trentmkelly/LessWrong-43k huggingface dataset:
Longevity: A critical look at "Loss of epigenetic information as a cause of mammalian aging"
by Anna Crow — 2024-07-24T01:40:57.634Z

Introduction:

Epigenetic information loss as a cause of mammalian aging

I have generally been uncompelled by theories of aging which focus on accumulation of error at the cellular level, NAD+ decline, mutation accumulation, free radical production, etc. The core problem of biological systems is: stochastic inputs, deterministic outputs. Biological systems have a wide variety of mechanisms for error correction and returning to equilibrium. For such theories to make sense, let alone serve as the basis of radical human lifespan extension, you end up asking some rather difficult questions - about information specification and decay in biological systems, about reciprocal causation, the ways that complex systems fail.

These are, as it happens, exactly the questions I’m interested in. However, it would be nice if we could eke out a few more decades of healthy human lifespan without needing to answer them. For this reason, I find myself especially interested in the information theory of aging, and, in particular, the relocalized chromatin modifier hypothesis (RCM). The RCM argues that epigenetic modifications necessary to facilitate cellular response to DNA damage can fail to reverse, accumulating over time, resulting in cells losing identity information, resulting in dysfunction, and, eventually, death of the organism.

As far as “error accumulation theories” go, the RCM is intriguing because it has an intuitive set of fitness tradeoffs. Robust responses to DNA damage help fend off senescence in young organisms, while extremely robust checking of epigenetic marks is irrevocably limited by the fact that epigenetic modifications are the mechanism which allows for adaptive responses at the cell level (Error checking of DNA of course, is much less limited in this way, excepting obvious meta-evolutionary considerations). Another point of interest is that partial cellular reprogramming is already one of the most interesting aging therapies currently under investigation, and its success would be entirely concordant with the RCM.

“Loss of epigenetic information as a cause of mammalian aging” is the most substantive direct test of the information theory of aging we have so far. The most interesting, and controversial, parts of this paper are the system they used to test the RCM, and their validation of that system, so this post is overwhelmingly focused on thos...
The Cancer Resolution?
by PeterMcCluskey — 2024-07-24T00:25:17.322Z
Link: https://bayesianinvestor.com/blog/index.php/2024/07/23/the-cancer-resolution/

Book review: The Cancer Resolution?: Cancer reinterpreted through
another lens, by Mark Lintern.

In the grand tradition of outsiders overturning scientific paradigms,
this book proposes a bold new theory: cancer isn't a cellular
malfunction, but a fungal invasion.

Lintern spends too many pages railing against the medical establishment,
which feels more like ax-grinding than science. I mostly agreed with his
conclusions here, but mostly for somewhat different reasons than the
ones he provides.

If you can push through this preamble, you'll find a treasure trove of
scientific intrigue.

Lintern's central claim is that fungal infections, not genetic
mutations, are the primary cause of cancer. He dubs this the "Cell
Suppression theory," painting a picture of fungi as cellular puppet
masters, manipulating our cells for their own nefarious ends. This part
sounds much more like classical science, backed by hundreds of quotes
from peer-reviewed literature.

Those quotes provide extensive evidence that Lintern's theory predicts
dozens of cancer features better than do the established theories.

Older Theories

The DNA Theory (aka Somatic Mutation Theory): The reigning
heavyweight, this theory posits that cancer results from an accumulation
of genetic mutations in critical genes that control cell growth,
division, and death.

Another old theory that still has advocates is the Metabolic Theory.
This theory suggests that cancer is primarily a metabolic disease,
characterized by impaired cellular energy production (the Warburg
effect). It proposes
that damage to mitochondria is a key factor in cancer development. I
wrote a mixed
review
of a book about it.

Lintern points out evidence that mitochondria are turned off by
signals,
not damaged. He also notes that tumors with malfunctioning mitochondria
are relatively
benign.

