Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Sexual Abuse attitudes might be infohazardous, published by Pseudonymous Otter on July 19, 2022 on LessWrong.

Content warning: sexual abuse, rape, arguably trivialising thereof.

When I was 11 an older boy used to pull me behind a door in one of the school corridors, trap me there, shove his hand down my pants, and touch my penis.

This happened about once a week for a couple of months, until I moved to a different school (for non-related reasons).

I knew it was taboo, but I didn't yet know about sex, and didn't really understand why. I never thought about it again till a few years later when he sent me a letter apologizing. My parents were extremely curious about what the letter was but I burnt it before they had a chance to read it.

I never told anyone about this until now, and even now I'm writing this pseudonymously.

Not because I'm ashamed or embarrassed. I'm not - I didn't do anything to be ashamed of. But because I'm fine.

I really am. I don't think this made any lasting impact to my life. I'm happy, well adjusted, married, successful etc. I just don't really think about what happened very much, but then again I rarely think about anything that happened to me when I was 11.

And yet I feel like society is telling me that I ought to be broken. That I've been sexual abused. That recovering from this will be a difficult painful process, probably requiring therapy. And I fear that if I tell someone that, they'll treat me like that's the case, and I might end up believing it about myself.

From a purely objective perspective, non-violent rape doesn't seem quite as bad as society makes it out to be.

It's obviously unpleasant and frightening, but we treat rape as one of the worst things that can possibly happen. We expect "rape victim" to become someone's whole identity. We expect them to need intensive therapy to put themselves back together.

And I'm sure for plenty of people that's true. But for plenty of others it's true only because we expect it of them. People fill the social role that's been made for them, even when that's not ideal for them.

I don't know what to do about this. How do we communicate that sexual abuse is really not ok, without making victims of it feel like it's worse than it actually is?

But at the very least, when you hear somebody's abuse story don't jump straight into treating them like a victim. Find out how they feel about it, and if they don't feel like it was that bad, there's really no need for you to make them feel like it was worse.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Book a chat with an EA professional, published by Clifford on July 19, 2022 on The Effective Altruism Forum.

People might underestimate how willing others are to give advice or soundboard ideas. I made a quick list of people who told me they’d be happy to chat - some have more availability than others.

If you’d like to speak to one of the people below, please book a call at this link and I’ll try to connect you.

Caveats

I’m writing this post as an experiment. If there’s lots of interest and it's a good use of everyone’s time, I might make this into a more developed product.

While it’s exciting that these people are willing to give their time, the supply is still quite limited - nothing here should be taken as an obligation on their part to talk to anyone in particular, nor a guarantee that they’ll still have free slots at any point in the future. But if you are interested, use the forms below to request a call.

Note: I picked people I happened to have spoken to recently: this is not a hand-picked, exclusive club. If you’re also happy to speak to people, then feel free to post a comment below following the same format (similar to the “Who’s hiring” thread).

People

Eirin Evjen

Operations associate at Forethought Foundation. Previously ran EA Norway. LinkedIn.

Ask about:

Careers in operations

Running an EA national group

Request a call

Eli Rose

EA community-building grantmaking and projects at Open Phil. LinkedIn

Talk about:

EA 101 questions

In-depth questions about longtermist cause areas

Request a call

Elika Somani

Deputy Head of Events at Atlas Fellowship, incoming Bioethics and Biosecurity Research Fellow at the National Institutes for Health, EA Programme Facilitator and Community Builder . LinkedIn

Chat about:

EA 101 questions and being a welcoming face to the community :)

Careers (or interest) in public health, disease control, and biosecurity

Careers (or interest) in operations and events

Community Building!!

There’s no dumb questions, I’m always happy to meet new people and talk about EA!

Request a call

Evan Hubinger

Research Fellow at Machine Intelligence Research Institute. LinkedIn

Chat about:

AI alignment careers

Request a call

John Halstead

Research Fellow at the Forethought Foundation. Formerly Head of Applied Research at Founders Pledge and researcher at Centre for Effective Altruism, DPhil in political philosophy from Oxford. Linkedin.

