trentmkelly/LessWrong-43k huggingface dataset:
Researching and Reasoning About Bardiya
by lionhearted (Sebastian Marshall) — 2021-01-31T00:24:20.869Z

It's both profitable and gratifying to use one's reasoning skills to attempting to figure out answers to complex and murky questions. Even failing the ability to say definitively what happened, we can still make and update our own statistical distributions of what could have possibly happened.

Unfortunately, it's typically questions like "Who killed JFK?" that draw the brunt of this type of speculative research and reasoning. Which is, I think, not a very good place to study and develop better skills for this type of thinking.

There's many reasons why, but just one will suffice — it's very helpful to be able to discuss and reason through these problems with another person, and almost any item that's emotionally charged in our culture will lead to less productive discussions.

An alternative for you.

I've found it better to train this type of thinking on incidents that are well-sourced and controversial in other cultures, but which are obscure or almost completely unknown in our own.

Things like —

(1) Was the 'Bardiya' that Darius the Great overthrew and killed the natural born son of Cyrus or not?

(2) Did Uesugi Kenshin die of natural causes?

(3) Did Scipio Africanus's brother misappropriate money?

And so on.

While it might be hard at first to get a friend interested in whether Bardiya was the actual Bardiya or a pretender, you can see how it's unlikely someone would have a heated opinion on the matter. It's a fun puzzle to work on with someone.

Many of these questions aren't definitively answerable — we'll probably never know for sure on Bardiya — but every now and then you find a problem that seems like it's unsolvable but for which you eventually find extremely conclusive evidence.

This is great when it happens.

I've come across a couple of these, where I was wracking my brain weighing evidence of whether a certain action during, say, the Russian Civil War was just an accident or coordinated — until I found a translation of an almost-certainly-not-forged-given-the-context set of orders.

It's like, you've been digging through the evidence and building a spectrum of probability in your mind, and then you get something like a 'wave function collapse' where you 99% know what happened...

It's very satisfying. Though, unfortunately, this class of problem resists documentation if you want it to remain answerable-only-with-digging. If you wrote down a list of such pr...
For those who advocate Anki
by jmh — 2021-01-31T00:01:20.698Z

I just read the start of TurnTrout's Lessons from his self-teaching efforts and his mention of Anki made me wonder about something.

Spaced repetition is a bit of a take away from real life experience. Typically we don't use everything we're learned every day, we get a form a natural spaced repetition (albeit somewhat biased in what is repeated by our own lives).

I've been (perhaps more pretending here) studying Korean for a while but in many ways not really accomplishing much. I've got a very limited vocabulary that I can recall in a "study" type setting (flash cards, reading the specific lesson dialogue...), understand common verb conjugation, various particle markers the language uses, know the character set and okay at making the right sound. But suggesting I have anything related to a working command of the language is a rather humorous fantasy in my view.

One challenge I have is I really don't know any Koreans so opportunities to practice in real life settings are very limited. I've decided that I will start using some of the online tutors to start making my self actualy use and so start really thinking and developing the "muscle" memory rather than just study, repeat saying, flashcards, online audio or Korean language shows.

Does anyone think that Anki is better than real life use for learning? Or is it perhaps more of a (possibly imperfect) substitute for when one cannot avail themself of a real life usage setting to apply what they have learned?
Catching the Spark
by LoganStrohl — 2021-01-30T23:23:57.383Z

Multiple readers have noted that this is very similar to a method called “Thinking At the Edge”, taught by Eugene Gendlin. I don’t know much about Thinking At the Edge, but what little I do know suggests that he and I have developed his focusing work in similar directions, perhaps for similar reasons. If you like this essay, you might want to check out

Gendlin’s introduction to Thinking At the Edge

.

There are a lot of different ways to dance.

In one kind of dancing, you move your body around spontaneously in response to music. That’s a fun dance that even infants can do. But some dances have a more specific goal than “fun”. Partner dances enable conversation through touch and motion rather than symbolic language. Folk dances encourage participation in the soul of a community. Ballet pairs choreography with music to tell a story. If you’ve ever tried any of these specialized dances, you know that they take deliberate effort to learn.

Similarly, there are a lot of different ways to engage with the world in a spirit of curiosity.

