trentmkelly/LessWrong-43k huggingface dataset:
Fertility Rate Roundup #1
by Zvi — 2023-02-27T13:30:01.015Z

Previously: On Car Seats as Contraception

[Editor’s Note: This post assumes the perspective that more people having more children is good, actually. I will not be engaging with any of the arguments against this, of any quality, whether they be ‘AI or climate change is going to kill everyone’ or ‘people are bad actually,’ other than to state here that I strongly disagree. AI content will continue later this week.]

A common theme in childhood roundups has been that existing efforts by governments, to increase the number of children born in various countries, have all been pathetically small in magnitude. The amounts spent and methods used pale in comparison to what is at stake. We reliably see signs that the policies work, even when poorly designed and implemented – the same way that when policies attempt to reduce the birth rate, those work as well.

The core problem is the dose is too low.

Yes. If you give parents money, more people choose to be parents.And the amount necessary to make this happen is, if you crunch the numbers, an amount rapidly aging societies can’t afford not to pay.

The other theme is, as I discuss in On Car Seats as Contraception, that there are lots of other government policies that have much bigger impacts on the felt costs of having and raising children, including the lifestyle and status impacts of raising children.

This is a roundup of related efforts that have crossed my desk recently, to illustrate that this is a highly solvable problem.

Childcare

Child care in America continues to be super expensive. People who understand economics understand that this is true because we combine large purchasing subsidies with onerous baseline requirements that drive up costs. Whereas you could (at least partly) solve this problem in the style of Vermont, by doing much less of both these things – removing price barriers for the bottom half and removing subsidizes for at least the top half, instead Giving Parents Money mostly in the form of lower taxes.

I also continue to think that it is madness to subsidize and massively favor professionally provided child care over family provided child care, where as far as I can tell most everyone agrees children are better off with family provided child care, which we are now essentially pricing out of the market. While in other contexts, of course, massively favoring family provided care v...
Something Unfathomable: Unaligned Humanity and how we're racing against death with death
by Yuli_Ban — 2023-02-27T11:37:36.112Z

I fear I may be becoming a mini-Yudkowsky.

I write this in response to multiple exclamatory remarks I've seen in recent weeks, excited over the prospect of all jobs being automated, of ultra-high unemployment, basic income, and radical abundance, now even further bolstered over the incredible hype over the imminency of artificial general intelligence.

Waking Up

For years now, perhaps even over a decade, I've been obsessed with the prospect of the Technological Singularity and all that comes with it. Starting in 2014, I even began considering myself a Singularitarian.

All the arguments seemed right to me. Technological change was progressing. Humans cannot think exponentially. Artificial intelligence will grow more powerful and generalized. We ought to accelerate to reach artificial general intelligence to maximize our potential, achieve immortality, and ultimately merge with the machines.

All that sounded fantastic. Every bit of progress in artificial intelligence that came excited me, and I'd dream of the day I lived in an AI-powered utopia so totally unlikely the mundane post-Y2K dead technoscape I considered contemporary life.

Then ChatGPT was released. Though GPT-2 had first convinced me that AGI was a real possibility, ChatGPT in December 2022 was the first time it ever felt truly tangible. And as I fiddled with its mighty capabilities, something about it felt.... off.

Some aspect of this new world of capabilities didn't feel right. It felt like too much of a vulgar display of power. But I still had my fun with it. During the Christmas gathering, I smugly believed against my increasingly technophobic relatives, "You people have absolutely no idea what's coming."

Unfortunately, I may have been terribly right.

All throughout January of 2023, I suffered a terrific crisis of confidence and decided that the only way to resolve it was to step back and examine my beliefs from a most critical eye. Some of which, I overcorrected— such as my erroneous belief that the laws of diminishing returns would extinguish any chance at an intelligence explosion or post-silicon advances in computing.

Others, I feel I undercorrected, such as my statements that synthetic media (popularly known as AI art) would change exactly nothing about the entertainment landscape beyond a temporary recession in creatives' fortunes.

In some ways, I found new re...
The idea of an "aligned superintelligence" seems misguided
by ssadler — 2023-02-27T11:19:57.679Z
Link: https://ssadler.substack.com/p/the-idea-of-an-aligned-superintelligence

For a while I’ve been reading LessWrong, and there’s something that doesn’t make much sense to me, which is the idea that it is possible to align an AGI/superintelligence at all. I understand that it’s probably not even a majority of discussion on LW that is optimistic about the prospect of AI alignment via technology as opposed to other means, but nonetheless, I think it’s skews the discussion.

