trentmkelly/LessWrong-43k huggingface dataset:
Crime and Punishment #1
by Zvi — 2025-04-21T15:30:06.420Z

This seemed like a good next topic to spin off from monthlies and make into its own occasional series. There’s certainly a lot to discuss regarding crime.

What I don’t include here, the same way I excluded it from the monthly, are the various crimes and other related activities that may or may not be taking place by the Trump administration or its allies. As I’ve said elsewhere, all of that is important, but I’ve made a decision not to cover it. This is about Ordinary Decent Crime.

Table of Contents

Perception Versus Reality.

The Case Violent Crime is Up Actually.

Threats of Punishment.

Property Crime Enforcement is Broken.

The Problem of Disorder.

Extreme Speeding as Disorder.

The Fall of Extralegal and Illegible Enforcement.

In America You Can Usually Just Keep Their Money.

Police.

Probation.

Genetic Databases.

Marijuana.

The Economics of Fentanyl.

Enforcement and the Lack Thereof.

Jails.

Criminals.

Causes of Crime.

Causes of Violence.

Homelessness.

Yay Trivial Inconveniences.

San Francisco.

Closing Down San Francisco.

A San Francisco Dispute.

Cleaning Up San Francisco.

Portland.

Those Who Do Not Help Themselves.

Solving for the Equilibrium (1).

Solving for the Equilibrium (2).

Lead.

Law & Order.

Look Out.

Perception Versus Reality

A lot of the impact of crime is based on the perception of crime.

The perception of crime is what drives personal and political reactions.

When people believe crime is high and they are in danger, they dramatically adjust how they live and perceive their lives. They also demand a political response.

Thus, it is important to notice, and fix, when impressions of crime are distorted.

And also to notice when people’s impressions are distorted. They have a mental idea that ‘crime is up’ and react in narrow non-sensible ways to that, but fail to react in other ways.

One thing that it turns out has very little to do with perceptions of crime is the murder rate, which in most places has gone dramatically down.

Hunter: Americans have a very distorted perception of how safe a city is.

If you go purely by state, if it counts then Washington DC is at the top, but 9 of the next 10 are red states. And of the 100 biggest cities, 5 of the 6 safest are in California.

Of course, there is a lot more to being safe than not being murdered. San Francisco has a very low murder rate, but what worries people are other crimes, ...
Improving CNNs with Klein Networks: A Topological Approach to AI
by Gunnar Carlsson — 2025-04-21T15:21:10.474Z

In our earlier post, we described how one could parametrize local image patches in natural images by a surface called a Klein bottle. In Love et al, we used this information to modify the convolutional neural network construction so as to incorporate information about the pixels in a small neighborhood of a given pixel in a systematic way. We found that we were able to improve performance in various ways. One obvious way is that the neural networks learned more quickly, and we therefore believe that they could learn on less data. Another very important point, though, was that the new networks were also able to generalize better. We carried out a synthetic experiment on MNIST, in which we introduced noise into MNIST. We then performed two experiments, one in which we trained on the original MNIST and evaluated the convolutional models on the “noisy” set, and another in which we trained on the noisy set and evaluated on the original set. The results are displayed below.

The upper boxes describe the accuracy on the test set, while the lower boxes show training loss. The orange, green, purple, and red curves belong to various versions of the networks which have been modified to incorporate the Klein bottle information, and the blue curve is a standard convolutional neural network. The left column describes the results when training on the noisy set and evaluating on the original MNIST, and the right the results when using the original MNIST for training and evaluating on the noisy set. It is clear that the Klein networks outperform standard CNN’s dramatically on generalization in this setting. We also considered generalization from one data set to another.

The curves on the left describe generalization from the SVHN data set of pictures of house numbers to MNIST, and vice versa. In this case, the orange, purple, and brown curves describe the performance of three different versions of the Klein modified constructions, while the green and blue describe two different versions of standard CNN architectures, one with pooling and one without. On the right, you see generalization between CIFAR and Kaggle data sets, for the question of classifying cats vs. dogs. In both cases, the Klein modified networks outperform standard constructions on generalization.

