trentmkelly/LessWrong-43k huggingface dataset:
Introducing Effective Self-Help
by Ben Williamson — 2022-04-12T15:01:29.229Z

An adapted, somewhat updated version of this article posted to the EA Forum a few months ago: https://forum.effectivealtruism.org/posts/i2Q3DTsQq9THhFEgR/introducing-effective-self-help
Effective Self-Help (ESH) is a pilot initiative researching the most effective ways people can improve their wellbeing and productivity. The project is currently funded by the EA Infrastructure Fund.

Improving your ability to function happily and productively should help you to be more effective in trying to solve the most pressing global problems. Effective Self-Help is an attempt to synthesise the best advice in service of this goal, slowly building a single, highest-quality source of information for anything related to wellbeing and/or productivity.

This post explains in greater depth what Effective Self-Help is, what I hope the project can achieve, and why I believe this work may be highly impactful. As this is a long post, I suggest reading the Summary section below and then skipping to whatever sections seem of greatest interest.

Summary

Effective Self-Help is currently an ongoing

series

of reports and articles researching key wellbeing and productivity topics. So far, we have published reports on sleep, stress, and habit formation interventions. We intend to develop the project into a home for the best guidance on how individuals can improve all significant aspects of their wellbeing and productivity.

In the What is Effective Self-Help? section, I discuss how I think ESH can be unique as a self-help resource by combining several key components:

Prioritisation

: Highlighting the most effective interventions and how effective they are, rather than just listing interventions that may be effective.

Research Quality

: Providing more rigorous and well-rounded advice that combines the findings of scientific literature with more Bayesian forms of evidence.

Presentation

: Maximising engagement through curated recommendations and the use of digital media (video, audio, and interactive programmes).

Breadth

: Covering topics across the full breadth of wellbeing and productivity, rather than just specific domains of expertise.

Practicality

: A focus on enabling behaviour change by highlighting practical actions and tailoring content to maximise the success of habit adoption.

Positive mental health

: Looking at how to improve the wellbeing of people who are not strugglin

...
Ukraine Post #10: Next Phase
by Zvi — 2022-04-12T13:40:01.035Z

This is another Covid-style Ukraine post in the ‘here are all the things that caught my attention since the last one of these posts’ tradition.

I have noticed that the rate at which I notice noteworthy new things while looking at my sources has declined steadily. The situation is becoming more static, and ambiguity over what is happening is steadily declining.

Thus, I expect to spend less time monitoring those sources. Doing these about once a week still might make sense, or it might not, we will have to see. I do want to start moving away from doing such an intense amount of taking in and reacting to current events, and find the time to also return to more long-term model building.

Military Progress and Conditions

Overview of what happened in the battle of Kyiv. Good detail on exactly how the Russian plan was supposed to work, and the various ways in which it didn’t.

Analysis of why Russia could not achieve its objectives and was forced to withdraw from the north. This kind of warfare depends on encircling and destroying the enemy forces, and Russia failed to do that, largely because their equipment was stuck on the roads.

6 April: Time lapse maps to toggle between for the previous week.

11 April: Report that 30% of Russian units are combat ineffective.

Kamil Galeev is back with a long and fascinating thread about what has happened militarily. He sees the Ukraine invasion as a replay of what the USSR did to Czechoslovakia in 1968, except against a real army and without adequate force, which matches many other observations. Among many other things, he explains that Soviet tanks were designed for use in a nuclear war, which makes them terrible in conventional war. Some review of old material from him, but a lot new as well.

Russia has appointed the first general from Syria to run its Ukraine war. This seems like a doubling down on atrocity-based strategies rather than putting the wartime generals in charge.

They do, however, seem to have an actual reasonable objective in mind now.

More time lapse video at the link, always good for perspective. Good to see the progress.

Very little happened in the days following – e.g. this April 11 map shows no changes.

What does the future hold?

Ukraine calls for some areas of Donbass to evacuate, Russia blows up the railway that would have enabled them to do so.

US military claims Russia is attempting to call u...
Is technical AI alignment research a net positive?
by cranberry_bear — 2022-04-12T13:07:56.289Z

I am concerned that technical AI alignment research could be increasing the risks from AGI, as opposed to decreasing them. Here are some reasons why alongside some possible interventions: (analogies are purposefully crude to help illustrate my points, but I recognize that in reality, the situation is not necessarily as clear cut / black and white)

Accelerating timelines: Promising results in AI alignment could lower researchers' assessments of danger, increase risk-taking and accelerate the development of AGI. Contrastingly, in a world where people are aware that misaligned AI could be dangerous to humanity, but there is not a lot of promising alignment research, researchers would be more cautious about developing and deploying AI. An analogy - if you believed self-driving cars were very dangerous and ideally should never be let on the road, you could plausibly be against attempts to make self-driving cars safer as this makes it more likely for them to be allowed on the road.

