Working on failures #1 (based on DP10 essay)
Working on failures
The plan of this essay is to look here, for where all I sucked in this practice session, and then take some of those cases and try some “high repetition”.
With this essay I take on identifying the following:
- When a claim is a belief
- When a claim cannot be tested
- Some special cases of splitting the Subject Predicate where I failed.
- Working on claims that have adjectives in them.
With every new section I start of with one example where I failed and then carry on with the practice.
Note: The link shown above, is not fully edited, but it was my manual attempt to classify all the errors according to patterns.
With every claim there is a checklist. The checklist in this essay looks like this: The first line has the following format: “Does-it-have-examples?; Is the claim true or false?”. The second line contains marks to identify the type of claim, marks if I failed, or marks if I am unsure or if it took too much time etc… This will become clear with the actual excercise hopefully. This is used for word processing in the terminal.
Checklist: no; neither;
no-example; failed; future-with-no-ex; unsure; future-with-example; failed;
Beliefs about right or wrong
STM:
(Crime)[0] should (not pay)[1], is (very simple common sense)[2].
Claims: Crime should not pay
Definition: checks out I think!
This is a predicate based on his beliefs about what is right and wrong, not on empirical examples. “Crime does not pay” is a predicate about the world and it would be false if the average crime led to a lot of money without imprisonment. “Crime should not pay” can be skipped for now. (“The Holy B says that crime should not pay” is testable, though.)
Pattern: “is common sense” or similar?
Source: https://youtu.be/g-9TdoU4Ay8?t=2683
If (you want to create a different pronoun for people who are intrasex)[1], that makes some sense to me.
Claims: [1] makes some sense to me.
Subject: If [1], what it does to me
Predicate: makes some sense to me.
Checklist: no; neither; no-example; belief-about-right-wrong;
Pattern: “A” makes sense to me.
But you do not get to (redefine fundamental terms of human biology)[1] simply because you have (a subjective feeling about yourself)[2], is my main objection.
Claims: You do not get to [1].
Subject: What you do not get to do.
Predicate: [1].
Checklist: no; neither; no-example ; belief-about-right-wrong; time (10mins)
Pattern: You do not get to do X.
People can say what they want particularly when (it happens to be true)[1]
Claims: People can say what they want when [1]
Checklist: no; neither; no-example; belief-about-right-wrong; time; (5mins)
Pattern: People can do/say X.
Right now they are trying to take title nine and apply to transgender people, which makes no sense at all.
Claims: Applying title nine to trans people makes no sense at all.
Checklist: no; neither; belief-about-right-wrong;
Pattern: A makes sense.
I don’t understand (how you can simultaneously claim that you are a feminist standing up for women and also claim that a man can be a women)[1]. That is (puzzling to me)[2]. There are (lot of internal contradictions here)[3], logically speaking
Claims: [1] is [2].
Checklist: no; neither;
belief-about-right-wrong
Pattern: A is puzzling to me.
Claims: There are [3], when logically speaking.
Checklist: yes; neither;
not-a-belief-about-right-wrong; definition-unclear;
What I do care about is (when my 5-year old daughter is in a bathroom with my wife and sees Logan paul (who identifies as a woman) in the bathroom)[1]. Is my wife wrong to feel a threat, The answer I think is no. Why in the world would she be wrong to feel a threat
Claims: I care about [1].
Checklist: yes; neither; not-a-belief-about-right-wrong
Claims: My wife is not wrong to feel the threat when [1].
Checklist: no; neither;
belief-about-right-wrong
Pattern: X is wrong/right.
it argues to me that there should be (a different level (lesser) of threat when my wife and child see someone with hormone treatment who looks like a female)[1].
Claims: There is [1].
Subject: When wife perceives a man to be a female, what the level of threat she perceives should be.
Predicate: lesser than that of a male.
Checklist: yes; neither;
because-should-due-to; not-a-belief-about-right-wrong
(The same people who are arguing that women ought to be afraid of toxic masculinity are arguing that a man can be a woman)[1]. How is that even logically coherent?
Claims: [1]
Checklist: yes; neither;
not-a-belief-about-right-wrong
Claims: [1] is not logically coherent.
Author suggests some hypocritism. I guess I can check for it.
