trentmkelly/LessWrong-43k huggingface dataset:
Legal Brief: Plurality Voting is Unconstitutional
by c.trout — 2022-10-21T04:55:25.722Z
Link: https://medium.com/@C.Trout/plurality-voting-is-unconstitutional-2d08fbae8e1a

Epistemic status: certain of nothing. I'm not a legal scholar, but my training in philosophy has prepared me for legalese: I believe there might be a case here? Something worth exploring? Feedback from those with legal expertise would be greatly appreciated. UPDATE: I'm ~90% certain the section on rational basis review is wrong – feel free to skip. Also, I shouldn't have put the section on ballot design vs voting method at the end, because I think it's one of the more interesting ideas here.

As Aaron Hamlin of the Center for Election Science has argued, improving our voting methods is a prime target for EA. Therefore, if anyone with the requisite status, money and connections thinks this case actually has a chance of succeeding, by all means, go for it. Maybe purposefully spoil your ballot in the midterms (voting for more than one candidate) and then file suit against the state?

The U.S. uses the least expressive voting method conceivable: doesn’t that violate voters’ freedom of speech? That’s what I will argue. Just because our voting method has never been tested in court doesn’t mean it shouldn’t be.

Background

This method is known as “plurality voting” in the literature (a.k.a. “first-past-the-post”, or “choose-one”) and we use it for nearly every state and federal election, supplemented by a run-off here and there. It’s a similar story at the municipal level. The two exceptions: Maine and Alaska which each use a form of Ranked Choice Voting (RCV).

Under plurality voting, voters may express approval for only one candidate. It grants no opportunity to rank candidates, score them, or even just approve of more than one. Any of these alternatives (RCV, range voting, and approval voting, respectively) would allow voters to express more about their preferences. And not in a small way: as a matter of raw math, the number of different ways a voter could express themselves under any of these alternative methods increases at polynomial or exponential rates as the number of candidates increases.

Credit: Aaron Hamlin, Center for Election Science. More details on the formulas here.

Meanwhile, that number only increases linearly under plurality voting. In fact, barring forced voting for at least one candidate, our “choose one or none” style ballot is as restrictive as it gets.

Political scientists have also identified mechanisms by which plurality voting favors a two-part...
Learning societal values from law as part of an AGI alignment strategy
by John Nay — 2022-10-21T02:03:25.359Z

This post argues that P(misalignment x-risk | AGI) is lower than anticipated by alignment researchers due to an overlooked goal specification technology: law.

P(misalignment x-risk | AGI that understands democratic law) < P(misalignment x-risk | AGI)

This post sheds light on a neglected mechanism for lowering the probability of misalignment x-risk. The mechanism that is doing the work here is not the enforcement of law on AGI. In fact, we don’t discuss the enforcement of the law at all in this post.[1] We discuss AGI using law as information. Unless we conduct further research and development on how to best have AGI learn the law and how we can validate a computational understanding of the law as capabilities scale to AGI, it is less likely that AGI using law as information reduces misalignment x-risk. In other words, we do not believe that alignment will be easy; we believe it will involve difficult technical work, but that it will be different than finding a one-shot technical solution to aligning one human to one transformative AI.[2]

Specifically, there is lower risk of bad outcomes if AGI can learn to follow the spirit of the law. This entails leveraging exclusively humans (and not AI in any significant way) for the “law-making” / “contract-drafting” part (i.e., use the theory and practice of law to tell agents what to do), and build AI capabilities for the interpretation of the human directives (machine learning on data and processes from the theory and practice of law about how agents interpret directives, statutes, case law, regulation, and “contracts”).

At a high level, the argument is the following.

Law is the applied philosophy of multi-agent alignment and serves as a knowledge base of human values. (This has been previously underappreciated by much of the AI alignment community because most lawyers know little about AI, and most AI researchers know little about legal theory and law-making.)
Many arguments for AGI misalignment depend on our inability to imbue AGI with a sufficiently rich understanding of:

what individual humans want; and
how to take actions that respect societal values more broadly.

AGI could learn law as a source of human values. (Before we reach AGI, we can make progress on AI learning law, so we can validate this.)

Private law (contracts and their methods) can teach AGI how to align with goals of ind

...
Covid 10/20/22: Wait, We Did WHAT?
by Zvi — 2022-10-20T21:50:00.878Z

The what, in this case, was the type of thing that could cause another pandemic:

This has not made enough people very angry, and is insufficiently widely regarded as a bad move. It happened, and we are paying for similar things to happen again.

There’s a section on that below (you can Ctrl-F for Gain of Function).

It was a surprisingly news-filled week for Covid in other ways as well. None of the other developments should scare you.

Executive Summary

We did Gain of Function research. Such research must stop.

New variants are coming, unclear how big a wave might follow.

