trentmkelly/LessWrong-43k huggingface dataset:
Boring machine learning is where it's at
by George3d6 — 2021-10-20T11:23:22.272Z
Link: https://cerebralab.com/Boring_machine_learning_is_where_it's_at

It surprises me that when people think of "software that brings about the singularity" they think of text models, or of RL agents. But they sneer at decision tree boosting and the like as boring algorithms for boring problems.

To me, this seems counter-intuitive, and the fact that most people researching ML are interested in subjects like vision and language is flabergasting. For one, because getting anywhere productive in these fields is really hard, for another, because their usefulness seems relatively minimal.

I've said it before and I'll say it again, human brains are very good at the stuff they've been doing for a long time. This ranges from things like controlling a human-like body to things like writing prose and poetry. Seneca was as good of a philosophy writer as any modern, Shakespear as good of a playwright as any contemporary. That is not to say that new works and diversity in literature isn't useful, both from the perspective of diversity and of updating to language and zeitgeist, but it's not game-changing.

Human brains are shit at certain tasks, things like finding the strongest correlation with some variables in an n-million times n-million valued matrix. Or heck, even finding the most productive categories to quantify a spreadsheet with a few dozen categorical columns and a few thousand rows. That's not to mention things like optimizing 3d structures under complex constraints or figuring out probabilistic periodicity in a multi-dimensional timeseries.

The later sort of problem is where machine learning has found the most amount of practical usage, problems that look "silly" to a researcher but implacable to a human mind. On the contrary, 10 years in, computer vision is still struggling to find any meaningfully large market fits outside of self-driving. There are a few interesting applications, but they have limited impact and a low market cap. The most interesting applications, related to bioimaging, happen to be things people are quite bad at; They are very divergent from the objective of creating human-like vision capabilities, since the results you want are anything but human-like.

Even worst, there's the problem that human-like "AI" will be redundant the moment it's implemented. Self-driving cars are a real challenge precisely until the point when they become viable enough that everybody uses them, afterwards, every car is running on softwar...
关于中国的“巫”的部分解释（Some explanations of "wu" in China）

by Axioms — 2021-10-20T08:56:47.779Z

According to the feedback, I have adjusted it to the Chinese version only, and the Chinese version will be modified later. The English version will only write outlines until I have the ability to explain the whole article in English. Thank you.

中文版本（Chinese version

那就先从“巫”开始。

中国古代的“巫“一直和“胡巫”（北方草原地区的巫）有很大关系，经常听到的“萨满”实际上只是“胡巫”的一种。

先说萨满，萨满是由俄语saman转音译过来的，来源是西伯利亚。据说是通古斯语族的基，本意是激动不安和疯狂的人，传统萨满形象也多是身体残疾，精神错乱或者大病不死许愿当巫的人，其中以女性尤其多；其降神手段是舞，也就是俗话说的跳大神。他们往往集医ト星相各种术为一身，用以满足民间的各种需要。汉语地区熟知这个词是宋代以来，年代较晚。不过西方熟知这个词也是在俄国占领西伯利亚之后，年代更晚。

而中国古文献中的巫是与“祝宗卜史”类似的神媒，合称都叫巫，细分的话女性叫巫，男性就叫残，这些在很多著名或者不著名的古书中都有提到。例如《左传》中的信公二十一年“焚巫尪“就是其一，商代时《山海经》也有所谓“六巫“和”十巫”。

这些人以舞降神，多为女性，多为残疾人，多有较为癫狂的精神状态，多被视为无所不知智能超常，这些点和萨十分相似，其类似特征也多见于其他地区的巫。

常听说“巫即萨满”，实际上并不尽然，这个说法还没有被证实。

萨满也本来只是个比较狭窄的概念（通古斯民族的概念），现在被泛化（用指亚美地区或更大范围的宗教传统），几乎等同早期宗教的代名词，所以说它和巫在周秦汉时期的实际地位是有一定出入的。

在西方的汉学研究中，巫之所以引人注目，原因是它同中国宗教／科学背景有关，这样的背景对理解中国文化以及在世界历史上的地位很重要。

西方汉学家习惯的看法是，对任何文化来说宗教统治都必不可少，要么“王”之上祭司僧侣之流，要么“王”本身就是祭司僧侣，早期文化更是如此。而甲骨文作为中国最早的文字材料又正好支持了这个想法，虽然事实上并不是这样。但很恰巧的，甲骨文的发现正好是在中国学术“现代化”的关口，很多学者都把这类材料直截了当的看作商代的史料，而且是唯一的史料。

