trentmkelly/LessWrong-43k huggingface dataset:
The Future of Work: How Can Policymakers Prepare for AI's Impact on Labor Markets?
by davidconrad — 2024-06-24T14:18:55.023Z

This is a brief overview of a more detailed research paper conducted as part of the AI Safety Camp. Our aim is to give those outside the economic discourse a deeper understanding of the labor market impacts of AI and to discuss potential policies in response.

The draft report can be found here. All feedback greatly appreciated.

AI researchers and economists broadly agree that AI has the potential to disrupt labor markets, and policymakers broadly agree that “something must be done” about it. Labor markets touch every aspect of the economy and society, and transformative AI has the potential to cause immense dislocation across countries and industries in the next few years.

However beneath this surface-level agreement lies a wide diversity of opinion. There is a notable lack of consensus in the literature for how the labor market might be affected by advanced AI in the coming decades and, in turn, how governments and regulators should respond.

We offer a straightforward framework to those outside this economic debate by which they can conceptualize the potential futures we are facing. To address this uncertainty we have

written a paper

that summarizes the range of labor market predictions for the future of AI in a simple model with two factors - the net task displacement effect and the aggregate productivity effect. This framework allows us to understand four potential labor markets that are possible under transformative AI. With this framework in hand we then categorize the key policy levers that are relevant under each of these different possible futures.

This framework provides a simple way to categorize and discuss predictions about the future of the labor market under transformative AI and, in turn, allows for better decision making.

Understanding AI's Dual Impact on Labor Markets

Based on recent economic theory

, we argue that AI's influence on labor demand will depend on two primary factors: the net task displacement effect and the aggregate productivity effect. The net displacement effect measures AI's overall impact on labor requirements by balancing task displacement and task creation in the labor market. Task displacement occurs when AI automates tasks previously done by humans, reducing labor demand. However, while transformative AI is likely to automate many tasks it will also create new tasks and jobs that don’t currently exist. T...
LLM Generality is a Timeline Crux
by eggsyntax — 2024-06-24T12:52:07.704Z

Four-Month Update

[EDIT: I believe that this paper looking at o1-preview, which gets much better results on both blocksworld and obfuscated blocksworld, should update us significantly toward LLMs being capable of general reasoning. See update post here.]

Short Summary

LLMs may be fundamentally incapable of fully general reasoning, and if so, short timelines are less plausible.

Longer summary

There is ML research suggesting that LLMs fail badly on attempts at general reasoning, such as planning problems, scheduling, and attempts to solve novel visual puzzles. This post provides a brief introduction to that research, and asks:

Whether this limitation is illusory or actually exists.
If it exists, whether it will be solved by scaling or is a problem fundamental to LLMs.
If fundamental, whether it can be overcome by scaffolding & tooling.

If this is a real and fundamental limitation that can't be fully overcome by scaffolding, we should be skeptical of arguments like Leopold Aschenbrenner's (in his recent 'Situational Awareness') that we can just 'follow straight lines on graphs' and expect AGI in the next few years.

Introduction

Leopold Aschenbrenner's recent 'Situational Awareness' document has gotten considerable attention in the safety & alignment community. Aschenbrenner argues that we should expect current systems to reach human-level given further scaling[1], and that it's 'strikingly plausible' that we'll see 'drop-in remote workers' capable of doing the work of an AI researcher or engineer by 2027. Others hold similar views.

Francois Chollet and Mike Knoop's new $500,000 prize for beating the ARC benchmark has also gotten considerable recent attention in AIS[2]. Chollet holds a diametrically opposed view: that the current LLM approach is fundamentally incapable of general reasoning, and hence incapable of solving novel problems. We only imagine that LLMs can reason, Chollet argues, because they've seen such a vast wealth of problems that they can pattern-match against. But LLMs, even if scaled much further, will never be able to do the work of AI researchers.

It would be quite valuable to have a thorough analysis of this question through the lens of AI safety and alignment. This post is not that[3], nor is it a review of the voluminous literature on this debate (from outside the AIS community). It attempts to briefly introduce the dis...
On Claude 3.5 Sonnet
by Zvi — 2024-06-24T12:00:05.719Z

There is a new clear best (non-tiny) LLM.

If you want to converse with an LLM, the correct answer is Claude Sonnet 3.5.

It is available for free on Claude.ai and the Claude iOS app, or you can subscribe for higher rate limits. The API cost is $3 per million input tokens and $15 per million output tokens.

This completes the trifecta. All of OpenAI, Google DeepMind and Anthropic have kept their biggest and more expensive model static for now, and instead focused on making something faster and cheaper that is good enough to be the main model.

You would only use another model if you either (1) needed a smaller model in which case Gemini 1.5 Flash seems best, or (2) it must have open model weights.