I should humbly clarify here that I don’t consider myself in the league of most LW posters, but I have very much enjoyed reading the forum nonethless. The alignment question is a bit like the the Collatz conjecture, seductive, only the alignment question intuitively seems much harder than Collatz!

Alignment is a subject of intense ongoing debate on LW and other places, and of course the developers of the models, though they admit that it’s hard, are still optimistic about the possibility of alignment (I suppose they have to be).

But my intuition tells me something different; I think of superintelligence in terms of complexity, eg, it has a greater ability to manage complexity than humans, and to us it is very complex. In contrast, we are less complex to it and it may find us basic and rather easy to model / predict to a good degree of confidence.

I think complexity is a useful concept to think about, and it has an interesting characteristic, which is that it tends to escape your attempts to manage it, over time. This is why, as living organisms, we need to do continual maintenance work on ourselves to pump the entropy out of our bodies and minds.

Managing complexity takes ongoing effort, and the more complex the problem is, the more likely that your model of it will fall short sooner or later, so pretty much guaranteed, in the case of a superintelligence. And this is going to be a problem for AI safety, news to pretty much no-one I expect…

And this is just how life is, and I havn't seen an example in any other domain that would suggest we can align a superintelligence. If we could, maybe we could first try to align a politician? Nope, we havn't managed that either, and the problem isn't entirely dissimilar. What about aligning a foreign nation state? Nope, there are only 2 ways, either there is mutual benefit to alignment, or there is a benefit to one party to behave well because the other has the circumstantial advantage.

I Googled "nature of complexity" just to see if the...
EA & LW Forum Weekly Summary (20th - 26th Feb 2023)
by Zoe Williams — 2023-02-27T03:46:39.842Z

Supported by Rethink Priorities
This is part of a weekly series summarizing the top posts on the EA and LW forums - you can see the full collection here. The first post includes some details on purpose and methodology. Feedback, thoughts, and corrections are welcomed.

If you'd like to receive these summaries via email, you can subscribe here.

Podcast version: Subscribe on your favorite podcast app by searching for 'EA Forum Podcast (Summaries)'. A big thanks to Coleman Snell for producing these!

Philosophy and Methodologies

Make RCTs cheaper: smaller treatment, bigger control groups

by Rory Fenton

When your intervention is expensive relative to data collection, you can maximize statistical power for a given cost by using a larger control and smaller treatment group. The optimal ratio of treatment sample to control sample is the square root of the cost per treatment participant divided by the square root of the cost per control participant.

Object Level Interventions / Reviews

AI

What AI companies can do today to help with the most important century

by Holden Karnofsky

Grounded suggestions (ie. more than one AI lab has made a serious effort at each suggestion) for major AI companies to help the most important century go well:

Prioritize alignment research, strong security, and safety standards (eg. figuring out what behaviors are dangerous, and what to do if they see them).
Avoid hype and acceleration (eg. publish fewer flashy demos or breakthrough papers).
Prepare for difficult decisions via setting up governance, employee and investor expectations to allow for decisions that aren’t profit-maximizing.
Balance the above with conventional / financial success (so they stay relevant).

Holden is less excited about the following interventions labs sometimes take: censorship of AI models, open-sourcing AI models, and raising awareness of AI with governments and the public.

Sam Altman: "Planning for AGI and beyond"

by LawrenceC

Linkpost for this blog post from OpenAI’s CEO Sam Altman on their AGI roadmap. Key points include:

Goal

OpenAI’s mission is to ensure artificial intelligence benefits all humanity. They operate as if risks are existential and want to successfully navigate these.
They think short timelines and slow takeoff seem like the safest scenario, since it minimizes compute overhang and gives maximum time to empirically solve safety and adapt to AGI.

Short-term Stra...
[Simulators seminar sequence] #2 Semiotic physics - revamped
by Jan — 2023-02-27T00:25:52.635Z

Update February 21st: After the initial publication of this article (January 3rd) we received a lot of feedback and several people pointed out that propositions 1 and 2 were incorrect as stated. That was unfortunate as it distracted from the broader arguments in the article and I (Jan K) take full responsibility for that. In this updated version of the post I have improved the propositions and added a proof for proposition 2. Please continue to point out weaknesses in the argument; that is a major motivation for why we share these fragments.

For comments and clarifications on the conceptual and philosophical aspects of this article, please read metasemi's excellent follow-up note here.