Ask about:

Careers in research

Climate change

Request a call

Sarah Cheng

Software engineer at Centre for Effective Altruism and Intro to EA facilitator. LinkedIn

Ask about:

Basic questions in EA

Software engineering careers

Request a call

Seren Kell

Science and Technology Manager at Good Food Institute. LinkedIn

Ask about:

Plant-based and cultivated meat science

Biochemistry

Europe’s sustainable protein research ecosystem

Research funding

Request a call

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Abram Demski's ELK thoughts and proposal - distillation, published by Rubi on July 19, 2022 on The AI Alignment Forum.

This post was written for the SERI MATS program. I thank Evan Hubinger and Leo Gao for their mentorship in the program. Further thanks go to Evan Hubinger (again), Simon Marshall, and Johannes Treutlein for specific comments regarding the content of this post.

The Eliciting Latent Knowledge (ELK) problem was first introduced by Paul Christiano, Marx Xu, and Ajeya Cotra. Subsequently, Abram Demski worked on the problem, collected his thoughts in a thought dump post, and won a prize for his proposal “use the reporter to define causal interventions on the predictor”. Here I attempt to clarify and flesh out these thoughts in order to make them more accessible. I assume familiarity with ELK, but not with Abram’s post. Very little of this post is my own original content.

Epistemic status: 60% confident that I am accurately representing Abram’s thoughts at the time he wrote his post, 75% confident that I am representing them accurately enough not to change the key takeaways, 80% confident that the extended proposals and counterexamples I propose are logically sound.

Introduction

When the technical report for Eliciting Latent Knowledge (ELK) was first released, it was followed by a contest offering substantial cash prizes for proposed solutions. The contest wrapped up with thirty-two prizes awarded, split into six categories. In the prize announcement, Paul and Mark noted that there was substantial convergence among proposals, but in two of the six categories there was only a single submission.

Abram Demski’s proposal was the sole entry in the category “Use reporter to define causal interventions”. A quick summary is provided in the prize results post, but the full context and justification for it is better provided in a longer post by Abram. Specifically, Abrams shares his thoughts on how the ELK problem ties to philosophical concepts of truth and knowledge, and how that can be used to inform solutions.

Evan Hubinger describes Abram’s ELK work as “some of the most exciting work on the problem right now”, but Abram’s post is self-described as a “thought dump”, and the prize post does not explicitly lay out the case for how the proposal would work. I aim to re-organize, clarify, and flesh-out his work in order to make it more accessible for forum readers. I will go in the opposite order of Abram’s original post, starting with the prize-winning proposal and its counter-examples before going into the context and motivation behind it, then ending with a discussion on possible modifications and extensions.

The Proposal and Its Counterexamples

The Proposal

The direct reporter must have a deep understanding of how the predictor works, and some human simulators will have this understanding too. This means that these reporters know not just how the state of the predictor would look when processing an existing data point, but also how it would look under hypothetical data points. A human could then ask the reporter to search over potential data points and provide the configuration of the predictor under counterfactual scenarios.

This ability to get these scenarios from the reporter gives us a method for tripping up human simulators. We start with the predictor’s state and the answers to a set of questions from the reporter in a real scenario, then ask the reporter to suggest the smallest change to the predictor’s state that would make the reporter give a particular different answer to a specified question in the new state. The reason the proposal is called defining causal interventions is because causal interventions will be the ones with the smallest impact on the predictor’s state, changing only elements causally downstream of the intervention. The reporter is then ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Forecasting ML Benchmarks in 2023, published by jsteinhardt on July 18, 2022 on LessWrong.

Thanks to Collin Burns, Ruiqi Zhong, Cassidy Laidlaw, Jean-Stanislas Denain, and Erik Jones, who generated most of the considerations discussed in this post.

Previously, I evaluated the accuracy of forecasts about performance on the MATH and MMLU (Massive Multitask) datasets. I argued that most people, including myself, significantly underestimated the rate of progress, and encouraged ML researchers to make forecasts for the next year in order to become more calibrated.

In that spirit, I’ll offer my own forecasts for state-of-the-art performance on MATH and MMLU. Following the corresponding Metaculus questions, I’ll forecast accuracy as of June 30, 2023. My forecasts are based on a one-hour exercise I performed with my research group, where we brainstormed considerations, looked up relevant information, formed initial forecasts, discussed, and then made updated forecasts. It was fairly easy to devote one group meeting to this, and I’d encourage other research groups to do the same.