The ability to ...
Eulogy to the Obits
by Niko_McCarty — 2025-04-21T14:10:27.211Z

By Xander Balwit

With death all but obsolete, Jamie’s life felt moot and emaciated. The Obituary Desk at The Times, where he worked, had turned into a ghost town he presided over with the bearing of a man who had given everything up for the bitter disappointment of a mine devoid of mineral riches. He would go on long walks around the deserted halls, choosing a different desk each day from which to work on whatever writing projects he could find. It was a shame, for death was what he liked writing about most.

Lives had been easier to frame when they’d been time-bound. A man at 60 is hardly the same man at 153, let alone 154, with how quickly things were changing. Happily, at least for Jamie, there were still the unfortunate saps who gave up the ghost to sheer accident or, lacking technological enthusiasm or a spirit of curiosity, elected to bow out early — or had never opted in to begin with.

Deaths were rare enough that when one was announced, those still employed in the Obits clamored, begged, and connived their way into a piece of the action. But after an ugly incident involving a bribe, a bottle of cognac, and the burning of an office chair, the holdouts had taken to drawing straws.

As luck would have it, Jamie drew the next reported incident. He didn’t know when exactly it would take place because chance, rather than age, had come to herald death’s arrival. No longer could one preempt those venerated elders whose lives had been haunted by finitude as now they sustained themselves through a regimen of drugs, diets, and therapies: products of the decades and countless billions poured into longevity medicine.

The call came a little after noon, just after Jamie had returned from the short walk down to his favorite cafe for his third cup of coffee that morning. The cafe line crept along at a glacial pace because their barista was one of the older task models — attractive, but inordinately slow — steaming milk with the rigidity of someone who detests babies but has found themselves holding a squalling one. There was an awkwardness about her that Jamie liked, a charm in the mechanical smile and uncanny valley of her handwriting on the cup he now tossed aside as he made his way out of the building to the scene.

Hurrying across the park, he could see the uprooted tree lying horizontally across the path before he saw the police tape. It had been a windy few days, a...
Research Notes: Running Claude 3.7, Gemini 2.5 Pro, and o3 on Pokémon Red
by Julian Bradshaw — 2025-04-21T03:52:34.759Z

Disclaimer: this post was not written by me, but by a friend who wishes to remain anonymous. I did some editing, however.

So a recent post my friend wrote has made the point quite clearly (I hope) that LLM performance on the simple task of playing and winning a game of Pokémon Red is highly dependent on the scaffold and tooling provided. In a way, this is not surprising—the scaffold is there to address limitations in what the model can do, and paper over things like lack of long-term context, executive function, etc.

But the thing is, I thought I knew that, and then I actually tried to run Pokémon Red.

A Casual Research Narrative

The underlying code is the basic Claude scaffold provided by David Hershey of Anthropic.[1] I first simply let Claude 3.7 run on it for a bit, making observations about what I thought might generate improvements. Along the way there were many system prompt changes, tweaking this and that.

For instance, after turning the navigation tool[2] on, Claude pretty much never used it. Even now it still only does so intermittently. While arguably a big tool assist, it ends up being mostly QoL for human observers, making things go by faster. Every once in a while it will path the model past a small obstacle it was struggling with. I don't consider it a big help as-is.

Claude is currently trying to go down the stairs which he is "right on top of".

My first additions were to reintroduce something I knew Claude and GeminiPlaysPokemon had already done: writing the grid coordinates onto the screenshot provided to the model.

Left: game in emulation. Right: game screenshot as Claude will see it.

Whoops don't look at this, I got it right on the first try.

This suppressed a behavior I had noticed early: Claude was awful at using the navigation tool because he simply didn't know what tile was which. It also was necessary for my first change.