CDC adds Covid-19 to childhood vaccination schedule.

Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: 234k cases (+0%) and 2,500 deaths (+2%).

Result: 223k cases (-5%) and 2,319 deaths (-6%).

Prediction for next week: 218k cases (-3%) and 2,160 deaths (-7%).

Now that the decline sustained itself past the holiday and its aftermath, we can be confident that existing strains continue to be on their way down. The question now shifts to the new variants, and when, whether and to what extent they will cause trouble. See the variant section for further discussion of this. Once again, the pandemic is ‘resetting’ in this fashion.

Deaths

Indiana didn’t report deaths or cases last week and this week likely filled in the backlog, which is probably most of why the Midwest went up instead of down.

Cases

The south continues to decline, half of which is Mississippi and North Carolina not reporting cases. I chose not to adjust.

Variants

Paper says BA.2.75.2 ‘exhibits extensive escape from neutralizing antibodies,’ which is not news.

Which new variant will take over next?

In the equilibrium model, any reproduction advantage is scary, since it will continue to compound until behaviors adjust or enough people get infected. What we have learned over the last few years is that there is a big reproduction advantage to being the new variant and starting from a small base. It takes a huge edge during growth to be the kind of game changer that Alpha, Delta and Omicron were.

Here is a graph for America.

It is clear that BA.5 has peaked and is in decline, with BQ.1.1 looking most likely to be next and looking like it has a higher reproductive advantage than Moritz is suggesting. That is enough relative growth to...
When apparently positive evidence can be negative evidence
by cata — 2022-10-20T21:47:37.873Z
Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841483/

This is a thought-provoking journal article discussing cases where:

You have something with two possible states, A and B, and you can perform fairly accurate, mostly independent tests which distinguish between A and B.
However, there is some failure case with a low prior probability, in which the actual state may be B, but something confounds all your tests and makes them look like they favor A regardless.
If you then perform many tests, and they all favor A, the first few tests will make you update towards A, but then if you keep performing more tests whose results favor A, you will actually start updating towards B, because you will start to think you are in the failure case.

An interesting anecdote which I haven't verified:

Under ancient Jewish law one could not be unanimously convicted of a capital crime—it was held that the absence of even one dissenting opinion among the judges indicated that there must remain some form of undiscovered exculpatory evidence.

Via Hacker News.
Plans Are Predictions, Not Optimization Targets
by johnswentworth — 2022-10-20T21:17:07.000Z

Imagine a (United States) high-schooler who wants to be a doctor. Their obvious high-level plan to achieve that goal is:

Graduate high school and get into college
Go to college, study some bio/chem/physiology, graduate and get into med school
Go to med school, make it through residency
Doctor!

Key thing to notice about that plan: the plan is mainly an optimization target. When in high school, our doctor-to-be optimizes for graduating and getting into college. In college, they optimize for graduating and getting into med school. Etc. Throughout, our doctor-to-be optimizes to make the plan happen. Our doctor-to-be does not treat the plan primarily as a prediction about the world; they treat it as a way to make the world be.

And that probably works great for people who definitely just want to be doctors.

Now imagine someone in 1940 who wants to build a solid-state electronic amplifier.

Building active solid-state electronic components in the early 1940’s is not like becoming a doctor. Nobody has done it before, nobody knows how to do it, nobody knows the minimal series of steps one must go through in order to solve it. At that time, solid-state electronics was a

problem we did not understand

; the field was preparadigmatic. There were some theories, but they didn’t work. The first concrete plans people attempted failed; implicit assumptions were wrong, but it wasn’t immediately obvious which implicit assumptions. One of the most confident predictions one might reasonably have made about solid-state electronics in 1940 was that there would be surprises; unknown unknowns were certainly lurking.

So, how should someone in 1940 who wants to build a solid-state amplifier go about planning?

I claim the right move is to target robust bottlenecks: look for subproblems which are bottlenecks to many different approaches/plans/paths, then tackle those subproblems. For instance, if I wanted to build a solid-state amplifier in 1940, I’d make sure I could build prototypes quickly (including with weird materials), and look for ways to visualize the fields, charge densities, and conductivity patterns produced. Whenever I saw “weird” results, I’d first figure out exactly which variables I needed to control to reproduce them, and of course measure everything I could (using those tools for visualizing fields, densities, etc). I’d also look for patterns amon...
Introduction to abstract entropy
by Alex_Altair — 2022-10-20T21:03:02.486Z

This post, and much of the following sequence, was greatly aided by feedback from the following people (among others): Lawrence Chan, Joanna Morningstar, John Wentworth, Samira Nedungadi, Aysja Johnson, Cody Wild, Jeremy Gillen, Ryan Kidd, Justis Mills and Jonathan Mustin.

