trentmkelly/LessWrong-43k huggingface dataset:
Trust-Building: The New Rationality Project
by DirectedEvolution — 2020-05-28T22:53:36.876Z

“I did not write half of what I saw, for I knew I would not be believed"

-Marco Polo

It's enlighteningly disturbing to see specifically how the "distrust those who disagree" heuristic descends into the madness of factions.

-Zack M Davis

Old LessWrong: we fail to reach the truth because of our cognitive biases.

New LessWrong: we fail to reach the truth because of reasonable mistrust.

What causes mistrust? It's a combination of things: miscommunication and mistakes; the presence of exploitative actors; lack of repeated trust-building interactions with the same people; and high payoffs for epistemically unvirtuous behavior. Enough mistrust can destroy our ability to converge on the truth.

Once mistrust has led us into factionalism, can we escape it?

Factionalism is not a bad thing. It means that we are willing to accept some evidence from other people, as long as it's not too divergent from our priors. Factions are worse than unity, but better than isolation.

What we want isn't a lack of factionalism, it's unity.

This suggests an activism strategy. Let's form three categories of factions:

Your Community: You have high trust in this network, and believe the evidence you receive from it by default. Although you don't trust everyone who calls themselves a part of this network or community, you know who belongs and who doesn't, who's reliable and who's not, and how to tell the difference when you meet someone new in the network.
Strangers: This network is untested, and your community doesn't have a strong opinion on it. It would take substantial work to learn how to navigate this foreign network. But because they are relatively isolated from your own community, they have evolved a different constellation of evidence. The existence of factionalism is itself evidence that you'd have something new to gain by trading information with them.
Enemies: This network has been examined, either by you or by your community, and labeled a toxic breeding ground of misinformation. Treating your enemies as though they were merely strangers would only alienate you from your own community. They might be exploitative or stupid, but either way, engaging with them can only make things worse. All there is to do is fight, deprogram, or ignore this lot.

To increase unity and pursue the truth, your goal is to find foreign c...
The principle of no non-Apologies
by agentydragon — 2020-05-28T19:58:44.765Z

Original on my website

TL;DR: Principle of no non-Apologies: “Distinguish between saying I’m sorry and apologizing. Don’t give non-Apologies.” Do not Apologize when you don’t agree that you fucked up. When you fucked up, own the fuck-up and, if it’s systematic, commit to reducing future fuck-ups.

Everyday “I’m sorry” is usually not an Apology

“I’m sorry” can be used in several ways.

One way is using it as a conciliatory gesture, basically saying “you’re stronger than me, I submit, please don’t hurt me”. It’s one possible way I might react when under threat by someone stronger making demands I don’t agree with.

Another way is to say “this was accidental, I didn’t intend to hurt you”, like when you bump into someone when boarding your tram.

But when you use the words that way, you are not making an Apology. And it’s useful to distinguish between these uses of “I’m sorry” andactual Apologies.

Apologies and non-Apologies

Courtesy of an unknown source that I can’t immediately recall, you are Apologizing when you:

Communicate understanding that you behaved badly (and own responsibility for it),
try to fix the negative consequences of that behavior, and
commit to work on not acting similarly in the future.

An Apology which holds to this definition makes you vulnerable (because you are open about the weakness that caused the behavior), and it’s not to be made lightly, because of the commitment. It is also virtuous to own your mistakes or systematic problems, and to work on them.

On the other hand, if you use the ritual apologetic words but do not meet these criteria, let’s call that a non-Apology.

A prototypical example is “I’m sorry you feel that way”, which happens when a sociopath in charge is forced by overwhelming force to “Apologize”.

“I’m sorry” that you tell your boss just to make them stop grilling you is also, under my use of the word, a non-Apology.

So is, in many (but not all) cases, a “sorry I’m late” I might say when coming to a meeting. Also the “bump into someone on the tram” example, and the “I yield I’ll do what you demand” example.

(So, notice that I’m no...
looking for name/further reading on fallacy I came across
by Yitz — 2020-05-28T18:01:34.692Z

I’ve recently been reading Rabbi Dr. Gottlieb’s book “Reason To Believe,” which promises to give “sufficient evidence” that Judaism is true (and in my personal opinion, has failed rather spectacularly as of what I’ve read so far). One of the central arguments given is that Judaism contains a legend that could not have been falsified (and if that legend is true, the rest must follow). The reason for that is because the legend is supposedly one-of-a-kind, (specifically “a national event of a type that would be hard to forget”—I believe that any proposed counter-examples given would be dismissed as not having enough witnesses present, while this case supposedly had 600,000 witnesses), and that the problem for the critic is that there are no other known cases anything like it (supposedly) to draw evidence from. I don’t even know where to start with this fallacy, I don’t even know what to call it by, and would appreciate it if anyone could recommend further reading on what exactly is going on here, logically.
Wrist Issues
by jefftk — 2020-05-28T16:50:03.189Z

I've been having trouble with my wrists on and off for years. The
first time I remember, I think I was in seventh grade and I'd learned
how to play a blues riff on the guitar that involved stretching widely
while bending sharply at the wrist. It suddenly started hurting, and
I needed to stop. This started a familiar pattern, where something
would make my wrists hurt and I would learn not to do the thing that
made them hurt.

