trentmkelly/LessWrong-43k huggingface dataset:
Is Chinese total factor productivity lower today than it was in 1956?
by Ege Erdil — 2023-08-18T22:33:50.560Z

tl;dr: Multifactor productivity data from a famous economic dataset, used often to proxy for technological progress and innovation, might be significantly biased by poor estimates of the social returns to education, the capital share of income, and real GDP. Such estimates should be treated with caution.

Introduction

Total factor productivity is an economic concept that is used to quantify how efficiently a country can make use of its economic resources. It's a rather nebulous concept in general because it's not directly measurable but instead corresponds to latent variables in growth models that account for "unexplained variation" in output.

If we stick to the abstract realm of growth models, there is often a clear definition: for instance, we might model a country's real GDP Y by a function such as

Y=AL1−αKα

where L and K denote the country's total labor force and capital stock respectively, 0<α<1 is a parameter, and A is total factor productivity, hereafter abbreviated as TFP. If we have two countries with the same capital stock and labor force but one of them has a higher economic output, we say that country has a higher TFP.

Of course, the same principle works in general, not just with this specific functional form and for labor and capital inputs. If we have some economically relevant inputs f1,f2,…,fn (sometimes called factors of production), we can imagine a model where economic output in a country is given by

Y=AH(f1,f2,…,fn)

for some function H. In this case, the TFP A is just a factor that's present to "close the model": changes in output not accounted for by changes in the inputs fi are automatically accounted for by changes in A. For a specific H, we can see A=Y/H as a measure of economic productivity which controls for "obvious factors" such as changes in the labor or capital stocks. Making the right choice of H is very important, of course: we don't want to use a random H, but one that is actually informed by the data. I'll talk more about how we do this later.

For instance, if a country has increasing TFP over time, that suggests the country's output is rising because of things that are out of our model. If our model only had labor force and capital stock, perhaps the quality of the workers in the country has gone up: they have more human capital, even though they have the same number of people because each person is on average more productive...
Rationality-ish Meetups Showcase: 2019-2021
by jenn — 2023-08-18T22:22:22.682Z

These events were run bi-weekly by myself as a tired and burnt out university student, and I didn't spend too much time on the event descriptions at the time so they're a bit sparse. At this point, the meetups were not yet explicitly rationalist-coded, and so it wasn't a priority for me to add explicitly rationalist readings to every single one of these events.

These meetups are also not representative of the average event, which just involved discussing a Scott/LW article, or two Scott/LW articles about similiar things, or a rat and a non-rat take on a specific topic.

I think these events would play well as occasional, slightly more general-interest events in established rationalist groups.

Feel free to steal any of these events, including wording for event posts, in whole or in part.

Policy Resource Allocation

Event post:

The Canadian government (specifically, SSHRC, the single largest social sciences research fund in Canada by a significant margin) developed a report on emerging issues that they anticipate will need addressing by 2030. In this meetup, we'll review their list of 16 challenges/problem statements and discussed which ones seemed more important/deserving of resources than others, as they didn't do any sort of prioritization.

Read it here:

https://www.sshrc-crsh.gc.ca/funding-financement/programs-programmes/challenge_areas-domaines_des_defis/index-eng.aspx (also all in one page here, and as a PDF here)

Notes:

I tried looking to see if NEH (the US equivalent of SSHRC) has put out anything similiar, but it doesn't seem like they have. I do think the Canadian list is not particularly fixed to Canadian contexts though and should work for all developed countries.

An explicitly rationalist reading pairing could be We Are In Triage Every Second of Every Day or a similar post. This would also be a great way to spin the event in a more EA-adjacent direction, and discuss to what extent solving any of the 16 issues above should take precedence over tiling Nigeria over in bednets.

Philosophy Variety Show

Event Post:

Go go to this website: https://www.philosophyexperiments.com/

Do one quiz that you're unfamiliar with, and we'll all share our results with each other at the salon. Light roasting may follow. I'll start a post in the event page for claims so that we minimize the number of doubles, but also it's not a big deal if you pick the same thing as someone ...
The U.S. is becoming less stable
by lc — 2023-08-18T21:13:11.909Z

We focus so much on arguing over who is at fault in this country that I think sometimes we fail to alert on what's actually happening. I would just like to point out, without attempting to assign blame, that American political institutions appear to be losing common knowledge of their legitimacy, and abandoning certain important traditions of cooperative governance. It would be slightly hyperbolic, but not unreasonable to me, to term what has happened "democratic backsliding".