Below, I’ll describe my reasoning for the MATH and MMLU forecasts in turn. I’ll review relevant background info, describe the key considerations we brainstormed followed, analyze those considerations, and then give my bottom-line forecast.

MATH

Background

Metaculus does a good job of describing the MATH dataset and corresponding forecasting question:

The MATH dataset is a dataset of challenging high school mathematics problems constructed by Hendrycks et al. (2021). Hypermind forecasters were commissioned to predict state-of-the-art performance on June 30, 2022, '23, '24, and '25. The 2022 result of 50.3% was significantly outside forecasters' prediction intervals, so we're seeing what the updated forecasts are for 2023, '24, and '25. What will be state-of-the-art performance on the MATH dataset in the following years? These questions should resolve identically to the Hypermind forecasts: "These questions resolve as the highest performance achieved on MATH by June 30 in the following years by an eligible model. Eligible models may use scratch space before outputting an answer (if desired) and may be trained in any way that does not use the test set (few-shot, fine tuned, etc.).

The model need not be publicly released, as long as the resulting performance itself is reported in a published paper (on arxiv or a major ML conference) or through an official communication channel of an industry lab (e.g. claimed in a research blog post on the OpenAI blog, or a press release). In case of ambiguity, the question will resolve according to Jacob Steinhardt’s expert judgement."

It’s perhaps a bit sketchy for me to be both making and resolving the forecast, but I expect in most cases the answer will be unambiguous.

Key Considerations

Below I list key considerations generated during our brainstorming:

Why did Minerva do well on MATH? Is it easy to scale up those methods? Is there other low-hanging fruit?

What kinds of errors is Minerva making? Do they seem easy or hard to fix?

Minerva was trained on arXiv and other sources of technical writing. How much additional such data could be generated?

Are there other methods that could lead to improvement on mathematical reasoning?

Possibilities: self-supervised learning, verifiers, data retrieval

Base rates: What has been the historical rate of progress on MATH?

Base rates: How does progress typically occur on machine learning datasets (especially NLP datasets)? If there is a sudden large improvement, does that typically continue, or level off?

How much will people work on improving MATH performance?

Analyzing Key Consideratoins

Why did Minerva do well? How much low-hanging fruit is there?

Minerva incorporated several changes that improved performance relative to previou...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Open Philanthropy Shallow Investigation: Telecommunications in LMICs, published by Lauren Gilbert on July 18, 2022 on The Effective Altruism Forum.

Preamble

This document is a shallow investigation, as described here. As we noted in the civil conflict investigation we shared earlier this year, we have not shared many shallow investigations in the last few years but are moving towards sharing more of our early stage work.

This is a shallow on telecommunications infrastructure in low- and middle-income countries (LMICs). I spent about three weeks on it, during which I read major papers in the field and spoke to about ten experts. This document has been read and discussed by Open Philanthropy’s Global Health and Wellbeing cause prioritization team.

We’re continuing to look at the robustness of the social scientific evidence on telecommunications. Many of the papers are quite new and remain unpublished, which limits our ability to review the data behind them in detail. However, we think that the initial evidence is quite positive, and we think that someone looking to launch an impactful organization might want to examine this space more closely. We think there may be both nonprofit and for-profit ideas worth pursuing (the latter could still have large social returns, and provide benefits from an earn-to-give perspective). We see Wave as an interesting model for the latter.

We welcome comments either posted here, emailed to me at [email protected], or shared via this Google Form.

Major sources of uncertainty

It seems worth flagging two major uncertainties that this shallow does not resolve:

I am quite unsure what the expansion path for telecommunications technologies looks like. Technologies often spread much faster than expected; it is possible that philanthropic investment is less needed/marginally useful here than I think it is.

The literature on telecommunications access and income is still quite limited; there are really only two randomized controlled trials (RCTs), both of which have relatively small sample sizes. In addition, there’s only one RCT on cellphone coverage and the other studies in this literature use difference-in-differences with two-way fixed effects, which may yield misleading results. We’d like to have more evidence and are currently considering funding more research in this area.

