management of COVID-19 more challenging.

Third, if we delay desperately needed universal net coverage campaigns, and bednets are the m

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What can we call the mistaken belief that something can't be measured?
by yhoiseth — 2020-03-27T22:10:47.330Z

In his book How to Measure Anything, Douglas W. Hubbard defines measurement as a quantitative reduction in uncertainty. He also shows how anything can be measured, i.e. how it's always possible to quanitatively reduce uncertainty.

However, it's common for people to flat-out believe that many things — such as the value of a human life — can't be measured. I believe they are wrong, and I would like a name for being wrong in this way. Is there already a name for this fallacy? If so, what is it? If not, what do you think of the immeasurability fallacy? Or do you have other ideas?
Coronavirus Research Ideas for EAs
by habryka — 2020-03-27T22:10:35.767Z
Link: https://forum.effectivealtruism.org/posts/AcksHPwQupvPRKcZm/coronavirus-research-ideas-for-eas

Peter Hurford wrote a large, very comprehensive, research agenda over on the EA Forum. Here are all the questions:

Meta-Research

1.) Just how bad are things right now? How bad might we expect it to get? What is the current state of play and what are various plausible scenarios forward? [PRIORITY]

2.) What paths to impact are available to us with regard to coronavirus related work? What is our rough guess of how this impact might compare to typical EA work?

3.) An insane amount of research is being produced at a rapid pace. How can we best consolidate and synthesize this information and get people up to speed quickly? [PRIORITY]

4.) An insane number of people are working hard on this, both inside and outside the EA movement. How should we best coordinate? [PRIORITY]

5.) What else could and should EAs do, other than these research questions?

6.) Are there places EAs should donate that focus on coronavirus response that are particularly promising to donate to, relative to existing charities EAs like?

7.) Does EA need a rapid response task force to coordinate inevitable future catastrophes? How can we be better prepared to respond to the next one?

8.) What do our biosecurity and pandemic prevention specialists think the rest of us should be doing right now?

Policy Response

9.) What, more precisely, is the “endgame scenario” for getting out of the coronavirus situation with minimal deaths? What kind of timeline might we be looking at?

10.) Will there be some situations or risks that could arise that make some of the desired endgames impossible or much more difficult to implement?

11.) How have various countries, states, counties, cities, private companies, non-profits, civil societies, etc. successfully or unsuccessfully handled the coronavirus policy response so far?

12.) How might we expect COVID to change policy advocacy over 2020-2022?

13.) What, if anything, can be done to change coronavirus response policy for the better? What about for future pandemics / crises?

14.) Low- and middle-income countries seem at particular risk of not being able to handle the coronavirus problem. How bad will this situation be? What should be done to mitigate it? Do we have any viable paths to impact?

15.) What is the actual cost-benefit of various pandemic responses? [PRIORITY]

16.) What opportunities does this give for us to improve institutions and policies more broadly?

17.) Are there a

...
COVID-19 growth rates vs interventions
by Bucky — 2020-03-27T21:33:25.851Z

It’s been a couple of weeks since I posted regarding the growth rates of COVID-19 cases in various countries. Now, after these countries have implemented various control measures it is more clear how each measure effects growth rate. This post looks at the 10 countries with the highest number of confirmed cases.

Death growth rates roughly match confirmed case growth rates

Firstly, to check whether the confirmed case growth rates roughly reflect the actual growth rates, they can be compared to death rates (suggested in a comment by Unnamed on my previous post). This doesn’t show what the ratio is between the number of cases and the number of detected cases, it only shows whether the fractional growth per day in detected cases roughly represents the actual growth rate of the virus.

Obviously the death growth rate doesn’t perfectly indicate the spread of the virus but it has different biases so if the two are similar then they probably at least somewhat reflect the actual growth rate.

A graph to compare growth rates of confirmed cases & deaths of the 10 worst affected countries is here. (Select country at the top)

(Note: For the y-axis I have used doubling time of infectious cases, which I’ve defined as 10 days fairly arbitrarily. The actual length of time doesn’t matter too much to the results but it’s important to have some limit otherwise the recent China and South Korea results particularly make less sense. This makes the definition of doubling time for deaths a bit odd but it's good enough for the purposes of comparing the rates.)

Generally the rates match well. Iran and USA are the main ones where there is significant divergence (and both look more reasonable recently), the others all seem sensible.

In theory there should be a lag between cases and deaths which we seem to see in Italy and Spain but the data is too noisy to say for sure.

Growth rates vs interventions

I’ve created 4 different displays of the cases data:

1. Cumulative confirmed cases

2. New cases per day

3. Doubling time for infectious cases

4. Fractional change in infectious cases per day

This last one is analagous to the effective reproduction number (R) and could be converted to that if I knew the mean infectious period. I could take a stab at this but I think it's best to leave it as it is. It's useful to know that 1 on this axis is ... ,
Some examples of technology timelines
by NunoSempere — 2020-03-27T18:13:19.834Z

"Weather forecasts were comedy material. Now they're just the way things are. I can't figure out when the change happened."

Introduction

I briefly look into the timelines of several technologies, with the hope of becoming marginally less confused about potential A(G)I developments.

Having examples thus makes some scenarios crisper:

(AGI will be like perpetual motion machines: proven to be impossible)

AGI will be like flying cars: possible in principle but never in practice.

AI will overall be like contact lenses, weather forecasts or OCR; developed in public, and constantly getting better, until one day they have already become extremely good.

AI will overall be like speech recognition or machine translation: Constant improvement for a long time (like contact lenses, weather forecasts or OCR), except that the difference between 55% and 75% is just different varieties of comedy material, and the difference between 75% and 95% is between "not being usable" and "being everywhere", and the change feels extremely sudden.

AGI will be like the iPhone: Developed in secret, and so much better than previous capabilities that it will blow people away. Or like nuclear bombs: Developed in secret, and so much better than previous capabilities that it will blow cities away.

(AGI development will be like some of the above, but faster)

(AGI development will take an altogether different trajectory)

I did not use any particular method to come up with the technologies to look at, but I did classify them afterwards as:

After the event horizon: Already in mass production or distribution.

In the event horizon: Technologies which are seeing some progress right now, but which aren't mainstream; they may only exist as toys for the very rich.

Before the event horizon: Mentioned in stories by Jules Verne, Heinlein, Asimov, etc., but not yet existing. Small demonstrations might exist in laboratory settings or by the DIY community

I then give a summary table, and some highlights on weather forecasting and nuclear proliferation which I found particularly interesting.

After the event horizon

Weather forecasting

A brief summary: Antiquity's weather forecasting methods lacked predictive power in the short term, though they could understand e.g., seasonal rains. Advances were made in understanding meteorological phenomena, like rainbows, in estimating the size of the stratosphere, but no

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