trentmkelly/LessWrong-43k huggingface dataset:
What is a reasonably complete set of the different meanings of "hard work"?
by Pee Doom — 2019-12-07T04:54:30.190Z

The concept of "hard work" seems very confusing to me.

I think it could mean combinations of many different things. It doesn't seem that helpful for actually carving reality at the joints for engineering purposes and getting better at whatever traits are needed. It seems like a messy construct composed of many dimensions, and in fact the dials on those dimensions could be set to very different settings and still be called "hard work" without distinguishing between the different underlying factors.

I want to be able to reductionistically decompose what another person could mean when they use the phrase, so I could then narrow it down to the precise anticipation of experience that they are implying. I have some guesses as to some of those parts below.

What I want is a list of Hard-Workingness Theories, composed of particular precise-as-possible lower-level pieces, rank-ordered by plausibility/usefulness/interestingness at capturing what you think people in general mean, and/or your best attempt at defining it. Maybe there is some general feature that neatly explains most of it.

Spoiler alert if you want to think before seeing my thoughts (also, apologies for redundancies and bad grouping in my list). If someone wants (someone else) to work hard, it could mean any combination of the following components:

Working for a long period of time, relative to some baseline, measured in hours.
Muscular usage, measured by literal sweat, heart rate, and soreness the day after
How physically fast a person is at completing a task
Engaging in activities that correlate with high trait Conscientiousness (this still makes me unsatisfied with how much it passes the buck but at least it's reasonably operationalized)
How ritualized a person's schedule is
How much a person's attention is focused on the work
How much a person's attention is required for success at the work
How much working memory is required to perform a task
How valuable others think the task(s) performed are, measured in dollars
How meaningful a person felt a task was afterwards
How long the time is between effort and payoff (deferment of gratification, time preference)
How quickly a person does a task after the task is received
How much enthusiasm a person displays for doing a task
How much the person worked compared to other people doing the same thing
How much VUCA the person is capab

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The New Age of Social Engineering
by VivaLaPanda — 2019-12-07T00:23:24.725Z

Why have so many online social networks failed to form healthy communities, and instead gained notoriety as hostile spaces? I argue that the reason these platforms have failed is because they didn’t learn the lessons taught by the High Moderns when humans were first faced with the challenge of engineering alongside systems that were built through millennia of natural evolution. In a chaotic environment such as human social relations, a different engineering approach is necessary to ensure that more good is done than harm. To gain the skills necessary to make these projects a success we need to learn from the history of social environments themselves, and of human engineering strategies. What follows is the story of social evolution becoming social engineering, how the meaning of both has changed radically in the last 20 years, and what this means for designers in the new Information Era.

Part 1 — Ten millenniums of social engineering

A key part of my thesis is that the way our social environment is formed has changed over the course of human history, and more rapidly in recent years. How do we know that to be true? Much of the work I’m building on comes out of the accounts provided by The Secret of Our Success by Joseph Henrich, as well as Seeing Like a State by James C. Scott. There are many things that I disagree with in these works, but I think they both get to the core idea that there exist two main ways in which human society develops. One of those ways is via an evolutionary process, where some societies develop some technique that aids in survival and flourishing, pass it on, and end up growing and outcompeting other societies. The people practicing these traditions often don’t have concrete knowledge as to why they work, but they become enshrined as tradition because they help the group succeed. This goes from knowledge about what plants are edible, to complex ideas like how the group should be structured. On the other hand, there is social engineering. In social engineering, explicit models of human behavior are used to derive new social conventions and structures. Usually this doesn’t mean designing something new from whole cloth, but instead an effective synthesis of ideas that the culture has generated over time into a compelling ideological canon or into new distinct institutions.

For most of human history, we rel...
What subfields of mathematics are most useful for what subfields of AI?
by Rudi C — 2019-12-06T20:45:31.606Z

I’ve seen https://www.lesswrong.com/posts/snzFQJsNYqzPZS2nK/course-recommendations-for-friendliness-researchers, but that seems to be specialized to AI alignment.

Bonus points for sorting the prerequisites into different import tiers.