Checklist: yes; neither;
not-a-belief-about-right-wrong;
You feel (pretty safe when we have one room but the toilets are all locked off)[1]
Claims: You feel [1]
Checklist: yes; neither;
not-a-belief-about-right-wrong
I think when we lock it away and say there’s no improvements we can make to that system, that’s where it can kind of become very segregating.
Checklist: yes; neither; not-a-belief-about-right-wrong
If you want to come up with solutions that make sense, I am all for it.
Checklist: no; neither;
belief-about-right-wrong
Pattern: if X makes sense then Y
I am completely at peace with new age identification systems with gay marriage.
Checklist: yes; neither; not-a-belief-about-right-wrong
I support gay marriage
Checklist: yes; neither; not-a-belief-about-right-wrong
I am a huge fan of the LGBTQ community
Checklist: yes; neither; not-a-belief-about-right-wrong
The problem that I see, is that (once you start encoding that in state law, the next move is to call everyone who disagrees, a bigot and to remove their tax-exempt status)[1]
Claims: [1]
Checklist: yes; neither; not-a-belief-about-right-wrong
As a libertarian I find this very scary.
Checklist: yes; neither; not-a-belief-about-right-wrong
(People should be allowed to do whatever they want)[1]. (I don’t owe you a duty to bake you a cake)[2]. You don’t like my way of baking a cake, go find some other baker.
Claims: [1]
Checklist: no; neither;
belief-about-right-wrong
Pattern: A should be allowed to do B.
Claims: [2]
Checklist: yes; neither; not-a-belief-about-right-wrong; unsure
If you don’t have a claim on me and yet you are pointing a government gun at me, you are the bad guy in the scenario
Checklist: no; neither;
belief-about-right-wrong
Pattern: You are bad.
If I say that you owe me, you have to make dinner tonight and you don’t want to make me dinner, so I get the government to point a gun at you and you make me dinner, I think I am the bad guy.
Checklist: no; neither;
belief-about-right-wrong
Pattern: You are bad.
(Businesses)[1] should be allowed to (do bad things that I don’t like (e.g., not serving gay people), so long as they are not forcing me to do anything.)[2]
Claims: [1] are to be allowed to [2].
Checklist: no; neither;
belief-about-right-wrong
Pattern: A should not be allowed to do B
“People able to boycott things like discrimination”, I think is a good part of American discourse.
Claims: ^^
Checklist: no; neither;
belief-about-right-wrong
future with and without examples
Source: https://80000hours.org/problem-profiles/positively-shaping-artificial-intelligence/
STM:
Claim: Human civilization is at stake (because of AI).
Example:
The fate of Gorillas currently depends on the actions of humans. They are currently endangered. Similarly the fate of humanity may come to depend on the actions of machines than our own.
In other words, we have no concrete example. If they said that diseases put human civilization at stake, we can point to the Black Plague, which killed nearly half the people of Western Europe. Or nukes (Japan). Or asteroids (dinosaurs).
Claims: Human civilization is at stake (because of AI).
Checklist: no; neither;
*future-without-example;
Pattern: about the future; X is at stake;
I think the goal here shall be to see if things can be tested or not. For example the above example cannot be tested. There is no AI that puts Human civilization at stake currently. (I realize this only during re-reading my work that this is about testing).
A growing number of experts believe that (a third revolution will occur during the 21st century)[1], through the invention of machines with intelligence which far surpasses our own.
Claims: [1]
Checklist: no; neither;
future-without-example;
Pattern: about the future; will
Rapid progress in machine learning has raised the prospect that (algorithms will one day be able to do most or all of the mental tasks currently performed by humans)[1].
Claims: [1]
Checklist: no; neither;
future-without-example;
Pattern: about the future; will
This could ultimately lead to machines that are much better at these tasks than humans.
Claims: ^^
Checklist: no; neither;
future-without-example
Pattern: about the future; could lead to
(These advances could lead to extremely positive developments, presenting solutions to now-intractable global problems)[1], but (they also pose severe risks)[2].
Claims: [1].
Checklist: no; neither;
future-without-example
Pattern: about the future; could lead to
Claims: [2]
Checklist: no; neither;
future-without-example
Pattern: about the future; implied could
(If machines surpass humans in intelligence)[1], then just as the fate of gorillas currently depends on the actions of humans, (the fate of humanity may come to depend more on the actions of machines than our own.)[2]
Claims: If [1], [2].
