trentmkelly/LessWrong-43k huggingface dataset:
Did Christopher Hitchens change his mind about waterboarding?
by Isaac King — 2024-09-15T08:28:09.451Z

There's a popular story that goes like this: Christopher Hitchens used to be in favor of the US waterboarding terrorists because he thought it wasn't bad enough to be considered torture. Then he had it tried on himself, and changed his mind, coming to believe it is torture and should not be performed.

(Context for those unfamiliar: in the ~decade following 9/11, the US engaged in a lot of... questionable behavior to prosecute the war on terror, and there was a big debate on whether waterboarding should be permitted. Many other public figures also volunteered to undergo the procedure as a part of this public debate; most notably Sean Hannity, who was an outspoken proponent of waterboarding, yet welched on his offer and never tried it himself.)

This story intrigued me because it's popular among both Hitchens' fans and his detractors. His fans use it as an example of his intellectual honesty and willingness to undergo significant personal costs in order to have accurate beliefs and improve the world. His detractors use it to argue that he's self-centered and unempathetic, only coming to care about a bad thing that's happening to others after it happens to him.

But is the story actually true? Usually when there are two sides to an issue, one side will have an incentive to fact-check any false claims that the other side makes. An impartial observer can then look at the messaging from both sides to discover any flaws in the other. But if a particular story is convenient for both groups, then neither has any incentive to debunk it.

I became suspicious when I tried going to the source of this story to see what Hitchens had written about waterboarding prior to his 2008 experiment, and consistently found these leads to evaporate.

The part about him having it tried on himself and finding it tortureous is certainly true. He reported this himself in his Vanity Fair article Believe me, It's Torture.

But what about before that? Did he ever think it wasn't torture?

His article on the subject doesn't make any mention of changing his mind, and it perhaps lightly implies that he always had these beliefs. He says, for example:

In these harsh [waterboarding] exercises, brave men and women were introduced to the sorts of barbarism that they might expect to meet at the hands of a lawless foe who disregarded the Geneva Conventions. But it was something that Americans were being trained to

...
Not every accommodation is a Curb Cut Effect: The Handicapped Parking Effect, the Clapper Effect, and more
by Michael Cohn — 2024-09-15T05:27:36.691Z
Link: https://perplexedguide.net/2024/08/09/not-every-accommodation-is-a-curb-cut-effect-the-handicapped-parking-effect-the-clapper-effect-and-more/

In the fields of user experience and accessibility, everyone talks about the curb cut effect: Features that are added as accommodations for people with disabilities sometimes become widely useful and beloved. But not every accommodation becomes a "curb cut," and I've been thinking about other patterns that come up when accommodations intersect with wider society.

The original Curb Cut Effect

The eponymous curb cut -- the place at the intersection where the sidewalk slopes down to the street instead of just dropping off -- is most obviously there to for wheelchair users. But it's also great for people who are pulling a suitcase, runners who want to avoid jarring their ankles, and people who are walking their bikes. Universal captioning on TV, movies, and video is nominally for Deaf or hearing-impaired people, but captions are handy to anyone who's watching TV in a noisy restaurant, or trying to make sense of a show with artistically muddy audio, or trying to watch a video at 3x speed and the audio is unintelligible. When we make products easier to use, or spaces easier to access, it's not just some essentialized group of people with disabilities who benefit -- accessibility is good for everyone.

Why the idea is useful: First, it breaks down the perspective of disability accommodations as being a costly charity where "we" spend resources to help "them." Further, it breaks down the idea of disability as an essentialized, either-or, othered type of thing. Everybody has some level of difficulty accessing parts of the world some of the time, and improving accessibility is an inherent part of good design, good thinking, and good communication.[1] Plus, it's cool to be aware of all the different ways we can come up with to hack our experience of the world around us!

I think there's also a dark side to the idea -- a listener could conclude that we wouldn't invest in accommodations if they didn't happen to help people without disabilities. A just and compassionate society designs for accessibility because we value everybody, not because it's secretly self-interested.

That said, no society spends unlimited money to make literally every experience accessible to literally every human. There's always a cost-benefit analysis and sometimes it might be borderline. In those cases there's nothing wrong with saying that the benefits to the wider population tip the balance in fa...
AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space
by Bogdan Ionut Cirstea — 2024-09-14T23:23:26.296Z
Link: https://arxiv.org/abs/2406.18344

Authors: Huzheng Yang, James Gee, Jianbo Shi.