Possible solution: focus more on making people aware of risks from AI as opposed to trying to mitigate those risks.

Subtle misalignment: If we become better at aligning the goals of AI with our own goals, misalignment will become more subtle and much harder to spot. A slightly misaligned AI could be much more dangerous than a very misaligned AI. For example, it could take us much longer to notice that the AI system was misaligned and by the time we do, multiple negative cascades have been set in motion. For those concerned with S-risks, it seems like a slightly misaligned AI is more likely to lead to a long period of human suffering. An analogy - a clearly dangerous axe-wielding maniac is plausibly less dangerous than a calculating psychopath who appears very normal but secretly plans to kill as many people as possible.

Possible solution: focus more on techniques for detecting misalignment as opposed to techniques for achieving alignment.

Increased usefulness for malicious actors: There is a lot of talk of "alignment", but less about what we are actually aligning with. The ability to align the goals of AI with complex human-level goals could make AI-based systems much more effective weapons/tools for malicious actors than AI's that aren't able to encapsulate human values as well. An analogy - say we have a destructive weapon that, when used, wipes out a random area of the earth.

...
What is your advice for elder care, particularly taking care of dementia patients?
by RasmusHB — 2022-04-12T11:33:49.209Z

I think I already have a good sense of what lifestyle changes can slow down aging and the progression of dementia and to what extent, but I don't know how to implement these changes. There is lots of advice here on how to change your life, which I appreciate, but not that much on how to change another person's life (please do correct me if I'm wrong), particularly if they are older and set in their ways or even suffering from dementia and find it difficult to learn new things while not quite comprehending the urgency of change. In this case, I couldn't even convince the primary caregiver (which is not, or at least should not be me) of the importance of diet and exercise.

How did/would you deal with these problems or any other problems in elder care in general? (Since my model of average rationalist told me that someone would bring up cryonics, I asked and they don't want that.)

As a side note, I feel like this topic does not receive enough attention on LessWrong, for instance it does not have a tag, unlike e.g. parenting.
Reward model hacking as a challenge for reward learning
by Erik Jenner — 2022-04-12T09:39:35.161Z

This post discusses an issue that could lead to catastrophically misaligned AI even when we have access to a perfect reward signal and there are no misaligned inner optimizers. Instead, the misalignment comes from the fact that our reward signal is too expensive to use directly for RL training, so we train a reward model, which is incorrect on some off-distribution transitions. The agent might then exploit these off-distribution deficiencies, which I’ll refer to as reward model hacking.

I’m sure that others have thought about this issue before, but I didn’t find much discussion focused on it. So I’m writing this post so that either someone can explain to me why this isn’t a big deal, or to give it a name and some explicit analysis. Depending on how hard reward model hacking is to deal with, it could present a significant challenge to the entire approach of doing RL + reward learning, and my main goal is figuring out whether that’s the case.

The Setting

I will focus on the case where the policy learned via RL is able to do some online planning or reasoning about the world—it can come up with action sequences that lead to high reward without ever having tried out those action sequences before. I don’t care much here whether we have a learned mesa-optimizer, or a search process that we built explicitly, or just a bunch of really good heuristics that when taken together yield similar behavior.

Reward model hacking is also an issue without online planning. But the online planning setting makes it clearer that reward model hacking could be a really hard to deal with fundamental issue for reward learning, rather than a small technical problem. I'm much less sure whether that's also the case without online planning capabilities.

Apart from that, I’ll make rather optimistic assumptions to isolate reward model hacking from other potential failure modes:

We have access to a perfect reward signal,[1] but it’s expensive to evaluate (e.g. because it requires human feedback)
We train an RL agent in parallel with a reward model (for concreteness, you can imagine a setup similar to Deep RL from human preferences) [2]
The agent is trying to optimize its reward according to the reward model (in particular, it hasn’t learned some other misaligned inner objective)

If we furthermore assumed that the reward model had learned the reward signal perfectly, then we would ha...
How I use Anki: expanding the scope of SRS
by CallumMcDougall — 2022-04-12T08:28:49.018Z

Intro to this post

So maybe you’ve heard about spaced repetition, and you’re familiar with the basic ideas for why it can be highly beneficial for learning. But despite this, you don’t really use it all that much / at all, maybe because you find it kinda boring, or you don’t think the things that you are trying to learn are suitable for spaced repetition. Even though there’s a bunch of meta-level advice floating around sites like LessWrong, there doesn’t seem to be as much on the object-level - i.e. how to actually implement spaced repetition in a way that fits with your learning style - and you’re not sure how to go about doing this.