Checklist: no; neither;
future-without-example
Pattern: about the future; may come to
(This might be the most important transition of the next century)[1] – either ushering in an unprecedented era of wealth and progress, or heralding disaster.
Claims: [1].
Checklist: no; neither;
future-without-example
Pattern: about the future; might be
We’ve also come to believe (the technical challenge can probably be overcome if humanity puts in the effort)[1].
Claims: [1].
Checklist: no; neither;
future-without-example
Pattern: about the future; probably
(Working on a newly recognized problem)[1] means that (you risk throwing yourself at an issue that never materializes or is solved easily)[2] – but it also means that (you may have a bigger impact by pioneering an area others have yet to properly appreciate, just like many of the highest impact people in history have done.)[3]
Claims: [1] means [2].
Can possibly give an example from the past
Checklist: yes; neither; no-example
Claims: [3]
Checklist: yes; neither; no-example
Many experts believe that (there is a significant chance that humanity will develop machines more intelligent than ourselves during the 21st century)[1]. (This could lead to large, rapid improvements in human welfare, but there are good reasons to think that it could also lead to disastrous outcomes)[2].
Claims: [1].
Checklist: no; neither;
future-without-example
Pattern: about the future; will
Claims: [2].
Checklist: no; neither; no; neither; future-without-example
Pattern: about the future; could lead to
(If AI research continues to advance without enough work going into the research problem of controlling such machines, catastrophic accidents are much more likely to occur.)[1]
Claims: [1].
Checklist: no; neither;
future-without-example
Pattern: about the future; are likely; AI
(We estimate that the risk of a serious catastrophe caused by machine intelligence within the next 100 years is between 1 and 10%.)[1]
Claims: [1].
Checklist: no; neither;
future-without-example
Pattern: about the future; prediction of future;
We think a doubling of effort would reduce the size of the existing risk by around 1%.
Claims: [1].
Checklist: no; neither;
future-without-example
Pattern: about the future; prediction of future;
Source: https://80000hours.org/podcast/episodes/the-world-desperately-needs-ai-strategists-heres-how-to-become-one/
Those organizations have an interest in making sure that AI is as beneficial as possible, and they’re keenly aware of the fact that they can be misused and that (there might be accident risks associated with them)[1].
Claims: [1]
Subject: Accident risks associated with AI
Predicate: might exist.
Example:
By the afternoon of May, 6, 2010, US equity markets were already down 4% on worries about the European debt crisis. At 2:32 p.m., a large seller (a mu- tual fund complex) initiated a sell algorithm to dispose of a large number of the E-Mini S&P 500 futures contracts to be sold off at a sell rate linked to a measure of minute-to-minute liquidity on the exchange. These contracts were bought by algorithmic high-frequency traders, which were programmed to quickly eliminate their temporary long positions by selling the contracts on to other traders. With demand from fundamental buyers slacking, the algorithmic traders started to sell the E-Minis primarily to other algorithmic traders, which in turn passed them on to other algorithmic traders, creating a “hot potato” effect driving up trad- ing volume—this being interpreted by the sell algorithm as an indicator of high liquidity, prompting it to increase the rate at which it was putting E-Mini contracts on the market, pushing the downward spiral. At some point, the high-frequency traders started withdrawing from the market, drying up liquidity while prices con- tinued to fall. At 2:45 p.m., trading on the E-Mini was halted by an automatic circuit breaker, the exchange’s stop logic functionality. When trading was restarted, a mere five seconds later, prices stabilized and soon began to recover most of the losses. But for a while, at the trough of the crisis, a trillion dollars had been wiped off the market, and spillover effects had led to a substantial number of trades in in- dividual securities being executed at “absurd” prices, such as one cent or 100,000 dollars. After the market closed for the day, representatives of the exchanges met with regulators and decided to break all trades that had been executed at prices 60% or more away from their pre-crisis levels (deeming such transactions “clearly erroneous” and thus subject to post facto cancellation under existing trade rules). — SuperIntelligence chapter 1.
Definition: checks out!
Checklist: yes; true; future-with-example;
Pattern: about the future; might
Source: https://80000hours.org/articles/us-ai-policy/
one of the most impactful things that people can work on is ensuring that the transition to a world with advanced AI technology benefits all of humanity.
Claims: ^^
Checklist: no; neither;
future-without-example
Pattern: about the future; transitioning to a world with advanced AI.
