trentmkelly/LessWrong-43k huggingface dataset:
Currency Collapse
by prue — 2025-04-11T03:48:01.469Z
Link: https://www.prue0.com/2025/04/11/currency-collapse/

A short story on the cascading impacts of tariffs

Year One: The Warning Signs

Eleanor Vega’s office window faced east, allowing the morning sun to illuminate the economic indicators flashing red on her screen. On this particular morning at the Treasury Department, she stared at projections that seemed almost apocalyptic, yet mathematically inevitable.

“The President is about to make the announcement,” her assistant said from the doorway.

Eleanor nodded without turning from her display. The models presented a cascade of consequences with 87% confidence intervals that narrowed as temporal distance increased, a rare phenomenon in economic forecasting that signaled high probability outcomes.

On the wall-mounted screen, President William Harmon stepped to the podium with his characteristic swagger. “Today, I’m signing Executive Order 14721, establishing the American Sovereignty Initiative. We’re putting tariffs on everything. Everything! Thirty percent minimum. Some industries, fifty percent. China’s been eating our lunch for decades. Europe’s been taking advantage of us. No more.”

Eleanor listened as financial analysts offered shocked commentary. Markets responded with immediate, brutal volatility; the S&P 500 dropping 4.7% within minutes. The VIX fear index spiked to levels not seen since the 2020 pandemic. Trading algorithms executed sell orders faster than human comprehension, creating liquidity vacuums in multiple sectors simultaneously. Within hours, news alerts pinged about critical imported medical supplies facing delays and price hikes, while automakers reliant on foreign components announced potential production slowdowns. The methodology for calculating the Consumer Price Index immediately became a political flashpoint.

Later that afternoon, in the windowless conference room deep within the Treasury building, a room already thick with tension, Eleanor presented her analysis to the Secretary and senior staff. Some career officials shifted uncomfortably, aware of the implications but constrained by the political climate; whispers of quiet mitigation efforts through regulatory interpretation had already begun in some departments.

“The trade deficit will decrease temporarily,” she explained, highlighting a section of her report. “But the collateral effects are severe. These projections indicate a 78% probability that within four years, we’ll see coordinated di...
Why do misalignment risks increase as AIs get more capable?
by ryan_greenblatt — 2025-04-11T03:06:50.928Z

It's generally agreed that as AIs get more capable, risks from misalignment increase. But there are a few different mechanisms by which more capable models are riskier, and distinguishing between those mechanisms is important when estimating the misalignment risk posed at a particular level of capabilities or by a particular model.

There are broadly 3 reasons why misalignment risks increase with capabilities:

As AIs get more capable, the chance of egregious misalignment increases: AIs with more powerful opaque reasoning abilities are more likely to become egregiously misaligned—sufficiently misaligned that they strategically deceive and work against their developers—without this being detected or trained out. We should expect opaque reasoning abilities generally increase as models get more capable, though it isn't entirely obvious what the relationship between opaque and non-opaque cognition will be in future AI systems. Additionally, I expect that as AIs get more capable their capabilities will increasingly come from sources (like long horizon RL) which make misalignment more likely and I also expect the architecture will increasingly have properties (like opaque "neuralese" recurrence) that make misalignment more likely. I discuss all of these factors more in the post "How will we update about scheming?".

More capable AIs will be used more and given greater affordances: Currently, AIs aren't used in higher stakes settings and this would give AIs (better) opportunities to take actions that could potentially have bad consequences. They aren't used in this way mostly because current AIs aren't capable enough for it to make sense to use them this heavily. When AIs are used, typically their affordances are quite limited and humans check their outputs for correctness, especially in cases where mistakes would be very costly. These affordances and this amount of human supervision make it more difficult for AIs to cause big problems. But in the future, powerful AIs will likely be run fully autonomously at massive scale and with substantial affordances, because this will be very productive (in the absence of misalignment risks at least). So, we should expect substantially elevated risk.

