trentmkelly/LessWrong-43k huggingface dataset:
Does freeze-dried mussel powder have good stuff that vegan diets don't?
by DanielFilan — 2019-01-12T03:39:19.047Z

Personal background for this question:

I'm a vegan for ethical reasons, meaning that I don't want to contribute to the farming of beings that can suffer.

I think that oysters and mussels probably aren't sentient.

Oysters and mussels seem pretty disgusting to me.

I would like to be healthy.

I am already taking B12 and Omega-3 supplements.

Oysters and mussels seem like they might have important nutrients that one otherwise can't get in a vegan diet without supplementation, just because that animals are different to plants/fungi/etc., and humans ate animals in the EEA. However, due to their disgustingness, I can't quite bring myself to eat them.

Luckily, it turns out that one can buy capsules that contain freeze-dried green-lipped mussel powder. I'm pretty excited about this prospect, but am somewhat worried that the freeze-drying and powderifying process would destroy various nutrients, or render them mysteriously ineffective in the ways that I gather vitamin pills are mysteriously less effective than vitamins in food. I also imagine that maybe the really important nutrients are only found in (say) land mammals. Can anybody shed light on this?
Why is so much discussion happening in private Google Docs?
by Wei Dai — 2019-01-12T02:19:19.332Z

I've noticed that when I've been invited to view and comment on AI-safety related draft articles (in Google Docs), they tend to quickly attract a lot of extensive discussion, including from people who almost never participate on public forums like LessWrong or AI Alignment Forum. The number of comments is often an order of magnitude higher than a typical post on the Alignment Forum. (Some of these are just pointing out typos and the like, but there's still a lot of substantial discussions.) This seems kind of wasteful because many of the comments do not end up being reflected in the final document so the ideas and arguments in them never end up being seen by the public (e.g., because the author disagrees with them, or doesn't want to include them due to length). So I guess I have a number of related questions:

What is it about these Google Docs that makes people so willing to participate in discussing them?

Would the same level of discussion happen if the same draft authors were to present their drafts for discussion in public?

Is there a way to attract this kind of discussion/participants to public posts in general (i.e., not necessarily drafts)?

Is there some other way to prevent those ideas/arguments from "going to waste"?

I just remembered that LessWrong has a sharable drafts feature. (Where I think the initially private comments can be later made public?) Is anyone using this? If not, why?

Personally I much prefer to comment in public places, due to not wanting my comments to be "wasted", so I'm having trouble understanding the psychology of people who seem to prefer the opposite.
A general framework for evaluating aging research. Part 1: reasoning with Longevity Escape Velocity
by emanuele ascani — 2019-01-11T21:33:51.691Z

Summary

To this day there is a lack of systematic research to evaluate a cause area with immense potential: aging research. This is the first of a series of posts in which I'll try to begin research to address this gap. The points made in this post are about how to evaluate impact using the concept of Longevity Escape Velocity. Bringing the date of Longevity Escape velocity closer by one year would save 36,500,000 lives of 1000 QALYs, using a conservative estimate. Other sources of impact that arise from the same concept include: increasing the probability of Longevity Escape Velocity, making Longevity Escape Velocity spread faster, and making a new future portion of the population reach Longevity Escape Velocity by increasing its life expectancy. Aging research could also positively impact the cost-effectiveness of other interventions by increasing the probability that Longevity Escape Velocity will be attained in the recipients' lifetimes. I will also discuss why the probability of Longevity Escape Velocity is substantial and why QALYs should be the measure of impact, and I'll give mathematical proofs that the adoption speed of the technologies that arise from research doesn't impact cost-effectiveness analyses.

The need of a theoretical foundation to evaluate aging research

I think one important approach to research in Effective Altruism is to try to lay theoretical foundations and put together tools for helping to evaluate a specific cause area that can be generalised to any intervention inside that cause area. Such work is often not possible because of lack of time and expertise, making it preferable, sometimes, to scout specific promising interventions or refine existing research.

One cause area that absolutely needs this kind of more systematic groundwork is aging research. The current EA research about aging is lacking in number and in what I think are crucial considerations, even though informal discussion with members of the community reveals that many people regard it as potentially promising. The expertise required to make such an analysis possible is rare to find. It requires people with a strong quantitative background who are also interested not only in biology but in the biology of aging in particular, and they must be accustomed to predicting the future of scientific research and making cost-effectiveness evaluations. I obser... ,
One Website To Rule Them All?
by anna_macdonald — 2019-01-11T19:14:51.445Z

Epistemic status: I'm not the first person to think of this, and I'm not sure if this is possible. But I want to flesh out some options for consideration and get feedback. Sorry if it's long.