于是就出现了“商代历史的内容主要是占卜，而且商王本人都参与占卜，商代是个由巫统治的时代。过去某甲骨文专家曾提出“商王为群巫之长”一说在西方学术界很受欢迎，原因就在
这里。

对于判断巫在中国早期历史上的地位，“祝宗卜史”的出现很关键，这四个字分别有各自的意义，祝管祭祀，宗管世系，卜管占卜，史管记事，分工的很明确。虽然按理来讲（中国词汇中常把巫和这几个字联系起来，例如巫祝）祝宗卜史可能来源于巫，但巫的其中一个特点是服务范围较狭小，职能技巧缺乏分化（祭祀占卜不分就算了，数术方技也不分），更倾向于民间；而祝宗卜史则是偏向于王宫，分工细而覆盖广，因此能猜想祝宗卜史一旦出现，巫的作用就完全不同，地位必然下降。

巫自商代以来地位比较低，这从几点可以看出。一是他们常常被用作牺牲，或者被人用水火（焚烧或者淹死），二是他们地位不仅在王之下，在祝宗卜史之下，还被中国官僚知识界（士大夫）所贱视，地位每工匠，商置一类相近，是禁止做官的，重是古代法律也参以巫术（尤其想黑巫术）为旁门左道，往住因为其对民众有息响塘事力而深感恐惧，必想要加以禁止或者限定。与印度代表神权统治的第一种姓婆罗门的地位有很大区别。

由此可以说中国历史上一直有巫和品的影响存在，但这种影响从很早就被限制，不再具有支配地位。

此外关于甲骨文，可能是因为初中历史只需要了解大概，并没有着重提到古代文字多种多样，甲骨文也只是其中一种，只是当时王室占卜的流水帐。
古时礼仪中也包含有这类东西，中国人对天地君亲师的崇拜是来源于早期对天地祖的崇拜，天地祖崇拜当然涉及天人关系。这里就简单讲，从总的趋势来看，中国人不但离“天”越来越远（敬而远之），而且“祖”的概念也日益分化，逐渐让位于君（国家）亲（伦常）师（知识），有点日益政治化的味道。

既然要对神灵进行祭拜，祠嗣一类也是必不可少。但中国早期的祭祀遗址、史籍缺略，很难详考，只有《史记》封禅书和《汉书》郊祀志有专门讲过一些可以作为线索，以王莽为分界线，可以把祠的规制历史分为前半期和后半期，因为各个人物时代立的祠太多而且对这个主題没什么太大关系所以就不详述了。（？）

以及提到神就不得不提到中国古代对“太一”的崇拜。太一崇拜在古代神衹崇拜和宗教仪式中地位很突出，它与古代的天文学以及占星，式法等，还有中国古代哲学中的本体概念“道”，“太极”密切相关。汉代的史料容易使人造成错觉，以为祭祀太一只是从汉武帝以来才有，但古人明确提到太一最早似乎是在战国晚期的文献。

在汉代的祭祀制度中，封禅和郊祀都包含祭天，但对于太一的祭祀主要还是属于郊祀制度，以前为了祭祀太一还建了两座太一坛。与太一相关的还有一个天一，似乎还和古代星图有联系，在先秦时代已经是一种兼有星，神和终极物三重意义的概念。

除了这些，还有一些在华北地区尤其风行的民俗信仰，即所谓“四大门”，四大门指的就是狐狸（胡门），黄鼠狼（黄门），刺猬（白门）和蛇（常门），除了四大门还有五大门的说法，以及坛仙家仙财神爷之类，因为太繁多了不多赘述，大概就是比较属于农村宗教的类别。

由此可见不同“神灵”的存在对于生活在中国古代社会的各个阶级的人有着不同的重要意义。

在这之后常见的关于这类事物的分支之一就是《周易》。

《周易》这类所处的分支就是占星，卜筮，历日，占梦，炼丹，养生等等看似与神灵没有联系...
Moravec's Paradox Comes From The Availability Heuristic
by james.lucassen — 2021-10-20T06:23:52.782Z
Link: https://jlucassen.com/moravecs-paradox-comes-from-the-availability-heuristic/

Epistemic Status: very quick one-thought post, may very well be arguing against a position nobody actually holds, but I haven’t seen this said explicitly anywhere so I figured I would say it.