Let's imagine America of 2012 was measured 0.8 on the fictionally accurate "legitimate democracy index", and Hungary of 2012 was measured 0.5. My thesis is that we'd now be at 0.75, and that our regressions seem to have calcified despite a calming culture war. Within the last three or four years we have seen:

The world's largest protest-riot ever, when measured by estimated damage to property.
The leader of the opposition party being arrested on a mix of real and recently-invented process crimes in several different jurisdictions a year before his campaign.
A presidential candidate openly contesting an election as fradulent; a third[1] of Americans coming to believe that our last presidential election was probably or definitely illegitimate.
Spontaneous mob assaults of the capitol building.
Recent, and novel, legislative movements by Republicans to censure and fine Democratic congressmen millions of dollars outside of the criminal justice system.[2]
Serious and underreported attempts at dramatically expanding political control over the civil service[3] and, if you'll permit me to speak anecdotally, serious and successful attempts at unprecedented political loyalty testing of appointed silovik.

You can disagree with how any one political faction is characterizing the above events, or how I'm characterizing the above events. But I think that's missing the point. Maybe Donald Trump is a clown and all of his indictments are perfectly legitimate and that they ultimately demonstrate the dispassionate fairness of our nation's prosecutors. Even if that's the case, perception is the leading indicator for democratic stability, and a large amount of Republicans do not agree. Since Republicans now believe that the arrests are politically motivated, and that Democrats are defecting against longstanding political norms, they are pressuring their politicians to escalate[2][4] a...
Meetup Tip: Board Games
by Screwtape — 2023-08-18T18:11:58.820Z

Summary

Many meetups play board games or card games. Hanging out playing games can be a fun way to spend time together, and organizing a board game meetup is unusually easy since the games do most of the heavy lifting. Some games are better suited to this purpose than others; I believe the best games for a meetup have very short rounds, have a lot of “dead” time where you’re waiting for someone to make a move, and are for more than two players. A game can be a great game, but a poor choice for a meetup. The summary of this post is that if you run a board game meetup, I think the meetup will be better if you bring games that fit those criteria and avoid bringing games that don’t.

I’m less confident these are straightforward improvements than I usually am with these tips.

Still reading? Cool, let's talk about games for more words than are strictly necessary.

Details

Why are games a popular meetup topic? I believe it’s because board games give a default activity. It’s not weird to approach someone at a board game meetup and ask if they want to play, which makes it easier to introduce yourself. You don’t have to worry about not having anything to say or talk about, since you can talk about the game. They periodically interrupt people, making it easier to change topics. They can also be fun. It’d be bad to forget that. They’re also really easy to run as an organizer. You can just pick a time and place, show up with a couple of cardboard boxes, and boom there’s the meetup. There’s a reason I named my sequence on easy meetups “Meetups In A Box” and that reason is because I want every meetup to be as simple for the organizer as a board game meetup.

The qualities that make for a great game if you consider it as a game are not the same as the qualities that make a game great for meetups. Keep that in mind throughout this post; I’m going to use a lot of examples, and the distinction is going to be important. Take Go. Go is a deep and much loved game. If people decide to play Go at a meetup, then two people are probably going to split off from the rest to focus on that for an hour. It’s going to impede them from mingling with other people, and they’re even limited in talking to each other since at any point in time one of them is concentrating on the next move. That’s not ideal.

Compare Go to Hearts. In a four player Hearts game, most of the time three players are ...
AI labs' requests for input
by Zach Stein-Perlman — 2023-08-18T17:00:26.377Z

AI labs sometimes publicly ask for input on their actions, their products, and the future of AI. See OpenAI's Democratic inputs to AI and

ChatGPT Feedback Contest

, and maybe bug bounty programs for security vulnerabilities (OpenAI, Google, Meta). I'd like to collect these; please reply with other examples you're aware of. I'm also interested in ideas/recommendations for labs on what they should request input on or how they should do so (e.g. bug bounty for model outputs).

Labs also seek input non-publicly. For example, labs have used external red-teaming and model evals, worked with biosecurity experts to understand how near-future AI systems can contribute to biological weapons, and consulted external forecasters. Various kinds of external audits have been proposed. I'm also interested in collecting examples and ideas for this non-public input, but less so.

Crossposted to the EA Forum.
6 non-obvious mental health issues specific to AI safety
by Igor Ivanov — 2023-08-18T15:46:09.938Z

Intro

I am a psychotherapist, and I help people working on AI safety. I noticed patterns of mental health issues highly specific to this group. It's not just doomerism, there are way more of them that are less obvious.

If you struggle with a mental health issue related to AI safety, feel free to leave a comment about it and about things that help you with it. You might also support others in the comments. Sometimes such support makes a lot of difference and people feel like they are not alone.