Some advice on how to balance and order the learning time between math, coding, and CS theoretic parts will be appreciated.
What is Abstraction?
by johnswentworth — 2019-12-06T20:30:03.849Z

Let's start with a few examples (borrowed from here) to illustrate what we're talking about:

We have a gas consisting of some huge number of particles. We throw away information about the particles themselves, instead keeping just a few summary statistics: average energy, number of particles, etc. We can then make highly precise predictions about things like e.g. pressure just based on the reduced information we've kept, without having to think about each individual particle. That reduced information is the "abstract layer" - the gas and its properties.
We have a bunch of transistors and wires on a chip. We arrange them to perform some logical operation, like maybe a NAND gate. Then, we throw away information about the underlying details, and just treat it as an abstract logical NAND gate. Using just the abstract layer, we can make predictions about what outputs will result from what inputs. Note that there’s some fuzziness - 0.01 V and 0.02 V are both treated as logical zero, and in rare cases there will be enough noise in the wires to get an incorrect output.
I tell my friend that I'm going to play tennis. I have ignored a huge amount of information about the details of the activity - where, when, what racket, what ball, with whom, all the distributions of every microscopic particle involved - yet my friend can still make some reliable predictions based on the abstract information I've provided.
When we abstract formulas like "1+1=2*1" and "2+2=2*2" into "n+n=2*n", we're obviously throwing out information about the value of n, while still making whatever predictions we can given the information we kept. This is what abstraction is all about in math and programming: throw out as much information as you can, while still maintaining the core "prediction" - i.e. the theorem or algorithm.
I have a street map of New York City. The map throws out lots of info about the physical streets: street width, potholes, power lines and water mains, building facades, signs and stoplights, etc. But for many questions about distance or reachability on the physical city streets, I can translate the question into a query on the map. My query on the map will return reliable predictions about the physical streets, even though the map has thrown out lots of info.

The general pattern: there’s so...
Comment on Coherence arguments do not imply goal directed behavior
by Ronny Fernandez — 2019-12-06T09:30:25.882Z

In coherence arguments do not imply goal directed behavior Rohin Shah argues that a system's merely being at all model-able as an EU maximizer does not imply that it has "goal directed behavior". The argument as I understand it runs something like this:

1: Any behavior whatsoever maximizes some utility function.

2: Not all behaviors are goal directed.

Conclusion: A system's behavior maximizing some utility function does not imply that its behavior is goal directed.

I think this argument is technically sound, but misses an important connection between VNM coherence and goal directed behavior.

Shah does not give a formal definition of "goal directed behavior" but it is basically what you intuitively think it is. Goal directed behavior is the sort of behavior that seems like it is aimed at accomplishing some goal. Shah correctly points out that a system being goal directed and being good at accomplishing its goal is what makes it dangerous, not merely that it is good at maximizing some utility function. Every object in the universe perfectly maximizes the utility function that assigns 1 to all of the actual causal consequences of its behavior, and 0 to any other causal consequences its behavior might have had.

Shah seems to suggest that being model-able as an EU maximizer is not very closely related to goal directed behavior. Sure, having goal directed behavior implies that you are model-able as an EU maximizer, but so does having any kind of behavior whatsoever.

The implication does not run the other way according to Shah. Something being an EU maximizer for some utility function, even a perfect one, does not imply that its behavior is goal directed. I think this is right, but I will argue that nonetheless, it is true that it being a good idea for you to model an agent as an EU maximizer does imply that its behavior will seem goal directed (at least to you).

Shah gives the example of a twitching robot. This is not a robot that maximizes the probability of its twitching, or that wants to twitch as long as possible. Shah agrees that a robot that maximized those things would be dangerous. Rather, this is a robot that just twitches. Such a robot maximizes a utility function that assigns 1 to whatever the actual consequences of its actual twitching behaviors are, and 0 to anything else that the consequences might have been.

This system i...
The Actionable Version of "Keep Your Identity Small"
by Hazard — 2019-12-06T01:34:36.844Z

(cross posted on my roam blog)

There's an old Paul Graham Essay, "Keep Your Identity Small". It's short so it's worth it to read the whole thing right now if you've never seen it. The yisbifiefyb ("yeah it's short but i'm functionally illiterate except for your blog") is roughly "When something becomes part of your identity, you become dumber. Don't make things part of your identity."

I read that post some time in high school and thought, "Of course! You're so right Paul Graham. Cool, now I'll never identify as anything." I still think that Paul Graham is pointing out a real cluster of Things That Happen With People, but over time the concept of identity, and identifying as BLANK have started to feel less clear. It feels right to say "People get dumb when their identity is challenged" and it even feels sorta axiomatic. Isn't that what it means for something to be part of your identity? Thinking about it more I came up with a bunch of different ways of thinking of myself that all felt like identifying as BLANK, but it felt like unnecessary dropping of nuance to smoosh them all into the single concept of identity.