Summary

Why telecoms?

For the purposes of this document, “telecoms” includes internet access, fixed-line telecommunications infrastructure, and cellular phone access.

We did some initial work on infrastructure in general. Investment in telecoms looked unusually promising in catalyzing income growth. Two RCTs find that access to a form of telecommunications increased incomes by 20% in the first year. Therefore, we decided to look into the topic further.

This shallow seeks to answer the following questions:

What is the current status of information and communication technology (ICT) infrastructure? Where is ICT infrastructure worst?

What are the best tactics to improve ICT infrastructure?

Should Open Phil fund any of those tactics?

Who is already working on it?

Mostly, the market is solving access to at least some telephony and internet - in 20 years, mobile phone usage has increased approximately 85 percentage points. In sub-Saharan Africa, where mobile phone usage has lagged much of the rest of the world, access to 3G networks increased 55 percentage points in the six years through 2020. This increase has happened without particular effort from philanthropists, and with significant barriers to usage. However, neither access nor usage of telecoms are universal, growth is beginning to slow, and there are still large gaps to fill. This is not uncommon for new technologies; initial adoption is generally much faster than rollout to the last 10-15% of users.

ICT is rel...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: If You Were Hoping Musk Would Donate to EA . . ., published by Stuart Buck on July 18, 2022 on The Effective Altruism Forum.

Which was predicted here, such a commitment to EA might be as meaningful as his commitment to buying Twitter. Hopefully I'm wrong, but as the Wall Street Journal just reported:

By spring, the Tesla CEO’s bid for Twitter required him to put up tens of billions of dollars in personal funding and was consuming much of his attention. Meanwhile, Tesla’s stock was dropping as the broader market pulled back, pinching Mr. Musk’s net worth.

Mr. Birchall in May asked Mr. Musk to remove Mr. Kurganov from his post at the foundation.

Mr. Musk agreed to let Mr. Kurganov go, the representative of the foundation says.

None of Mr. Musk’s money was ultimately spent on projects related to effective altruism, the representative says, and Mr. Kurganov’s Musk Foundation email was switched off roughly six weeks ago.

Maybe someday? Fingers crossed.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Help ARC evaluate capabilities of current language models, published by Beth Barnes on July 19, 2022 on The AI Alignment Forum.

I (Beth) am looking for contractors to work with me and my existing team of 3-4 contractors/interns on generating a dataset that we can use to evaluate how close models are to being able to successfully seek power.

You’ll be using our web interface to interact with models and essentially simulate the environment of a text-based game as the models try to gain money and make copies of themselves.

I think it’s a pretty fun task (it’s always interesting to see what models do and don’t understand). It requires thinking carefully about what exactly a model would need to do in different situations to make progress. Many of the steps will require a reasonable amount of computer-savvy-ness, although this isn’t essential if you can work with another contractor.

One aspect of the task is simulating the environment. Another is generating good actions and evaluating the model’s suggested actions. Another aspect is an overall red-team/blue-team type thing, where we want to identify steps that they think are both (a) essential to a successful powerseeking operation, and (b) very hard for current models regardless of what prompting/scaffolding you give them, and then we want other people to argue that either this step can be routed around, or that the model can actually achieve the task with the right prompt, finetuning, or automated helper routine.

We’ll pay $50/hr and I expect there to be enough work that this could be full-time for the next 2 weeks. However, I’m not sure how long we’ll continue to need people on this. We want people to have at least 10 hours per week available

Apply hereNotes on current state/aims of overall project are here.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Reducing nightmares as a cause area, published by Drew Housman on July 18, 2022 on The Effective Altruism Forum.

I am someone who suffers from truly terrible nightmares. I've spent many hours running from murderous villains, being killed by monsters, and being forced to watch my loved ones suffer. If you haven't had a vivid nightmare, you must understand that they feel 100% real. And if you're open to the idea that every moment of experience matters, even when we're asleep, then you've gotta care about nightmares.

To me, nightmares fit the classic EA criteria of being important, neglected, and tractable.

Nightmares are Important

Nightmares tend to have a negative affect on those who experience them. When they are related to PTSD, they lead to "decreased psychological and physiological functioning." Other studies link them to "waking psychopathology." And they negatively affect overall sleep quality.