Setting Up The Paradox

According to Wikipedia:

Moravec’s paradox is the observation by artificial intelligence and robotics researchers that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources.

-https://en.wikipedia.org/wiki/Moravec’s_paradox

I think this is probably close to what to Hans Moravec originally meant to say in the 1980’s, but not very close to how the term is used today. Here is my best attempt to specify the statement I think people generally point at when they use the term nowadays:

Moravec’s paradox is the observation that in general, tasks that are hard for humans are easy for computers, and tasks that are easy for humans are hard for computers.

-me

If you found yourself nodding along to that second one, that’s some evidence I’ve roughly captured the modern colloquial meaning. Even when it’s not attached to the name “Moravec’s Paradox”, I think this general sentiment is a very widespread meme nowadays. Some example uses of this version of the idea that led me to write up this post are here and here.

To be clear, from here on out I will be talking about the modern, popular-meme version of Moravec's Paradox.

And Dissolving It

I think Moravec’s Paradox is an illusion that comes from the availability heuristic, or something like it. The mechanism is very simple – it’s just not that memorable when we get results that match up with our expectations for what will be easy/hard.

If you try, you can pretty easily come up with exceptions to Moravec’s Paradox. Lots of them. Things like single digit arithmetic, repetitive labor, and drawing simple shapes are easy for both humans and computers. Things like protein folding, the traveling salesman problem, and geopolitical forecasting are difficult for both humans and computers. But these examples aren’t particularly interesting, because they feel obvious. We focus our attention on the non-obvious cases instead – the examples where human/computer strengths are opposite, counter to our expectations.

Now, this isn’t to say that the idea behind Moravec’s Paradox is wrong and bad and we should throw it out. It’s definitely a us...
Book review: Lost Connections by Johann Hari
by maia — 2021-10-20T02:40:09.443Z

Why this book is interesting

Well, it's about depression, which is generally interesting to LW readers. For instance, 34% of SSC readers said they were diagnosed or thought they had it in 2020 (

source

).

This book asserts that most of us are thinking about depression in a fundamentally wrong way, which would be very important if true. It also presents some interesting possible solutions for solving depression in one’s own life and solving depression as a social, collective-action problem.

It’s not really fully fleshed out and supported, nor is it a good self-help guide. The book provides a vague model pointing towards how we should think about depression differently, and even some specific causes to look at, but there’s clearly some big gaps in the supporting evidence, and there’s not much concrete advice that a depressed person won’t have considered before (“maybe you would be less depressed if you made friends, or had more money”). Many of the claims made in the book seem to rely on anecdote and individual studies. The author seems to be taking a journalistic approach -- find one or two experts and one or two random individuals whose stories support his narrative, describe a couple of studies -- rather than a more rigorous “but why should we actually believe this over alternative models?”

Another major issue is that it conflates “biopsychosocial model of depression” with a more generalized position of “antidepressants bad, all other treatments good,” which really doesn’t help the author’s case. More on this in “Critiques”.

Nonetheless, I found the book useful because it provides a more social and practical lens on the problem of depression, which we really don’t talk about much because it doesn’t fit into the standard “treatment of illness” narrative. And some of the early results in e.g. social prescribing sound very promising.

Summary of the book

High-level claims:

The biological model of depression is mostly wrong; instead, we should think of it as biopsychosocial, meaning that there are some biological and some social and some individual components. We should focus more on the psycho- and social- components, because right now we mostly treat it biologically.
Depression is a signal in your brain that tells you something is wrong, like pain or nausea; you should listen to it and try to fix the underlying causes, rather than trying to just treat the symptom.

...
[Update] Without a phone for 10 days
by Benjamin Spiegel — 2021-10-20T01:17:43.391Z

I wrote a post about going without a phone for 10 days. Ten days have now passed, and I'm evaluating my options. This post is about my experience being phoneless and my thoughts about having a phone moving forward.

The last ten days have been extraordinarily peaceful! After a break-in phase of frequently checking my pant pocket for a phantom phone, I began to feel more at ease. After about three days, I felt a calmness that I hadn't enjoyed since middle school. After a week, I became more aware of the passage of time -- my days felt closer to a single drawn-out experience, as opposed to a cluttered collection of moments. During errands, I was forced to spend time waiting for as long as 30 minutes. Being without a phone, I spent these periods thinking to myself. There was immense value in maintaining my attention during these moments; I would compare them to a weak form of mindfulness meditation, something once part of my daily routine.