This is basically the position I was in at the start of this year. But since then, I’ve started using it a lot more than I used to, in the form of the spaced repetition software Anki. I’ve realised that the customisability Anki offers allows you to greatly expand the scope of things SRS is traditionally used for, to encompass many different fields and types of knowledge. Hence, this post should be seen as an attempt to combat the problem outlined above, and give specific object-level advice about how to make Anki work for you.

Before I get into the main body of this post, I should add a caveat here. It’s understandable why object-level advice seems undersupplied relative to meta-level, because the latter can be relevant to everyone, whereas the effectiveness of the former often varies a lot from person to person. As such, this post should be read with a great big dose of YMMV. I expect a large group of people will probably get very little value out of it. However, if a couple of people find it extremely helpful for setting up / improving their own spaced repetition systems, I will consider it having been worth writing!

Another caveat - maybe the most valid criticism of Anki is that it's great for discrete memorisation, less so for linking ideas together. I totally agree with this, and I think there are some great posts on LessWrong like this one about how you can build systems to help you synthesise knowledge. I'm a big believer in PKM software like Obsidian which is built on the ideas of bidirectional links and knowledge graphs. I believe Anki can and should be complimentary to these kinds of systems, rather than replacing them.

Depending on why you’re reading this, you might find different parts of this post more relevant than oth...
What do you think will most probably happen to our consciousness when our simulation ends?
by ArtMi — 2022-04-12T08:23:17.859Z

I would like to introduce this Question-Post with more information about the increasing Capital and Human Resources directed to A.I.

. . .

There is an open debate about whether or not we are in a simulation. This Question-Post argument is based on the assumption that there is a high probability that we are living in a simulation. I believe the ending stance of our simulation will be when AGI is invented and that this simulation has the goal of information gathering.
What do you think will happen with our self awareness when our simulation ends?
I came up with the argument that: As there is not Universal mercy in our reality, there will probably not be with our minds. So it would be an instant Shutdown?
Favorites & Performers
by Soma — 2022-04-12T05:50:08.716Z

Childhood is such a magical time. As a parent, I feel even more pressure to preserve this sanctity for my own children. Based on pure anecdote, I have noticed a trend: if there are at least two children in a family, there are two archetypes: a favorite and a performer.

The favorite in the family was the one that was, knowingly or unknowingly, doted on. Parents were well meaning, but they probably unintentionally engaged in enabling relationships with the favorite. This has consequences when it comes to our harsh, competitive world.

The performer, on the other hand, was someone who had to prove herself.

I do consider myself the performer in my family. It was subtle. I felt loved. I never felt I was wronged. But there must have been a range of behaviors while being around the favorite that had undue influence on my performance as an adult.

I was able to achieve more milestones in adult life than my brother. I felt more responsibility for the outcomes of the people I loved. I was able to change my physics whenever I needed to without the help of my parents. I will not claim I am self made, but I never asked for anything.

I find a lot of career and purpose driven people to be performers who weren’t the favorites in the family. It is a slippery slope to retrospectively explain what happened to me, by generalizing how I was treated in childhood.

But these thoughts make me feel cautious about my own parenting. Especially around my blind spots on how I treat my children.

I don’t want to favor anyone. I want them all to perform.
A broad basin of attraction around human values?
by Wei Dai — 2022-04-12T05:15:14.664Z

Followup to: Morality is Scary, AI design as opportunity and obligation to address human safety problems

In Corrigibility, Paul Christiano argued that in contrast with ambitious value learning, an act-based corrigible agent is safer because there is a broad basin of attraction around corrigibility:

In general, an agent will prefer to build other agents that share its preferences. So if an agent inherits a distorted version of the overseer’s preferences, we might expect that distortion to persist (or to drift further if subsequent agents also fail to pass on their values correctly).

But a corrigible agent prefers to build other agents that share the overseer’s preferences — even if the agent doesn’t yet share the overseer’s preferences perfectly. After all, even if you only approximately know the overseer’s preferences, you know that the overseer would prefer the approximation get better rather than worse.