Updates to their larger and smaller models, Claude Opus 3.5 and Claude Haiku 3.5, are coming later this year. They intend to issue new models every few months. They are working on long term memory.

It is not only the new and improved intelligence.

Speed kills. They say it is twice as fast as Claude Opus. That matches my experience.

Jesse Mu: The 1st thing I noticed about 3.5 Sonnet was its speed.

Opus felt like msging a friend—answers streamed slowly enough that it felt like someone typing behind the screen.

Sonnet’s answers *materialize out of thin air*, far faster than you can read, at better-than-Opus quality.

Low cost also kills.

They also introduced a new feature called Artifacts, to allow Claude to do various things in a second window. Many are finding it highly useful.

Benchmarks

As always, never fully trust the benchmarks to translate to real world performance. They are still highly useful, and I have high trust in Anthropic to not be gaming them.

Here is the headline chart.

Epoch AI confirms that Sonnet 3.5 is ahead on GPQA.

Anthropic also highlight that in an agentic coding evaluation, Claude 3.5 Sonnet solved 64% of problems versus 38% for Claude Opus, discussed later.

Needle in a haystack was already very good, now it is slightly better still.

There’s also this, from Anthropic’s Alex Albert:

You can say ‘the recent jumps are relatively small’ or you can notice that (1) there is an upper bound at 100 rapidly approaching for this set of benchmarks, and (2) the releases are coming quickly one after another and the slope of the line is accelerating despite being close to the maximum.

Human Evaluation Tests

We are stil...
Book Review: Righteous Victims - A History of the Zionist-Arab Conflict
by Yair Halberstadt — 2024-06-24T11:02:03.490Z

I originally entered this to the ACX Book Review competition. Since it has not been selected as a finalist I'm now free to post it here. In truth it's a followup to my review of Morris's history of Israel's War of Independence.

In the wake of the October 7th attack on Israel and Israel’s response, everyone seemed to agree that one side of the conflict was the epitome of evil, the reincarnation of the Nazis, with warfare in their blood and a pure unfiltered hatred of the enemy in their minds. The other side was a force for good, who just wanted peace and was doing the best they could in a difficult situation. The only problem is no one could agree which side was which.

This is unfair. While the loudest voices may paint the world in black and white, as soon as you ignore them, you begin to encounter a whole range of more nuanced views - yet still find yourself no less confused.

Now for the most part my view is that unless you’re willing to put in the effort to deeply understand conflicts in far off lands, you’re best off not having an opinion on them, and definitely not one fed to you by the twitter or tiktok feed. Expressing loud, confident opinions on unfamiliar conflicts often does more harm than good.

Alas this conflict is not in a far away land. I live 20km from the border with Gaza. Most of my friends were called up to do reserve duty in the IDF. My children almost certainly will have to do the same once they grow up. Far too much of my income goes towards military spending rather than my bank account. I can’t take the easy way out, so I have to do things the hard way. So I bought a copy of Benny Morris’s Righteous Victims at exorbitant cost[1], and plowed through it. And I thought I’d share with you what I learned, so that if you do decide to opine on the Israel Palestine conflict, your opinion will hopefully be more educated.

Righteous Victims is a history of the Arab Zionist conflict from 1881 till 2001, written by one of the most respected historians of this conflict.

Bias

Morris is a liberal Zionist, but one whose aim in studying history was to strip back the comforting lies he'd been taught as a child, and find out the actual truth.

None of his (serious) critics accuse him of lying, and his mastery of the primary sources is undisputed. Instead there are two main accusations leveled against him.

The first he readily admits himself in the introduction. ...
The Living Planet Index: A Case Study in Statistical Pitfalls
by Jan_Kulveit — 2024-06-24T10:05:55.101Z
Link: https://www.nature.com/articles/s41467-024-49070-x

In case you sometimes read an alarming headline about biodiversity loss, like Global wildlife populations have declined by 69% since 1970 or Researchers Report a Staggering Decline in Wildlife, there is a decent chance it is based on a metric called The Living Planet Index (LPI). My colleagues at CTS looked into how is LPI calculated, and show that current version of LPI is probably best understood as a cautionary example of effects of mathematical biases, but poor measure of biodiversity. [1] I do expect this result will get way less media coverage than the alarmist headlines; also it is methodologically interesting, so posting a short summary.

How is LPI calculated?

The paper has a helpful diagram:

So, the whole process is

Addition of a constant of 1% of the population mean (the mean from all non-zero values) to all values of the time series if the time series contains zero in any year.
Estimation of the new population values by two methods (also the way to estimate missing values, i.e. values for years without population records):

GAM method, which is basically curve-fitting in log() space. It is used if the length of the time series is equal to or longer than 6 records and only if the GAM fits well.
Chain method is used if the length of the time series is less than 6 records or if the GAM does not fit well (or if all population values are the same). It is a linear interpolation for missing values in the population series in logspace Ni=Np(NsNp)i−^ps−^p
where i is the year when the value is interpolated, p previous year and s subsequent year.