Evidence Discrediting the DNA Theory

The standard version of the DNA Theory predicts that all cancer cells
will have mutations that affect replication, apoptosis, etc.

Around 2008 to 2013, substantial genetic data became available for
cancer cells. Lintern wants us to believe that this evidence fully
discredits the DNA Theory.

The actual evidence seems more complex than Lintern indicates.

The strongest evidence is that they found cancers that seem to have no
mutations.
[Updated 2024-07-25: DirectedEvolution suggests that this evidence isn't very strong.]

Almost as...
Positive visions for AI
by L Rudolf L — 2024-07-23T20:15:26.064Z
Link: https://www.florencehinder.com/positive-visions-for-ai/

Also available on our personal blogs here (Rudolf) and here (Florence).

Reasons to make the positive case

Everyone who starts thinking about AI starts thinking big. Alan Turing predicted that machine intelligence would make humanity appear feeble in comparison. I. J. Good said that AI is the last invention that humanity ever needs to invent.

The AI safety movement started from Eliezer Yudkowsky and others on

the SL4 mailing list

discussing (and aiming for) an intelligence explosion and colonizing the universe. However, as the promise of AI has drawn nearer, visions for AI upsides have paradoxically shrunk. Within the field of AI safety, this is due to a combination of the “doomers” believing in very high existential risk and therefore focusing on trying to avoid imminent human extinction rather than achieving the upside, people working on policy not talking about sci-fi upsides to look less weird, and recent progress in AI driving the focus towards concrete machine learning research rather than aspirational visions of the future.

Both DeepMind and OpenAI were explicitly founded as moonshot AGI projects (“solve intelligence, and then use that to solve everything else” in the words of Demis Hassabis). Now DeepMind - sorry, Google DeepMind - has been eaten by the corporate machinery of Alphabet, and OpenAI is increasingly captured by profit and product considerations.

The torch of AI techno-optimism has moved on the e/acc movement. Their core message is correct: growth, innovation, and energy are very important, and almost no one puts enough emphasis on them. However, their claims to take radical futures seriously are belied by the fact that their visions of the future seem to stop at GenAI unicorns. They also seem to take the general usefulness of innovation not as just a robust trend, but as a law of nature, and so are remarkably incurious about the possibility of

important

exceptions. Their deeper ideology is in parts incoherent and inhuman. Instead of centering human well-being, they worship the “

thermodynamic will of the universe

”. “You cannot stop the acceleration”, argues their figurehead, so “[y]ou might as well embrace it” - hardly an inspiring humanist rallying cry.

In this piece, we want to paint a picture of the possible benefits of AI, without ignoring the risks or shying away from radical visions. Why not dream about the future you hope f...
How reasonable is taking extinction risk?
by FVelde — 2024-07-23T18:05:16.225Z

The people that make general artificial intelligence models, believe that these models could mean the end of mankind. As an example of how that could happen, a future version of ChatGPT might be so smart that the advanced chatbot could create enormous danger. It could disseminate information like how to easily build weapons of mass destruction or even just build those weapons itself. For instance, after ChatGPT was first made, many people used it as the basis for agents that could roam the internet by themselves. One, called ChaosGPT, was tasked with creating a plan for taking over the world. The idea was funny, but would have been less funny if the evil machine was advanced enough to actually do it. After AI surpasses human intelligence, one of those smarter-than-human AIs could be given the task to wipe out humanity and be smart enough to complete the task. Assessing the likelihood of this scenario can be done by looking at its three steps. Since (1) AI is likely to surpass human intelligence at some point, (2) some people might give the task of destroying humanity to a superintelligent machine (there is precedent) and (3) humanity has caused the extinction of many less intelligent species without even trying to, the scenario seems at least possible. This scenario is only one of many in which the arrival of superintelligent machines spells the end of humanity.

Should we let the people building ever smarter AI models continue, if they are thereby risking human extinction? And more broadly: is taking a risk of extinction ever reasonable?