Mostly this has been fine. I haven't had to give up things that are
especially important to me, and I've been able to figure out how to do
all the things I need to do. It did get pretty bad for me about two years ago, where I wasn't able to
figure out any sort of mouse I could use for a while. Through all of
this, though, I've been able to type, as long as I keep good posture
with my wrists.

A week ago this changed. I'd been doing a lot of typing, and my wrists started
hurting even when I typed. It didn't feel better in the morning and I
decided I was going to try and spend the long weekend without typing
at all or swiping on my phone. I put my computer away, set up voice
control on my phone, and tried to give my wrists as much of a rest as
possible.

Unfortunately, even after the whole weekend of resting, my wrists were
hurting within about an hour of starting work on Tuesday
morning. I set up voice control on my computer, but by this
point I had been using my voice enough ("scroll down", "scroll down")
that I was starting to hurt it as well, and I realized I needed to be
careful with my voice too.

Normally when things happen in my life I tend to write blog posts
right away, but I couldn't do that in this case since it was my
ability to write that was affected.

My wrists might be doing okay enough that as long as I type
primarily by dictation I can still use my wrists for
navigation. Navigating entirely verbally is a lot slower.

One thing I found with dictation is that different systems vary
enormously and how well they interpret speech. So far I've tried voice
typing on my Pixel 3A, voice access on my Pixel, Mac OS's dictation,
Mac OS's voice control, and Google Docs dictation. Of these, the
Pixel's voice typing is by far the best. It runs locally on the
device, is highly responsive, and it makes far fewer errors than the
others. I'm dictating this blog post to my phone in Keep because
weirdly enough that's the b...
Speculations on the Future of Fiction Writing
by johnswentworth — 2020-05-28T16:34:45.599Z

If we look at major movies, writing is generally a relatively small chunk of the overall budget. A handful of examples from

Wikipedia

:

Unbreakable: story rights + screenplay ~8.1% of budget
Tomb Raider - Cradle of Life: story rights + screenplay ~3.4%
Terminator 3: screenplay ~2.8%
Spider-Man 2 - 2004 version: screenplay ~5%

Eyeballing these numbers, I’d guess that if you could consistently get a 10% better movie by spending twice as much on the writing, that would be a great deal.

Why aren’t studios already spending twice as much for 10% better movies? It doesn’t seem like it ought to be that hard; it’s not like the tropes on the

bad writing index

are hurting for examples. I’d guess that this is mainly a case of the

difficulty of hiring experts

: it’s hard to hire people with better taste than whoever’s in charge.

On the other side of the equation,

writing techniques for intelligent characters

are proving not just

popular

, but robustly popular. Scroll down the list at

topwebfiction.com

, and you’ll see an awful lot of consistent intelligence. The internet’s top writing doesn’t rely on artificial stupidity.

On top of that, the techniques for writing intelligent characters (at least

level 1 intelligent

) are largely agnostic to setting and plot. It’s largely about sprinkling in plausible in-universe reasons why characters don’t just do the obvious thing. Such techniques are well-suited to fanfiction for exactly that reason: they can be stitched in after-the-fact without completely throwing out the whole story.

Put these two pieces together. On one side, we have movie studios (and video game studies, and…) who’d love to throw more money at writing in order to make it better, but don’t have consistent formulas for how to do that. On the other side, we have a handful of setting/plot-agnostic techniques for writing intelligent characters, and such writing is already proving very popular online.

Sounds like there’s probably a market for intelligentification of characters.

At its simplest, this would be a service which takes in stories - screenplay, storyboard script, traditional books, what have you - and performs minimally-invasive surgery to make the characters not-stupid. It would involve tweaking background details of the universe to remove obvious exploits, or adding in-universe problem constraints to drive plot-relevant decisions rather than somebody holding the

idiot bal

...
Updated Hierarchy of Disagreement
by B Jacobs — 2020-05-28T15:57:57.570Z

In 2008 Paul Graham created the Hierarchy of Disagreement.

Then Loudacris had the genius idea to make this hierarchy into an easily shareable picture:

And thus internet debate was rational forever.

