trentmkelly/LessWrong-43k huggingface dataset:
Do Women Like Assholes?
by Jacob Falkovich — 2020-06-22T02:14:43.503Z

Cross-posted from Putanumonit where a lively discussion is already going.

Thank you to Ben Pace for helping migrate this post to LessWrong.

Insofar as Putanumonit promotes a normative stance, it boils down to the following:

Be savvy about statistics and research

Be nice and cooperative

Saying “sex is cool, but have you considered…” is cool, but

have you considered having sex

?

These are not unrelated.

Being savvy

with math

can help

your romantic life. Being

nice and cooperative

can really help your romantic life. At least, that’s what I believe based on my experience.

Some readers enjoy my posts and profitably employ my philosophy and then write me lovely messages about it. They are the reason why I get up in the morning and put so much time and effort into this blog.

But some readers don’t enjoy my posts. They tell me that I’m a fool or a liar, that women date jerks and disdain nice guys, that

the gender wars are real

and must be fought ruthlessly, that all this talk about win-win romance and compatible goals is a blue pill conspiracy to oppress men.

I’ve been mostly ignoring and mocking these latter readers. But recently, they started posting links to research papers supposedly proving their point. And so, in the name of stance #1, I got up in the morning and put way too much time and effort into my own research project to investigate: do assholes really do better romantically, or is there hope for men and women to get along after all?

Literature Review

The literature sucks. That’s it, that’s the review.

Granted, this question is hard to measure empirically. It’s hard to define who is an asshole, let alone to identify them, let alone to measure how well they do with women in the long run — I struggled with those issues in my own research. But that’s far from the only problem.

The study I was sent most often is

The Dark Triad personality: Attractiveness to women

by Carter et al (2013). The dark triad is the combination of narcissism (entitlement, grandiose self-image), psychopathy (callousness, lack of empathy), and Machiavellianism (insincere manipulation). The attractiveness was measured by asking women in an online questionnaire to read descriptions of men and say how attractive they find them. The women are 128 college undergrads in psychology.

The result of the study was a positive but statistically insignificant boost to the attractiveness of the dark triad des

...
Plausible cases for HRAD work, and locating the crux in the "realism about rationality" debate
by riceissa — 2020-06-22T01:10:23.757Z

This post is my attempt to summarize and distill the major public debates about MIRI's highly reliable agent designs (HRAD) work (which includes work on decision theory), including the discussions in Realism about rationality and Daniel Dewey's My current thoughts on MIRI's "highly reliable agent design" work. Part of the difficulty with discussing the value of HRAD work is that it's not even clear what the disagreement is about, so my summary takes the form of multiple possible "worlds" we might be in; each world consists of a positive case for doing HRAD work, along with the potential objections to that case, which results in one or more cruxes.

I will talk about "being in a world" throughout this post. What I mean by this is the following: If we are "in world X", that means that the case for HRAD work outlined in world X is the one that most resonates with MIRI people as their motivation for doing HRAD work; and that when people disagree about the value of HRAD work, this is what the disagreement is about. When I say that "I think we are in this world", I don't mean that I agree with this case for HRAD work; it just means that this is what I think MIRI people think.

In this post, the pro-HRAD stance is something like "HRAD work is the most important kind of technical research in AI alignment; it is the overwhelming priority and we're pretty much screwed if we under-invest in this kind of research" and the anti-HRAD stance is something like "HRAD work seems significantly less promising than other technical AI alignment agendas, such as the approaches to directly align machine learning systems (e.g. iterated amplification)". There is a much weaker pro-HRAD stance, which is something like "HRAD work is interesting and doing more of it adds value, but it's not necessarily the most important kind of technical AI alignment research to be working on"; this post is not about this weaker stance.

Clarifying some terms

Before describing the various worlds, I want to present some distinctions that have come up in discussions about HRAD, which will be relevant when distinguishing between the worlds.

Levels of abstraction vs levels of indirection

The idea of levels of abstraction was introduced in the context of debate about HRAD work by Rohin Shah, and is described in this comment (start from "When groups of humans try to build complicated stuff"). For more background,

...
Training our humans on the wrong dataset
by George3d6 — 2020-06-21T17:17:07.267Z
Link: https://blog.cerebralab.com/Training_our_humans_on_the_wrong_dataset

I really don't want to say that I've figured out the majority of what's wrong with modern education and how to fix it, BUT

1. We train ML models on the tasks they are meant to solve

When we train (fit) any given ML model for a specific problem, on which we have a training dataset, there are several ways we go about it, but all of them involve using that dataset.

Say we’re training a model that takes a 2d image of some glassware and turn it into a 3d rendering. We have images of 2000 glasses from different angles and in different lighting conditions and an associated 3d model.