Claims: AI safety needs progress
This can be tested, if AI they talk about is the current existing technologies and not some super intelligence
Checklist: yes; neither;
no-example; not-future-without-example ; unsure
Claims: AI policy needs progress
Checklist: yes; neither;
no-example; not-future-without-example ; unsure
(ensuring that advanced AI, benefits all humanity, requires substantial progress to be made on both AI safety and AI policy)[1], particularly work with a longer-term perspective that considers more advanced AI systems.
Claims: [1].
Checklist: not-sure; neither; no-example; future-without-example; time
Pattern: advanced AI; X needs progress
So here I spent 20 mins figuring out if it is a claim of the future just like (“human civilization is at stake”).
One element of this coordination problem is that the perceived rewards from (accelerating the development of AI capabilities may create a race-to-the-bottom)[1].
Claims: [1].
Checklist: yes; neither; no-example future-with-example; time
Pattern: may
Racing in this way may be counterproductive even from actors’ self-interest.
Claims: ^^
Checklist: yes; neither; no-example future-with-example; time
Pattern: may
(Perceptions or misperceptions of a race could exacerbate rivalrous development)[1], as the nuclear arms race did during the cold war, potentially even leading to conflict.
Claims: [1].
Example: nuclear arms race during cold war.
Checklist: yes; neither;
future-with-example
Pattern: about the future; could
Claims: [1] could lead to conflict
Example: nuclear arms race during cold war.
Checklist: yes; neither;
future-with-example
Pattern: could lead to
(In such a scenario, a lack of coordination risks a worst-case outcome for all actors)[1], while (a coordinated response in which parties credibly pre-commit to the broad sharing of benefits could allow a good outcome for all.)[2]
Claims: [1].
Checklist: yes; neither;
no-example
Claims: [2].
Checklist: yes; neither;
future-with-example; no-example
Pattern: about the future; could
IF
STM
(These areas)[6a] can be (particularly worth pursuing)[7] if you’re (especially motivated by one of them)[8].
Your response:
For [6a] we think of, working in promoting EA as in the above example.
For [8], we think of a personal fit of more than 50%
For [7], we think of an impact of 5300*50%=2650 lives which is better than working a DS job resulting in 530 net people.
But that doesn’t use [8] at all. Why is it particularly worth pursuing if you are “especially motivated”? The “impact” [7] you pointed out would seem to be the same if you had [8] or if you didn’t.
I would expect an example where someone who had this magical [8] went on to have particularly great “impact”. And I suspect that they do not have that example.
Consider this: You can bowl a particularly high bouncer if you’re 6 feet tall. Showing Bhuvaneshwar Kumar bowling a bouncer is not an example. You have to show Courtney Walsh bowling a bouncer that is one foot higher than usual bouncers.
Source: https://80000hours.org/key-ideas/
if (an area already receives plenty of attention)[1], then there will usually already be (people)[2] working on the (most promising interventions)[3].
Claims: If [1], then there will already be [2] working on [3].
Subject: If [1], what [2] will already be working on.
Predicate: most promising interventions.
Example: For [1], we think of “health in poor countries” where about 300 billion$ is spent on health each year. Contrast that to the spending of 10 to 100 million$ on factory farming.
For “what [2] are working on”, we think of AMF working on delivering bednets to people in Africa.
For [3], we cite 80khours suggesting here that delivering bednets to people in Africa is most needed.
Get everyone exposed to malaria sleeping under bednets.
Definition: Checks out.
Checklist: yes; true;
In the 1950s, the large-scale production of nuclear weapons meant that, for the first time, (a few world leaders )[1] gained the ability to kill (hundreds of millions of people)[2] — and possibly many more if (they triggered a nuclear winter)[3], which would make it nearly impossible to grow crops for several years.
Claims: [1] could kill [2], if [3].
Subject: If [3], What [1] could kill.
Predicate: [2].
Example: No example for nuclear winter ([3]) from the past.
Definition: -
Checklist: no; neither; no-example; future-without-example;
However, over the past eight years, we’ve come to realize that the (present generation)[1] is capable of (putting the entire future of civilisation at stake)[2] if (it doesn’t wisely navigate the development of these technologies)[3].
Claims: [1] is capable of [2], if [3].