Abstract:

We study the intriguing connection between visual data, deep networks, and the brain. Our method creates a universal channel alignment by using brain voxel fMRI response prediction as the training objective. We discover that deep networks, trained with different objectives, share common feature channels across various models. These channels can be clustered into recurring sets, corresponding to distinct brain regions, indicating the formation of visual concepts. Tracing the clusters of channel responses onto the images, we see semantically meaningful object segments emerge, even without any supervised decoder. Furthermore, the universal feature alignment and the clustering of channels produce a picture and quantification of how visual information is processed through the different network layers, which produces precise comparisons between the networks.
How you can help pass important AI legislation with 10 minutes of effort
by ThomasW — 2024-09-14T22:10:50.386Z

Posting something about a current issue that I think many people here would be interested in. See also the related

EA Forum post

.

California Governor Gavin Newsom has until September 30 to decide the fate of SB 1047 - one of the most hotly debated AI bills in the world. The Center for AI Safety Action Fund, where I work, is a co-sponsor of the bill. I’d like to share how you can help support the bill if you want to.

About SB 1047 and why it is important

SB 1047 is an AI bill in the state of California. SB 1047 would require the developers of the largest AI models, costing over $100 million to train, to test the models for the potential to cause or enable severe harm, such as cyberattacks on critical infrastructure or the creation of biological weapons resulting in mass casualties or $500 million in damages. ​​AI developers must have a safety and security protocol that details how they will take reasonable care to prevent these harms and publish a copy of that protocol. Companies who fail to perform their duty under the act are liable for resulting harm. SB 1047 also lays the groundwork for a public cloud computing resource to make AI research more accessible to academic researchers and startups and establishes whistleblower protections for employees at large AI companies.

So far, AI policy has relied on government reporting requirements and voluntary promises from AI developers to behave responsibly. But if you think voluntary commitments are insufficient, you will probably think we need a bill like SB 1047.

If SB 1047 is vetoed, it’s plausible that no comparable legal protection will exist in the next couple of years, as Congress does not appear likely to pass anything like this any time soon.

The bill’s text can be found here. A summary of the bill can be found here. Longer summaries can be found here and here, and a debate on the bill is

here

. SB 1047 is supported by many academic researchers (including Turing Award winners Yoshua Bengio and Geoffrey Hinton), employees at major AI companies and organizations like Imbue and Notion. It is opposed by OpenAI, Google, Meta, venture capital firm A16z as well as some other academic researchers and organizations. After a recent round of amendments, Anthropic said “we believe its benefits likely outweigh its costs.”

SB 1047 recently passed the Cal...
Calibration training for 'percentile rankings'?
by david reinstein — 2024-09-14T21:51:55.705Z

Does this tool/site exist? What's the best/easiest way to build it?

I'm looking to find or build a tool for calibration training for 'percentile rankings'.
E.g., give me a list of dog photos to rate out of a common pool, and ask me to give percentile rankings for each ('as a percentage, where does this dog fall in the disribution of dog quality, among all the dog photos in our database?).

The tool should graph my percentile rankings against a uniform distribution. It should provide a metric for 'the probability that my rankings were drawn from a uniform distribution.'

It might be ideal to have these spaced out to keep me from ~cheating by remembering my previous rankings and alternating high and low, etc. So maybe I rate one item a day.

I also should be able to choose the domain and the criteria: e.g., rate academic paper abstracts for their writing quality, or for the plausibility of their claims; politicians' statements for their right/left wing alignment, or for their appeal; pictures of pumpkins for their weight, or their orange hue, etc.

Context

Evaluation percentile ratings

At The Unjournal we commission evaluators and ask them to (evaluate, discuss and) rate particular research papers on a percentile scale relative to a reference group according to various criteria (see guidelines).

You can see the data from these evaluator ratings in our dashboard here (note, as of 14 Sep 2024 the data needs data updating -- it has incorporates less than half of our evaluations to date).

We don't know whether these are well-calibrated – as we're asking them to rate these relative to external reference group.

But we'd like to find or build tools to help evaluators test and train their 'percentile ratings calibration'.

Internal prioritization ratings

In choosing which research papers and projects to commission for evaluation, we also ask the people on our team (the ones who suggest and give the 'second opinion' on a paper)

... to rate its relevance (suitability, potential for impact etc.) on a percentile scale relative to our existing database research in consideration (a superset of the research shared here).