More capable AIs can more acquire power or cause problems by undermining human control: Successfully acquiring power and undermining human control are difficult, so AIs would need to

...
Six reasons why objective morality is nonsense
by Zero Contradictions — 2025-04-11T02:11:04.775Z
Link: https://coelsblog.wordpress.com/2013/07/29/six-reasons-why-objective-morality-is-nonsense/

Note: I didn't write this essay, nor do I own the blog where it came from. I'm just sharing it. The essay text is displayed below this line.

Whether morality is an objective property of the universe, or instead the subjective opinion of humans, is one of the longest running issues in philosophy. Jerry Coyne recently returned to the theme, arguing that morality was subjective, and, as I usually am, I was surprised by the number of commentators arguing the contrary.

This debate seems hampered by a lack of clarity on what “objective” and “subjective” moralities are. Coyne gave a sensible definition of “objective” morality as being the stance that something can be discerned to be “morally wrong” through reasoning about facts about the world, rather than by reference to human opinion.

If morality were objective, it would have to be conceivable that the statement “George’s actions were wrong and he deserves to be punished” would be true even if every human in the world were of the opinion, “George’s actions seem fine to me, perhaps even laudable”.

Thus, if morality were an absolute set by a god, something could be immoral even if every human disagreed. If, instead, human feelings and desires are what ultimately count, then that is a subjective morality.

Thus, a subjective morality is strongly preferable to an objective one! That’s because, by definition, it is about what we humans want. Would we prefer to be told by some third party what we should do, even if it is directly contrary to our own deeply held sense of morality?

Given that an objective morality would be highly undesirable, why do so many philosophers and others continue to try hard to rescue an objective morality?

I suspect that they’re actually trying to attain objective backing for what is merely their own subjective opinion of what is moral. This is the trick the religious have long played, inventing a god in their own image who can back them up by turning “I want …” into “God wants …”.

Secular philosophers should not play this game by hankering after objective morality, we should have confidence in the simple and honest “I want …”. We humans have a lot to be proud of: by thinking it through and arguing amongst ourselves, we have advanced morality hugely, with Western society today giving vastly better treatment to individuals, to women, children, religious minorities, foreigners, those of other races, the ...
Refining MAIM: Identifying Changes Required to Meet Conditions for Deterrence
by David Abecassis — 2025-04-11T00:49:53.360Z
Link: https://intelligence.org/2025/04/11/refining-maim-identifying-changes-required-to-meet-conditions-for-deterrence/

This is part of the MIRI Single Author Series. Pieces in this series represent the beliefs and opinions of their named authors, and do not claim to speak for all of MIRI.

In an

op-ed

published in TIME Magazine in 2023, Eliezer Yudkowsky called for an international agreement to establish a moratorium on frontier AI development. If a country outside of the agreement was in violation, signatories should “be willing to destroy a rogue datacenter by airstrike.”

The key assumptions are that there is a level of AI capabilities progress which can be monitored for and interdicted, such that successful interdiction forestalls the development of smarter-than-human systems.

In their recent work,

Superintelligence Strategy

, Dan Hendrycks, Eric Schmidt, and Alexandr Wang propose a deterrence regime in which the threat of sabotage is used to motivate countries (specifically, the U.S. and China) to forestall dangerous AI development. This regime is called Mutual Assured AI Malfunction (MAIM), and includes limiting AI development domestically and using the threat of sabotage to coerce rivals to do the same.

This essay argues that MAIM, as conceived in Superintelligence Strategy, is unlikely to provide an effective or stable deterrence regime. While MAIM correctly frames the strategic dynamic, this analysis reveals significant flaws. Specifically, MAIM struggles with unclear and unmonitorable red lines for AI development, questionable threat credibility (particularly the capability of sabotage to deny progress), and a volatile deterrence calculus driven by immense stakes and uncertainty.