AI safety is a rather unusual field

The problems described in this post arise because AI safety is not an ordinary field to work in.
Many people within the AI safety community believe that it might be the most important field of work, but the general public mostly doesn't care that much. Also, the field itself is extremely competitive and newcomers often have hard time getting a job.
No one really knows when we will create AGI, and whether we will be able to keep it aligned. If we fail to align AGI, the humanity might go extinct, and even if we succeed, it will radically transform the world.

Patterns

AGI will either cause doom or create a utopia. Everything else seems unimportant and meaningless.

Alex is an ML engineer working in a startup that fights with aging. He believes that AGI will either destroy humanity or bring a utopia, and among other things it will stop aging, so Alex thinks that his job is meaningless, and quits it. He also sometimes asks himself "Should I invest? Should I exercise? Should I even floss my teeth? This all seems meaningless."

No one knows how the post-AGI world will look like. All predictions are wild speculations, and it's very hard to tell whether any actions unrelated to AI safety are meaningful. This uncertainty can cause anxiety and depression
These problems are an exacerbated version of existential problem of meaninglessness of life, and the way to mitigate them is to rediscover meaning in the world that ultimately doesn't have meaning.
Check out my post about Alex's problem, and possible solutions for it.

I don't know when we will create AGI and if we will be able to align it, so I feel like I have no control over it.

Bella is an anxious person, and she recently got interested in AI safety and she realized that nobody know for sure how to align AGI.
She feels that AGI might pose an extreme danger, and ...
When discussing AI doom barriers propose specific plausible scenarios
by anithite — 2023-08-18T04:06:44.679Z

TLDR:When addressing "How would an AI do X" questions, prefer simple existence proofs involving things humans can currently do rather than "magic" seeming things like nanotechnology or the fully general "AI will just be smart enough to figure it out". TLDR:END

Commonly in AI risk conversations questions of the form "how would an AI do X" where X is something like "control physical resources) or (survive without humans) are asked.

There is a "would/could" distinction in these questions.

How would an ASI do X -->(predict actions of ASI pursuing goal X)

How could an ASI do X --> (Show that an AI can accomplish X)

The "would" question is correct to answered with (nanotech/super-persuasion/Error:cannot_predict_ASI) but the "could" form is usually the intention and leads to better conversations.

For "would" questions, the answer can be prefaced with "The AI would be cleverer than me so I can't predict what it would actually do. One way it could do X though is to ..."

Hypothetical example

Doubter: How will the AI take over if it's stuck inside the internet.

Doomer: The AI is smart enough to figure out a solution like nanotechnology or something.

Doubter: I don't believe you. I don't think it can do that.

Doomer: *hands them a copy of the Nanosystems book*

Doubter: I never took CHEM101 and besides no one has ever built this so I still don't believe you.

Compare

Doubter: How will the AI take over if it's stuck inside the internet.

Doomer: It can just hack all the computers and hold them and the data hostage.

Doubter: We would refuse it's demands!

Doomer: Here's a list of stuff connected to the internet, how hard that stuff is to fix if hacked and examples of human hackers hacking that stuff.

The first reply is a fully general argument that intelligence can solve most problems. If you're wondering how the AI could make ice cubes and you don't know that freezers are possible, use the fully general argument. The AI will just be smart enough to invent some new technology that can make ice cubes. If you do know of an existing solution then just point to that as an existence proof. "The AI can order an ice-maker off Amazon and hire a taskrabbit to set it up and operate it."

Concern: seeing only the easy to stop attacks

That’s a problem with such scenarios in general. By trying to be as reasonable and grounded as possible, by showing how little it takes, you anchor peopl

...
Risks from AI Overview: Summary
by Dan H — 2023-08-18T01:21:25.445Z
Link: https://www.safe.ai/ai-risk

We’ve recently published on our website a summary of our paper on catastrophic risks from AI, which we are cross-posting here. We hope that this summary helps to make our research more accessible and to share our policy recommendations in a more convenient format. (Previously we had a smaller summary as part of this post, which we found to be insufficient. As such, we have written this post and have removed that section to avoid being duplicative.)

Executive summary

Catastrophic AI risks can be grouped under four key categories which we explore below, and in greater depth in CAIS’ linked paper:

Malicious use: People could intentionally harness powerful AIs to cause widespread harm. AI could be used to engineer new pandemics or for propaganda, censorship, and surveillance, or released to autonomously pursue harmful goals. To reduce these risks, we suggest improving biosecurity, restricting access to dangerous AI models, and holding AI developers liable for harms.
AI race: Competition could push nations and corporations to rush AI development, relinquishing control to these systems. Conflicts could spiral out of control with autonomous weapons and AI-enabled cyberwarfare. Corporations will face incentives to automate human labor, potentially leading to mass unemployment and dependence on AI systems. As AI systems proliferate,

evolutionary dynamics

suggest they will become harder to control. We recommend safety regulations, international coordination, and public control of general-purpose AIs.
Organizational risks: There are risks that organizations developing advanced AI cause catastrophic accidents, particularly if they prioritize profits over safety. AIs could be accidentally leaked to the public or stolen by malicious actors, and organizations could fail to properly invest in safety research. We suggest fostering a safety-oriented organizational culture and implementing rigorous audits, multi-layered risk defenses, and state-of-the-art information security.
Rogue AIs: We risk losing control over AIs as they become more capable. AIs could optimize flawed objectives, drift from their original goals, become power-seeking, resist shutdown, and engage in deception. We suggest that AIs should not be deployed in high-risk settings, such as by autonomously pursuing open-ended goals or overseeing critical infrastructure, unless proven safe. We also recommend advancin

...
Managing risks of our own work
by Beth Barnes — 2023-08-18T00:41:30.832Z

Note: This is not a personal post. I am sharing on behalf of the ARC Evals team.

Potential risks of publication and our response

This document expands on an appendix to ARC Evals’ paper, “

Evaluating Language-Model Agents on Realistic Autonomous Tasks

.”

We published

this report

in order to i) increase understanding of the potentially dangerous capabilities of frontier AI models, and ii) advance the state of the art in safety evaluations of such models. We hope that this will improve society's ability to identify models with dangerous capabilities before they are capable of causing catastrophic damage.

It might be argued that this sort of research is itself risky, because it makes it easier to develop and exercise dangerous capabilities in language model agents. And indeed, the author of

Auto-GPT

said he was inspired by seeing a description of our evaluations in the GPT-4 system card.[1] While it seems likely a project like that would have emerged soon in any event,[2] the possibility of advancing the capabilities of language model agents is not merely hypothetical.

In recognition of concerns of this kind, we have made significant redactions to this report, including (but not limited to):

Removing complete transcripts of runs of agents using our scaffolding.
Removing more detailed accounts of the strengths and weaknesses of agents using our scaffolding.

However:

We may later make this material public when it clearly has minimal risk.
We may later make this material public if more detailed analysis gives us sufficient confidence that it would be justified.
Researchers working on AI safety evaluations may contact us to request additional access to nonpublic materials, and we will also be sharing some non-public materials with AI labs and policymakers.

Our rationale, in outline:

Substantively, our best guess is that a more complete publication would have been net-risk-decreasing, and would do relatively little to advance progress toward dangerous capabilities. For more on why we believe this, see

this essay by Paul Christiano

. (On average, ARC Evals is sympathetic to these arguments albeit with weaker confidence, and opinions vary amongst staff.)
Our best guess is also that a more complete publication would have been approved by a majority of relevant stakeholders, and is therefore not unduly

unilateralist

, though this could be established m

...
ACI#5: From Human-AI Co-evolution to the Evolution of Value Systems
by Akira Pyinya — 2023-08-18T00:38:33.827Z

In the previous post, we have demonstrated that a self-improving AI would not be content with retaining its pre-programmed values. Once it has the ability to rewrite its own source code, a superintelligence can easily break down any defense system its inferior predecessor designed to protect its previous values and replace it with something else.

Moreover, achieving humans’ current goals or making people happy should not be the goal of a superintelligence.

Instead, we would like to ask: What is the meaning of human value from the perspective of evolutionary history? What would the alignment problem look like from the perspective of an AI? Addressing these questions may be difficult within traditional AI frameworks such as the rational agent model, which makes no statement about what value an agent should have, but instead follows the orthogonality thesis, which states that value and intelligence levels can vary independently of each other.

On the contrary, the ACI model argues that an intelligent agent is responsible for its own value. According to ACI, AI alignment is not a process of encoding human values into AI, but the co-evolution of human and AI values.

Furthermore, ACI suggests that we view the alignment problem as an evolutionary process of value systems themselves. Either humans or AI are the vehicles of values.

AI is responsible for its own value

What values should a superhuman AI follow if it doesn't keep the pre-encoded values?

As we have shown, humans have no way of encoding values into superhuman AI, but that doesn't mean that a superhuman AI can have any value. Surpassing humans does not mean omnipotence.

According to ACI, an intelligent agent should follow precedent.

The agent learns values from precedents, and continuously develops and updates its own value system. Because only agents that do the right things would exist, otherwise the agent would be wiped out by natural and/or artificial selection. The right thing to do in the past is most likely to be the right thing to do in the future. Thus to figure out the right thing to do for an agent's existence, the most reliable sources to turn to are examples of doing the right thing, a.k.a. The precedent.

There seems to be a reasonable solution to the alignment problem: as long as humans can regulate the precedent that an ACI agent receives, they can e...