How do we go about training the model? Well, arguable, we could start small then feed the whole dataset, we could use different sizes for test/train/validation, we could use cv to determine the overall accuracy of our method or decide it would take to long... etc

But I'm fairly sure that nobody will ever say:

I know, let's take a dataset of 2d images of cars and their 3d rendering and train the model on that first.

If you already have a trained model that does some other 2d image processing or predicts 3d structure from 2d images, you might try doing some weight transfer or using part of the model as a backbone. But that's just because the hard part, the training, is already done.

To have a very charitable example, maybe our 3d rendering is not accurate enough and we've tried everything but getting more data is too expensive. At that point, we could decide to bring in other 2d to 3d datasets and also train the model on that and hope there's enough similarity between the two datasets that the model will get better at the glassware task.

One way or another, we'd try to use the most relevant dataset first.

2. We don't do this with humans

I'm certain some % of the people studying how to implement basic building blocks (e.g. allocators, decision trees, and vectors) in C during a 4-year CS degree end up becoming language designers or kernel developers and are glad they took the time to learn those things.

But the vast majority of CS students go on to become frontend developer or full-stack developers where all the "backend" knowledge they require is how to write SQL and how to read/write from file and high-level abstractions over TCP or UDP sockets.

At which point I ask something like:

Well, why not just teach tho

...
The Bright Side of rationalization
by Alexey Smirnov — 2020-06-21T16:30:58.466Z

Usually, people talk about rationalization ("the act, process, or result of rationalizing : a way of describing, interpreting, or explaining something (such as bad behavior) that makes it seem proper, more attractive, etc" https://www.merriam-webster.com/dictionary/rationalization) in a negative context: a person does something bad, or at least does it out of reprehensible motivations, but then rationalizes his act, bringing worthy motives under it.

But recently I have come across the rationalization of good behavior, through selfish motives. In other words, a person is not free to do good things, and they need a selfish motivation to look practical in their own eyes. For example, a person donates clothes and other things to an orphanage and says that they do it for their own sake, because they have a system of biological encouragement for socially positive actions. But he innocently forgets that it was not in his will that such a system of unconditional encouragement was laid down in him by evolution. Or a person becomes a vegetarian, not for ethical reasons, but because it is "good for their personal health".

That is, some inherently good people are so dependent on a certain layer of culture that forbids them to do altruistic actions of their own free will that they are forced to rationalize good actions by saying that these actions are in some way beneficial to them personally.
Are Humans Fundamentally Good?
by Nyarlathotep — 2020-06-21T16:29:56.976Z

I've been doing some reading on political philosophy recently, with a focus on the works that influenced the founding fathers of the United States, and one of the ideas that comes up a lot is the idea of the social contract. One of the assumptions that drives this theory is that humans are not fundamentally good.

Are humans not fundamentally good?

In real live examples of anarchy, does society devolve because humans are not fundamentally good or because of some other reason?

I am an absolute newcomer to political philosophy so forgive me if I am misrepresenting social contract theory.
My weekly review habit
by benkuhn — 2020-06-21T14:40:02.607Z

Every Saturday morning, I take 3-4 hours to think about how my week went and how I’ll make the next one better.

The format has changed over time, but for example, here’s some of what I reflected on last week:

I noticed I’d fallen far short of my goal for written output. I decided to allocate more time to reading this week, hoping that it would generate more ideas. And I reorganized my morning routine to make it easier to start writing in the morning.

I looked at some stats from RescueTime and Complice about what I’d spent time on and accomplished. I noticed that my time spent on Slack was nearing dangerous levels, so I decided to make a couple experimental tweaks to get it down:

I tried out Contexts, a replacement for the macOS window switcher, which I configured to only show windows from my current workspace—hoping that this would prevent me from cmd+tabbing over to Slack and getting distracted.

I decided to run an experiment of not answering immediately when coworkers called me in the middle of a focused block of time, and keeping a paper “todo when done focusing” list to remind myself to call them back, check Slack, etc.

I noticed that it felt hard for me to get useful info from the time-tracking data in RescueTime and Complice, so I revisited what questions I actually wanted to answer and how I could make them easy to answer.

I realized that I should be using Google Calendar, not RescueTime or Complice, to track my time spent in meetings, so I added that to my time-tracking data sources.

I also made several tweaks to the way I used Complice to make it easier to see various stats I was interested in.

And so on. By the end of the review I had surfaced lots of other improvements for the coming week.

While each individual tweak is small, over the weeks and years they’ve compounded to make me a lot more effective. Because of that, this weekly review is the most useful habit (or habit-generating meta-habit) I’ve built. Here are some of the improvements I’ve made that have come out of weekly reviews:

I decided to experiment with time tracking, realized that it needed to be zero-effort to succeed, and identified RescueTime as the best option, giving me much better data on how I was actually spending my time.