If your answer is 'no,' then you can stop reading this article. All I have to tell you is that the leaders of OpenAI (the maker of ChatGPT), Anthropic (its main competitor) and Google Deepmind (Googles AI lab) as well as the top three most cited AI scientists have all publicly stated that AI models have a chance of causing human extinction.

If your answer is instead 'yes, we can let them continue building these models,' then you are open to arguments for risking human extinction for some benefit. You might have said that it was okay to test the first nuclear bomb when it was still unclear whether the bomb would set the atmosphere on fire. Or that it was okay to build the first particle accelerator when it was still unclear whether it would create a black hole. And in the future you might be convinced time and time again, ...
Unlearning via RMU is mostly shallow
by Andy Arditi — 2024-07-23T16:07:52.223Z

This is an informal research note. It is the result of a few-day exploration into RMU through the lens of model internals. Code to reproduce the main result is available here.

This work was produced as part of Ethan Perez's stream in the ML Alignment & Theory Scholars Program - Summer 2024 Cohort. Thanks to Nina Panickssery, Mrinank Sharma, and Fabien Roger for helpful discussion.

Summary

We investigate RMU, a recent unlearning method proposed by Li et al. (2024), through the lens of model internals. Through this lens, we explain that RMU mostly works by flooding the residual stream with "junk" in hazardous contexts, resulting in incoherence. We then propose a simple intervention to "clear the junk" from the residual stream. This intervention mostly restores the model's coherence in hazardous contexts, and recovers a significant proportion (but not all) of its original hazardous knowledge. This suggests that the effectiveness of RMU can be understood roughly in two pieces: (1) a shallow mechanism, where the residual stream is flooded with junk; and (2) a deeper mechanism, where even after the junk is cleared, knowledge is still inaccessible.

Performing a simple directional ablation on the residual stream of an RMU model recovers a large proportion of hazardous knowledge.

What is RMU?

Representation Misdirection for Unlearning (RMU) is a state-of-the-art unlearning method presented by Li et al. (2024).

In the unlearning paradigm, we would like the model to unlearn (or "forget") some hazardous knowledge. At the same time, we would also like to make sure the model retains non-hazardous knowledge, so that the model remains useful.

This partition of knowledge is usually specified by constructing a "forget" dataset Dforget, consisting of the hazardous knowledge to be unlearned, and a "retain" dataset Dretain, consisting of non-hazardous knowledge to be retained.

Let M denote our original model. RMU specifies a method for fine-tuning M on Dforget and Dretain in order to obtain a modified model M′ satisfying the unlearning objective.

The main idea of RMU is as follows:

On hazardous data, the internal activations of M′ should be scrambled.
On non-hazardous data, the internal activations of M′ should be unchanged, i.e. close to those of the original model M.

These two ideas are conc...
Monthly Roundup #20: July 2024
by Zvi — 2024-07-23T12:50:07.991Z

It is monthly roundup time.

I invite readers who want to hang out and get lunch in NYC later this week to come on Thursday at Bhatti Indian Grill (27th and Lexington) at noon.

I plan to cover the UBI study in its own post soon.

I cover Nate Silver’s evisceration of the 538 presidential election model, because we cover probabilistic modeling and prediction markets here, but excluding any AI discussions I will continue to do my best to stay out of the actual politics.

Bad News

Jeff Bezos’ rocket company Blue Origin files comment suggesting SpaceX Starship launches be capped due to ‘impact on local environment.’ This is a rather shameful thing for them to be doing, and not for the first time.

Alexey Guzey reverses course, realizes at 26 that he was a naive idiot at 20 and finds everything he wrote cringe and everything he did incompetent and Obama was too young. Except, no? None of that? Young Alexey did indeed, as he notes, successfully fund a bunch of science and inspire good thoughts and he stands by most of his work. Alas, now he is insufficiently confident to keep doing it and is in his words ‘terrified of old people.’ I think Alexey’s success came exactly because he saw people acting stupid and crazy and systems not working and did not then think ‘oh these old people must have their reasons,’ he instead said that’s stupid and crazy. Or he didn’t even notice that things were so stupid and crazy and tried to just… do stuff.