During these past ten days I've also seen greater productivity, which I attribute to an overall decrease in desire for stimulation. I finally got a simple academic personal website up and running. On the whole, I feel more capable of directing my attention.

Granted, there were some inconveniences to not having a phone. Most inconvenient was being unable to authenticate my university login. I still cannot authenticate, and in order to generate backup codes I need to get in touch with my school's IT department. I was also inaccessible to close friends whom I wanted to speak to, could not order food for delivery (though now I realize I can order it from my computer), and could not easily use car service. I was, however, able to chat with friends using iMessage on my laptop.

Having spent the past ten days without a phone -- and being disappointed by the recent launch of the Google Pixel 6 (a contributing factor to this phoneless experiment) -- I would like to continue life without a phone. I'm willing to give up Uber, Uber Eats, Google Maps, Google Pay, Slack, GMail, and Snapchat. Happy to, actually. However, I need to be able to make calls and texts. Also, having used iMessage for the first time, I was very impressed by how conversational it feels and how convenient it is to chat from my laptop. If I want to get texts on a feature phone, I will have to choose SMS over iMessage. I briefly considered getting an iPhone 12 mini for this ...
Book Review: 'Predicting the Next President: The Keys to the White House'
by TranscendetalIdealist — 2021-10-20T00:42:14.826Z

Part one: what is this book and should you read it?

Predicting the Next President: The Keys to the White House (henceforth rendered “The Keys”) is an ambitious book. Penned by historian Allan Lichtman, The Keys explains his system for forecasting the outcome of US presidential elections. This system is also called “The Keys”. So, to help readers figure out whether I’m talking about the book or the system, I’m only going to italicize The Keys when I’m explicitly referring to the book. The Keys is a 13-strong checklist of “true-or-false” statements that measure how well the incumbent party has performed during its term in office. If six or more statements are judged to be “false” then the election goes to the challenger. Five or fewer, and the incumbent takes it.

According to Lichtman, The Keys reflect a number of axiomatic truths about the democratic system in the US. And although he’s keen to caution against superposing these truths non-reflectively on democratic systems in other countries, he also believes The Keys speak to political processes in liberal democracies more generally.

In his introductory chapter, Lichtman describes a “pragmatic” American electorate that chooses a president “according to the performance of the party holding the White House” and theorises: “If candidates and the media could come to understand that governing, not campaigning, counts in presidential elections, we could have a new kind of presidential politics.” (Lichtman, 2016, pp. 8). This new kind of politics would dump the attack ads and go deep on policy promises, outlining substantial agendas of what candidates actually planned to do with their desired four years in office.

I should be clear from the outset that I think Lichtman’s vision is an appealing one. I also think The Keys is well written, and The Keys is well conceived. Assuming you’re in the target audience, you will probably enjoy reading this book, and might come away from it with something useful. What is the target audience? Probably people who enjoy reading long copy about predictive methodology and political history (given where you’re reading this, I don’t think it’s a stretch to assume that includes you, dear reader).

With all that out of the way, let me tell you what’s coming next. In part two, I’ll give a brief overview of what’s in this book and how I’m going to address it. In the third section, I’m...
Some of the best rationality essays
by Jack R — 2021-10-19T22:57:48.843Z

Meta: Send this to anyone who is interested in learning more about "rationality"

A refresher: what is “rationality?” [1]

Rationality is the art of thinking in ways that result in

accurate beliefs

and

good decisions

[as understood by the LessWrong community; this understanding of rationality differs from

others

, some of which are more common in colloquial usage than “LessWrong rationality”]. It is the primary topic of LessWrong.

Rationality is not only about avoiding the vices of

self-deception

and obfuscation (the failure to

communicate clearly

), but also about the virtue of

curiosity

, seeing the world more clearly than before, and

achieving things

previously unreachable

to you

. The study of rationality on LessWrong includes a theoretical understanding of ideal cognitive algorithms, as well as building a practice that uses these idealized algorithms to inform

heuristics

,

habits

, and

techniques

, to successfully reason and make decisions in the real world.

Resources and materials

To learn more about rationality, I recommend (in order of usefulness per unit of effort):

Subscribing to the

LessWrong digest

(subscribe at the sidebar),
Reading the essays from the list below,
Reading

Rationality A to Z

(podcast form

here

),
Reading some of the best works from Scott Alexander.