WANTED:

A crowd-sourced site that reliably presents important pros and cons on any/all topics and hopefully leads us collectively closer to consensus.

This has been on my mind for a few years now. Figuring out truth in the digital age is often tricky and sometimes downright impossible. (Is minimum wage a good thing? Should I adopt a paleo or keto or vegan or Shangri-la diet? What do we really know and not know about the historical Jesus?) Especially when it comes to political discussion, answers often depend on more details than an average person has time or energy to research—and even if they did, they may still leave out considerations or solutions that someone else may think of.

Some links where other rationalists think semi-related things: Robin Hanson wants AI to help us towards consensus because Wikipedia isn't up for the job. Scott Alexander promoted adversarial collaborations to see if they could reach consensus. (I swear, I saw more examples of people wishing for something like a debate website, but I wasn't writing them down and searching for them has failed.)

THE WISHFUL THINKING PART

Now let me be more specific about what my imagined website (hereafter, Site) needs to do.

On matters of truth, it needs to support epistemic arguments for why we should believe or not believe particular claims. On matters of action, it needs to provide important pro/cons of taking that action. Site must have a method of allowing the best arguments to rise to the top. That's the most basic functionality.

Additional Feature A: Because the Site is crowd-sourced, anyone can propose possible actions and possible modifications to actions. In theory, this could mean that Site could be a platform where a crowd-written law could have its details hammered out by many affected parties. Creative solutions could have a chance to be reviewed and, if well-thought-out, could rise higher in the public consciousness.

Optional Feature B: I really, really would like it if, within the structure of arguments (for either epistemic-truth topics or proposed-actions), Site distinguished between truth-claims and value-claims. This might be especially unrealistic, since no one natura...
Hierarchical system preferences and subagent preferences
by Stuart_Armstrong — 2019-01-11T18:47:08.860Z

During NeurIPS, I had a discussion with Dylan Hadfield-Menell about how to fomalise my methods for extracting human values.

He brought up the issue of how to figure out the goals of a hierarchical system - for example, a simplified corporation. It's well known that not everyone in a corporation acts in the best interests of the corporation. So, a corporation will not act at all like a rational shareholder value maximiser. Despite this, is there a way of somehow deducing, from its behaviour and its internal structure, that its true goals are shareholder maximisation?

I immediately started taking the example too literally, and objected to the premise, mentioning the goals of subagents within the corporation. We were to a large extent talking past each other; I was strongly aware of the fact that even if a system had its origin in one set of preferences, that didn't mean it couldn't have disparate preferences of its own. Dylan, on the other hand, was interested in formalising my ideas in a simple setting. This is especially useful, as the brain itself is a hierarchical system with subagents.

So here is my attempt to fomalise the problem, and present my objection, all at the same time.

The setting and actions

I'm choosing a very simplified version of the hierarchical planning problem presented in this paper.

A robot, in green, is tasked with moving the object A to the empty bottom storage area. The top level of the hierarchical plan formulates the plan of gripping object A.

This plan is then passed down to a lower level of the hierarchy, that realises that first it must get B out of the way. This is passed to a lower level that is tasked with gripping B. It does so, but, for some reason, it first turns on the music:

Then it moves B out of the way (this will involve moving up and down the planning hierarchy a few times, by first gripping B, then moving it, then releasing the grip, etc...):

Then the planning moves up the hierarchy again, which re-issues the "grip A" command. The robot does that, and eventually moves A into storage:

The irrationality of music

Now this seems to be mainly a rational plan for moving A to storage, except for the music part. Is the system trying to move A but is being irrational at one action? Or does the music reflect some sort of preferences for the hierarchical system?

Well, to sort that out, we can look within the agent. Let's look at the l

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Megaproject management
by ryan_b — 2019-01-11T17:08:37.308Z

Megaproject management is a new-ish subfield of project management. Originally considered to be the special case of project management where the budgets were enormous (billions of dollars), it is developing into a separate specialization because of the high complexity and tradition of failure among such projects. The driving force behind treating it as a separate field appears to be Bent Flyvbjerg, previously known around here for Reference Class Forecasting as the first person to develop an applied procedure. That procedure was motivated by megaprojects.

I will make a summary of the paper "What you should know about megaprojects, and why: an overview" from 2014. For casual reading, there is an article about it from the New Yorker here.

History

Megaprojects got their name from the association of mega with big, so think mega-city rather than mega-joule. It did match the unit prefix in the beginning however, as such projects were mostly dams, bridges, or very large buildings in the early 20th century.

The next shift upward took place with the Manhattan Project and then the Apollo program, which are also frequently drawn on as positive examples. The term 'megaproject' picked up steam in the 1970s, at the same time project costs crossed over into the billions.