Still, Superintelligence Strategy deserves credit for advancing the key problem: states cannot be expected to sit idly by and accept the consequences of unrestricted AI development. What characteristics should alternatives to MAIM possess? As a precursor to answering this question, this analysis introduces a framework based on established conditions for successful deterrence, drawing insights from works such as Michael Mazarr’s

Understanding Deterrence

. The following sections will outline this framework, apply it systematically to the MAIM proposal, and reveal where MAIM, in its current form, fails to meet the necessary conditions for stable deterrence.

A Deterrence Framework

Simply put, deterrence is the imposition of costs on some ac...
DOGE Might Be Worth Influencing
by LTM — 2025-04-11T00:40:34.866Z
Link: https://routecause.substack.com/p/doge-might-be-worth-influencing

TL;DR DOGE is central to initiatives important to senior decision makers in the US government. There are legitimate concerns about their longevity of influence, politicised nature, and independence from existing agendas. However, their proximity to decision-makers and growing administrative authority makes them an unusually accessible channel for advancing AI safety considerations. Their influence could significantly shape America's approach during critical technological developments, offering a cost-effective point of intervention.

Introduction

DOGE, the

Department of Government Efficiency

, has gathered widespread attention in recent months. Its secretive nature, apparent influence over president Trump, and the questionable credentials and experience of its members has made the quasi-department both an object of ridicule and a source of genuine concern.

Despite this, it presents a unique opportunity for raising the political salience of AI safety concerns through influencing US government policy. DOGE staff skews notably young and technologically capable (although a lot of information about the semi-government department is difficult to verify). They have shown remarkable willingness to upend decades of convention and participate in the executions of initiatives that would have been unthinkable under any previous administration. [1]

Most significantly, DOGE appears to have direct access to the highest echelons of the US government. Influencing a government is hard, with most estimates of the total cost of US lobbying settling on the

low billions

of dollars a year. Yet this small cohort of relatively inexperienced 20-somethings have found themselves with the ear of at least the richest man in the world, and possibly much of Trump’s cabinet.

DOGE offers two distinct paths to shaping a beneficial post-AGI future for humanity.

Channeling their significant discretion in how specific policies are executed across the US government.
Using their proximity to senior officials to raise AI safety awareness among those who will make critical decisions during technological transitions.

Directly influencing the US government

Although they are new, small, and widely criticised, DOGE has demonstrated amazing potential to acquire decision-making authority and divert significant resources. In their brief existence, they’ve collectively already gained temporary

admi

...
Can I learn language faster? Or, perhaps, can I memorize the foreign words and recall them faster?
by jmh — 2025-04-11T00:01:25.530Z

I've been learning Korean for a while now. I am finding a rather asymmetric aspect to my vocabulary learning process and results. I use Anki as the learning tool for vocabulary. What I find is that fairly quickly I get to a good recognition[1] performance. But I find my recall performance is significantly behind. I've got some ideas on why this type of asymmetry occurs but the bottom line is that I am beginning to think I've been approach vocabulary building the wrong way.

A quick google about the asymmetry suggests that this is a fairly common problem for language learners. I wonder if it's not because most settings for developing one's vocabulary in a new language tend to follow the same approach I see in the Anki decks I've downloaded or created myself. You get the foreign word on the front and then learn what the native language word is on the back.

So what I've though is maybe that is backwards. I'm starting a test to see flipping the approach, putting the word I already know up first and then having to come up with the Korean word as the "answer" might perhaps improve my recall performance, which I suspect would have no affect on my recognition ability.

If my supposition holds here then I expect I will improve my learning speed and more quickly. As it is now it's a bit of a slow slog of, what seems like, the process of hammering things into my head until I can recall the word on demand rather than recognizing the word when seen (or to some extent heard). And, at the end of the day that is the goal as I don't want to just read but be able to actually interact with others in a day-to-day type setting.