To learn more about systematically doing good (i.e. effective altruism) I recommend:

Subscribing to the 80,000 Hours mailing list,
Reading this 80,000 Hours post about the importance of career choice,
Participating in an EA Virtual Program,
Reading/skimming this comprehensive set of readings on effective altruism.

Some of the best rationality essays [2]

Below you can find some of the best writings on rationality, in no particular order:

Affective Death Spirals

Uncritical Supercriticality

Cached Thoughts

We Change Our Minds Less Often Than We Think

Hold Off On Proposing Solutions

Knowing About Biases Can Hurt People

Update Yourself Incrementally

The Bottom Line

Avoiding Your Belief's Real Weak Points

Motivated Stopping and Motivated Continuation

Anti-Epistemology

The Proper Use of Humility

You

Can

Face Reality

The Meditation on Curiosity

Something to Protect

Crisis of Faith

Mind Projection Fallacy

Reductionism

Explaining vs. Explaining Away

Angry Atoms

Making Beliefs Pay Rent (in Anticipated Experiences)

Humans are not automatically strategic

Local Validity as a Key to Sanity and Civilization

Twelve Virtues of Rationality

The nonc

...
Ideal Format/Frequency of Information Consumption
by Avi — 2021-10-19T15:39:16.483Z

It seems that for every type of information input, there is an ideal:

1. Source/Format

2. Frequency

3. Time of Day

4. Energy State

1. Source/Format

Examples: Twitter, blog, books, podcasts etc.

Don’t waste your time following your favorite author’s Twitter account if 99% of their value is derived from their books.

2. Frequency

The most effective frequency to consume a particular information source might be:

- Live/on-demand/push notifications

- Daily

- Weekly

- Monthly

- Annually

- On a Random Schedule

Match the frequency to the information source, and aim to maximize value gained for time input. Apply the 80/20 rule, then apply it again.

In general, if you feel like a source of information input is taking up too much of your time relative to the value gained, try reducing the frequency.

3. Time of Day

Some information is best consumed in the morning, some during your lunch break, some while exercising, some late at night etc.

4. Energy State

Example: Deep work is only possible when you’re in a state where your mind is able to focus and concentrate, not when you’re exhausted.

Example: If you have a favorite TV show, you can probably watch it when you’re exhausted in the evening and still enjoy it, whereas you can’t complete serious ‘productive’ work in that energy state.

Note: All of these factors are unique to each person and change at different stages of life.

Note: Many information sources don’t pass any filters and their ideal source/frequency etc. is zero, never.
Listen to top LessWrong posts with The Nonlinear Library
by KatWoods — 2021-10-19T12:24:43.564Z

Crossposted from the EA Forum.

We are excited to announce the launch of The Nonlinear Library, which allows you to easily listen to top EA and rationalist content on your podcast player. We use text-to-speech software to create an automatically updating repository of audio content from the EA Forum, Alignment Forum, LessWrong, and other EA blogs.

In the rest of this post, we’ll explain our reasoning for the audio library, why it’s useful, why it’s potentially high impact, its limitations, and our plans. You can read it here or listen to the post in podcast form here.

Listen here: Spotify, Google Podcasts, Pocket Casts, Apple, or elsewhere

Or, just search for it in your preferred podcasting app.

Goal: increase the number of people who read EA research

A koan: if your research is high quality, but nobody reads it, does it have an impact?

Generally speaking, the theory of change of research is that you investigate an area, come to better conclusions, people read those conclusions, they make better decisions, all ultimately leading to a better world. So the answer is no. Barring some edge cases (1), if nobody reads your research, you usually won’t have any impact.

Research → Better conclusion → People learn about conclusion → People make better decisions → The world is better

Nonlinear is working on the third step of this pipeline: increasing the number of people engaging with the research. By increasing the total number of EA and rationalist articles read, we’re increasing the impact of all of that content.

This is often relatively neglected because researchers typically prefer doing more research instead of promoting their existing output. Some EAs seem to think that if their article was promoted one time, in one location, such as the EA Forum, then surely most of the community saw it and read it. In reality, it is rare that more than a small percentage of the community will read even the top posts. This is an expected-value tragedy, when a researcher puts hundreds of hours of work into an important report which only a handful of people read, dramatically reducing its potential impact.

Here are some purely hypothetical numbers just to illustrate this way of thinking:

Imagine that you, a researcher, have spent 100 hours producing outstanding research that is relevant to 1,000 out of a total of 10,000 EAs.

Each relevant EA who reads your research will generate $1,000 of posi

...