Identity Menagerie

Lets look at some examples of what identifying as a BLANK can look like:

Blake: "I do Cross Fit. "
Jane: "I'm smart. In fact I'm normally among the smartest in the room. I'm able to solve a lot of problems by just finding a clever solution to them instead of having to get stuck in grunt work. People often show awe and appreciation for my depth and breadth of knowledge."
Jay: "I the peacekeeper, the one always holding the group together."

Self-Concept

Steve Andreas outlines the idea of a self-concept quite nicely:

Your self-concept is a sort of map of who you are. Like any other map, it is always a very simplified version of the territory. [...] Your self-concept, your "map" you have of yourself, has the same purpose as a map of a city—to keep you oriented in the world and help you find your way, particularly when events are challenging or difficult.

The thing you'll notice is it's nigh impossible to avoid having a self-concept. When Jane thinks of herself and how she can act on the world, "being smart" is a chunk of self-concept that summarizes a lot of her exp...
Understanding “Deep Double Descent”
by evhub — 2019-12-06T00:00:10.180Z

If you're not familiar with the double descent phenomenon, I think you should be. I consider double descent to be one of the most interesting and surprising recent results in analyzing and understanding modern machine learning. Today, Preetum et al. released a new paper, “Deep Double Descent,” which I think is a big further advancement in our understanding of this phenomenon. I'd highly recommend at least reading the summary of the paper on the OpenAI blog. However, I will also try to summarize the paper here, as well as give a history of the literature on double descent and some of my personal thoughts.

Prior work

The double descent phenomenon was first discovered by Mikhail Belkin et al., who were confused by the phenomenon wherein modern ML practitioners would claim that “bigger models are always better” despite standard statistical machine learning theory predicting that bigger models should be more prone to overfitting. Belkin et al. discovered that the standard bias-variance tradeoff picture actually breaks down once you hit approximately zero training error—what Belkin et al. call the “interpolation threshold.” Before the interpolation threshold, the bias-variance tradeoff holds and increasing model complexity leads to overfitting, increasing test error. After the interpolation threshold, however, they found that test error actually starts to go down as you keep increasing model complexity! Belkin et al. demonstrated this phenomenon in simple ML methods such as decision trees as well as simple neural networks trained on MNIST. Here's the diagram that Belkin et al. use in their paper to describe this phenomenon:

Belkin et al. describe their hypothesis for what's happening as follows:

All of the learned predictors to the right of the interpolation threshold fit the training data perfectly and have zero empirical risk. So why should some—in particular, those from richer functions classes—have lower test risk than others? The answer is that the capacity of the function class does not necessarily reflect how well the predictor matches the inductive bias appropriate for the problem at hand. [The inductive bias] is a form of Occam’s razor: the simplest explanation compatible with the observations should be preferred. By considering larger function classes, which contain more candidate predictors compatible with the data, we are able to find interpolating fu

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Tapping Out In Two
by philh — 2019-12-05T23:10:00.935Z

I'm one of those people who feels personally called out by xkcd's "Duty Calls" ("someone is wrong on the internet").

(Not as much as I used to. At some point I stopped reading most of the subreddits that I would argue on, partly for this reason, and Hacker News, for unrelated reasons, and now I don't do it as much.)

As pastimes go, there's nothing particularly wrong with internet arguments. But sometimes I get involved in one and I want to stop being involved in one and that's not easy.

I could just, like, stop posting. Or I could let them say something, and then respond with something like, "yeah, I'm still not convinced, but I don't really have time to get into this any more". But then the other person wins, and that's terrible. It potentially looks to onlookers like I stopped because I couldn't find something to say any more. And in at least some cases, it would feel kind of rude: if they've put a lot of thought into their most recent post, it's a bit dismissive to just leave. In the past, when people have done that to me, I've disliked it, at least some times. (Well, at least once. I remember one specific occasion when I disliked it, and there may have been others.)

Another thing I could do is respond to their most recent post, and then say "and now I'm done". But that feels rude, too, and I certainly haven't liked it when people have done it to me. (Why not? It puts me in the position where I don't know whether to respond. Responding feels petty, and kind of a waste of time; not responding feels like they win, and that's terrible.) If they do reply, then I'm shut off completely; I can't even make a minor clarification on the order of "no, you somehow interpreted me as saying exactly the opposite of what I actually said".

So I don't like those tactics. There are probably mental shifts I could make that would bring me more inclined towards them, but… about a year ago I came up with another tactic, which has seemed quite helpful.