Meta: Over the past few months, we've held a seminar series on the Simulator theory by janus. As the theory is actively under development, the purpose of the series is to uncover central themes and formulate open problems. A few high-level remarks upfront:

Our aim with this sequence is to share some of our discussions with a broader audience and to encourage new research on the questions we uncover.
We outline the broader rationale and shared assumptions in Background and shared assumptions. That article also contains general caveats about how to read this sequence - in particular, read the sequence as a collection of incomplete notes full of invitations for new researchers to contribute.

Epistemic status: Exploratory. Parts of this text were generated by a language model from language model-generated summaries of a transcript of a seminar session. The content has been reviewed and edited for conceptual accuracy, but we have allowed many idiosyncrasies to remain.

Three questions about language model completions

GPT-like models are driving most of the recent breakthroughs in natural language processing. However, we don't understand them at a deep level. For example, when GPT creates a completion like the Blake Lemoine greentext, we

can't explain why it creates that exact completion.
can't identify the properties of the text that predict how it continues.
don't know how to affect these high-level properties to achieve desired outcomes.

We can make statements like "this token was generated because of the multinomial sampling after the softmax" or "this behavior is implied by the training distribution", but these statements only imply a form of descriptive adequacy (or saying “Al...
A Somewhat Functional Definition of Philosophy
by Richard Henage — 2023-02-27T00:25:29.561Z

There are many broad definitions of philosophy, including Merriam-Webster's simple "pursuit of wisdom"[1]. I think something useful can be learned from thinking about this a little harder. When I reread the definition I put forward in this post, I understand the goal of philosophy[2] much more clearly and have an idea of how to troubleshoot hard philosophical problems. Here is my proposed definition:

Philosophy is the process of creating a definition D for concept C such that:

when average people read D, they intuitively feel that it is a good definition for C, and
all cases are treated the same under D as they are under C (i.e. the thing-space boundary for D is the same as the thing-space boundary for C), and
D is more useful or more simple or more obvious in its interpretation than C

As an example, Plato's definition of knowledge was "justified, true, belief"[3]. This 1) sounds intuitively right, and 2) seemed to cover all the same areas that the word "knowledge" covered (in the sense of "knowing that", as opposed to "knowing how" or "knowing by aquantance"). It also fulfilled 3) because it provided a useful sort of checklist. However, Gettier showed cases where someone would be said to have justified, true, belief but not knowledge. Philosophers have since begun providing alternate definitions of knowledge that attempt to better cover requirement 2 (accuracy) while still covering 1 (intuitiveness) and 3 (usefulness).

^

https://www.merriam-webster.com/dictionary/philosophy

^

Of the 7 main philosophical areas listed on Wikipedia as of writing this, I think this definition applies best to Epistemology and Ethics while working somewhat for Aesthetics, Logic, Metaphysics, Philosophy of mind, and Philosophy of science.

^

https://academy4sc.org/video/the-gettier-problem-true-justified-belief-without-knowledge/
Respect Chesterton-Schelling Fences
by Shmi — 2023-02-27T00:09:30.815Z

This post is inspired by the recent Ziz-revelations posted here and elsewhere.

Chesterton Fence injunction: Do not remove a protective barrier unless you know why it was put there in the first place.

Schelling fence injunction: Do not cross a generally agreed upon guardrail, lest you end up sliding down an increasingly slippery slope, not noticing until it is too late.

I think a term like a Chesterton-Schelling Fence injunction might be useful: Respect an ethical injunction even if you think you know why it was put there in the first place.

A somewhat simplified example: There is a rather strong Schelling fence against, say, killing someone. Suppose the stated reasoning behind it is "God commanded so". Some day, you deconvert and start questioning the tenets of your faith, throwing one injunction after another, assuming you know why it was there, not realizing that this particular Chesterton fence is fake, the real reason is an unstated Schelling fence that has little to do with religion, but a lot with living in a society.

I said "respect" not "obey", because it is often hard to tell whether there is a hidden Schelling fence behind a Chesterton fence, and how strong the former is. Or vice versa. Or how many of the various hidden fences are there. Is it okay to cheat in an unhappy marriage? Maybe, maybe not, but noticing that this is an unsafe territory, that respecting the societal norms is generally a safe default, and that crossing it is likely yo backfire in both expected and unexpected ways can be quite useful.
Curiosity as a Solution to AGI Alignment
by Harsha G. — 2023-02-26T23:36:55.541Z

AGI (Artificial General Intelligence) seems to be just around the corner. Or maybe not. Either way it might be humanity’s last ever invention— the greatest of all time or the ultimate doom machine. This is a “thinking-out-loud” piece about how we can avoid the doom machine scenario of AGI.

Firstly, we need an objective function for the AI to align with. I think curiosity can help.

