trentmkelly/LessWrong-43k huggingface dataset:
Cognitive Dissonance is Mentally Taxing
by SorenJ — 2025-04-24T00:38:25.535Z

Cognitive dissonance is the discomfort we feel when our beliefs don't line up with our actions. More generally, the two dissonant things don't need to be belief and action. It can be the discomfort we feel when different beliefs of ours are in contradiction. It can also arise when our different actions lead to conflicting goals. Often, we aren't even fully conscious of this discomfort. It may be hard to notice in ourselves, but it is easy to spot in others.
Here are some of the old, classic, examples from the empirical literature, served up and summarized courtesy of Gemini 2.5 Pro. It's unclear if these studies would survive replication attempts, but let's examine them nonetheless:

The Classic Forced Compliance Study (Festinger & Carlsmith, 1959):

Behavior: Participants performed extremely dull and repetitive tasks (like turning pegs on a board for an hour). Afterwards, they were asked to tell the next participant (who was actually a confederate) that the tasks were very interesting and enjoyable.
Manipulation: Participants were paid either $1 or $20 (a significant amount in 1959) to lie. A control group did the tasks but didn't lie.
Dissonance: Those paid only $1 experienced high dissonance. Their behavior (lying) conflicted strongly with their belief (the task was incredibly boring), and they had insufficient external justification for the lie ($1 wasn't really enough to justify misleading someone). Those paid $20 had sufficient external justification – they could tell themselves they lied for the money.
Belief Change: When later asked to rate how enjoyable the tasks actually were, the participants paid $1 rated the tasks significantly more enjoyable than those paid $20 or those in the control group. To reduce the dissonance caused by lying for a paltry sum, they unconsciously changed their belief about the task itself, convincing themselves it wasn't so bad after all. The $20 group didn't need to change their belief; the money justified the lie.

Effort Justification Study (Aronson & Mills, 1959):

Behavior: Female college students volunteered to join a group that would discuss the psychology of sex.
Manipulation: To be admitted to the group, participants had to undergo an "embarrassment test" (initiation). This initiation was either severe (reading obscene words and lurid sexual passages aloud), mild (reading less embarrassing sex-related words), or

...
My Favorite Productivity Blog Posts
by Parker Conley — 2025-04-24T00:32:47.594Z
Link: https://parconley.com/my-favorite-productivity-blog-posts/

I’ve read at least a few hundred blog posts, maybe upwards of a thousand.

Agreeing

with Gavin Leech, I believe I’ve gained from essays more than any other medium. I’m an intellectually curious college student with a strong interest in both the practical and philosophical. This blog post provides a snippet into the most valuable blog posts I’ve read on the topic of productivity. I encourage you to scan what I’ve gotten valuable out of the blog posts and read deeper if interesting.

On systems - Living a life of zero willpower

by Neel Nanda. This post inspired me to sign up for Focusmate. I can confidently say that Focusmate (1) has increased my productivity by >20% in a lasting way and (2) houses more than half of my social life. I’ve met many of my closest friends through the app. The other advice in Neel’s blog post is also great. (If you use Focusmate, feel welcome to favorite

me

and lock in a coworking session!)

How to write good prompts

by Andy Matuschak. Empirically, I have this blog post to thank for my >2 year long Anki habit. It was a three-hour read, but it was well worth it (being a full-time student spending 1-2h/day using Anki).

How to learn at college

by Brennan Colberg. Led me to skip 70% of my classes last college semester.

Honesty about reading

by Holden Karnofsky. I skim a lot. I mostly don’t finish books. This blog post by Holden gave me even more permission to continue skimming a lot.

Internet Search Tips

by Gwern. Two things I got from this: (1) I have

single-press hotkeys

for LLMs, and (2) I now use site: in search queries. I use these all the time. Having Claude/ChatGPT one click away is life-changing, highly recommend.

My tech stack

by Andrew Conner. It was either this blog post, another blog post, or a conversation with Andrew that pushed me over the edge into trying out

raindrop.io

. I love raindrop, and use it daily. It’s basically a bookmarks bar that actually works.
What Physically Distinguishes a Brain with False Beliefs Using a Swimming Pool Example
by YanLyutnev — 2025-04-24T00:01:41.589Z

Question the article attempts to answer: How does a brain that remembered a pool depth of 180cm differ from a brain that remembered 200cm? And how is this causally connected to a scenario where I'm drowning in a pool because physically in my brain there was a difference in... what? (a difference from the universe where I remembered the height as 200cm and was afraid)

My motivation for writing this article: I was in a swimming pool and noticed a non-obvious pattern in the moment about how my lack of knowledge about a depression in the pool affects my motivations. In this article, I want to create my best formalization of the model and see where this predictive model transfers to other cases. An additional goal is to ground the model maximally to acquire simple associations in memory. The model is falsifiable (testable).