What I do now is say something to the effect of: "after this, I'm limiting myself to two more replies in this thread."

This has various advantages. It feels less rude. It doesn't look like I'm quitting because I have no reply. It helps the conversation reach a more natural conclusion. And it also feels a lot easier to do, partly for the above reasons and partly for the normal reasons precommitment helps me to do things.

For some quantitat...
Values, Valence, and Alignment
by Gordon Seidoh Worley — 2019-12-05T21:06:33.103Z

I have previously advocated for finding a mathematically precise theory for formally approaching AI alignment. Most recently I couched this in terms of predictive coding and longer ago I was thinking in terms of a formalized phenomenology, but further discussions have helped me realize that, while I consider those approaches useful and they helped me discover my position, they are not the heart of what I think is import. The heart, modulo additional paring down that may come as a result of discussions sparked by this post, is that human values are rooted in valence and thus if we want to build AI aligned with human values we must be able to understand how values arise from valence.

Peter Carruthers has kindly and acausally done me the favor of laying out large parts of the case for a valence theory of value ("Valence and Value", Philosophy and Phenomenological Research, Vol. XCVII No. 3, Nov. 2018, doi:10.1111/phpr.12395). He sets out to do two things in the linked paper. One is to make the case that valence is a "unitary natural-psychological kind" (another way of saying it parsimoniously cuts the reality of human minds at the joints). The other is to give an account of how it is related to value, arguing that valence represents value against the position that valence is value. He calls these positions the representational and the hedonic accounts, respectively.

I agree with him on some points and disagree on others. I mostly agree with section 1 of his paper, and then proceed to disagree with parts of the rest, largely because I disagree with his representational account of valence because I think he flips the relationship between valence and value. Nonetheless, he has provided a strong jumping off point and explores many important considerations, so let's start from there before moving towards a formal model of values in terms of valence and saying how that model could be used in formally specifying what it would mean for two agents to be aligned.

The Valence-Value Connection

In the first section he offers evidence that valence and value are related. I recommend you read his arguments for yourself (the first section is only a few pages), but I'll point out several highlights:

It is widely agreed, however, that all affective states share two dimensions of valence and arousal (Russell, 1980, 2003; Reisenzein, 1994; Rolls, 1999). All

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LW Team Updates - December 2019
by Ruby — 2019-12-05T20:40:39.473Z

This is the once-monthly updates post for LessWrong team activities and announcements.

Summary

In the past month we rolled out floating comment guidelines and launched the inaugural Lesswrong 2018 review. Work has continued on the LessWrong editor and on a prototype for the new tagging system.

December will see more work on the editor, the 2018 review process, and analytics.

Recent Features

The LessWrong 2018 Review

Much of the recent weeks has been devoted to getting the inaugural Lesswrong 2018 Review into full swing:

LessWrong is currently doing a major review of 2018 — looking back at old posts and considering which of them have stood the tests of time. It has three phases:

Nomination (ends Dec 1st at 11:59pm PST)
Review (ends Dec 31st)
Voting on the best posts (ends January 7th)

Authors will have a chance to edit posts in response to feedback, and then the moderation team will compile the best posts into a physical book and LessWrong sequence, with $2000 in prizes given out to the top 3-5 posts and up to $2000 given out to people who write the best reviews.

Read Raemon's full post to hear for the full rationale for the evaluation of historical posts.

NOMINATED POSTS ARE NOW OPEN FOR REVIEW

The nomination phase just ended a few days ago. 34 nominators made 204 nominations on 98 distinct posts written by 49 distinct authors. Of these, 74 posts have received the 2+ nominations required to proceed to the review phase.

How to start reviewing

The frontpage currently has a LessWrong 2018 Review section. It shows a random selection of posts which are up for review and has buttons to the Reviews Dashboard and the list of reviews and nominations you've made so far.
The Reviews Dashboard (located at www.lesswrong.com/reviews) is another way to find posts to review.

The 2018 Review section currently on the homepage.

The Reviews Dashboard.
Note the "Expand Unread Comments" button.

3. When you click Review on a review-able post, you will be taken to the post page and a Review Comment Box will appear.

The Review Comment Box

Reviews are posted as comments and can be edited after they are posted like regular comments.

Reasons to review

All users are encouraged to writes reviews. Reviews help by:

Giving authors feedback which they can use to revise, update, and expand their posts before users vote on them and they possibly get included in the physical book that will be published

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