trentmkelly/LessWrong-43k huggingface dataset:
Notes on Justice (as a virtue)
by David Gross — 2020-11-05T20:27:57.142Z

This post examines the virtue of justice. I wrote this not as an expert, but as someone who wants to learn. I hope it will help people who want to know more about this virtue and how to nurture it.

Defining what justice is is a major topic of ethical and political philosophy. There is no way I can do it justice (see what I did there?) in a brief post like this one. Instead I will concentrate on justice as a virtue. Bracketing what justice is or what it consists of, what does it mean to be characteristically, habitually just: to exhibit the virtue of justice?

Justice as a virtue

Justice is usually thought of as existing between people: as a balance, with opposing claims of different people weighed against each other.

Justice as a virtue is different.[1] It is something within a person (though it often exhibits itself through how that person behaves towards others).

It is not contractarian in the social-contract sense of being willing to submit to the restraint of justice in exchange for the security of knowing that your neighbors are similarly restrained. It is also not merely personal obedience or acquiescence in the face of a particular standard of justice.

To a person without the virtue of justice, justice is a double-edged sword: If that person is cheated unjustly, they may hope to appeal to justice to right the wrong. But if that person is on the favorable side of a shady deal, they may hope justice remains thwarted so they can get away with it.

A person with the virtue of justice evaluates such situations differently. To such a person, justice itself is something they value: the life they consider to be most worth living is a just life. The possession of a just character is worth more to them than the possession of things successfully swindled away from someone else unjustly. Because of this desire to be just, the person with the virtue of justice has developed the habit of characteristically seeking out just outcomes.

An example of someone exhibiting the virtue of justice might be someone who notices that they were inadvertently handed a $20 instead of a $10 in change at the cafe that morning, so they return at lunchtime to give back the extra money. Even though they did nothing wrong, even though nobody but them knows of the unjust state of affairs, even though that injustice worked to their material advantage, they feel the desire to set things right an...
Impostor Syndrome as skill/dominance mismatch
by Viliam — 2020-11-05T20:05:54.528Z

I am surprised that there is nothing about Impostor Syndrome on Robin Hanson's website, when to me it seems obviously connected to status. To use the standard formula: Impostor Syndrome is not about lack of skills.

(Also related to: humility, status regulation, unpopularity of nerds.)

Let me quote my older article:

Robin Hanson calls the two basic forms of status "dominance" and "prestige"; the fear-based and the admiration-based sources of social power respectively. He also notes how people high in "dominance" prefer to be perceived as (also) high in "prestige" [1, 2]. Simply said, a brutal dictator often wants to be praised as kind and smart and skilled (e.g. the "coryphaeus of science" Stalin), not merely powerful and dangerous.

[...] If you are not ready to challenge the chieftain for the leadership of the tribe, and if you don't want to risk being perceived as such, the safe behavior is to also downplay your skills as a hunter.

Although humans have two mechanisms of constructing social hierarchies, at the end of the day both of them compete for the same resource: power over people. Thus we see powerful people leveraging their power to also get acknowledged as artists or scientists; and successful artists or scientists leveraging their popularity to express political opinions.

The hierarchy of "dominance" is based on strength, but is not strength alone. The strongest chimp in the tribe can be defeated if the second-strongest and third-strongest join against him. Civilization makes it even more complicated. Stalin wasn't the physically strongest man in the entire Soviet Union.

(In theory, the most powerful person shouldn't need physical strength at all, if they have an army and secret police at command. But in practice, I suppose our instincts demand it; a physically weak leader would probably be a permanent magnet for rebellions. Therefore leaders flaunt their health and strength.)

Similarly, the hierarchy of "prestige" is based on skill, but is not skill alone. The most skilled person can be... what? Outskilled by a coalition of opponents? Nah, sounds like too much work. It is easier to stop them flaunting their skill, either by taking away their tools, or by threatening to break their arms and legs if you see them performing publicly again.

Which makes the prestige ladder a mixed one. To get on the top, you need a combination of superior skill and at least aver...
What considerations influence whether I have more influence over short or long timelines?
by Daniel Kokotajlo — 2020-11-05T19:56:12.147Z

As my timelines have been shortening, I've been rethinking my priorities. As have many of my colleagues. It occurs to us that there are probably general considerations that should cause us to weight towards short-timelines plans, or long-timelines plans. (Besides, of course, the probability of short and long timelines) For example, if timelines are short then maybe AI safety is more neglected, and therefore higher EV for me to work on, so maybe I should be systematically more inclined to act as if timelines are short.