I joke, but I do genuinely believe that this image helped the quality of internet-debate in some small way. Even if not many people see it, making these kinds of guides is a net benefit for humanity.

It also made the rounds on LessWrong. Scott Alexander created a related guide and Lukeprog used the critiques of Black Belt Bayesian to expand the hierarchy.

Black Belt Bayesian writes:

If you’re interested in being on the right side of disputes, you will refute your opponents' arguments. But if you're interested in producing truth, you will fix your opponents' arguments for them. To win, you must fight not only the creature you encounter; you [also] must fight the most horrible thing that can be constructed from its corpse.

Which Lukeprog argues should be the top layer called: "Improve the Argument, then Refute Its Central Point"

I don't know about you, but to me this new top layer feels separate from the other layers. This top layer, for one, should probably only be used after you already refuted the existing argument. You don't want your interlocutor to feel like you are either misrepresenting or humiliating him. Improving an argument is still desirable, but don't sour the debate.

But secondly, this layer goes beyond countering your interlocutor and ascends into the realm of active truth seeking. Whereas the other layers are a linear process of knocking down an argument, this new layer is more circular. You can continue on creating and knocking down new versions of the old argument again and again, always gaining new insights but never reaching a perfect conclusion.

Nonetheless, I think this addition is extremely important. It shows people that the purpose of debate is not an adversarial brawl, but ultimately a way to reach truth. The hope is this layer will change peoples mindset into a more curious and cooperative one.

Unfortunately no one has made an easily shareable visualization of this new hierarchy. So I decided to make one myself with two additional updates.

1) I hate it when there is a guide on the internet and I click on it to copy the text, only to realize it is a picture and I have to manually copy all the information ...
One systemic failure in particular
by MoritzG — 2020-05-28T05:25:58.335Z

I think there are areas of major systematic failure:

Human Resources (including Bull-Shit-Jobs as defined by David Graeber)

Education [A], [B], [C]

Population control

Market failure: military Industrial complex [D], Medical sector pricing and treatment selection

Democracy and Voting

Financial sector

Inter-/Super-national regulations and taxation (Heavens for tax-evasion, money-laundering, pollution, work-safety)

Inheritance and long term resource distribution both of production and natural

Media/Press [E], [F]

Next is a List of areas that are not made up (imaginary) and filled with Quacks, Con-men, Oracles, Fortunetellers and False-Experts (as defined by N.N. Taleb):

Nutrition & "Health/fitness"

Economics

Medicine

Consulting and Training

HR

A correlation with the list of areas of human societies biggest failures is evident.

Why are there such massive systemic failures? That is a massive generic question that this thread will not be about or able to answer.

In short mentioning some reasons: People are lazy thinkers and see the world form their perspective instead of a global/birds view. Because it is hard to impossible. Because it is done unscientifically. Because costs can be externalized and companies have become more than just used to it, they have come to expect others to pay. Because the personal is unqualified, because there is no one answer but people “refuse” to accept it, because there is no convenient solution, there are bad incentives, because of poor intuition/bias, because innovation and especially social change is hard, because of ideology, deep seated and institutional historical misconceptions, All the usual stuff: fallacies, biases, bad incentive structure, incomplete information.

HR is important because it is a consequence, a cause, and a solution. Change has positive feedback. We know that good people attract more good people and bad people draw in bad people.

The big issue of un(der)employment is in part because of failures in HR. I am certain that plenty very capable and trustworthy people are underemployed.

I find the issue of HR similar to population control in that it is not talked about even though it is very consequential.

I chose HR as an example because I think it has the highest product of consequence and neglect and it is personal to all of us who have to play along every few years. It is not rational to fail so widely at a

...
Shuttling between science and invention
by jasoncrawford — 2020-05-27T21:25:23.037Z
Link: https://rootsofprogress.org/transistors-science-and-invention

“The Link Between Science and Invention: The Case of the Transistor,” a 1962 paper by Richard Nelson at the RAND Corporation, provides a fascinating case study in invention at the frontiers of science, and the relation between the two. In it, we can see that research—and researchers—do not always fall neatly into categories like “basic” versus “applied,” nor is there a strict linear progression from one to the other.

First, some background. Engineers design electronic devices such as clocks, microwaves or computers by constructing electrical circuits out of a toolbox of basic components. One such component is the amplifier. An amplifier, as the name implies, produces an electrical output signal that is proportional to its input signal, but stronger.