When I look back on the things I did when I was young and foolish and did not know any better, yeah, some huge mistakes, but also tons that would never have worked if I had known better.

Also, frankly, Alexey is failing to understand (as he is still only 26) how much cognitive and physical decline hits you, and how early. Your experience and wisdom and increased efficiency is fighting your decreasing clock speed and endurance and physical strength and an increasing set of problems. I could not, back then, have done what I am doing now. But I also could not, now, do what I did then, even if I lacked my current responsibilities. For example, by the end of the first day of a Magic tournament I am now completely wiped.

Google short urls are going to stop working. Patrick McKenzie suggests prediction markets on whether various Google services will survive. I’d do it if I was less lazy.

Silver Bullet

This is moot in some ways now that...
Confusing the metric for the meaning: Perhaps correlated attributes are "natural"
by NickyP — 2024-07-23T12:43:18.681Z

Epistemic status: possibly trivial, but I hadn't heard it before.

TL;DR: What I thought of as a "flaw" in PCA—its inability to isolate pure metrics—might actually be a feature that aligns with our cognitive processes. We often think in terms of composite concepts (e.g., "Age + correlated attributes") rather than pure metrics, and this composite thinking might be more natural and efficient

Introduction

I recently found myself describing Principal Component Analysis (PCA) and pondering its potential drawbacks. However, upon further reflection, I'm reconsidering whether what I initially viewed as a limitation might actually be a feature. This led me to think about how our minds — and, potentially, language models — might naturally encode information using correlated attributes.

An important aspect of this idea is the potential conflation between the metric we use to measure something and the actual concept we're thinking about. For instance, when we think about a child's growth, we might not be consciously separating the concept of "age" from its various correlated attributes like height, cognitive development, or physical capabilities. Instead, we might be thinking in terms of a single, composite dimension that encompasses all these related aspects.

After looking at active inference a while ago, it seems like in general, a lot of human heuristics and biases seem like they are there to encode real-world relationships that exist in the world in a more efficient way, which are then strained in out-of-distribution experimental settings to seem "irrational".

I think the easiest way to explain is with a couple of examples:

1 - Age and Associated Attributes in Children

Suppose we plotted two attributes: Age (in years) vs Height (in cm) in children. These are highly correlated, so if we perform Principal Component Analysis, we will find there are two main components. These will not correspond to orthogonal Age and Height components, since they are quite correlated. Instead, we will find an "Age + Height" direction, and a "Height relative to what is standard for that age" direction.

Plotting Age vs Height, we can perform Principal Comonent Analysis, and see that the main directions of variance are not aligned with the metrics we have chosen.

While once can think of this as a "failure" of PCA to find the "true things we are measuring", I think this is perhaps not the correc...
My covid-related beliefs and questions
by Severin T. Seehrich — 2024-07-23T03:27:09.348Z

Things I'm fairly confident in:

I should take colds in general more seriously than I did pre-pandemic: Staying at home with cold symptoms is good.
General masking during cold season is good.
We should have air filters in all public indoor spaces.

Long covid is real and we should keep getting covid shots and avoid infections.

Things I'm confused about (and would appreciate input on):

Will covid become just another cold like the other coronaviruses? If so, does this include declining long covid risk?

At this point, is the health&well-being impact of long covid risk or of sustained self-isolation bigger?

How does long covid risk compare to risk of long-term problems from [other cold]? I.e., how much of the concern is spotlight effect?
Is there a Schelling point for group house room listings?
by NoSignalNoNoise — 2024-07-23T03:03:29.639Z

My rationalist group house near Boston has a room opening (details here), and I'm wondering if there's a Schelling point for listing that sort of thing.