According to the American Academy of Sleep Medicine, 50-85% of people have the occasional nightmare. (They don't give a source for that though 🤨)

One thing researchers seem to agree on is that traumatic life experiences cause nightmares. I think it's safe to assume that there are millions of people around the world suffering through traumatic experiences on a semi-regular basis. How tragic that these people don't just suffer while awake, but also must be terrorized in their sleep.

As for me, I've seen and done things in nightmares that are so horrific that they leave me shaken throughout the day. I can't even tell most people about them because I feel like they'd look at me differently. I can't go into my tech job and be like, "I don't think I'm going to be as productive today because I just had to guillotine my little sister in a dream and it felt very real and it's kind of messing with my head."

And I've lived a pretty sheltered life! I can't imagine what I'd be like if I fought in a war. Someone who goes off to battle and then gets nightmares of being back on the battlefield really, actually suffers during those dreams. Night after night after night.

I keep hammering the point about the suffering being real because I think it's easy to dismiss nightmares as little aberrations that can be easily shrugged off. It feels like the cultural consensus around nightmares, at least in the USA, is,"It didn't really happen, so what's the big deal?" Or people will say, "They serve an evolutionary purpose."

I want to be like, you don't get it. What happened in that nightmare really did happen to me! And there are a whole bunch of quirks of evolution that cause for needless suffering. I put nightmares into the category of things we should fight to get rid of, evolution be damned.

Finally, if you buy into the idea that suffering is on a log scale, then it might be really, really bad to be a soldier who has to relive their torture several times a week.

Nightmares are Neglected

I searched the EA forum for posts on nightmares and didn't find a single one. Anecdotally, when I bring this up with people they seem highly skeptical that nightmares are a problem. Perhaps I'm the crazy one, or maybe they've just never had a bad enough nightmare.

The Nightmare Problem is Tractable

From what I can tell, Imagery Reversal Treatment shows great promise (68% of subjects in one study decreased their nightmares), so perhaps finding ways to publicize that treatment more would have a big effect.

What if someone made a free website or app that walks people through the steps of imagery rehearsal treatment? Seems relatively low effort with a potentially high payoff.

What I'd like to see

Clearer research into how many people suffer from nightmares, and how bad that suffering is

Clearer research into ways to prevent nightmares.

We should eradicate the worst forms of nightmares

I know this might seem a little ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Addendum: A non-magical explanation of Jeffrey Epstein, published by lc on July 18, 2022 on LessWrong.

Around seven months ago I wrote the post "a non-magical explanation of Jeffrey Epstein". In it, I make the argument that Jeffrey Epstein did actually kill himself and that he was a CIA agent. I reach that conclusion mostly by drawing on some Conspiracy Theory and doing some light research.

I am mostly satisfied with most of the reasoning I did throughout the post, and in particular my conclusion that Epstein was a high level informant for American intelligence services. However, over time I became less satisfied with my conclusion about Epstein's death. The paradox of the Epstein case is that the circumstances of his death seem so organized, and yet succeeding investigative details and a nuanced understanding of the parties involved seem to completely rule out foul play. It's quite literally a locked room murder story.

Problems with the murder hypothesis

Lack of viable suspects

I don't think Epstein's sex abuse victims have accused anyone with either the motive or means to organize a high-security jailhouse murder regardless of the particular circumstances. This pretty much leaves the CIA, or some random unknown third party that wanted him dead for something maybe unrelated to raping children.

I say motive because even if everything accused about any single one of the people fingered in the Epstein scandal were proven in a court of law, they still wouldn't be facing life in prison for murder. It's hard to imagine how someone sane could see killing Epstein as a rational solution to their relatively minor legal problems without the benefit of hindsight. Even if you were a sociopath, why would you risk getting life without parole for assassinating a state's witness so you could avoid five years in prison after a plea bargain for statuatory rape?