I've been trying this approach for a bit more than a week now. Too soon to say if it's actually working better for me but I have that "fuzzy" feeling I'm doing better. I am highly confident that reversing the process here cannot reduce my vocabulary acquisition rate.

I'm wondering if anyone else has tried this and if they found it helped or not. Or what people think of the shift in approach in general.

^

I will use recognition and recall in a pretty obvious way but for clarity, recognition is about seeing the Korean word and knowing what English word would have the same meaning. Recall is the ability to, without any English prompt, come up with the Korean word.
Anti-memes: x-risk edition
by WillPetillo — 2025-04-10T23:35:30.756Z

I first heard of the concept of an anti-meme while listening to an

interview

with Connor Leahy (relevant section is from 1:02:28-1:05:42). Leahy's definition of an anti-meme is:
"An idea that, by its very nature, resists being known."
While memes are defined by their ability to stick in one's mind and propagate, anti-memes are slippery, tending to fade from conscious thought. I do not know if Leahy picked up this concept from somewhere else; an Internet search mostly leads to examples following other definitions, such as

images that subvert expectations

. This post will be using Leahy's definition of anti-meme, as I have since found it to be exceptionally useful in explaining concepts in AI safety as well as in attuning my own mind to concepts I might otherwise miss. Some of the content in this post will be from Leahy's description, but most is based on my own experience.
What anti-memes are not
Arbitrary: ideas that people don't remember because what they simply aren't important enough to bother with. Examples include random phrases, jokes without a punchline, and boring or useless facts. What makes anti-memes worth naming as a concept is their membership in a class of ideas that resist being known despite their worthiness of being known. In fact, the anti-memes I will be cataloging in this post all have a fair number of people putting considerable effort into spreading, but this effort is a consequence of the ideas' perceived utility, not the appeal of the idea itself.
Controversial: ideas that people are predisposed to disbelieve. When saying something controversial, one is often met with clear, deliberate rejection. In contrast, when explaining an anti-meme, people often agree...but then continue to speak & act in a way that is inconsistent with the concept. When using an anti-meme in an argument, you will often find yourself being weirdly ignored or consistently misunderstood as saying something very different.
Complicated: anti-memes can be very simple—even boringly so. What makes them slippery is that they are difficult to internalize, especially on the emotional level. Anti-memes' lack of emotional connection also makes them difficult to remember...which has made this post rather difficult to write.
The common theme driving all of these negative categories is alternative explanations for an idea resisting being known. If an idea is arbitrary, contro...
Forecasting time to automated superhuman coders [AI 2027 Timelines Forecast]
by elifland — 2025-04-10T23:10:23.063Z
Link: https://ai-2027.com/research/timelines-forecast

Authors: Eli Lifland,[1] Nikola Jurkovic,[2] FutureSearch[3]

This is supporting research for AI 2027. We'll be cross-posting these over the next week or so.

Assumes no large-scale catastrophes happen (e.g., a solar flare, a pandemic, nuclear war), no government or self-imposed slowdown, and no significant supply chain disruptions. All forecasts give a substantial chance of superhuman coding arriving in 2027.

Summary

We forecast when the leading AGI company will internally develop a superhuman coder (SC): an AI system that can do any coding tasks that the best AGI company engineer does, while being much faster and cheaper. At this point, the SC will likely speed up AI progress substantially as is explored in our takeoff forecast.

We first show Method 1: time-horizon-extension, a relatively simple model which forecasts when SC will arrive by extending the trend established by METR’s report of AIs accomplishing tasks that take humans increasing amounts of time.

We then present Method 2: benchmarks-and-gaps, a more complex model starting from a forecast saturation of an AI R&D benchmark (RE-Bench), and then how long it will take to go from that system to one that can handle real-world tasks at the best AGI company.

Finally we provide an “all-things-considered” forecast that takes into account these two models, as well as other possible influences such as geopolitics and macroeconomics.

We also solicited forecasts from 3 professional forecasters from FutureSearch (bios here).