Author's knowledge status: I'm reading 5 books on neurobiology from scratch, a couple of popular science books, and watching videos. I'm not an expert, and this article is my attempt at rough formalization of the question. I acknowledge that the model may be incorrect in places (for calibration, I've consulted with DeepSeek). I'm not postulating what's written below as true patterns, as is usually the case in articles. Please consider this when reading. Criticism and model refinement in the comments are welcome.

Article accuracy verification status: I tested the theses from the article against DeepSeek. Those whom I asked to proofread didn't provide detailed comments.

Background: Swimming Pool I visited a new swimming pool and started swimming in circles. I can swim, but I experience stress anticipating that if I run out of strength or disrupt my swimming technique, I might involuntarily sink underwater, water would get into my ears, and my eyes would sting badly, as in the past—significant stress, but the biggest stress of course is if water gets into my nose or throat, because it would be unexpected. Fear of drowning.

Therefore, I really wanted to avoid submerging my head underwater. I walked inside the pool along the edge and developed a model that since my feet reached the bottom, it meant that opposite a certain point near the wall, it was safe to stop swimming and hope that I would feel the bottom with my feet

This was an involuntary, automatic, wordless generalization, which turned out to be false—what proved false was the assumed distance fr...
OpenAI Alums, Nobel Laureates Urge Regulators to Save Company's Nonprofit Structure
by garrison — 2025-04-23T23:01:27.641Z
Link: https://garrisonlovely.substack.com/p/breaking-openai-alums-nobel-laureates

Converting to a for-profit model would undermine the company's founding mission to ensure AGI "benefits all of humanity," argues new letter

This is the full text of a post from Obsolete, a Substack that I write about the intersection of capitalism, geopolitics, and artificial intelligence. I’m a freelance journalist and the author of a forthcoming book called Obsolete: Power, Profit, and the Race to Build Machine Superintelligence. Consider subscribing to stay up to date with my work.

Don’t become a for-profit.

That’s the blunt message of a recent letter signed by more than 30 people, including former OpenAI employees, prominent civil-society leaders, legal scholars, and Nobel laureates, including AI pioneer Geoffrey Hinton and former World Bank chief economist Joseph Stiglitz.

Obsolete obtained the 25-page

letter

, which was sent last Thursday to the attorneys general (AGs) of California and Delaware, two officials with the power to block the deal.

Made public

early Wednesday, the letter argues that OpenAI's

proposed transformation

from a nonprofit-controlled entity into a for-profit public benefit corporation (PBC) would fundamentally betray the organization's founding mission and could even be unlawful — placing the power and responsibility to intervene squarely with the state AGs.

OpenAI and the offices of the California and Delaware attorneys general did not reply to requests for comment.

The letter was primarily authored by

Page Hedley

, a lawyer who worked at OpenAI from 2017-2018 and recently left an AI policy role at

Longview Philanthropy

; Sunny Gandhi, political director of

Encode AI

; and Tyler Whitmer, founder and president of

Legal Advocates for Safe Science and Technology

.

(Encode AI

receives funding

from the Omidyar Network, where I am currently a

Reporter in Residence

, and I worked as a media consultant for Longview Philanthropy in 2022.)

Nonprofit origins

In 2015, OpenAI’s founders established it as a nonprofit research lab as a counter to for-profit AI companies like Google DeepMind. When executives felt they couldn't raise enough capital to compete at AI's increasingly expensive cutting edge, they launched a for-profit in 2019 that was ultimately controlled by the nonprofit. This unusual corporate structure was designed to keep the organization loyal to its mission: to ensure that artificial general intelligence (AGI) bene...
What AI safety plans are there?
by MichaelDickens — 2025-04-23T22:58:06.885Z

I'm trying to make a list of every written plan for how to make AGI go well. I am interested in hearing about any plans that I missed, or any lists of plans that have already been compiled.

Here are the plans I've found so far, in no particular order:

Google https://deepmind.google/discover/blog/taking-a-responsible-path-to-agi/

OpenAI https://openai.com/index/updating-our-preparedness-framework/

Anthropic https://www.anthropic.com/news/announcing-our-updated-responsible-scaling-policy

https://www.lesswrong.com/posts/8vgi3fBWPFDLBBcAx/planning-for-extreme-ai-risks

https://www.lesswrong.com/posts/bb5Tnjdrptu89rcyY/what-s-the-short-timeline-plan

https://sleepinyourhat.github.io/checklist/

https://www.narrowpath.co/

https://situational-awareness.ai/

https://gradual-disempowerment.ai/

https://time.com/6266923/ai-eliezer-yudkowsky-open-letter-not-enough/ or https://intelligence.org/2024/05/29/miri-2024-communications-strategy/ (note: these both touch on MIRI's plan but they are not a complete breakdown of a plan)

https://aligned.substack.com/p/alignment-mvp

https://www.lesswrong.com/posts/vZzg8NS7wBtqcwhoJ/nearcast-based-deployment-problem-analysis

https://drive.google.com/file/d/1LbQgcz_aehcnhFMupCtXqoR-BV5dX5Rv/view

https://pauseai.info/proposal

It's somewhat arbitrary as to how much detail something needs to qualify as "a plan". I guess I would define a plan as "if we do these things and they all work, then we will be okay." By this definition Google is the only company with an actual plan (as far as I know) but I included the closest thing I could find from OpenAI + Anthropic.
o3 Is a Lying Liar
by Zvi — 2025-04-23T20:00:05.429Z

I love o3. I’m using it for most of my queries now.