But the suggester's ratings don't seem to be coming out uniformly distributed. They rarely use the bottom of the percentiles

Example 'suggester' ratings grouped by (hidden) research/cause field

Suggester ra

...
OpenAI o1, Llama 4, and AlphaZero of LLMs
by Vladimir_Nesov — 2024-09-14T21:27:41.241Z

GPT-4 level open weights models like Llama-3-405B don't seem capable of dangerous cognition. OpenAI o1 demonstrates that a GPT-4 level model can be post-trained into producing useful long horizon reasoning traces. AlphaZero shows how capabilities can be obtained from compute alone, with no additional data. If there is a way of bringing these together, the apparent helplessness of the current generation of open weights models might prove misleading.

Post-training is currently a combination of techniques that use synthetic data and human labeled data. Human labeled data significantly improves quality, but its collection is slow and scales poorly. Synthetic data is an increasingly useful aspect of post-training, and automated aspects of its generation scale easily. Unlike weaker models, GPT-4 level LLMs clearly pass reading comprehension on most occasions, OpenAI o1 improves on this further. This suggests that at some point human data might become mostly unnecessary in post-training, even if it still slightly helps. Without it, post-training becomes automated and gets to use more compute, while avoiding the need for costly and complicated human labeling.

A pretrained model at the next level of scale, such as Llama 4, if made available in open weights, might initially look approximately as tame as current models. OpenAI o1 demonstrates that useful post-training for long sequences of System 2 reasoning is possible. In the case of o1 in particular, this might involve a lot of human labeling, making its reproduction a very complicated process (at least if the relevant datasets are not released, and the reasoning traces themselves are not leaked in large quantities). But if some generally available chatbots at the next level of scale are good enough at automating labeling, this complication could be sidestepped, with o1 style post-training cheaply reproduced on top of a previously released open weights model.

So there is an overhang in an open weights model that's distributed without long horizon reasoning post-training, since applying such post-training significantly improves its capabilities, making perception of its prior capabilities inadequate. The problem right now is that a new level of pretraining scale is approaching in the coming months, while ability to cheaply apply long horizon reasoning post-training might follow shortly thereafter, possibly unlocked by t...
Forever Leaders
by Justice Howard — 2024-09-14T20:55:39.095Z

“dictators die… and so long as men die liberty will never perish…”

This is an abbreviated quote from Charlie Chaplin’s “The Great Dictator” which stuck with me ever since the clip was played for me in middle school. Political but prescient, these words describe a natural limiter on any man’s ambitions. A check and balance that has guaranteed the downfall of many a dark figure, from Genghis Khan to Stalin.

But, and here it is, technology advances.

Advances in medicine, genetics and nano technology may soon combine to make death no longer inevitable. By solving one problem we move up the chain to face new ones. So even though we still die today, we may, regardless, want to start thinking about what happens when dictators do not, in fact, die.

Men and women with unchecked ambition may evolve into what I call “Forever Leaders”. They would persist without the problem of succession, ever consolidating power in their hands past any inside challenge. We can each postulate a few such individuals today that would-so evolve if radical life extension were to be invented tomorrow.

Perhaps the solution is simply the “term limit”, but only one check is too fragile. I wonder what other mechanisms may help balance the systems that govern our societies. For that I hope to start a small discussion.
Emergent Authorship: Creativity à la Communing
by gswonk — 2024-09-14T19:02:07.635Z

I’m an agnostic, on my most curmudgeonly days an atheist, but there’s something spiritual about writing. And I like writing. I can only describe being a writer as someone who expresses the landscape of their inner world while making contact with the landscape of the outer world. Mystical, freaky, and semi-creepy—right?

The best writing is unpredictable. As I write this sentence, and as you read it, it’s ever becoming what it is, its meaning ever emerging from the process of its being written. If it sounds as if my writing has no plan, it’s because you’re right. Serendipity steers my mind and keyboard and pen—and frankly my life—into unknown territory, largely by the seat of my pants. The best sentence structure is unpredictable: it teeters, some words and phrases sidestepping the reader’s and my own forecasts of how the sentence will develop. Some writing is formulaic—our educational institutions have unfortunately inculcated “convention” into its pupils—but the best embraces the stochasticity of the mind that produces it.

What’s remarkable is that despite the feelings of fortuity and unpredictability that an author has when writing, the words, sentences, paragraphs, and whole works that emerge seem intentional and predictable post hoc. Moments that seemed to have teetered mid-sentence, when the author was intuiting where their mind would pilot their pen’s next move, appear after the fact less functions of erratic mental oscillations than entailments of calculated logic. The magic of how the words appear on the page disappears if we only care for the finished product, the words on the page, not the process which brings them into being.