Each method’s results are summarized below:

Eli’s SC forecast (median, 80% CI)	Nikola’s SC forecast (80% CI)	FutureSearch aggregate (80% CI) (n=3)
Time-horizon-extension model	2027 (2025 to 2039)	2027 (2025 to 2033)	N/A
Benchmarks-and-gaps model	2028 (2025 to >2050)	2027 (2025 to 2044)	2032 (2026 to >2050)
All-things-considered forecast, adjusting for factors outside these models	2030 (2026 to >2050)	2028 (2026 to 2040)	2033 (2027 to >2050)

All model-based forecasts have 2027 as one of the most likely years, which is when an SC is achieved in the AI 2027 scenario. The code for our simulation is here.

Defining a superhuman coder (SC)

Superhuman coder (SC): An AI system for which the company could run with 5%[4] of their compute budget 30x as many agents as they have human research engineers, each of which is on average accomplishing coding tasks involved in AI re...
AI could cause a drop in GDP, even if markets are competitive and efficient
by Casey Barkan — 2025-04-10T22:35:16.290Z

I present a macroeconomic model of a competitive economy that, counterintuitively, predicts that increases in the productivity of automation technology will decrease economic production. In the model, the decrease in production occurs over an intermediate range of automation productivities, and once automation productivity is high enough that all labor is replaced, production begins to rise.

Most discussions of the economic impacts of AI assume that automation will increase total economic output. Even though wages may decline, the common assumption is that total production will rise. However, in this post I show that a basic model that follows a standard macroeconomic modeling framework predicts otherwise. The model starts with a simple general equilibrium model of a competitive economy and incorporates an automation technology whose productivity increases over time. This model predicts that the increasing productivity of automation causes GDP to decline until all production is automated, at which point GDP begins to rise again and eventually surpasses its pre-automation level. Even a temporary decline in GDP could cause lasting effects: for example, falling tax revenue could destabilize governments, undermining universal basic income (UBI) and AI safety policies.

The mechanism responsible for the drop in GDP is that automation causes wages to fall, and as they fall the amount of labor that workers provide also falls. The drop in labor causes a drop in production that can be greater than the increase in production due to higher productivity automation. To my knowledge, this conceptual argument was first proposed by Artūrs Kaņepājs[1]. Later, I developed a model showing that AI can decrease GDP in a non-competitive economy[2][3]. It turns out that a drop in GDP can occur even in a competitive economy with frictionless markets, as shown in this post.

The model is defined and solved in the section below, and Figure 1 shows the equilibrium quantities as a function of the productivity of automation technology, denoted Aauto. Note that I used the simplest (not the most realistic) choice of parameters to make the figure. The economy's output (left panel, blue curve) drops sharply when Aauto surpasses the threshold R0, and falls until labor employed is zero, at which point output begins to rise. At low Aauto, returns to labor and capital are eq...
EA Reflections on my Military Career
by TomGardiner — 2025-04-10T19:01:42.844Z
Link: https://forum.effectivealtruism.org/posts/f6XmkJ9PWFfn9GvqD/ea-reflections-on-my-military-career

Four years ago, I commissioned as an Officer in the UK's Royal Navy. I had been engaging with EA for four years before that and chose this career as a coherent part of my impact-focused career plan, and I stand by that decision.

Early next year, I will leave the Navy. This article is a round-up of why I made my choices, how I think military careers can sensibly align with an EA career, and the theories of impact I considered along the way that don't hold water. Military service won't be the right call for most in this community, but it could be for some. Hopefully, this is informative for those people. Furthermore, I spent a whole year being trained in leadership. Someone I met at an EAGx conference said the offhand nugget of Military Leadership 101 wisdom I gave them was the "best advice I received in the whole event". This is probably worth sharing.

Noting how unconventional my experience is for EAs, I also write this to help communicate the value of my CV to prospective employers.