My first change was an idea I had been ruminating on for some time, as a way of bypassing vision and memory limitations. The models would be provided with a tool to dictate labels at map coordinates for permanent storage. For instance, if a coordinate was a door from A -> B, it would be able to note that. These labels would then be provided in the text input as well as printed as a label on the screenshot.

Notice what is labeled as stairs here. Quote at the time:
> kind of tempted to switch to gemini

...
Not All Beliefs Are Created Equal: Diagnosing Toxic Ideologies
by Big_friendly_kiwi — 2025-04-21T03:18:48.677Z

Epistemic status: exploratory but confident. This essay presents a general framework for identifying and analyzing ideologies based on recurring structural patterns. It draws on observation, theory, and examples from across the political spectrum. The goal is to improve mental defenses against manipulative belief systems, not to discredit all ideological thinking. I welcome critique and refinement.

We live in an online society saturated with diverse ideologies—identity politics, political populism, libertarianism, conspiracy theories, and others. The internet and social media enable these ideologies to spread rapidly, significantly influencing real-world politics and everyday life, often negatively. This essay outlines the defining features of ideologies, examines their essential structural components, explores why they spread effectively, identifies their harmful consequences, and provides illustrative examples.

Defining Ideology

An ideology is fundamentally a simplified worldview, offering a lens through which individuals interpret social and economic relationships. It combines descriptive and prescriptive elements: it explains the world as it supposedly is and advocates for how it should be. While superficially resembling scientific or rational frameworks, ideologies crucially differ in their resistance to falsification.

Importantly, this critique does not imply that all ideological frameworks are inherently harmful. Many ideologies begin as efforts to understand injustice or improve human flourishing. Feminism, for example, has made undeniable contributions to gender equality and has advanced critical conversations around consent, labor, and representation. Libertarian critiques have usefully highlighted government overreach and championed civil liberties. Marxist theory has illuminated the dynamics of class, power, and economic exploitation. Even populist movements have occasionally acted as corrective forces when mainstream institutions failed to address public concerns. These frameworks can be valuable when treated as starting points for inquiry, rather than as closed systems of belief.

The issue arises when these frameworks harden into unchallengeable dogmas, spread primarily for social signaling or emotional reinforcement, and become resistant to falsification. This essay is concerned with these virulent forms—not with thoughtful versions grounded in evi...
AI 2027 is a Bet Against Amdahl's Law
by snewman — 2025-04-21T03:09:40.751Z

EDIT: I've written a followup post, summarizing and responding to the key themes raised in the comments.

AI 2027 lies at a Pareto frontier – it contains the best researched argument for short timelines, or the shortest timeline backed by thorough research[1]. My own timelines are substantially longer, and there are credible researchers whose timelines are longer still. For this reason, I thought it would be interesting to explore the key load-bearing arguments AI 2027 presents for short timelines. This, in turn, allows for some discussion of signs we can watch for to see whether those load-bearing assumptions are bearing out.

To be clear, while the authors have short timelines, they do not claim that ASI is likely to arrive in 2027[2]. But the fact remains that AI 2027 is a well researched argument for short timelines. Let's explore that argument.

(In what follows, I will mostly ignore confidence intervals and present only median estimates; this is a gross oversimplification of the results presented in the paper, but sufficient for my purpose here, which is to present the overall structure of the model and give a flavor of how that structure is fleshed out without going into overwhelming detail.)

Timeline to ASI

AI 2027 breaks down the path from present-day capabilities to ASI into five milestones[3]:

Saturate RE-Bench. This milestone is achieved when an AI reaches an average score of 1.5 on the RE-Bench suite of AI R&D tasks[4], which the authors feel matches the ability of the best human coders.
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[5]."
Superhuman AI researcher (SAR): "An AI system that can do the job of the best human AI researcher but faster, and cheaply enough to run lots of copies."
Superintelligent AI researcher (SIAR): "An AI system that is vastly better than the best human researcher at AI research."
Artificial superintelligence (ASI): "An AI system that is much better than the best human at every cognitive task."