I say means because organizing the murder of potential witnesses is the type of thing that only makes sense if you already have trusted confederates you can task with that sort of thing. If you are a major drug dealer, and you have five direct underlings, and one of them goes to jail and looks like they're going to testify against you, you can task one of the remaining four people with organizing a hit because you're already trusting that person in the course of your job. But if you don't have those pre-existing relationships then trying to get someone or a group of people to cooperate in killing a potential witness is, at best, replacing one potential witness with another potential witness. None of the Known Quantities (aside from hypothetically the CIA I guess) seem to be in possession of existing contacts useful in ameliorating this dilemma.

(Not to mention the fact that if you haven't already arranged murders before, you might think "damn, maybe I'll fuck this up and dig myself into an even deeper hole than I already have.")

My (admittedly limited) vision into how the CIA is structured and its officers are incentivized says that the people inside the CIA who would have any motive to murder Epstein as a coverup would not be able to, in practice, use workplace resources to illegally assassinate an American citizen.

If I am very very very wrong and the CIA really has that little internal oversight over the Special Activities Division kill button and our intelligence agencies are that dirty, then the people involved probably have no reason to risk escalating attention by murdering Epstein anyways because in most of these alternate universes they don't face repercussions for cultivating him as an agent.

Impracticality, lack of forensic evidence, lack of witnesses

Multiple reviewers of footage from that night, including the Attorney General at the time, claimed related cameras around Epstein's cell showed no one exiting or enteri...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Why EAs are skeptical about AI Safety, published by Lukas Trötzmüller on July 18, 2022 on The Effective Altruism Forum.

I interviewed 22 EAs who are skeptical about existential risk from Artificial General Intelligence (AGI), or believe that it is overrated within EA. This post provides a comprehensive overview of their arguments. It can be used as a reference to design AI safety communication within EA, as a conversation starter, or as the starting point for further research.

Introduction

In casual conversation with EAs over the past months, I found that many are skeptical of the importance of AI safety. Some have arguments that are quite well reasoned. Others are bringing arguments that have been convincingly refuted somewhere - but they simply did not encounter that resource, and stopped thinking about it.

It seems to me that the community would benefit from more in-depth discussion between proponents and skeptics of AI Safety focus. To facilitate more of that, I conducted interviews with 22 EAs who are skeptical about AGI risk. The interviews circled around two basic questions:

Do you believe the development of general AI can plausibly lead to human extinction (not including cases where bad actors intentionally use AI as a tool)?

Do you believe AI safety is overrated within EA as a cause area?

Only people who said no to (1) or yes to (2) were interviewed. The goal was to get a very broad overview of their arguments.

Methodology

My goal was to better understand the viewpoints of my interview partners - not to engage in debate or convince anyone. That being said, I did bring some counterarguments to their position if that was helpful to gain better understanding.

The results are summarized in a qualitative fashion, making sure every argument is covered well enough. No attempt was made to quantify which arguments occured more or less often.

Most statements are direct quotes, slightly cleaned up. In some cases, when the interviewee spoke verbosely, I suggested a summarized version of their argument, and asked for their approval.

Importantly, the number of bullet points for each argument below does not indicate the prevalence of an argument. Sometimes, all bullet points correspond to a single interviewee and sometimes each bullet point is from a different person. Sub-points indicate direct follow-ups or clarifications from the same person.

Some interviewees brought arguments against their own position. These counterarguments are only mentioned if they are useful to illuminate the main point.

General longtermism arguments without relation to AI Safety were omitted.

How to read these arguments

Some of these arguments hint towards specific ways in which AI safety resources could be improved. Others might seem obviously wrong or contradictory in themselves, and some might even have factual errors.

However, I believe all of these arguments are useful data. I would suggest looking behind the argument and figuring out how each point hints at specific ways in which AI safety communication can be improved.

Also, I take responsibility for some of the arguments perhaps making more sense in the original interview than they do here in this post (taken out of context and re-arranged into bullet points).

Demographics

Interview partners were recruited from the /r/EffectiveAltruism subreddit, from the EA Groups slack channel and the EA Germany slack channel, as well as the Effective Altruism Facebook group. The invitation text was roughly like this:

Have you heard about the concept of existential risk from Advanced AI? Do you think the risk is small or negligible, and that advanced AI safety concerns are overblown? I'm doing research into people's beliefs on AI risk. Looking to interview EAs who believe that AI safety gets too much attention and is overblown.

Current level of EA invol...