Example: no-example
Checklist: yes; neither; not-chapter; no-example;
It (global catastrophe that leads to billions of deaths) seems like (such an event would be among the worst things that could happen)[1]. This is especially true if (one takes a longtermist perspective)[2], because extinction would also mean the loss of the potential welfare of all future generations.
Claims: [1] if [2].
Example: no example.
Definition: -
Checklist: no; neither; future-without-example; no-example
Some other issues we’ve focused on in the past include (ending factory farming and improving health in poor countries)[1]. They seem especially promising if (you don’t think people can or should focus on the long-term effects of their actions)[2].
Claims: [1] seems promising if [2].
Subject: if [2], what [1] seems to be.
Predicate: promising
Example: 80khours ranks most promising areas to work on here.
if [2], then this list cuts across to factory farming and health in poor countries only, neglecting things like climate change or AI safety.
Definition: checks out.
Checklist: yes; true;
(These areas (i.e., issues 80khours hasn’t been able to look into))[1] can be particularly worth pursuing if (you’re especially motivated by one of them)[2]. We cover this more in the section on ‘personal fit’ below.
Claims: [1] can be particularly worth pursuing if [2].
Example: no example
Definition: -
Checklist: yes; neither; ; not-in-chapter; no-example;
Given our take on the world’s most pressing problems and the most pressing bottlenecks these issues face, we think the following five broad categories of career are a good place to start generating ideas if you have the flexibility to consider a new career path.
Checklist: yes; neither;; not-in-chapter no-example
Research is the most difficult to enter of the five categories, but it has big potential upsides, and in some disciplines, going to graduate school gives you useful career capital for the other four categories. This is one reason why (if you might be a good fit for a research career, it’s often a good path to start with)[1] (though we still usually recommend exploring other options for 1-2 years before starting a PhD unless you’re highly confident you want to spend your career doing research in a particular area).
Claims: [1]
we would like an example of someone who is a “good fit” for a research career and see that “it is a good path”
No way am able to give examples for this. This is too broad and pointless for me to attempt to give an example.
Checklist: yes; neither;
no-example
Source: https://80000hours.org/problem-profiles/health-in-poor-countries/
People with HIV live nearly normal lifespans, and rarely pass on the virus to others, if promptly and consistently treated with anti-retroviral drugs.
Example: “Among nearly 1,000 male couples across Europe where one partner with HIV was receiving treatment to suppress the virus, there were no cases of transmission of the infection to the HIV-negative partner during sex without a condom.”—link
Definition: This is the best example I could get quickly. checks out.
Checklist: yes; true;
If (you look at experience)[1], it certainly does not look like the (deciding factor regarding this)[2].
Claims: If [1], it does not relate to skill.
Subject: If [1], What experience does not relate to.
Predicate: to skill.
Example: My colleague is 50 years old with 20+ years of design experience. Despite that I was the one who did all the important calculations and reviewed his design.
Definition: checks out.
Checklist: yes; true;
If talent existed and refused to show itself even after so many years of life, it beckons if inate ability even exists.
Claims: If talent existed, and it refused to show itself even after so many years of life, then talent doesn’t exist.
Checklist: no; neither;
no-example: failed (not sure how the example could look).
Claims: Everyone would achieve greatness do X, if it were easy and fun.
Example: I think the closest I can come to giving an example, is to give an example of a certain X and not ‘greatness’ (as I wouldn’t have an example for that claim)
Every Friday night my colleagues religiously plan outings with friends and get wasted. It costs very little and is fun.
Definition: checks out.
Checklist: yes; true;
Source: http://agent18.github.io/deliberate-practice.html
(You didn’t really have to know much about a field)[1] if you knew the (best ways to analyze a problem and think it through)[2], and (you needed to know even less)[3] if your (analysis and reasoning power could be juiced by a computer)[4]
Claims: Knowledge about field was not required (to be successful), if you knew [2].
The if statement is unclear. I can falsify the claim without the if statement, but with it is very hard.
Checklist: no; neither;
if; time; failed (failed as I did not know how to answer this claim)
Claims: You needed to know lesser than someone with [2], if [4].
Don’t have an example for [2].
Checklist: yes; neither;
Claims: You needed to know very little about the field to be successful, if [4].
Example: The AlphaGo system that defeated Lee Sedol (4-1). It required tens of millions of games of training data.
Definition: falsifies claim.