We are at this point very unsure what the most important considerations are, and how they balance. So I'm polling the hive mind!
Generalized Heat Engine
by johnswentworth — 2020-11-05T19:01:32.699Z

I’d like to be able to apply more of the tools of statistical mechanics and thermodynamics outside the context of physics. For some pieces, that’s pretty straightforward - a large chunk of statistical mechanics is just information theory, and that’s already a flourishing standalone field which formulates things in general ways. But for other pieces, it’s less obvious. What’s the analogue of a refrigerator or a carnot cycle in more general problems? How do “work” and “heat” generalize to problems outside physics? The

principle of maximum entropy

tells us how to generalize temperature, and offers one generalization of work and heat, but it’s not immediately obvious why we can’t extract “work” from “heat” without subsystems at different temperatures, or how to turn that into a useful idea in non-physics applications.

This post documents my own exploration of these questions in the context of a relatively simple problem, with minimal reference to physics (other than by analogy). Specifically: we’ll talk about how to construct the analogue of a heat engine using biased coins.

Intuition

The main idea I want to generalize here is that we can “move uncertainty around” without reducing uncertainty. This is exactly what e.g. a refrigerator or heat engine does.

Consider the viewpoint of a refrigerator-designer. All the microscopic dynamics of the (fridge + environment) system must be reversible, so the number of possible microscopic states will never decrease on its own as time passes. The only way to reduce uncertainty about the microscopic state is to observe it. But the fridge designer is designing the system, deciding in advance how it will behave. The designer has no direct access to the environment in which the fridge will run, no way to measure the exact positions the atoms will be in when the fridge first turns on. The designer, in short, cannot directly observe the system. So, from the designer’s perspective, there’s uncertainty which cannot be reduced.

(In statistical mechanics, there are several entirely different justifications for why observations can’t reduce microscopic uncertainty/entropy - for instance, in one approach, macroscopic variables are chosen in such a way that we can deterministically predict future macroscopic observations. Another comes from Maxwell’s demon-style arguments, where the demon’s memory has to be included as part of the system. I’ll...
Sub-Sums and Sub-Tensors
by Scott Garrabrant — 2020-11-05T18:06:44.421Z

This is the eighth post in the Cartesian frames sequence. Here, we define new versions of the sum and tensor of Cartesian frames that can delete spurious possible environments from the frame.

1. Motivating Examples

The sum C⊕D of C and D is supposed to represent an agent that can do anything either C or D can do, while the tensor, C⊗D, is supposed to represent an agent that can do anything C and D can do working freely together on a team. However, sometimes these operations produce Cartesian frames with more environments than we would expect.

Consider two players, Alice and Bob, in a prisoner's dilemma. We will let W={0,1,2,3} be the space of utilities for Alice. The Cartesian frame for Alice looks like

C0=(2031),

where the top row represents Alice cooperating, and the left column represents Bob cooperating.

Let C1=(20) represent Alice committed to cooperating, and let C2=(31) represent Alice committed to defecting. Since the real Alice can either cooperate or defect, one might expect that Alice (C0) would equal the sum (C1⊕C2) of Alice cooperating with Alice defecting. However,

C1⊕C2=(20203113).

The last two columns are spurious environments that represent Bob copying Alice's move, and Bob doing the opposite of Alice's move. However, since Bob cannot see Alice's move, Bob should not be able to implement these policies: Bob can only choose to cooperate or defect.

Next, consider a unilateralist's curse game where two players each have access to a button that destroys the Earth. If either player pushes the button, the Earth is destroyed. Otherwise, the Earth is not destroyed. W={0,1}, where 0 represents the world being destroyed and 1 represents the world not being destroyed.

Here, both players have the Cartesian frame

D1=(0001),

where the first row represents pressing the button, and the first column represents the other player pressing the button.

The two players together can be expressed with the Cartesian frame

D2=⎛⎜
⎜
⎜⎝0001⎞⎟
⎟
⎟⎠,

where the rows in order represent: both players pressing the button; the first player pressing the button; the second player pressing the button; and neither player pressing the button.

One might expect that D1⊗D1 would be D2, but in fact,

D1⊗D1=⎛⎜
⎜
⎜⎝00000010⎞⎟
⎟
⎟⎠.

The second possib...
Defining capability and alignment in gradient descent
by Edouard Harris — 2020-11-05T14:36:53.153Z

This is the first post in a series where I'll explore AI alignment in a simplified setting: a neural network that's being trained by gradient descent. I'm choosing this setting because it involves a well-defined optimization process that has enough complexity to be interesting, but that's still understandable enough to make crisp mathematical statements about. As a result, it serves as a good starting point for rigorous thinking about alignment.

Defining inner alignment

First, I want to highlight a definitional issue. Right now there are two definitions of inner alignment circulating in the community. This issue was first pointed out to me by Evan Hubinger in a recent conversation.