“Naturalism” (an allusion to

19th century naturalists

) is the name I use for a specific way of engaging curiously with the world. It’s a method of inquiry that uses patient observation and original seeing to build models that were previously unthinkable. It takes longer to learn than the spontaneous curiosity of pure exploration, but this specialized method helps you to make deliberate progress in areas where your existing concepts are leading you astray.

It takes much more than one essay to talk about naturalism as a whole, so this essay is just about the very first piece: catching the spark.

Imagine two pieces of flint colliding with each other and sending sparks flying. By default, the sparks will go dark and fall to the ground. But if you catch one in a bundle of dried grass, and then blow on it gently, you can make a flame.

Curiosity is like that. It's being sparked all the time, but most of the sparks flare out and disappear.

(If you watch your experience closely for just thirty seconds, with attention to teeny tiny searching sensations, you can probably feel it happening [1].)

This essay is about my strategy for turning some of those sparks into steady flames. When skillfully tended, a steady flame of curiosity can sustain a whole line of research, even when neither you nor anyone else knows how to think about the...
Notes on Judgment and Righteous Anger
by David Gross — 2021-01-30T19:31:21.314Z

This post examines the virtues of judgment and righteous anger. It is meant mostly as an exploration of what other people have learned about these virtues, rather than as me expressing my own opinions about them, though I’ve been selective about what I found interesting or credible, according to my own inclinations. I wrote this not as an expert on the topic, but as someone who wants to learn more about it. I hope it will be helpful to people who want to know more about these virtues and how to nurture them.

What are these virtues?

Judgment, or censure, is (at least) the ability and willingness to identify and call out something that is unjust or someone who is acting unjustly. Sometimes it goes beyond questions of justice: you can also judge someone unwise, unkind, unreasonable, and so forth.

Righteous anger is a motivating fury, usually provoked by injustice. If you are righteously angry you have noticed something (or suffered something) that violates your sense of justice in a way that provokes an emotional response that encourages you to do something about it (and often, to signal your displeasure to those around you).

We contain multitudes

I have already written about the virtues of good temper, patience & forbearance, and forgiveness & clemency and you might wonder if I’ve lost the plot by pivoting to something like “righteous anger” that seems at first to contradict those. In my defense:

These might not be as contradictory as they appear. You may have the virtues of forbearance, forgiveness, good temper, and the like, but still have a limit beyond which you get angry in a virtuous way. It may be that there is a time for forbearance and a time for judgment, a time for good temper and a time to be furious, and that you need skill in both modes in order to best flourish as a human being. Even forgiveness guru Jesus thought that judgment came first, forgiveness after: “If a brother or sister sins, you must rebuke the offender, and if there is repentance, you must forgive.”[1]
I am being ecumenical in my research into virtues. Different virtues were prized by different cultures and traditions in different periods. I should not be surprised if the collection of virtues I unearth does not cohere seamlessly.
Some virtues are commonly given the name of one of their more imbalanced extremes, when the opposite extreme is more common or more harmful or when the p

...
Fight Akrasia and Decision Fatigue with DIY Productivity Software
by DanB — 2021-01-30T18:38:20.279Z

Akrasia is the condition of knowing what you should be doing, but being psychologically unable to motivate yourself to do it. Decision fatigue is the condition of being unable to select between competing choices because of mental exhaustion.

There's a whole sector of the software industry that's dedicated to helping people fight these conditions. Some tools help you build habits, while others try to hold you accountable for your mistakes, to give you an incentive to make better choices.

Many people, including myself, have experimented with these tools and found them lacking, for a wide variety of reasons. Sometimes the problem is a crucial missing feature - and sometimes the problem is too many features. Some people have been burned by technology churn - why invest your time to learn the most recent fad, when history indicates that software often declines in quality over time, and is then abandoned?

Perhaps most importantly, it often feels that the author of the tool is guilty of other-optimizing - the techniques that are useful and relevant for him/her simply aren't as valuable for other people... because people are actually quite different internally.

I claim that you can solve these problems by writing your own productivity software.

To many people, this idea might seem preposterous: software is hard to build, so it only makes economic sense if you sell it to a large market.

But actually the hardness of software development mostly comes from the challenges of scaling up and serving large numbers of users. It turns out that writing software just for yourself is actually pretty easy - especially if 1) you're a decent developer, as many LessWrongers are, and 2) you have some good tools.