Once I started using RescueTime, I eliminated a bunch of distractions that it flagged. As a result I improved my focused time by 50%

...
The affect heuristic and studying autocracies
by Tim Liptrot — 2020-06-21T04:07:21.061Z

General Juan Velasco Alvarado was the military dictator of Peru from 1968 to 1975. In 1964-5 he put down revolutionary peasant guerilla movements, defending an unequal and brutally exploitative pattern of land ownership. Afterward he became frustrated with the bickering and gridlock of Peru’s parliament. With a small cadre of military coconspirators, he planned a coup d’état. Forestalling an uprising by pro-peasant parties, he sent tanks to kidnap the democratically elected president. The parliament was closed indefinitely. On the one year anniversary of his coup, Velasco stated “Some people expected very different things and were confident, as had been the custom, that we came to power for the sole purpose of calling elections and returning to them all their privileges. The people who thought that way were and are mistaken”.[1]

What would you expect Velasco’s policy toward land ownership and peasants to be? You would probably expect him to continue their exploitation by the oligarchy land-owning families. But you would be mistaken. Velasco and his successor redistributed 45% of all arable land in Peru to peasant lead communes, which were later broken up. Land redistribution is a rare spot of consensus in development economics, improving both the lives of the poor and increasing growth. [2]

I told you this story to highlight how your attitudes toward the actor affect your predictions. It is justifiable to dislike Velasco for his violence, for ending Peruvian democracy, for his state-controlled economy. But our brains predict off of those value judgements. The affect heuristic (aka the halo/horn effect) is when one positive/negative attribute of an actor causes people to assume positive/negative attributes in another area. The affect heuristic causes attractive candidates to be hired more often, or honest people to be rated as more intelligent. Subjects told about the benefit of nuclear power are likely to rate it as having fewer risks, et cetera. Our moral attitudes toward the coup are not evidence for Velasco’s policy preference, but our brains treat them as evidence.[3] That is bad if you think predicting what policies autocrats will adopt is important. Which I do.

The same problem applies to agents you like. One of my research projects involved interviewing Jordanian policymakers. I studied policies for rural-ur...
How to make a predicted AI alignment event/meeting calendar
by rmoehn — 2020-06-20T22:43:43.901Z

This is the process I used for maintaining the Predicted AI alignment event/meeting calendar. I shared it with Linda Linsefors, since she wanted to create an AI Safety Google Calendar. And I think it might be useful for others, too, so I'm sharing it here.

I did the following every month for my calendar in list format. You'll
need to adapt the process if you put the events in a Google Calendar.

Check the websites of the entries on the calendar.

If something has changed, update the entry accordingly.

If the event has passed, add its website to the List of Things to
Be Checked Again. Also, if it's a repeating event, project when it
will happen next and add an entry for that to the calendar. In my
LessWrong calendar, you can see this for the MIRI Summer Fellows
Program, for example.

Check the websites on the List of Things to Be Checked Again and add
entries to the calendar if necessary. Here is my current list:

https://www.lesswrong.com/upcomingEvents

https://intelligence.org/workshops/

https://intelligence.org/ai-risk-for-computer-scientists/

MIRI Summer Fellows Program (No constant website. Not happening
this year, anyway?)

https://safeai.webs.upv.es/

https://www.ai-safety.org/

http://dataia.eu/evenements/workshop-dataia-safety-ai

https://iclr.cc/Conferences/2020/Dates (Need to change to 2021.
Workshops might be announced in November.)

https://sites.google.com/view/safeml-iclr2019/home (Getting old.)

https://www.lesswrong.com/users/david_kristoffersson

https://www.lesswrong.com/users/linda-linsefors

Announce the update on my LessWrong shortform. (Apparently, this
wasn't very effective, given that nobody asked me to continue
maintaining the calendar. (Now someone did!) While I was working on AI safety, I found
it useful just for myself, though.)

Anytime I noticed upcoming events on AI Safety-related newsletters etc.,
I also added them to the calendar.
Insights Over Frameworks
by  — 2020-06-20T19:52:18.602Z

Once someone has made several insightful self-help observations, I think it's often tempting to then go and find some sort of cohering framework which can explain all of those observations. In this essay, I'll argue for why I think it can be better to skip the post-hoc theorizing altogether and just present the insights in a modular way.

First, let me be clear about definitions for the terms I'm using.

By "insight", I mean some sort of observation about how the world works. For example, someone trying to figure out how to do better in social situations might realize that finishing their thoughts before speaking aloud helps them be more eloquent. Of course this doesn't mean that insights are free from an underlying framework, but no attempt is made to bring the assumptions and models into the foreground. The focus is on some small mechanism or model.

By "framework", I mean some sort of set of beliefs that form an overarching model about how the world works. For example, some people have tried to model all of social interactions as an intelligent social web. Insights about social interactions are then contextualized under this framework. Perhaps, under this framework, finishing your thoughts help with eloquence because they give off the impression that you are thoughtful, which coheres with the framework's claims about interactions being similar to a theatrical performance. The focus is on the framework, and the insights are corollaries.