For the first milestone, they fit a logistic curve to historical progress on RE-Bench, and estimate that a score of 1.5 will be reached "some time in 2026":

To estimate timelines for the remaining milestones, they use a two-step process. First, they estimate the time that would be required to reach that milestone (starting from the previ...
Severance and the Ethics of the Conscious Agents
by Crissman — 2025-04-21T02:21:49.202Z

***Severance Spoilers!***

Nick Bostrom talks about coherent, extrapolated ethics as the goal of AI alignment, specifically to avoid calcification from our current moral code, which likely contains many things future generations would find unethical, just as we would previous generations. Since reading that, I've been wondering what things we accept today might alter the trajectory of the future.

Another of Bostrom's conjectures is the Simulation Hypothesis, which posits that the future is likely awash with consciousness that wants to create historical simulations, which would include orders of magnitude more consciousnesses, which means we are probably a historical simulation consciousness.

My personal counterargument to this is that our morals are likely to evolve against the creation of large amounts of consciousness. The EA movement includes avoiding causing pain to other creatures, even when their consciousness is questionable ("Save the shrimp!"). Another facet of this is forced labor, or is it acceptable to create a consciousness to do work for you?

Eventually, I expect we will solve what consciousness "is" and be able to confirm if an entity is conscious or not. After that point, it's likely we'll be able to design agents with or without consciousness.. Having unconscious entities do our work may be less computationally expensive, which could be another motivator against conscious agents.

The Severance TV series, in which separate consciousnesses are housed within the same body, foreshadows the developing morality around consciousness. In the TV show, failing to continue to provide experiences for an artificially created consciousness, even when the body and original consciousness survive, is frequently labelled as murder. Retiring the consciousness of multiple "innies" (work-only consciousnesses) is tantamount to mass murder.

What do you think? Is it ethical now to make a conscious agent to do work for you, and then retire it afterwards? Will it be considered so in the future?
March-April 2025 Progress in Guaranteed Safe AI
by Quinn — 2025-04-20T19:00:07.879Z

Say hi at ICSE in Ottawa, I’ll be at the reception Thursday, this colocated event on Friday, and the LLM4Code workshop on Saturday.

As usual there are no benefits to the paid subscription.

Sorry for consolidating two months into one post again after I said I wouldn’t.

Subscribe now

Fermstimate of the cost of patching all security relevant open source software

Niplav writes

So, a proposal: Whenever someone claims that LLMs will d/acc us out of AI takeover by fixing our infrastructure, they will also have to specify who will pay the costs of setting up this project and running it.

I’m almost centrally the guy claiming LLMs will d/acc us out of AI takeover by fixing infrastructure, technically I’m usually hedging more than that but it’s accurate in spirit.

If transformative AI is developed soon, most open source projects (especially old ones relevant to internet infrastructure) are going to be maintained by humans with human response times. That will significantly increase the time for relevant security patches to be reviewed and merged into existing codebases, especially if at the time attackers will submit AI-generated or co-developed subtle exploits using AI systems six to nine months behind the leading capabilities, keeping maintainers especially vigilant.

I usually say we prove the patches correct! But Niplav is correct: it’s a hard social problem, many critical systems maintainers are particularly slop-phobic and won’t want synthetic code checked in. That’s why I try to emphasize that the two trust points are the spec and the checker, and the rest is relinquished to a shoggoth. That’s the vision anyway– we solve this social problem by involving the slop-phobic maintainers in writing the spec and conveying to them how trustworthy the deductive process is.

Niplav’s squiggle model:

Median $~1b worth of tokens, plus all the “setting up the project, paying human supervisors and reviewers, costs for testing infrastructure & compute, finding complicated vulnerabilities that arise from the interaction of different programs…” etc costs. I think a lot’s in our action space to reduce those latter costs, but the token cost imposes a firm lower bound.