Checklist: yes; false;
Given a (word)[1] it is easier to remember if (it is familiar)[3] and with little effort we can spell it backwards even, but if the letters are in a random order, it is going to be pretty hard. What chess players could be seeing is words.
Claims: Given [1] it is easier to remember if [3].
Example: Contrast remembering “pneumonoultramicroscopicsilicovolcanoconiosis” (which is not familiar), with “Mitochondria” (which is familiar).
Definition: checks out.
Checklist: yes; true
Claims: If the letters are in random order, it is going to be pretty hard to remember.
Example: “asdfjahsdkfjhagskdfjhg”
Definition: checks out.
Checklist: yes; true;
If (we would like to become an expert in our field)[1] (we would read tons about our field, the history, read everything that the experts are doing, get insights from colleagues etc…)[2]
Claims: If [1], then [2].
Checklist: yes; neither;
future-with-no-ex
In simple terms, when the tasks are easy then it gets boring. When tasks involved are challenging enough that they just stretch us beyond our skills, then we are in flow. If (it gets too challenging)[1] then (we get frustrated)[2]. Top-level performers in sports seem to rate practice high on the scale of enjoyableness. But the violinists seen in Ericsson’s study seem to rank it as not enjoyable. The sharp contrast continues.
Claims: If [1] then [2].
Example: It is very very hard for me to reach 181bpm average. On the day when I made it I wanted to quit after the first 5 minutes as I was unable to maintain the heart rate.
Definition: checks out.
Checklist: yes; true; if; time; example-matching-subject; unsure
if you look at the (early lives of Welches, Ogilvies, and Rockefellas,)[1] there was (no hint at the giant success that was about to come)[2] (since a young age).
Claims: If you look at [1], we see [2].
Example: Jack Welch did his masters and PhD in Chemical engineering in a top school by the age of 25 and started a job in GE in chemical development. Whereas he became the most influential business manager of his time.
Definition: checks out.
Checklist: yes; true.
IQ’s didn’t help predict if someone was going to be good or bad at betting on horses.
Claims: ^^
Example: In the study, a person with an IQ of 85, was able to pick out the top horse in 10/10 races. Whereas a person with IQ 118, picked up the top horse for 3/10 cases.
Definition: checks out
Checklist: yes; true.
Claims: If you toggle the input factor, you can see if it has any impact on the output.
Example: Blondlot claimed that if you have an aluminium prism and a treated thread of cadmium sulfide, you will get a faint glowing in the dark.
“One day Blondlot had given a demonstration of N-Rays. The lights had turned out, and his assistant had called off the brightening and darkening as Blondlot performed his manipulations.”
“It had been a normal demonstration, all the results going as expected. Even though an American scientist named Robert Wood had quietly stolen the aluminium prism from the center of Blondlot’s mechanism.”
This way we know the prism did jack shit to the output.
Definition: checks out.
Checklist: yes; true.
Claims: if you don’t toggle the input factor, you won’t know if it has any effect at all.
Example: Blondlot didn’t toggle the prism, and he didn’t know that it had no effect on his experiment or the production of N-rays.
Definition: checks out.
Checklist: yes; true.
Subject predicate split
It is B
“There was a little girl in California who was part of the second class to integrate her public schools and she was bused to school everyday, and that little girl was me,” she said. “So, I will tell you: on this subject, (it)[1] cannot be (an intellectual debate among Democrats)[2]. We have to take it seriously. We have to act swiftly.”
Claims: [1] cannot be [2].
Subject: Discussion amongst Democrats
Predicate: cannot be intellectual
Example: Very vague labels and hard to give examples for this
Definition: No idea how to test against the predicate, nor do I have an example.
Checklist: yes; neither; subject-predicate-split; time; failed
pattern: “It cannot be BC DEF”
https://en.wikipedia.org/wiki/2020_Democratic_Party_presidential_debates_and_forums
It is important to note (that previously DNC policy has been passed down orally, and only confirmed later by statements to the press, without any official ruling, as was done with the Bullock controversy above)[1]. — link
Claims: It is important to note [1].
Subject: The importance of noting [1].
Predicate: is important.
Checklist: -
An identical DNC approved poll conducted on the 1st of July was also located in the data, but it is unclear which category was used for (the qualification for the debates, as no candidate had 2% in one category and 1% in the other, although FiveThirtyEight claims the above DNC source told them the sample for the “debate qualification will be the adult sample”, and Politico used the “registered” column for their data compilation)[1]. — link
Claims: It is unclear which category was used for [1].