As I mentioned above, I think people like coming up with frameworks. I think people like having neat explanations that can explain all of the neat things they've been observing. I am also guilty of this. There is something satisfying about finding some crystallization of all your past thoughts, like putting them through a strainer.

However, recently, I've bowed to pragmatism, and I think that frameworks are not as useful as I'd previously thought.

Firstly, there are too many frameworks. Most pieces of self-help content do not just promise a few (potentially) helpful observations, but rather an entire system. Sure, the system might have some good parts, but it's difficult to evaluate an entire framework without putting in the mental effort to really inhabit it for a while. And this is costly; you can't expect to do this each time a new self-help guru comes out with another new analogy or metaphor for the world.

Secondly, frameworks ar

...
When is it Wrong to Click on a Cow?
by  — 2020-06-20T18:23:07.420Z

[Speculative philosophy. Originally on Grand, Unified, Crazy.]

Three Stories

Imagine, for a moment, three young adults recently embarked on the same promising career path. The first comes home from work each day, and spends their evenings practising and playing a musical instrument. The second comes home from work each day, and spends their evenings practising and playing a video game. The third comes home from work each day, and spends their evenings hooked up to a machine which directly stimulates the pleasure and reward centres of their brain.

How do these people make you feel?

For some people with more libertarian, utilitarian, or hedonistic perspectives, all three people are equally positive. They harm no-one, and are spending their time on activities they enjoy and freely chose. We can ask nothing more of them.

And yet this perspective does not line up with my intuitions. For me, and I suspect for many people, the musician’s choice of hobby is laudable, the gamer’s is relatively neutral, and the “stimmer”‘s (the person with the brain-stimulating machine) is distinctly repugnant in a way that feels vaguely ethics-related. It may be difficult to actually draw that repugnance out in clear moral language – after all, no-one is being harmed – but still… they’re not the kind of person you’d want your children to marry.

The Good and The Bad

Untangling the “why” of these intuitions is quite an interesting problem. Technically all three hobbies rely on hijacking the reward centres of the brain, whose original evolutionary advantages were more to do with food, sex, and other survival-related tasks. There’s a fairly short path from arguing that the stimmer’s behaviour is repugnant to arguing that all three cases are repugnant; after all none of them result in food or anything truly “productive”. But this tack also seems to go a bit against our intuitions.

Fortunately, the world has a lot of different video games, and we can use that range to draw out some more concrete differences. At the low-end are games like Cow Clicker and Cookie Clicker, which are so basic as to be little more than indirect versions of the reward-centre-stimulating machine. More complex games seem to intuitively fare a little better, as do games with a non-trivial social element. Games tha...
Types of Knowledge
by Elizabeth — 2020-06-20T17:20:02.229Z

This is a system for sorting types of knowledge. There are many like it, but this one is mine.

First, there is knowledge you could regurgitate on a test. In any sane world this wouldn’t be called knowledge, but the school system sure looks enthusiastic about it, so I had to mention it. Examples:

Reciting the symptoms of childbed fever on command

Reciting Newton’s first law of motion

Reciting a list of medications’ scientific and brand names

Reciting historical growth rate of the stock market

Reciting that acceleration due to gravity on Earth is 9.807 m/s²

Second, there is engineering knowledge- something you can repeat and get reasonably consistent results. It also lets you hill climb to local improvements. Examples:

Knowing how to wash your hands to prevent childbed fever and doing so

Driving without crashing

Making bread from a memorized recipe.

What are the average benefits and side effects from this antidepressant?

Knowing how much a mask will limit covid’s spread

Investing in index funds

Knowing that if you shoot a cannon ball of a certain weight at a certain speed, it will go X far.

Knowing people are nicer to me when I say “please” and “thank you”

Third, there is scientific knowledge. This is knowledge that lets you generate predictions for how a new thing will work, or how an old thing will work in a new environment, without any empirical knowledge.

Examples:

Understanding germ theory of disease so you can take procedures that prevent gangrene and apply them to childbed fever.

Knowing the science of baking so you can create novel edible creations on your first try.

Knowing enough about engines and batteries to invent hybrid cars.

Actually understanding why any of those antidepressants works, in a mechanistic way, such that you can predict who they will and won’t work for.

A model of how covid is spread through aerosols, and how that is affected by properties of covid and the environment.

Having a model of economic change that allows you to make money off the stock market in excess of its growth rate, or know when to pull out of stocks and into crypto.

A model of gravity that lets you shoot a rocket into orbit on the first try.

A deep understanding of why certain people’s “please”s and “thank you”s get better results than others.

Engineering knowledge is a lot cheaper to get and maintain than scientific knowledge, and most of the time it work...