The first definition is the one from last year's Risks from Learned Optimization paper, which Evan co-authored and which introduced the term. This paper defined the inner alignment problem as "the problem of eliminating the base-mesa objective gap" (Section 1.2). The implication is that if we can eliminate the gap between the base objective of a base optimizer, and the mesa-objectives of any mesa-optimizers that base optimizer may give rise to, then we will have satisfied the necessary and sufficient conditions for the base optimizer to be inner-aligned.

There's also a second definition that seems to be more commonly used. This definition says that "inner alignment fails when your capabilities generalize but your objective does not". This comes from an intuition (pointed out to me by Rohin Shah) that the combination of inner alignment and outer alignment should be accident-proof with respect to an optimizer's intent: an optimizer that's both inner- and outer-aligned should be trying to do what we want. Since an outer-aligned optimizer is one whose base objective is something we want, this intuition suggests that the remaining part of the intent alignment problem — the problem of getting the optimizer to try to achieve the base objective we set — is what inner alignment refers to.

Here I'll try to propose more precise definitions of alignment and capability in an optimizer, and explore what generalization and robustness might mean in the context of these properties. I'll also propose ways to quantify the capability and alignment profiles of existing ML systems.

But before doing that, I want to motivate these definitions with an example.

The base objective

The optimizer I'll be using as my ...
Covid 11/5: Don’t Mention the War
by Zvi — 2020-11-05T14:20:03.374Z

Mutation has always been an elephant in the Covid-19 room, and I haven’t paid as much attention to it as I should have. It is increasingly clear the Covid-19 has mutated, and the new strain is substantially more infectious than the old one and now virtually the only strain. One write-up from the Financial Times: Scientists warn a more infectious coronavirus variant spreading across Europe. The new strain doesn’t seem different from the old one once you are infected, in terms of your prognosis or chance of death, and immunity to either strain gives immunity to both.

I remember being told the new version was ‘ten times more infectious’ but I can no longer find the reference, and in any case case who knows what such phrases actually cash out into in terms of magnitude, what the new baseline R0 is, or how much we would need to tighten our precautions to compensate. If you do know, please let us know. My assumption is that the change probably cashes out to not that big a difference in practice, and likely a misleading way to categorize what’s happening, but I have no idea.

What I do know is that it makes sense that if your previous goal was to do exactly enough to keep things under control, or your people were using control systems to do the same thing, and then the virus gets more infectious, you are going to be in a lot of trouble until you can adjust. Especially if winter arrives at the same time. And adjusting sufficiently won’t be easy.

This also throws a wrench into any calculations regarding herd immunity. Previously I was confident that 50% immunity, if accumulated through uncontrolled spread, would be more than sufficient. If the baseline R0 is suddenly much higher, that could no longer be the case. How much not the case depends on the magnitude of the change. Again, I do not have a good read on that.

I also suspect, both due to dropping death rates (which likely have multiple compounding causes) and also purely on first principles, that the new strain is effectively less deadly. Initial viral load when infected likely matters for how often you die and how bad your prognosis is in general. If the new virus is more infectious due to things like changes in its spike proteins, but replicates at the same rate and is equally vulnerable to an immune system response once it starts, it seems likely that it results in lower average initial v...
Babble Challenge: 50 thoughts on stable, cooperative institutions
by Bird Concept — 2020-11-05T06:38:38.997Z

In

a recent LessWrong question

Anna Salamon asks “Where did stable, cooperative institutions come from (like bridges that stay up; the rule of law; or Google)?” She also worries that “the magic that used to enable such cooperative institutions is fading”.

Anna’s post is strong in babble. It does provide gears-level mechanisms and concrete hypotheses. But it also gestures at intuitions, felt senses, and insights-waiting-to-be-had.

This week’s challenge is simple: Have 50 thoughts about Anna’s post and questions.

Do you have a guess at the magic enabling human societies to build roads and postal services? Do you think institutions are actually getting stronger over time? What are 10 examples of how institutions changed in the last 100 years? Or 10 predictions about how they'll change in the future? Etc.

Your thoughts can be hypotheses, questions, anecdotes, confusions, disagreements, feelings... and so forth.

50 thoughts, no need for them to be longer than a sentence.

You have 1 hour.

Looking back

Here are the current rankings

.

(You gain a star for completing a challenge, and lose one for missing a week. I’m not including myself since it feels weird to be both gamemaster and participant.)

Great job everyone!

★★★★★ gjm

★★★★ Yonge

★★★ Tetraspace Grouping, Slider

★★ Mark Xu, Bucky

★ Turntrout, Harmless, Tao Lin, Daniel Kokotajlo, chasmani, supposedlyfun

Moving Forwards

This is week 6 of my 7-week babble sprint.