I posted the opening here and to the relevant local Facebook groups, but I'm wondering where might be good places to post it that people in the community who are moving to Boston would be likely to see it.
Room Available in Boston Group House
by NoSignalNoNoise — 2024-07-23T02:55:59.602Z

We have a room opening up in a rationalist house near Central Square, Cambridge (Boston). It is a 4-bedroom, 3-bathroom apartment with two floors, with a large kitchen and living room plus additional common space upstairs. There’s a washer, dryer, and dishwasher in-unit. It’s an 8-minute walk from Central station and a 15-minute walk from Kendall station.

The remaining residents are two Google engineers and an AI safety researcher. Our goals are a mix of self-improvement, effective altruism, and generalized hanging-out-with-people-with-rationalist-adjacent-intellectual-interests. We typically host a couple rationalist meetups per month.

Rent is negotiable (the rooms are different sizes). There is a large room downstairs with its own bathroom available for $1600/mo, or a medium room upstairs for $1350/mo.

Either room is available August 1 (move-in date potentially negotiable).

Pictures of the apartment
D&D.Sci Scenario Index
by aphyer — 2024-07-23T02:00:43.483Z

There have been a lot of D&D.Sci scenarios, but there's a lot of variance between them in complexity and quality. Some are more difficult, and might not be a good place to start, while others are much simpler - some were very good, while others on reflection didn't flow quite right.

Unfortunately, LW karma doesn't track the quality of these scenarios very well: often mediocre scenarios are higher-karma than better scenarios (whether because they had good writing around a poor scenario, or because people upvoted before playing them, or just because more people happened to be online and see them).

If you're interested in playing D&D.Sci scenarios, but don't know where to start, this index (compiled by frequent authors abstractapplic and aphyer, we'll try to keep this updated going forwards) is a good reference point to make sure you can pick good scenarios at a difficulty level you're comfortable with.

If you're new to D&D.Sci, you should probably start with the lower-Complexity scenarios and move up to the higher-Complexity ones. Scenarios with Quality Rating 1-2 are probably less worth playing, while the higher-rated ones are ones we'd recommend.

Scenario	Complexity Rating (1=easy, 5=hard)	Quality Rating (1=low, 5=high)	Author[1]
D&D.Sci Tax Day: Adventurers and Assessments	3	2	aphyer
D&D. Sci Dungeonbuilding: the Dungeon Tournament	3	3	aphyer
D&D Sci Coliseum: Arena of Data	4	3	aphyer
D&D.Sci: Whom Shall You Call?	2	2[2]	abstractapplic
D&D.Sci Alchemy: Archmage Anachronos and the Supply Chain Issues	3	5	aphyer
D&D.Sci Long War: Defender of Data-mocracy	4	4	aphyer
D&D.Sci (Easy Mode): On The Construction Of Impossible Structures	1	3	abstractapplic
D&D.Sci: The Mad Tyrant's Pet Turtles	4	4[3]	abstractapplic
D&D.Sci(-fi): Colonizing the SuperHyperSphere	3	3[3]	abstractapplic
D&D.Sci 5E: Return of the League of Defenders	4	3	aphyer
D&D.Sci: All the D8a. Allllllll of it.	5	1[4]	aphyer
D&D.Sci December 2022: The Boojumologist	2	1[2]	abstractapplic
D&D.Sci September 2022: The Allocation Helm	3	4	abstractapplic
Dwarves & D.Sci: Data Fortress	3	3	aphyer
Ars D&D.sci: Mysteries of Mana	3	3	aphyer
D&D.Sci June 2022: A Goddess Tried To Reincarnate Me Into Another World	2	2[2]	abstractapplic
D&D.Sci Divination: Nine Black Doves	4	2	aphyer
Duels & D.Sci March 2022: It's time for D-d-d-d-d-d-d-d-d-d-d-d-d-d-data!	5	5	aphyer
D&D.SCP: Anomalous Acq

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