I know this from experience. I've been developing my own customized "life-management" tools for the last several years, and it's had a transformative effect on my life. I have dozens of tools - I call them Widgets - that range over a wide variety of areas:

Fitness tracking and logging
Time tracking
Expense monitoring
TODO list and project management
Commitment review
Daily Checklist (inspired by the Checklist Manifesto)
Chinese character learning
Question / Answer system (my personal version of StackOverflow)
Alcohol and Junk Food consumption control
Chore reminders
Day Planning / Scheduling

Some of these are more complicated than others, but a typical ...
Limiting Causality by Complexity Class
by Bunthut — 2021-01-30T12:18:37.723Z

The standard account of causality depends on the idea of intervention: The question of what follows if X not just naturally occurs, but we bring it about artificially independently of its usual causes. This doesn't go well with embedded agency. If the agent is part of the world, then its own actions will always be caused by the past state of the world, and so it couldnt know if the apparent effect of its interventions isn't just due to some common cause. There is a potential way out of this if we limit the complexity of causal dependencies.

Classically, X is dependent on Y iff the conditional distribution of X on Y is different from the unconditional distribution on X. In slightly different formulation, there needs to be a program that takes Y as an input and outputs adjustments to our unconditional distribution on X, and where those adjustments improve prediction.

Now we could limit which programms we consider admissible. This will be by computational complexity of the program with respect to the precision of Y. For example, I will say that X is polynomially dependent on Y iff there is a programm running in polynomial time that fullfills these conditions. (Note that dependence in this new sense needn't be symmetrical anymore.)

Unlike with the unlimited dependence, there's nothing in principle impossible about the agents actions being polynomially independent from an entire past worldstate. This can form a weakened sense of intervention, and the limited-causal consequences of such interventions can be determined from actually observed frequencies.

Now, if we're looking at things where all dependencies are within a certain complexity class, and we analyse it with something stronger than that, this will end up looking just like ordinary causality. It also explains the apparent failure of causality in Newcombs problem: We now have a substantive account of what it is to act in intervention (to be independent in a given complexity class). In general, this requires work by the agent. It needs to determine its actions in such a way that they dont show this dependence. Omega is constructed to make its computational resources insufficient for that. So the agent fails to make itself independent of Omega's prediction. The agent would similarly fail for "future" events it causes where the dependence is in the complexity class. In a sense, this is what puts the events ...
Calibrated estimation of workload
by kerspoon — 2021-01-30T12:15:23.413Z

I've been using a spreadsheet to track my work in various ways for about 10 years. There are some fun benefits to it that I want to share. It has really helped me improve my calibration and precision in an important area - planning my workload - and want to encourage others to try something similar for themselves.

This post follows on from how-quickly-can-you-get-this-done-estimating-workload but you don't need to have read that for this to make sense.

Show me the data

Before I get to the details lets look at the data for this year so far.

The first chart answers the question: how well calibrated am I when someone asks me "what's the chances that you will be able to complete this by next week?". You can see in the chart that I complete 65% of tasks that I say have a 50% chance of being completed.

The second chart answers the question "when it say it will take X hours, how long will it actually take?".

Data Gathering

At the start of each week I write down the most important tasks to complete that week. I mostly take these tasks from my current goals, which are set at the start of the year. I try to make some progress on my yearly goals every week; this helps me ensure that I keep my day-to-day tasks aligned with my long-term goals. The rest come from a longer "todo" list which mostly fills up with general admin/emails/etc.
Next to the tasks I also write down how long I think it will take and my probability of completing it that week. To make the maths easier my probabilities are to the nearest 10% (I've added in another bucket at 95% because I want to be able to distinguish between 90% and 95%).
Here is how one week of tasks looks (I've hidden the names of the tasks):

For the duration I have learnt not to schedule tasks I expect to take more than 4 hours. My time estimates for these are always way off, and it's often unclear what "completed" even means for many of these. If I'm writing a draft of an article, for example, I will often realise part way through that I need to do more research. As you can see in the image I also highlight anything over 2 hours, just as a reminder to myself to see if I can easily break it down further. What I do with these large tasks is either split them into smaller tasks but I have to treat "deep work" tasks differently.

For some tasks, the quicker I tick them off the better e.g. clearing my email inbox. For others the more ti...
Play with neural net
by KatjaGrace — 2021-01-30T10:50:13.811Z

I found Tensorflow Playground pretty cool to play with, in case anyone is looking for the kind of intuitive understanding of neural nets that futzing with a thing with one’s hands contributes to.