Subject: The clarity of which category was used for [1].
Predicate: is unclear.
Checklist: -
It is unclear (how long Mr Sanders will need to recover from his surgery)[1], and whether it will affect his appearance in the next Democratic debate on 15 October. — link
Claims: It is unclear how long [1].
Subject: The clarity of how long [1] is.
Predicate: is unclear.
Checklist: -
It is unclear why. Iowa hosts the first voting contest in the US presidential race. — link
ditto
It is being held in (Houston and will also be available on streaming services)[1].— link
Claims: It is being held in Houston.
Subject: Where the Democratic debate is being held
Predicate: in Houston.
Checklist: -
But it is exactly because (Castro is polling at 1 percent)[1] that these moments may work for him. What Castro really needs at this point is anything to stand out. — link
Claims: It is exactly because [1] that these moments (where he attacks Biden) may work for him.
Subject: The reason for his (Castro’s) attacks on Biden, working for him (Castro).
Predicate: [1].
Checklist: no; neither;
time
Pattern: because; It is X that Y.
adjective
A is important
Claims: I have important things to do in life (and hence cannot marry)
Look at the example I gave
Example: I have spent roughly 500 hrs over one year on Concrete thinking and still struggle with ten phrases an hour.
Having trouble with proving something is important
Source: https://www.givewell.org/shallow/climate-change/extreme-risks
This discussion has been limited and conceptual. Below, we review a few non-systematic examples of how (damages from worse-than-expected outcomes could play an important role in an overall assessment of the harms of climate change)[1].
Claims: [1]
Checklist: no; neither;
future-with-no-ex
(These examples)[1] are only a few of the many possible low-probability, high-impact results of climate change that may play (an important role in the overall harms of climate change)[2].
Claims: [1] may play [2].
Checklist: no; neither;
future-with-no-ex
“What concrete funding opportunities exist to limit extreme risks from climate change?” is an important question
Claims: ^^
Checklist: no; neither;
no-example; failed (not sure how the example will look)
Source: http://pradeep90.github.io/toggle-curious-factor.html
Loud sound effects were definitely important.
Claims: ^^
Example: When the sound for the horror movie was lowered as opposed to the “original” sound, the movie appeared to be much less scary.
Definition: checks out.
Checklist: yes; true;
Visuals were not as important as the sound
Claims: ^^
Example: When the sound was on but the visuals were not, the movie was still “pretty scary”. Where as watching the Visuals with muted sound was not at all scary.
Definition: checks out.
Checklist: yes; true.
Source: Talent is overrated
Claims: Talents are much less important than we usually think.
Example: Lazlo Polgar, married someone with the intention of making his kids prodigies in some sport. He later chose Chess as that would be easy to measure progress in. Judit polgar became grandmaster at 15. Susan Polgar became top ranked female player at the age of 15. She is also a woman grandmaster. The odds that he was able to make all his kids grandmasters is the evidence for the claim.
Definition: checks out.
Checklist: yes; true.
Claims: critical thinking is obviously important in the real world.
Checklist: yes; neither;
no-example; :(
Coaches advice are important
Example: I go often to shoot balls in the court and practice. This particular day, had been after a few weeks of 0 practice. I kept shooting and I got something like 20% shooting over maybe 100 shots. I should probably give up this sport. My friend (who is extremely good) saw me and said I was all over the place with my action, performing different motions for every single shot, suggested I shoot from the forehead in one motion and for the next 10 shots kept criticizing my every move.
Almost instantaneously, I started nailing every single shot. It didn’t take that much effort even to push the ball. Averaged 50%, which was pretty sick for 50 shots and my highest in a while considering my recent hiatus.
Definition: checks out.
Checklist: yes; true.
running to the point of exhaustion was centrally important activity (for Jerry Rice’s success.)
I could look at Jerry before joining NFL and Jerry after he won his first season and compare what was the difference, but I don’t have such data.
Checklist: yes; neither; no-example; (hard to give an example here due to lack of data)
What DP means is critically important
Example: Not able to get real-life examples for this.
Definition: -
Checklist: yes; neither; no-example;
Choosing the aspects of performance is itself an important skill
Subject: How important is the skill of choosing the aspects of performance.