Thus an entire neighborhood of possible preferences lead the agent towards the same basin of attraction. We just have to get “close enough” that we are corrigible, we don’t need to build an agent which exactly shares humanity’s values, philosophical views, or so on.

But it occurs to me that the overseer, or the system composing of overseer and corrigible AI, itself constitutes an agent with a distorted version of the overseer's true or actual preferences (assuming a metaethics in which this makes sense, i.e., where one can be wrong about one's values). Some possible examples of human overseer's distorted preferences, in case it's not clear what I have in mind:

Wrong object level preferences, such as overweighting values from a contemporary religion or ideology, and underweighting other plausible or likely moral concerns.

Wrong meta level preferences (preferences that directly or indirectly influence one's future preferences), such as lack of interest in finding or listening to arguments against one’s current moral beliefs, willingness to use "cancel culture" and other coercive persuasion methods against people with different moral beliefs, awarding social status for moral certainty instead of uncertainty, and the revealed preferences of many powerful people for advisors who reinforce one’s existing beliefs instead of critical or neutral advisors.

Ignorance / innocent mistakes / insufficiently cautious meta level preferences in the face of dangerous new situ

...
AI governance student hackathon on Saturday, April 23: register now!
by mic — 2022-04-12T04:48:14.967Z

Cross-posted from the EA Forum

Columbia Effective Altruism and Roosevelt at Columbia are hosting an AI policy hackathon on Saturday, April 23, 2022, from 9am to 7pm EDT (UTC-4:00), open to virtual and in-person student participants! As artificial intelligence is expected to have a dramatic influence on our world in the coming decades, it’s increasingly urgent to develop policies for governments and corporations to reduce risks from advanced AI. During the hackathon, your team of 1–4 people will write and present a brief policy memo, developing your experience with AI governance and effective policy research. There will also be workshops to help students deepen their understanding of AI governance, effective policy research and writing, and other crucial skills. No prior experience with AI governance or policy writing is required. Up to $3,000 in prizes available. Register here!

We’re also looking for judges, mentors, and talk/workshop presenters! They can be in-person or virtual. We’re ideally looking for them to have some exposure to AI governance for reducing existential risk (e.g., have read content equivalent to EA Cambridge’s AI Governance program). We’re excited about the potential for this hackathon to introduce students to the field of AI governance and engage them in reducing long-term risks. Register to be a judge, mentor, or speaker

We’d also appreciate it if you shared the participant or judge/mentor/speaker form with anyone you think would be interested.

If you have any questions, feel free to comment here or message me on the LessWrong.
The Platonist’s Dilemma: A Remix on the Prisoner's.
by James Camacho — 2022-04-12T03:49:42.695Z

The Platonist’s Dilemma

“You two miscreants are going away for a long time. Here’s the deal: We have enough on you to put you behind bars for five years. If you talk, you can go home right now. No bid, no record. If you don’t, and your friend does, you’ll be in here for a dime. If you both get conversational, you’ll be guaranteed less than ten years.”

Phillip looked at the guy sitting next to him. Friend? Certainly not. Maybe his partner in crime, but he was never good at long-term relationships to begin with. He’d just met the dude—what was his name? Michael? Max?—a few hours ago, there was no reason to give him preferential treatment. Of course, that also meant M— should not receive unwarranted betrayal.

Phillip thought back to his childhood, playing Blockus with his friends. They’d quickly discovered the ideal strategy was to pair up, work together to block off half the board. Betrayal gave you a slight edge if the other person remained loyal, but not a significant one. Plus, it ended up hurting you both far more than you would benefit from the defalcated space. The utilitarian argument said Phillip should keep his silence.

Matthew looked over at Phillip, watching his mind turn like a 50MHz laptop. Doesn’t the idiot realize the detective can read every facial expression he makes, like the micro frown when the word “friend” was mentioned? Phillip was certainly not a friend; in fact he probably couldn’t even remember Matthew’s name. Regardless, Matthew needed to leave the room with the best possible outcome. Phillip clearly had an IQ at the top of the bell curve, so how could Matthew use that to his advantage?

Matthew enjoyed these kinds of problems. As a kid, his favorite game was Risk. Calculating the odds, buying loyalty and selling integrity, all to gain an upper hand. He always played with the gray pieces, and loved to see the board fill up with a miniature storm of soldiers. He’d gotten very good at the game. Somehow, the more he played, the more innocent he appeared to his competitors. Occasionally Matthew worried he had become a pathological liar, but then he remembered that he didn’t actually believe his lies. Maybe he was a good liar, but that was different; that was good.

&nbs...