One game you can play is pretending to be a training algorithm. How would you adjust the weights, if you wanted to classify which of two spirals a point comes from?
Against Sam Harris's personal claim of attentional agency
by mike_hawke — 2021-01-30T09:08:45.145Z

TL;DR: Sam Harris has an obviously terrible information diet, and it is correct to criticize him for this in the context of his meditation teachings. He should treat his engagement with news headlines and twitter as unfortunate addictions, not good intellectual citizenship.

Here is a recent podcast episode in which he fends off claims that his attentional priorities are incompatible with his meditation practice (starting at 13m55s). Nothing he says is quite wrong, but I couldn't help but feel like he was talking around something more important. It felt like a missed opportunity to loudly admit that he falls well short of a healthy mental lifestyle. Those claims by some listeners that he should not podcast about ongoing world events are obviously silly. But I think that they end up serving as a Weak Man argument that distracts from the related, non-silly criticisms. It concerns me that he rebuts those misplaced criticisms at length, but (as far as I know) has never spent a similar amount of time admitting his actual error and urging contemplative trainees to beware the same.

Harris has said that given his background (as someone with extensive training in both meditation and neuroscience, plus a large existing audience), he is uniquely positioned to produce such an app as Waking Up. He’s right. But at the same time, he has the significant drawback of setting a very bad example of how to use social media. It’s true that people who like his app can just ignore his podcast and social media activity. But that strikes me as a needlessly lossy compromise. If I had it my way, he would stop using twitter except under very controlled conditions, spend some of the freed attention on deliberate, high-quality information, and thereby increase the signal:noise ratio of his podcast. Maybe he would agree with me about this, but if so, it’s not very clear to me.

Perhaps the starkest aspect of all this is the way Harris related to Donald Trump's Twitter presence. So far as I can tell, it was in large part a concession to outrage addiction. I think that paying fine-grained attention to Donald Trump’s tweets was an important task that some small number of people were right to do. But it was absolutely counterproductive for huge chunks of the American public to do it, including Sam Harris. He could have easily said what he wanted to say about Donald Trump without constantly intaking...
Less Wrong Poetry Corner: Coventry Patmore's "Magna Est Veritas"
by Zack_M_Davis — 2021-01-30T05:16:26.486Z

(Previously)

This is my favorite poem that's secretly about the necessity of revealing information about deception!

Here, in this little Bay,

Full of tumultuous life and great repose,

Where, twice a day,

The purposeless, glad ocean comes and goes,

Under high cliffs, and far from the huge town,

I sit me down.

For want of me the world's course will not fail:

When all its work is done, the lie shall rot;

The truth is great, and shall prevail,

When none cares whether it prevail or not.

Truth will win out in the end—after no one has any remaining incentive to lie, because the liars have already gotten everything they wanted? Is that supposed to be comforting? I'm going to assume the contented tone is ironic, and that this is really a coded plea for the importance of speed. The truth is great and shall prevail ... and the work of rationality is to get it to prevail faster—the need of us to make the world's course fail.
AMA on EA Forum: Ajeya Cotra, researcher at Open Phil
by Ajeya Cotra — 2021-01-29T23:05:41.527Z
Link: https://forum.effectivealtruism.org/posts/QAqghTmp7FSMcJ4ch/ama-ajeya-cotra-researcher-at-open-phil

Hi all, I'm Ajeya, and I'll be doing an AMA on the EA Forum (this is a linkpost for my announcement there). I would love to get questions from LessWrong and Alignment Forum users as well -- please head on over if you have any questions for me!

I’ll plan to start answering questions Monday Feb 1 at 10 AM Pacific. I will be blocking off much of Monday and Tuesday for question-answering, and may continue to answer a few more questions through the week if there are ones left, though I might not get to everything.

About me: I’m a Senior Research Analyst at Open Philanthropy, where I focus on cause prioritization and AI. 80,000 Hours released a podcast episode with me last week discussing some of my work, and last September I put out a draft report on AI timelines which is discussed in the podcast. Currently, I’m trying to think about AI threat models and how much x-risk reduction we could expect the “last long-termist dollar” to buy. I joined Open Phil in the summer of 2016, and before that I was a student at UC Berkeley, where I studied computer science, co-ran the Effective Altruists of Berkeley student group, and taught a student-run course on EA.

I’m most excited about answering questions related to AI timelines, AI risk more broadly, and cause prioritization, but feel free to ask me anything!