Log transformation (base 10) of the population values.
Calculating the difference between the log population values between every two consecutive years = log ( ratio of population values ) = population growth = lambda
Calculating the arithmetic mean of lambdas (the logarithm of the geometric mean) of all populations of one species within one biogeographical realm (for an individual year). There are 5 (for the terrestrial and freshwater ecosystem) or 6 (for the marine ecosystem) biogeographical realms distinguished (see below).
... more steps of averaging by arithmetic mean by realms, across all taxa within one realm, all realms (for an individual year) and ecosystem-specific lambdas across all ecosystems.
The calculation of the LPI as 𝐼=𝐼𝑝×10𝜆, where Ip is the ind

...
Sci-Fi books micro-reviews
by Yair Halberstadt — 2024-06-24T09:49:28.523Z

I've recently been reading a lot of science fiction. Most won't be original to fans of the genre, but some people might be looking for suggestions, so in lieu of full blown reviews here's super brief ratings on all of them. I might keep this updated over time, if so new books will go to the top.

A deepness in the sky (Verner Vinge)

scifiosity: 10/10
readability: 8/10
recommended: 10/10

A deepness in the sky excels in its depiction of a spacefaring civilisation using no technologies we know to be impossible, a truly alien civilisation, and it's brilliant treatment of translation and culture.

A fire upon the deep (Verner Vinge)

scifiosity: 8/10
readability: 9/10
recommended: 9/10

In a fire upon the deep, Vinge allows impossible technologies and essentially goes for a slightly more fantasy theme. But his depiction of alien civilisation remains unsurpassed.

Across Realtime (Verner Vinge)

scifiosity: 8/10
readability: 8/10
recommended: 5/10

This collection of two books imagines a single exotic technology, and explores how it could be used, whilst building a classic thriller into the plot. It's fine enough, but just doesn't have the same depth or insight as his other works.

Children of Time (Adrian Tchaikovsky)

scifiosity: 7/10
readability: 5/10
recommended: 5/10

Children of Time was recommended as the sort of thing you'd like if you enjoyed a deepness in the sky. Personally I found it a bit silly - I think because Tchaikovsky had some plot points he wanted to get to and was making up justifications for them, rather than deeply thinking about the consequences of his various assumptions.

The Martian (Andy Weir)

scifiosity: 10/10
readability: 8/10
recommended: 9/10

This is hard sci-fi on steroids. Using only known or in development technologies, how could an astranaut survive stranded on Mars. It's an enjoyable read, and you'll learn a lot about science, but the characters sometimes feel one dimensional.

Project Hail Mary (Andy Weir)

scifiosity: 8/10
readability: 8/10
recommended: 7/10

This is more speculative sci-fi than the martian, but still contains plenty of hard science[1]. It focuses more on plot, but that's not really Weir's forte and the sciencey bits suffer as a result. Still enjoyable though.

Seveneves (Neil Stephenson)

scifiosity: 8/10
readability: 8/10
recommended: 7/10

This is really two books. The first is a hard sci-fi, how do we build things rapidly in space using current...
A Step Against Land Value Tax
by Blog Alt — 2024-06-24T05:13:48.949Z
Link: https://antematters.substack.com/p/against-lvt-as-an-acx-believer-now

Introduction

Many Nuance-oors like the promised pragmatic arguments for LVT (densification, lack of deadweight loss) and lament some of the principled arguments against (kicking out grandmas, unbounded gentrification). Here I build the intuition for the economic efficiency of the status quo (from here on SQ) over LVT which moved my opinion from "There is another way, have you heard of LVT?" to "Build more 1. houses 2. transport, it's literally the only way". It is not going to exhaustively prove any point or crunch many statistics but singularly gesture at a theoretical level why SQ has stronger economic arguments and LVT weaker ones than I have once believed. My intent is to put this perspective into the Overton Window, such that smarter people than myself may develop it. https://www.astralcodexten.com/p/does-georgism-work-is-land-really serves, as far as I know, the best low down on the idea. Please be familiar with LVT or read that article series first as a primer.