Amplifiers are crucial for a number of applications. Long-distance communication lines depend on them, because when a signal travels along a very long wire it spreads out and gets “blurry”. Long-distance telegraph initially solved this problem through electromechanical “repeaters”, but long-distance telephone was effectively impossible. An amplifier can be used to build an electronic repeater that creates a fresh, sharp copy of the signal, say, every hundred miles. Amplifiers were also needed for radio broadcasting, to boost the small signal from a microphone into a large signal coming out of a radio tower. And in their “binary” form of an on/off switch, amplifiers are the basis of computers, enabling circuits that perform logical and mathematical operations on ones and zeros.

The first amplifier was based on the vacuum tube triode, invented by Lee de Forest in 1906. A glass tube was created with three electrodes: one connected to a filament, one to a plate, and one to a metal grid in between them. The tube was pumped empty. Electrons could jump through the vacuum from the filament to the plate, but a negative charge on the grid would stop them. The vacuum tube amplifier enabled cross-country telephone service in the 1910s, the radio industry in the 1920s, and the computer in the 1940s.

Vacuum tubes, however, were relatively large and ran hot. They are the reason that radios were once the size of cabinets, and that the first computer, made from some 18,000 tubes, took up an entire room. It was clear that a smaller, light...
A taxonomy of Cruxes
by Eli Tyre — 2020-05-27T17:25:01.011Z

[Crossposted to Musings and Rough Drafts]

This is a quick theoretical post. I have little idea if this is interesting to others. Plus this is super finicky to read, but I don't know what to do about that.

In this post, I want to outline a few distinctions between different kinds of cruxes. Sometimes folks will find what seems to be a crux, but they feel some confusion, because it seems like it doesn’t fit the pattern that they’re familiar with, or it seems off somehow. Often this is because they’re familiar with one half of a dichotomy, but not the other.

Conjunctive, unitary, and disjunctive cruxes

As the Double Crux method is typically presented, double cruxes are described as single propositions, about which, if you changed your mind, you would change your mind about another belief.

But as people often ask,

What if there are two propositions, B and C, and I wouldn’t change my mind about A, if I just changed my mind about B or if I just changed my mind about C? I would my mind about A, only if I shift on both B and C.

This is totally fine. In this situation would would just say that your crux for A is a conjunctive crux of B and C.

In fact, this is pretty common, because people often have more than one concern in any given situation.

Some examples:

Someone is thinking about quitting their job to start a business, but they will only pull the trigger if a) they thought that their new work would actually be more fulfilling for them, and b) they know that their family won’t suffer financial hardship.
A person is not interested in signing up for cryonics, but offers that they would if a) it was inexpensive (on the order of $50 a month and b) if the people associated with cryonics were the sort of people that he wanted to be identified with. [These are the stated cruxes of a real person that I had this discussion with.]
A person would go vegetarian if, a) they were sure it was healthy for them and b) doing so would actually reduce animal suffering (or "how elastic is the supply curve for meat?").

In each of these cases there are multiple considerations, none of which is sufficient to cause one to change one’s mind, but which together represent a crux.

As I said, conjunctive cruxes are common, I will say that sometimes folks are too fast to assert that they would only change their mind if they turned ou...
[AN #101]: Why we should rigorously measure and forecast AI progress
by Rohin Shah — 2020-05-27T17:20:02.460Z

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

Danny Hernandez on forecasting and the drivers of AI progress

(Arden Koehler and Danny Hernandez) (summarized by Rohin): This podcast is a great introduction to the practice of forecasting and measurement in AI, and why it is important. I won't summarize everything in the podcast, but here are some of the points made.

Danny talks about the AI and Compute (AN #7) and AI and Efficiency (AN #99) work that he did at OpenAI. The former shows that the compute devoted to the largest-scale experiments has increased by a factor of 300,000 from 2012 to 2018, and the latter suggests that algorithms have been able to achieve similar performance with 25x less compute over the same time period (later updated to 44x from 2012 to 2019).

One thing I didn’t realize earlier was that the 25x / 44x factor should be thought of as a loose lower bound: in other areas such as language modeling, the factor looks higher. But more importantly, the methodology used doesn’t allow us to model the effects of an algorithm allowing us to do something we couldn’t do before (which we could interpret as something we could do, but with way more compute). Possibly this algorithmic progress should be thought of as a 100x or even 1000x improvement in efficiency. Overall, Danny sees both algorithmic progress and increase in compute as pretty big factors in predicting how AI will go in the future.

Unfortunately, it’s hard to draw strong implications from these measurements for the downstream things we care about -- should we think that AI progress is “slow” or “fast”, or “linear” or “exponential”, based on these results? It’s important to be specific about the units you’re using when thinking about such a question. Danny thinks the economic impact of AI is an important lens here. It seems to him that neural nets were having very little impact back in (say) 2008, but since then they have been having a lot more impact, e.g. by making ~15% of Google searches better (by using a new language mod...