But this is an EA Forum post, meaning the project is being evaluated as an EA cause area: is it cost effective? To be cost effective, the savings from alleviating some disvalue have to be worth the money you’ll spend. As a pro...
How to end credentialism
by Yair Halberstadt — 2025-04-20T18:50:05.920Z

The current University System is bad. Very bad. Half of young adults spend 3 to 4 years learning nothing of particular value so that they can get a piece of paper that gives them a leg up when applying for a job. They can't stop, because then somebody else will be first in line for an interview, and their CV will be tossed in the trash. In fact to make sure they have the best chance of getting a job, they might consider wasting another 5 years on a PhD! There's a shortage of Doctors, but in the US Doctors need to spend 4 years studying a random undergrad degree so that they can then spend another 4 years actually learning how to treat people.

We need to change things. But how?

The gist of my proposal is simple: every institution that offers credentials must allow anyone to receive the same credential by taking the same assessments as students at the institution would take, at cost price plus some reasonable profit. If I want a Bachelors from MIT all I need to do is turn up to the exams and hand in some coursework, and there's nothing they can do about it.

The fine print

Universities get most of their money from students, so they will have strong incentives to cheat. As such the law forcing this change must be written in such a way as to minimise the viability of this strategy.

I've written an initial draft of some rules that attempt to tackle this. There's no need to wade through this if you don't want to, but I would appreciate it that if you intend to comment with a reason why this wouldn't work, you skim through this to see if it's already addressed.

Regulations governing granting of credentials.[1]

Definitions and Student Acceptance:
a. Internal Student: An individual to whom the institution provides tutoring, instruction, or other educational resources intended to facilitate preparation for the credential’s assessments, irrespective of the student’s physical location or mode of study.
b. External Student: An individual seeking to obtain a credential solely by completing the required assessments, without receiving tutoring, instruction, or other educational resources from the institution specifically for said credential, beyond publicly available assessment information.
c. Selection and Acceptance: Institutions may establish their own criteria for selecting Internal Students, subject to applicable laws and regulations. Institutions may not deny access to assessmen

...
Spending on Ourselves
by jefftk — 2025-04-20T18:40:07.988Z

The Effective Altruism community has encouraged a range of different
approaches to doing good over time. Initially there was more focus on

frugality
as a way to increase how much you could donate, which was mostly
supplanted by emphasis on earning more. In late 2015 this started to
shift towards

doing
things that are directly useful, which accelerated

in
2021. Then the market

fell in
2022, FTX

turned out to
be a fraud, and there haven't been new donors near the scale of

Open Phil /

Good Ventures. Among many
changes, people are thinking more about frugality again: the

less
you can live on, the more you can stretch a given amount of
funding. [1]

To encourage myself to live more frugally and to give an example of
what I thought was a pretty fulfilling life at relatively low cost for
the US, I used to calculate numbers for how much we spent on
ourselves. This included housing, food, transportation, medical, etc
but not donations, taxes, or savings. At one point there were some
news
stories
comparing our spending to our income, and it was nice to have a simple
number to point at.

I was thinking it might be nice to start calculating these numbers
again, but when I looked back at why I stopped it's mostly that it's
actually a pretty tricky accounting question and I'm not sure there
are ways to draw the lines that make much sense. For example:

One of the main things I do for fun is play music. This costs
some money (instruments,
kids coming with
me to gigs, fun things while
traveling) and also earns some money. How should I account for
this? At one extreme I could say that income is income and expenses
are "spending on ourselves", but this doesn't match reality well:
there shouldn't be a difference between playing a dance weekend that
is $1,000 with reimbursed travel and one that's $1,500 but I need to
spend $500 on flights. At the other extreme I could look at the whole
activity on net, and subtract expenses from income, but should what's
essentially a family vacation tacked onto a gig really not be
"spending on ourselves"? In between I could count this the way the
IRS does (an approach I think is a good fit for determining income
for pledging purposes) but this is also not great. For example,
some portion of my
new keyboard should probably be "spending on ourselves" since I
was motivated in part by a desire to enjoy pl

...