Currently project costs of 50-100 billion are common, with even larger projects less common but not rare. If you were to view certain things which need dedicated management as a project, like the stimulus packages from 2008 or US defense procurement, then we have crossed over into the trillions and are entering a 'tera era' of megaprojects.

Ignoring these special cases, but counting infrastructure and industries where billion dollar projects are common, megaprojects account for ~8% of global GDP.

Four Sublimes

These are four reasons which drive the popularity of megaprojects. They are kind of a group bias for each type of stakeholder. They are:

Technological sublime: because engineers and technologists love making the newest/tallest/fastest things.
Political sublime: because politicians love being able to associate with huge projects and the publicity that comes with them.
Economic sublime: because unions, contractors, and business people love all the jobs and fees.
Aesthetic sublime: because designers love making beautiful things, and the public loves to adopt big beautiful things as di

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What shape has mindspace?
by Gurkenglas — 2019-01-11T16:28:47.522Z

Epistemological status: Babbling.

Let a map each mind in mindspace to how aligned it is. We are trying to optimize a. To that end, lemmata are helpful which talk about the shape of mindspace. That's why we try to call it a space even before defining what category C it lives in.

To optimize a function, start with a diverse enumeration of its domain. The deontological enumeration covers all others with constant-factor overhead, but the consequentialist enumeration gives us more properties to work with.

Every mind m has an implicit utility function u(m). a factors through u as a function, but not as a continuous function, let alone a C-morphism. That's why we've recently moved away from explicit utility maximizers.

Use mathematical language to tell our story! Then we might guess where it's going.
Non-Consequentialist Cooperation?
by abramdemski — 2019-01-11T09:15:36.875Z

This is a very rough intuition pump for possible alternatives to value learning.

In broad strokes, the goal of (ambitious) value learning is to define and implement a notion of cooperation (or helpfulness) in terms of two activities: (1) figuring out what humans value, (2) working to optimize that.

I'm going to try to sketch an alternative notion of cooperation/helpfulness. This intuition is based on libertarian or anarcho-capitalist ideas, but in some ways seems closer to what humans do when they try to help.

Autonomy

I was talking to Andrew Forrester about the suffering golem thought experiment. I'm not sure who came up with this thought experiment, but the idea is:

Suffering Golem: A golem suffers terribly in every moment of its existence, but it says it wants to keep living. Do you kill it?

The idea is that if you think it is good to kill it, you're a hedonic utilitarian: your altruistic motives have to do with maximizing pleasure / minimizing suffering. If you think it should not be killed, then you're more likely to be a preference utilitarian: your altruistic motives have to do with what the person values for themselves, rather than some other thing you think would be good for them. (I tend to lean toward preference utilitarianism myself, but don't think the question is obvious.)

Andrew Forrester was against killing it, but justified his answer with something like "it's none of your business. You could provide convenient means of suicide if you wanted..." This wasn't an expression of not caring about the welfare of the golem. Rather, it was a way of saying that you want to preserve the golem's autonomy.

I realized that although his answer was consistent with preference utilitarianism on the surface, it went beyond. I think he would likely have a similar response to the following thought experiment:

Confused Golem: A golem hates every moment of its existence, and would prefer to die, but it is unable to admit the fact to itself. It thinks that it loves life and wants to continue living. Perhaps it could eventually realize that it preferred not to exist if it thought about the question for long enough, but that day is a long way off. Do you kill it?

The autonomy-preserving move is to not kill the confused golem. You might talk to the golem about what it wants, but you wouldn't actively optimize for convincin...
Combat vs Nurture & Meta-Contrarianism
by abramdemski — 2019-01-10T23:17:58.703Z

My initial reaction to Combat vs Nurture was to think "I already wrote about that!" and "but there should be three clusters, not two". However, looking at my old posts, I see that my thinking has shifted since I wrote them, and I don't describe the three clusters in quite the way I currently would. So, here is how I think about it:

"Face Culture" / "Playing Team": When people offer ideas, their ego/reputation is on the line. It is therefore important to "recognize the value of every contribution" -- accepting or rejecting an idea has a strong undercurrent of accepting or rejecting the person offering the idea. This sometimes makes rational decision impossible; the value of team members is greater than the value of the specific decision, so incorporating input from a team member can be a higher priority than making the best decision. Much of the time, bad ideas can be discarded, but it involves a dance of "due consideration": an idea may be entertained for longer than necessary in order to signal strongly that the team valued the input, and downsides may be downplayed to avoid making anyone look stupid. Eventually you want someone on the team to point out a decisive downside in order to reject a bad idea, but ideally you coax this out of the person who originated the idea. (I called this "face culture", but I have heard people call it "playing team".)
Intellectual Debate: It is assumed that engaging with an idea involves arguing against it. Approving an idea doesn't necessarily signal anything bad, but unlike face culture, arguing against an idea doesn't signal anything bad either. Arguing against someone (genuinely or in a devil's-advocate position) shows that you find the idea interesting and worth engaging with. Concepts like burden of proof are often applied; one tends to operate as if there were an objective standard of truth which both sides of the debate are accountable to. This warps the epistemic standards in a variety of ways, for example, making it unacceptable to bring raw intuitions to the table without putting them in justifiable terms (even if a raw intuition is your honest reason). However, if you have to choose between face culture and intellectual debate culture, intellectual debate is far better for making intellectual progress.
Mutual Curiosity

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Book Recommendations: An Everyone Culture and Moral Mazes
by sarahconstantin — 2019-01-10T21:40:04.163Z

Epistemic Status: Casual

I highly recommend An Everyone Culture, by Robert Kegan, and Moral Mazes, by Robert Jackall, as companion books on business culture. Moral Mazes is an anthropological study of the culture and implicit ethics of a few large corporations, and is an eye-opening illustration of the problems that arise in those corporations. An Everyone Culture is an introduction to the idea of a “deliberately developmental organization”, an attempt to fix those problems, plus some case studies of companies that implemented “deliberately developmental” practices.

The basic problem that both books observe in corporate life is that everybody in a modern office is trying to conceal their failures and present a misleadingly positive impression of themselves to their employers and coworkers.

This leads to lost productivity.

For instance:

The longer one tries to cover up a mistake, the costlier it will be to fix it.

The less accurately credit is allocated for success or failure, the harder it will be to incentivize good work.

The more employees misinform their bosses, the worse-informed the bosses’ decisions will be.

The more people are concerned with maintaining appearances, the less cognitive capacity they will have for productivity and creativity.

The more unacceptable it is to acknowledge “personal” concerns (emotions, physical health, intrinsic motivation or lack thereof), the harder it is to fix productivity problems that arise from “personal” problems.

Moral Mazes basically takes the view that the Protestant work ethic really died in the mid-to-late nineteenth century, when an American economy defined by small business owners and freelance professionals was replaced by an economy defined by larger firms and the rise of the managerial profession. The Protestant work ethic declared that hard work, discipline, and honesty would bring success. The “managerial work ethic” holds that a good employee has quite different “virtues” — things like

ability to play politics

loyalty & willing to subordinate oneself to one’s manager

“flexibility” (the opposite of stubbornness — not holding strong individual opinions)

To give an outside example, the author of “The Western Elite from a Chinese Perspective” was coming from a “Protestant work ethic” culture of hard work (though not, of course, actually Protestant) and encountering the “managerial work ethic” culture of...
What is narrow value learning?
by Rohin Shah — 2019-01-10T07:05:29.652Z

Ambitious value learning aims to achieve superhuman performance by figuring out the underlying latent "values" that humans have, and evaluating new situations according to these values. In other words, it is trying to infer the criteria by which we judge situations to be good. This is particularly hard because in novel situations that humans haven't seen yet, we haven't even developed the criteria by which we would evaluate. (This is one of the reasons why we need to model humans as suboptimal, which causes problems.)

Instead of this, we can use narrow value learning, which produces behavior that we want in some narrow domain, without expecting generalization to novel circumstances. The simplest form of this is imitation learning, where the AI system simply tries to imitate the supervisor's behavior. This limits the AI’s performance to that of its supervisor. We could also learn from preferences over behavior, which can scale to superhuman performance, since the supervisor can often evaluate whether a particular behavior meets our preferences even if she can’t perform it herself. We could also teach our AI systems to perform tasks that we would not want to do ourselves, such as handling hot objects.

Nearly all of the work on preference learning, including most work on inverse reinforcement learning (IRL), is aimed at narrow value learning. IRL is often explicitly stated to be a technique for imitation learning, and early algorithms phrase the problem as matching the features in the demonstration, not exceeding them. The few algorithms that try to generalize to different test distributions, such as AIRL, are only aiming for relatively small amounts of generalization.

(Why use IRL instead of behavioral cloning, where you mimic the actions that the demonstrator took? The hope is that IRL gives you a good inductive bias for imitation, allowing you to be more sample efficient and to generalize a little bit.)

You might have noticed that I talk about narrow value learning in terms of actual observed behavior from the AI system, as opposed to any sort of “preferences” or “values” that are inferred. This is because I want to include approaches like imitation learning, or meta learning for quick task identification and performance. These approaches can produce behavior that we want without having an explicit representation of “preferences”. In pra...