But that damn model is a lying liar. Who lies.

This post covers that fact, and some related questions.

o3 Is a Lying Liar

The biggest thing to love about o3 is it just does things. You don’t need complex or multi-step prompting, ask and it will attempt to do things.

Ethan Mollick: o3 is far more agentic than people realize. Worth playing with a lot more than a typical new model. You can get remarkably complex work out of a single prompt.

It just does things. (Of course, that makes checking its work even harder, especially for non-experts.)

Teleprompt AI: Completely agree. o3 feels less like prompting and more like delegating. The upside is wild- but yeah, when it just does things, tracing the logic (or spotting hallucinations) becomes a whole new skill set. Prompting is evolving into prompt auditing.

The biggest thing to not love about o3 is that it just says things. A lot of which are not, strictly or even loosely speaking, true. I mentioned this in my o3 review, but I did not appreciate the scope of it.

Peter Wildeford: o3 does seem smarter than any other model I’ve used, but I don’t like that it codes like an insane mathematician and that it tries to sneak fabricated info into my email drafts.

First model for which I can feel the misalignment.

Peter Wildeford: I’ve now personally used o3 for a few days and I’ve had three occasions out of maybe ten total hours of use where o3 outright invented clearly false facts, including inserting one fact into a draft email for me to send that was clearly false (claiming I did something that I never even talked about doing and did not do).

Peter Wildeford: Getting Claude to help reword o3 outputs has been pretty helpful for me so far

Gemini also seems to do better on this. o3 isn’t as steerable as I’d like.

But I think o3 still has the most raw intelligence – if you can tame it, it’s very helpful.

Here are some additional examples of things to look out for.

Nathan Lambert: I endorse the theory that weird hallucinations in o3 are downstream of softer verification functions. Tbh should’ve figured that out when writing yesterday’s post. Was sort of screaming at me with the facts.

Alexander Doria: My current theory is a big broader: both o3 and Sonnet 3.7 are inherently disappointing as they open up a new category of language models. It’s not a chat an

...
Putting up Bumpers
by Sam Bowman — 2025-04-23T16:05:05.476Z

tl;dr: Even if we can't solve alignment, we can solve the problem of catching and fixing misalignment.

If a child is bowling for the first time, and they just aim at the pins and throw, they’re almost certain to miss. Their ball will fall into one of the gutters. But if there were beginners’ bumpers in place blocking much of the length of those gutters, their throw would be almost certain to hit at least a few pins. This essay describes an alignment strategy for early AGI systems I call ‘putting up bumpers’, in which we treat it as a top priority to implement and test safeguards that allow us to course-correct if we turn out to have built or deployed a misaligned model, in the same way that bowling bumpers allow a poorly aimed ball to reach its target.

To do this, we'd aim to build up many largely-independent lines of defense that allow us to catch and respond to early signs of misalignment. This would involve significantly improving and scaling up our use of tools like mechanistic interpretability audits, behavioral red-teaming, and early post-deployment monitoring. We believe that, even without further breakthroughs, this work we can almost entirely mitigate the risk that we unwittingly put misaligned circa-human-expert-level agents in a position where they can cause severe harm.

On this view, if we’re dealing with an early AGI system that has human-expert-level capabilities in at least some key domains, our approach to alignment might look something like this:

Pretraining: We start with a pretrained base model.
Finetuning: We attempt to fine-tune that model into a helpful, harmless, and honest agent using some combination of human preference data, Constitutional AI-style model-generated preference data, outcome rewards, and present-day forms of scalable oversight.
Audits as our Primary Bumpers: At several points during this process, we perform alignment audits on the system under construction, using many methods in parallel. We’d draw on mechanistic interpretability, prompting outside the human/assistant frame, etc.
Hitting the Bumpers: If we see signs of misalignment—perhaps warning signs for generalized reward-tampering or alignment-faking—we attempt to quickly, approximately identify the cause.
Bouncing Off: We rewind our finetuning process as far as is needed to make another attempt at aligning the model, taking advantage of what we’v

...
The AI Belief-Consistency Letter
by Knight Lee — 2025-04-23T12:01:42.581Z

Dear policymakers,

We demand that the AI alignment budget be Belief-Consistent with the military budget.