Example: X chose ABC, and Y chose XYZ. Y is grandmaster, X is not. How will I even get such examples. :(
Somehow, how the example could look became clearer when I wrote out the subject
Definition: -
Checklist: yes; neither no-example; time;
High repetition is the most important difference between deliberate practice of a task and performing the task for real, when it counts.
I do not think I can find an example for the above claim mainly because of “most”. How do I give an example that out of all the factors, “repetition” is the most important.
If I think of the basketball example I gave earlier, “feedback” was the most important rather than practice at that point.
Checklist: yes; neither; no-example
Most important thing you can do to improve performance is not fun
Claims: ^^
Subject: Most important thing you can do to improve your performance
Predicate: is no fun
Example: As a highest level figure skater, Shizuka Arakawa trained on the jumps she couldn’t do (most important). Arakawa’s road to the gold medal involved at least twenty thousand derriere impacts on an unforgiving surface.
Definition: It cannot be fun to fall on ice on your ass 20000 times.
Checklist: yes; true;
Another important variable is how much effort a person puts into it (practice)
Claims: ^^
Subject: The effort put by person.
Predicate: is an important variable.
Example: A study of singers found that when amateurs took a voice lesson, they experienced it as an enjoyable release of tension, but when professionals took a lesson, they experienced it as an intense, difficult effort.
Definition: checks out
Checklist: yes; true;
Comparing hours of practice by large numbers of people reveals important trends
Example: A study of violinists found that by the top violinists had 7410 hours of lifetime practice whereas the “ok” students at the institute clocked 3420 hours.
I concede that I have not dealt with the large numbers part but I assume the study took sufficiently large numbers
Definition: Checks out. These trends seem to inform us that more practice leads to greatness.
Checklist: yes; true;
Indeed, (the most important effect of practice in great performers)[1] is that it takes them beyond—or, more precisely, around—(the limitations that most of us think of as critical)[2].
Claims: [1] is that it takes them beyond [2].
Checklist: yes; neither; no-example; definition-unclear;
Reaction time doesn’t play an important role, but the stakes can be extremely high (regarding reading X-rays).
Claims: Reaction time doesn’t play an important role in reading X-rays
Example: A radiologist roughly takes 5 seconds to decide if a chest X-ray is normal. If he takes 1 second more or 10s more, the consequence is not much other than it adds cost to the hospital.
Where as a tennis player needs to be able to react to the ball in 0.47s otherwise he loses the game.
Definition: checks out.
Checklist: yes; true.
Excellent performers in other fields have learned to spot non-obvious information that’s important.
Claims: ^^
Subject: What excellent performers have learned to spot
Predicate: non-obvious information that’s important
Example: Top Tennis players look at a servers body and not at the tennis ball to understand where the ball is expected.
Definition: checks out.
Checklist: yes; true;
the most important ingredient in any expert system is knowledge
Claims: One of the most important ingredients in any expert system is knowledge.
Example: Despite having state of the art computer systems and programmers and programs rich in general inference methods, the AlphaGo still seems to need millions of games to train on before it defeated the GO champion 4-1.
Definition: checks out.
Checklist: yes; true.
An important part of prework self-regulation centers on attitudes and beliefs.
Claims: ^^
Subject: What an important part of prework self-regulation centers on
Predicate: attitudes and beliefs
Example: “Figuring out specific goals and plans for what you’ll be doing every day sounds hard and requires high motivation”
This is the best example I see from the book.
Definition: checks out!
Checklist: yes; true; example-matching-subject; unsure
(The most important self-regulatory skill that top performers use during their work)[1] is self-observation.
Claims: ^^
Subject: [1]
Predicate: is self-observation
Example:
I need more data for MOST. So I skip it assuming I am not going to be able to find an example here.
“Elite runners, by contrast, focus intensely on themselves; among other things, they count their breaths and simultaneously count their strides in order to maintain certain ratios.”
Definition: checks out!
Checklist: yes; true; subject-predicate-split; time
Statistics
Total claims: 103
Total time: 22hrs (6 days)
Round 1: 18 hrs
Round 2: 2 hrs
Round 3: 2 hrs
general clean up:
time/day:3.6hrs per day
time/claim: 13.2 mins per claim (avg over 22 hrs)
P.S Will add the “score of this session” later.