Introduction & motivation

In the course of researching optimization, I decided that I had to really understand what entropy is.[1] But there are a lot of other reasons why the concept is worth studying:

Information theory:

Entropy tells you about the amount of information in something.
It tells us how to design optimal communication protocols.
It helps us understand strategies for (and limits on) file compression.

Statistical mechanics:

Entropy tells us how macroscopic physical systems act in practice.
It gives us the heat equation.
We can use it to improve engine efficiency.
It tells us how hot things glow, which led to the discovery of quantum mechanics.

Epistemics (an important application to me and many others on LessWrong):

The concept of entropy yields the maximum entropy principle, which is extremely helpful for doing general Bayesian reasoning.

Entropy tells us how "unlikely" something is and how much we would have to fight against nature to get that outcome (i.e. optimize).
It can be used to explain the arrow of time.
It is relevant to the fate of the universe.
And it's also a fun puzzle to figure out!

I didn't intend to write a post about entropy when I started trying to understand it. But I found the existing resources (textbooks, Wikipedia, science explainers) so poor that it actually seems important to have a better one as a prerequisite for understanding optimization! One failure mode I was running into was that other resources tended only to be concerned about the application of the concept in their particular sub-domain. Here, I try to take on the task of synthesizing the abstract concept of entropy, to show what's so deep and fundamental about it. In future posts, I'll talk about things like:

How abstract entropy can be made meaningful on continuous spaces
Exactly where the "second law of thermodynamics"[2] comes from, and exactly when it holds (which turns out to be much broader than thermodynamics)
How several domain-specific types of entropy relate to this abstract version

Many people reading this will have some previous facts about entropy stored in their minds, and this can s...
Trajectories to 2036
by ukc10014 — 2022-10-20T20:23:34.164Z

TL;DR

This post follows on from an earlier, longer attempt to think through Holden Karnofsky's ‘

AI Could Defeat All of Us Combined

’ (AIDC). It spends less time on the specifics of precisely how a mass-copied human-level AI (HLAI) would kill, enslave, or disempower humans (i.e. who would copy the HLAIs, how they might establish an 'AI headquarters', how they might form malign plans, etc.), as both Karnofsky's and my earlier post consider these. The chart lays out a variety of (geopolitically-informed) trajectories, and the text below works through them to identify the different 'flavours' of AIDC or alignment that might result.

Gloss on AIDC

In another post I summarised Holden Karnofsky's ‘

AI Could Defeat All of Us Combined

’ (AIDC) as follows:

‘Human level AI (HLAI) is developed by 2036, is useful across a broad range of jobs, and is rolled out quickly. There is sufficient overhang of servers used to train AI at that time that a very large number of HLAIs, up to hundreds of millions running for a year, can be deployed. In a short period, the HLAIs are able to concentrate their resources in physical sites safe from human interference. Humans are unable to oversee these copies, which coordinate and conspire, leading to an existentially risky outcome for humanity.'

AIDC is a useful sketch of a possible future, but it seems at once too high-level and somewhat constrained. For instance, AIDC deals with an abstract deployment scenario, and implicitly posits 'all HLAIs' against 'all humans'. But that abstraction seems hard to think about without considering the possible geopolitical distribution and incentives of AI assets and their political masters.[1] AIDC also suggests a specific date, 2036, for HLAI deployment; and centres its analysis on human-level AI. Both choices are not without implication, which I discuss below and elsewhere.[2]

Assumptions & Caveats

14 years is a kinda long time: 2036, a slightly artificial choice of date, is both close enough to somewhat forecast, but far enough for radical shifts (along technological, economic, and strategic axes).[3] Hence, this chart is provisional, and it is possible that long before HLAI, there are major changes (a US-China kinetic war, a Chinese debt crisis, a renewed EU political/debt crisis, substantial US internal conflict, and of course, a U...
Rough Sketch for Product to Enhance Citizen Participation in Politics
by T431 — 2022-10-20T20:04:23.488Z

For politics and governance in the US, does there exist a tool that:

Prompts the user to enter their rough location (e.g., town, county, state)
Prompts the user to select interest keywords (e.g., housing, animal welfare, cannabis)
Lists pending local (town / county), state, and federal level laws and regulations w.r.t. these interests
Lists current local (town / county), state, and federal level laws and regulations w.r.t. these interests
Includes summaries of current local (town / county), state, and federal level laws and regulations for accessibility
Lists special interest groups that typically support/counter laws and regulations relating to these interests at the local (town / county), state, and federal level
Lists ways in which the user could influence laws and regulations w.r.t. these interests (e.g., a step-by-step tutorial for participating in a certain election)

If this tool doesn't exist, how much value would people get from it if it existed? How difficult would it be to implement each part? (please point me to any tools / organizations that roughly fulfill the duties outlined in the bullet points)