(also this is my first poast so if I make a big nono - let me know)

Land Values ARE Property Values

First let's put aside underground minerals, land marks, etc which Georgists would agree with and broadly define a property as a building ontop of land. The Georgist claim is that the value of the land is inexplicably independent from the value of the building. This is equivalent to "Here is AD and AS and yea.. that's the economy". This fundamentally claims that somehow property, land and buildings are all fungible amongst eachother. This is clearly incorrect but let us demonstrate why:

Property fungibility: A school next to a prison is not the same school when it is not next to that prison. A kindergarden adjacent to a primary school adjacement to a highschool is not the same as the set of properties in which these are split apart.
Building fungibility: A lighthouse next to the sea is not the same as a lighthouse not next to the sea or a more charitable example: a house with a sea view. A school next to public transport is not the same as a school not next to public transport.
Land fungibility: A building with a garden will not have that same garden if it's land is smaller. A large building physically imposes requirements on the land it is built upon (such is the case for most buildlings) which may be even impossible for other plots of land.

Even the same building on the same plot of land can be differen...
Different senses in which two AIs can be “the same”
by Vivek Hebbar — 2024-06-24T03:16:43.400Z

Sometimes people talk about two AIs being “the same” or “different” AIs. We think the intuitive binary of “same vs. different” conflates several concepts which are often better to disambiguate. In this post, we spell out some of these distinctions. We don’t think anything here is particularly novel; we wrote this post because we think it’s probably mildly helpful for people to think this through, and because Buck fairly often wants to reference it.

Some example distinctions:

In a debate protocol[1], people often refer to the judge and debaters as three “different” AIs because their tokens are subject to different training incentives. But you could also say that the two debaters are “the same AI” because their prompts and context look similar. Or you could say that all three are the “same AI” because they were fine-tuned from the same weights.
In a control setting, an untrusted monitor might be fine-tuned from the same base model as the model it’s monitoring, leaving it intuitively unclear whether they are “the same AI”.

We think that it’s often important to be able to distinguish different senses of “personal identity” when discussing various issues in AI safety, especially issues related to collusion. We frame each sense of personal identity as telling us whether two actions[2] belong to the “same AI” or “different AIs”. Here are the senses we distinguish in this post:

Model weights: Were the two actions produced by two different models?

Pretrain identity: Models are different if they come from separate pretraining runs
Parameter identity: Models are different even if they are fine-tunings of the same pretrained model

Shared context: Do the two actions have shared LLM context?
Shared activations: Are the two actions downstream of shared activations?
Shared memory: In a scaffold, did the processes producing the two actions have access to a shared memory bank?
Shared reward: In an RL setting, do the actions share the same rewards?
Shared role in training: In protocols like debate, the training incentive can be different in nature for different classes of prompts/contexts. Do the two actions come from contexts in the same class?

Model identity

Model identity is based on which model parameters were run to produce a given action. “Pretrain identity” conflates all models finetuned from the same pretraining run. Whereas “paramet...
Talk: AI safety fieldbuilding at MATS
by Ryan Kidd — 2024-06-23T23:06:37.623Z

I recently gave a talk to the AI Alignment Network (ALIGN) in Japan on my priorities for AI safety fieldbuilding based on my experiences at MATS and LISA (slides, recording). A lightly edited talk transcript is below. I recommend this talk to anyone curious about the high level strategy that motivates projects like MATS. Unfortunately, I didn't have time to delve into rebuttals and counter-rebuttals to our theory of change; this will have to wait for another talk/post.

Thank you to Ryuichi Maruyama for inviting me to speak!

Ryan: Thank you for inviting me to speak. I very much appreciated visiting Japan for the Technical AI Safety Conference in Tokyo. I had a fantastic time. I loved visiting Tokyo; it's wonderful. I had never been before, and I was pleased to meet some representatives from ALIGN at the conference. I welcome more opportunities to speak to the ALIGN Network and Japanese researchers.

The purpose of my talk today is to give an update on the state of AI safety field-building as I understand it, which might benefit the ALIGN Network and the Japanese research community. Perhaps others will find it interesting. I can speak to my experience at MATS, LISA, and various other projects I've been involved in regarding AI safety. I'll do my best, and we can have lots of questions.

I did a PhD in physics at the University of Queensland in Brisbane, Australia, which is where I grew up. During that PhD, I became interested in AI safety and did a limited amount of research on the topic. After my PhD, I realized there wasn't much of a pipeline into the field, so new researchers had a lot of problems entering AI safety. I joined a program called MATS as a scholar. There were five of us in the first round; it was a pilot program. I wanted to help this program grow, so I joined the leadership team, and the old leadership team left. I helped grow the program into what will now be a 90-person program, twice a year.

Along the way, I also helped co-found an office in London, the London Initiative for Safe AI (LISA). This is, as far as I understand, the preeminent AI safety community space for organizations pursuing AI safety projects in London and the UK. This complements offices such as Constellation and FAR Labs in Berkeley. I'm speaking from FAR Labs right now; this is in Berkeley, California, the epicenter of the AI safety movement, right next to San Francisco, the epi...