Belief-Consistency is a simple yet powerful idea:

If you spend 8000 times less on AI alignment (compared to the military),
You must also believe that AI risk is 8000 times less (than military risk).[1]

Yet the only way to reach Belief-Consistency, is to

Greatly increase AI alignment spending,

or

Become 99.95% certain that you are right, and that the majority of AI experts, other experts, superforecasters, and the general public, are all wrong.

Let us explain:

In order to believe that AI risk is 8000 times less than military risk, you must believe that an AI catastrophe (killing 1 in 10 people) is less than 0.001% likely.[2]

This clearly contradicts the median AI expert, who sees a 5%-12% chance of an AI catastrophe (killing 1 in 10 people), the median superforecaster, who sees 2.1%, other experts, who see 5%, and the general public, who sees 5%.

In fact, assigning only 0.001% chance contradicts the median expert so profoundly, that you need to become 99.95% certain that you won't realize you were wrong and the majority of experts were right!

If there was more than a 0.05% possibility that you study the disagreement further, and realize the risk exceeds 2% just like most experts say, then the laws of probability forbid you from believing a 0.001% risk.

We believe that foreign invasion concerns have decreased over the last century, and AGI concerns have increased over the last decade, but budgets remained within the status quo, causing a massive inconsistency between belief and behaviour.

Do not let humanity's story be so heartbreaking.

Version 1.1us

These numbers are for the US, but the same argument applies to NATO and other countries.

Please add your signature by asking in a comment (or emailing me). Thank you so truly much.

Signatories:

Knight Lee

References

Claude 3.5 drew the infographic for me.

“Military Budget: $800 billion”

[3] says it's $820 billion in 2024. $800 billion is an approximate number.

“AI Safety: $0.1 billion”:

The AISI is the most notable US government funded AI safety organization. It does not focus on ASI takeover risk though it may partially focus on other catastrophic AI risks. AISI's budget is $10 million according to [4]. Worldwide AI safety funding is between $0.1 billion and $0.2 billion according to [5].

“the median AI expert, who

...
Jaan Tallinn's 2024 Philanthropy Overview
by jaan — 2025-04-23T11:06:11.779Z
Link: https://jaan.info/philanthropy/#2024-results

to follow up my philanthropic pledge from 2020, i've updated my philanthropy page with the 2024 results.

in 2024 my donations funded $51M worth of endpoint grants (plus $2.0M in admin overhead and philanthropic software development). this comfortably exceeded my 2024 commitment of $42M (20k times $2100.00 — the minimum price of ETH in 2024).

this also concludes my 5-year donation pledge, but of course my philanthropy continues: eg, i’ve already made over $4M in endpoint grants in the first quarter of 2025 (not including 2024 grants that were slow to disburse), as well as pledged at least $10M to the 2025 SFF grant round.
Are we "being poisoned"?
by Tigerlily — 2025-04-23T05:11:56.985Z

I would like to revisit some of the concepts Scott explored in his 2020 article "For, Then Against, High-Saturated-Fat Diets". I'm hoping someone will have some novel/updated insights or new research to share concerning the impacts of the Western diet on health in 2025.

I'm about to turn 32, and I sense that I'm moving toward becoming a prime candidate for one of the gastrointestinal cancers that (among many others) are on the rise globally among people under the age of 50. Demographically, I am disproportionately at risk due to genetic, socioeconomic, and other factors. And, guys, I don't want to die any time soon.

This trend in cancer rates among young adults has seen the most dramatic uptick specifically in rich, industrialized Western nations, and the consensus seems to be that diet and lifestyle choices are a factor, albiet one of many, not all of which are clear right now.

Yesterday, I saw a video on social media of a woman discussing how, since moving to the US, she has started mysteriously gaining weight even though she's eating the same amount of food as before she moved here.

The comments were full of people with similar stories. Some of them claim that we are all "being poisoned" via our own food systems and the corporations that run them, as evidenced by the prevalence of growth hormones in meat, the presence of microplastics in everything, the preservatives and toxic chemicals in common food staples, the chemical pesticides that are present in the environment, the fields, the water supply, and by extension even in natural foods and animal products.

The woman's experience is consistent with my own experience of Western vs non-Western diets and their respective impacts on health. Without veering too far off into the anecdotal wilderness, I lived in Turkey for most of 2012 and was a total glutton while there. I ate my weight in delicious Turkish food and sweets every day and exercised very little. I also smoked cigarettes (but have since quit). In spite of my gluttony, I lost so much weight that the US customs agent who stamped my passport on my way back into the country pointed it out to me and congratulated me on it.

I want to know what, specifically, is driving this problem (doesn't everyone) and what an ordinary person can do to minimize the risk of serious illness. What are we all being exposed to that's wreaking ...