Lastly, w.r.t. the point

Includes summaries of current local (town / county), state, and federal level laws and regulations for accessibility

I think GPT-X might work well for summarizing and distilling legal language - has this been done already?
The heritability of human values: A behavior genetic critique of Shard Theory
by geoffreymiller — 2022-10-20T15:51:35.786Z

Overview (TL;DR):

Shard Theory is a new approach to understanding the formation of human values, which aims to help solve the problem of how to align advanced AI systems with human values (the ‘AI alignment problem’). Shard Theory has provoked a lot of interest and discussion on LessWrong, AI Alignment Forum, and EA Forum in recent months. However, Shard Theory incorporates a relatively Blank Slate view about the origins of human values that is empirically inconsistent with many studies in behavior genetics indicating that many human values show heritable genetic variation across individuals. I’ll focus in this essay on the empirical claims of Shard Theory, the behavior genetic evidence that challenges those claims, and the implications for developing more accurate models of human values for AI alignment.

Introduction: Shard Theory as an falsifiable theory of human values

The goal of the ‘AI alignment’ field is to help future Artificial Intelligence systems become better aligned with human values. Thus, to achieve AI alignment, we might need a good theory of human values. A new approach called “Shard Theory” aims to develop such a theory of how humans develop values.

My goal in this essay is to assess whether Shard Theory offers an empirically accurate model of human value formation, given what we know from behavior genetics about the heritability of human values. The stakes here are high. If Shard Theory becomes influential in guiding further alignment research, but if its model of human values is not accurate, then Shard Theory may not help improve AI safety.

These kinds of empirical problems are not limited to Shard Theory. Many proposals that I’ve seen for AI ‘alignment with human values’ seem to ignore most of the research on human values in the behavioral and social sciences. I’ve tried to challenge this empirical neglect of value research in four previous essays for EA Forum, on the heterogeneity of value types in humans individuals, the diversity of values across individuals, the importance of body/corporeal values, and the importance of religious values.

Note that this essay is a rough draft of some preliminary thoughts, and I welcome any feedback, comments, criticisms, and elaborations. In future essays I plan to critique Shard Theory from the perspectives of several other fields, such as evolutionary biology, animal behavior research, ...
A Conflict Between Longtermism and Veganism, Pick One.
by Connor Tabarrok — 2022-10-20T14:30:03.889Z
Link: https://alltrades.substack.com/p/a-longtermist-case-against-veganism?sd=pf

[This is a cross-post from my blog, which you can find here]

The EA space is certainly a unique intersection of people from many walks of life, each with their own priorities and goals. However, an interesting contradiction arose in a recent conversation I had over dinner with friends. As I state in the conclusion, this may be either a criticism of longtermism or of vegetarian/veganism, depending on your perspective.

If you are someone who subscribes to Longtermism, (the idea that future people hold equal moral weight as compared to present people, and that we should adjust our actions to be accordingly biased to creating future growth.) Then, it seems to me that it would actually be non-optimal of you not to eat the most convenient/delicious/nutritious meal that you can find, whenever possible, and without much regard for animal welfare.
ANIMAL WELFARE VS HUMAN PREFERENCES

The argument goes like this: Whatever people may do to make future people better off, they will probably do more of it/do it better if they are more satisfied/happier. There are some studies on this (

link

,

link

,

link

), that suggest it might be a difference somewhere between 10-20%. Anecdotally, just take a look at the sometimes ludicrous lengths that tech companies go to please their employees. This is not altruism, it’s just good business.

So okay, great. We agree that happy people are more productive. Now let’s consider this within the domain of diet choice.

Is veganism/vegetarianism a choice that makes people happy? Maybe for some people, but usually not in a vacuum. If you truly do enjoy eating vegetarian/vegan more than a meat based diet on the basis of taste and convenience alone, more power to you. However, it seems that around the world, there is a strong revealed preference for people to eat more meat as it becomes more available. We can tell this by looking at the rate of meat consumption vs. GDP per capita.

Many vegetarians/vegans do so for religious or moral reasons, but messages that our inherent preference to eat meat is morally bad can be harmful, because they don’t actually change how a meatless diet tastes, they simply add costs like guilt and disgust in an attempt to tip the scale in people’s choices, and do not make material changes in either diet.

Okay, so we’ve now established that there is a large cost to not eating meat for those whose inherent...
AI Research Program Prediction Markets
by tailcalled — 2022-10-20T13:42:55.113Z

John Wentworth requested that someone would create prediction markets for various AI research programs. I've now done so, for four programs:

Remember, though - Measure Lots Of Stuff: It would probably be good if other people made prediction markets that measured things from other angles. Make sure that you read John Wentworth's original post to know what is needed by alignment researchers.