Reminder of the thesis at hand: this process is mystical, freaky, and semi-creepy. When one writes they must predictively read minds: when one writes, they must understand the possible combinations of neural states, and therefore thoughts and emotions and moods and memories, their writing might evoke in their audience after it enters their eyeballs. The story of what occurs when we write becomes more mysterious when we consider that the author intends to convey something to their reader, and hence evoke something in their mind, but that this something, I’ll remind you, originates from a mind, the author’s, which itself less so operates on intention than serendipity at the time of writing that something. To furth...
Compression Moves for Prediction
by adamShimi — 2024-09-14T17:51:12.004Z
Link: https://epistemologicalfascinations.substack.com/p/compression-moves-for-prediction

Imagine that you want to predict the behavior of some system -- the weather, a computer program, a real-world language... However you go about it, I bet you will do some compression: you'll find simple and minimal and concrete ways to think about the vast complexity of your system that helps you predict what you want to predict.

At a fundamental level,

prediction is compression

.

Yet what does compression looks like in practice? Are all compressions done in the same way, with the same goals, considering the same tradeoffs?

No. In fact, there are distinct forms of compressions for different problem setups, different compression moves for solving different issues. The ones I want to discuss today are summarization, reduction, and precomputation:

When you are drowning in data, you need summarization
When you want to extend your model to different settings, you need reduction
When you are swamped by the computational difficulties of applying your model, you need precomputation.

Note that this post focuses exclusively on cases where we're predicting a system over which we have no control. This is the usual set up for natural sciences such as physics, chemistry, biology, or historical ones such as geology and historical linguistics. But note that in other settings, such as economics or engineering sciences, your ability to constrain the system unlocks a whole new range of moves to make the system easier to predict.[1]

Summarization: Taming The Ocean of Data

In many predictive endeavors, your first problem is the overabundance of data.[2]

Imagine massive tables and charts with eye watering rows of numbers after numbers.They could represent various temperature measurements, or the weights of a neural network, or the different versions of the the same word across language in a family...

How do you make sense of that? How do you even start to think about it with a human's small and puny brain?

Well, you summarize.

Instead of keeping all the data, you find some thread, some regularity, some pattern in them that you leverage for prediction. Such summaries can take many different forms.

The most well-know one, due to physics' cultural hegemony, are the quantitative regularities: relating core factors/components of your data into an equation. The list includes

the gas laws

relating pressure, volume, and temperature in gases;

large scale macroeconometric models

capturing relations betwee...
Pay-on-results personal growth: first success
by Chris Lakin — 2024-09-14T03:39:12.975Z
Link: https://chrislakin.blog/p/i-became-a-bounty-hunter

New, part II: Began a pay-on-results coaching experiment, made $40,300 since July

Thanks to Kaj Sotala, Stag Lynn, and Ulisse Mini for reviewing. Thanks to Kaj Sotala, Brian Toomey, Alex Zhu, Damon Sasi, Anna Salamon, and CFAR for mentorship and financial support

Update: Bob has recorded a 6-month follow-up here.

A few months ago, I hypothesized, “Radically effective and rapid emotional growth is possible with the right combination of facilitator and method”. E.g.: for resolving anxiety, agency, insecurity, need for validation.

To test my hypothesis, I began a pay-on-results coaching experiment. Clients pay only if they achieve their goal by working with me.

First bounty

Bob (pseudonymous) and I met at Manifest 2024 prediction market conference. Bob told me he had lifelong anxiety, and it was crushing his agency and relationships. He offered a $3k bounty if it could be resolved.

We spoke one day for about four hours.

One month later:

I’m choosing to work on problems beyond my capabilities, and get excited about situations where my weaknesses are repeatedly on display.

I actually feel excited about entering social situations where chances of things going worse than I would want them to were high.

(Shared with permission.)

He felt ready to pay his bounty and sent the $3k.

6-month follow-up

Now available: chrislakin.blog/p/bob

Other bounties hunting

A woman working in SF after 3 conversations, text support, and three weeks:

I went to Chris with a torrent of responsibilities and a key decision looming ahead of me this month. I felt overwhelmed, upset, and I didn’t want just talk

Having engaged in 9+ years of coaching and therapy with varying levels of success, I’m probably one of the toughest clients — equal parts hopeful and skeptical. Chris created an incredibly open space where I could easily tell him if I didn’t know something, or couldn’t feel something, or if I’m overthinking. He also has an uncanny sense of intuition on these things and a strong attunement to being actually effective

The results are already telling: a disappointment that might’ve made me emotionally bleed and mope for a month was something I addressed in the matter of a couple of days with only a scoop of emotional self-doubt instead of *swimming* in self-torture. The lag time of actually doing things to be there for myself was significantly quicker, warmer, and more effective

To-

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