Curiosity as a solution to the AGI Alignment Problem, by Midjourney

Why Curiosity? (And why won’t it be enough?)

I.

Children are curious for their own good. Mostly their curiosity helps them explore their environment and understand how to survive. It also helps their bigger versions (adults) teach them “values” and other means by which children don’t just survive as an individual but survive with the group, in a symbiotic relationship, which leads to better survival of the entire species. Collectivism has always been more important than individualism until maybe the last few centuries.

II.

Children are also curious at the expense of their own survival. They might burn themselves and die. Nature made it easy to kill yourself if you’re curious. Evolution got around this by building loops of positive and negative reinforcement (that we call pain and pleasure). Even if you consider consciousness to be “illusory”, these sensations are “real enough” for the child to not touch the fire again.

This tendency to be curious along with a conscious ability to plan and think long-term and have empathy towards objects and others— define our ability to cheat the game of natural selection and bend the rules to our will. Curiosity in this “post-natural-selection” kind of world has lead to knowledge creation and that to me is the most human-pursuit imaginable, leading to possibly infinite progress.

III.

Children also however have a tendency to be rather “evil”. It takes them more than a decade to align their values to ours and then too, not all of them are able to do it well. For these others, we have defined negative reinforcement loops (punishments) at a societal level as either social isolation (prisons) or error correction (like therapy), either of which still might not help with the value alignment. Either of which probably won’t work for AGIs.

IV.

Overall, curiosity has been instrumental in the evolution of humans, providing advantages in terms of adaptation, social and econom...
Learning How to Learn (And 20+ Studies)
by maxa — 2023-02-26T22:46:55.031Z
Link: https://max2c.com/learning-how-to-learn/

I have been interested in cognitive science and effective learning methods for years. I've read multiple books and articles and put many ideas to test. In this post, I aim to synthesize my notes and provide references to scientific studies.

Summary (TL;DR)

Effective Learning Strategies

Distributed learning. Study less in each session but more frequently.
Active recall. Actively test your knowledge and skills.
Distributed recall. Space the tests in time and adjust the intervals based on performance.
Interleaving. Practice multiple related yet different skills/concepts simultaneously.
Elaborative interrogation (quiz-and-recall). Ask yourself questions and use the material you've learned to answer them.
Self-explanation and the Feynman technique. Explain what you've just learned in simple terms.

Physiology and Brain's Health

Sleep
Exercise
Nutrition

Disclaimer and Introduction

I have no formal background in cognitive science or neuroscience, and this has been more of a side interest. My understanding is limited, and I still need to learn how to effectively and consistently apply all these ideas to practice.

That being said, I found some of the methods described in this article very useful. For example, I've used them to learn foreign languages, the basics of programming, and various disciplines covered during the two-year MBA program.

Effective Learning Strategies

Strategy #1: Distributed (Spaced) Learning Practice

In short, it's better to distribute one's practice over a period of time than cram it into one day.

In one study, elementary school students were asked to study in one of the three ways: massed, clumped, and spaced.

Massed = four lessons at a time
Clumped = two lessons on one day and two lessons on the next day
Spaced = one lesson per day for four days

The “spaced” group performed best, followed by the “clumped” group:

Another study compared comprehension scores under three different conditions:

Read a text once (“single”)
Read a text twice (“massed”)
Read a text twice with a week-long gap (“distributed”)

When tested immediately, the second group performed best. But when tested with a delay of two days, the third group performed best.

This method is also superior for learning motor skills.

How to apply this in practice:

Create a learning schedule or find time to practice a little bit every day or every few days instead of cramming all your learning into one or just a few day...
Bayesian Scenario: Snipers & Soldiers
by abstractapplic — 2023-02-26T21:48:00.788Z
Link: https://h-b-p.github.io/Snipers-and-Soldiers/

You are a soldier, planning to attack an enemy position. You have a foolproof strategy for taking positions held by ordinary enemies, which will get you killed if you use it on positions held by snipers. You can call in an airstrike that will reliably devastate the enemy position, but you'd prefer not to if you don't have to. Using an airstrike will overextend the army's air support; there's a Q% chance this will lead to a fellow soldier's death.

X% of enemies are snipers.

Snipers hit their target Y% of the time.

Ordinary enemies hit their target Z% of the time.

[Poke a helmet on a stick out of the trench]

[Call in an airstrike] [Attack!]

I made a miniature interactive scenario to help people develop and/or evaluate their understanding of Bayes' Theorem. I intend to use it to teach a class a week from now, and I'm hoping to get feedback before then. Help me (and possibly yourself) out?