It is said that sufficiently advanced technology is indistinguishable from magic.

I think something similar is true for building skills.

There are some skills of which you can see the contours. You can squint and see yourself wielding them, with practice. And there are some things which seem like magic. As if though the kinds of humans who wield them are fundamentally different from the kind of human you are. There's no set of steps that could get you to where they are at.

Intellectual creativity often falls in this bucket.

For whatever reason, culture loves to create the vision of a genius. The media writes about “the 14-year old who climbed Mount Everest and wrote software for America’s largest bank” when in fact they made an impressive-for-their-age contribution to an open source package and camped out at a lower base station reachable by walking.

Maybe because creativity is so ill...
Being Productive With Chronic Health Conditions 
by lynettebye — 2020-11-05T00:22:50.871Z

This post appeared first on the EA Coaching blog.
A fair number of clients who suffer from chronic health problems come though my metaphorical door. Personally, I’ve dealt with a chronic health condition called POTS since my early teens.

When you’re dealing with longer term, physical chronic health problems that cause fatigue, brain fog, or pain, standard productivity advice might not work. Common advice can even backfire, leaving you worse off. Similarly, shorter term problems such as a concussion or pregnancy will temporarily change what you can do.

Of course, I am not a doctor. This is a collection of productivity tips based on many conversations I’ve had, not proven medical advice. If you have a chronic health issue, you should be getting medical advice from appropriate professionals.

Invest in your capacity

Improving your habits and better managing your symptoms will almost certainly increase your productivity more than trying to power through. So work smarter, not harder. Here are some tips for improving your capacity to do work.

Spend time trying to find treatments as early as possible. The biggest productivity gains may come from finding better ways to manage your condition. You may need to talk to many specialists, try different medications, adopt new routines, or learn new ways of moving. The earlier you do these, the longer you will be enjoying the rewards. College is a great time to do this. Of course, grant yourself some compassion if navigating a complicated medical landscape seems like an overwhelming burden on top of dealing with your normal work.

Get enough sleep. Sleeping less than you need so that you have more time awake will reduce your productivity. If you have any kind of chronic fatigue issue, you just need more sleep than average. It’s common to need nine hours a night or more in such cases. If you’ve been running a sleep debt, you may need a lot of rest to catch up over many days.

Adapt your environment to support you. If people are getting annoyed that you’re taking longer than normal to reply to their emails, set up an autoreply that you’re slow to reply right now because of a health problem. If you’re struggling to remember to take your pills, get a weekly pill organizer or an electric bottle cap that tells you when you last took your meds. Build in redundant systems to make sure the vital habits happen.&nbs...
Covid-19 IFR from a Bayesian point of view
by mwacksen — 2020-11-04T23:55:21.362Z

This is mostly cross-posted from here , but edited slightly for the crowd here. Given that this is grand Bayesian Central, I'm surprised to see the lack of good Bayesian analysis as applied to covid-19. This seems to be a major failure of science, lesswrong should pick up the slack!

In my previous blog post, I had some comments on the statistical analysis present in some covid-19 studies. It is easy to criticize, but can I do better? Let's find out what the data tells us about the fatality of SARS-coV-2.

Data by itself tells us nothing

Well, almost nothing - remember that we are trying to find something that should be more generally true than just for the individual data points that we have. For this we need a model, a mathematical description of the process that created the data points including the unknown parameters (in this case, the IFR) that we want to discover.

Deciding on the model in advance

By tailoring a model to the data, one can strongly influence the results it shows. To avoid suspicions of this happening, and to avoid it happening inadvertently, one must/should always decide on the model before looking at the data. I have in fact seen some of the studies I'll be getting data from (see previous blog post). But I have not done any statististical analysis that looks at how age/sex affects the IFR. I will use this blog post to set a fixed methodology before I look further.

Garbage in, garbage out

No matter how good you are at statistics, your conclusions can only be as good as your data. I will therefore limit myself to studies of a certain type and quality. In particular, I will only look at antibody studies in Western countries that

try to get a random sample of the whole population.

do assay validation for their tests (or at least can link to some).

come from countries not counting "suspected" covid-19 deaths like "confirmed" ones.

have age/sex statistics of the sample. I need to be able to find covid-19 death statistics as well as age statistics for the total population being sampled.

As I only have so much time, I will not look for studies not covered by the two meta-analyses in the previous blog post.

The model

I start by binning ages into 10 year brackets, i.e. 0-10, 11-20, and so on, with a bin for each sex. I will label these by i=1,…,N. For each age bracket and sex, an IFR will be estimated which I denote by IFR[i]. We assume that these ...