trentmkelly/LessWrong-43k huggingface dataset:
The Fountain of Health: a First Principles Guide to Rejuvenation
by PhilJackson — 2023-01-07T18:34:12.094Z

I’d like to live in a world where age really is just a number. For the last ~3 years or so, I’ve been nerding out with increasing fervour about longevity biotech, having originally been inspired by one of Aubrey de Grey's many interviews. I want to explain how and why I've become optimistic about the short-term prospects of indefinite healthy life extension, in terms of how I've come to think about ageing, disease, medicine and rejuvenation.

A revolution is underway in how people think about health and medicine. Worst case scenario, it will unlock a whole new generation of therapies that can actually cure chronic age-related diseases, instead of just moderately slowing their progression at the expense of multiple poorly understood side effects. Best case scenario, it will enable us to live indefinitely in bodies that are functionally and cosmetically indistinguishable to those of 25 year olds. As with AGI, nobody can predict with certainty how long this will take, but optimism has grown tremendously over the past five years, and it is no longer unusual to encounter experts who believe that the ageing process is malleable.

This timeframe depends massively on how rapidly we can divert funding and talent toward longevity biotechnology, because we are not going to achieve this outcome by doing more of what mainstream biomedicine has been doing. You could liken our position to where SpaceX are right now on the road to Mars: there's a long way to go still, but no Nobel prizes are required, no major scientific breakthroughs, just a lot of engineering. In this post, I will explain what ageing is, why we suck so much at treating chronic illnesses, and how this problem will be solved by a new kind of medicine, one that directly modifies the structure of your body so as to make you healthier.

What is ageing?

In biology it is said that function follows form, and in fact this is true of all machines. Machines do the things they do because of the way they are set up, because of their shape, because of their structure.
Damage can be defined as any structural change that causes a loss of function.
Any machine will do damage to itself in the course of normal operation.
Ageing, then, is the process by which a machine loses function as a result of damage sustained from normal operation.
The human body is a machine. Biological ageing is a progressive lo

...
What's wrong with the paperclips scenario?
by No77e — 2023-01-07T17:58:35.866Z

In October 27th 2022, Eliezer Yudkowsky tweeted this:

So far so good. I may ask myself when this happened and why the paperclips example was the one that actually sticked, but so far I have no reason to worry about my understanding of AI safety. But here's more of the conversation that happened on Twitter:

When Eliezer says "this is exactly the misunderstanding I'm complaining about" and "the related lesson is false and not valuable" I start to question my own sanity. This example is used pretty much everywhere. In Bostrom's Superintelligence, Rob Miles' videos about the stamp collector (inspired by a blog post by Nate Soares), Wait But Why's introduction to AI risk and I don't know what else.

So here are questions:
1. I don't see how the paperclips metaphor is wrong. Or am I misunderstanding Eliezer?

2. If it's wrong, why is it used everywhere?
Building a Rosetta stone for reductionism and telism (WIP)
by mrcbarbier — 2023-01-07T16:22:55.465Z

This is a Work In Progress, significant updates are to be expected/hoped for in the near future.

See the introductory post of this sequence to make any sense of the following.

OVERALL THRUST OF THE PROJECT

I believe that we must in parallel:

1. Develop an autonomous theory of functional roles

Find a way to ask functional questions without ever referring to non-functional questions such as "what is this made of", "how did it emerge" etc.

2. Build a Rosetta stone between reductionism and telism

Interpret functional questions in terms of the natural objects of reductionism: atomic components, microscopic rules, sets and spaces of possibilities/configurations…

Then use this interpretation to identify candidates for the natural objects of telism.

Asking more classic questions such as:
- how to identify the boundaries of a functionally integrated subsystem (e.g. how to identify that the heart is an organ, whereas the heart + 20% of the lungs is not an organ)
- why a system-wide task should generally decompose into modular subtasks (having organs doing different things) – what kind of microscopic rules and system-wide task allow or prevent it

Why in parallel?

Because I don't think we can yet think through the former alone, even though it may be our ultimate goal.

I feel the relationship between these two goals should be a bit like topology & geometry: in the Rosetta stone project, we are building a geometry over a space whose metric properties stem from reductionism (e.g. configurations of parts); in the autonomous theory we forget about such details entirely and try to capture global properties and relations of functional roles.

Vocabulary note

Though I am trying to standardize my vocabulary, there may still be moments where I interchangeably use:
- system, organism = the starting point of our explanation, the level of organization at which a simple and understandable selection rule exists
- selection rule, constraint, task, goal = what should happen, which states are forbidden or allowed, or more or less likely (e.g. being alive or dead, performing a certain calculation or operation on the world, etc.)
- subsystem, coherent/integrated subsystem, organ = things that we want to explain by stating their role in the system; once we know that role, we know the selection rule applying to them, and they can in turn serve as a reference point to explain further sub-subsystems...
-...
What should a telic science look like?
by mrcbarbier — 2023-01-07T16:13:28.849Z

A brief (re-)introduction

In case you don't want to read introductory post of this sequence, here is a quick recap of the broad context for this work. This post will try to clarify my objectives, and the next one will start to provide some concrete formalisms.

When we want to understand an object or phenomenon, there are at least three approaches:

- zoom in: take the object in isolation (removing all external factors), cut it down until we find elementary components with simple rules (e.g. atoms following Newtonian mechanics), and build back up to understand how the focal object’s behaviors emerge from its composition and structure.

- zoom out: ignore anything that is internal to the object, look at a broader and broader context until we find it is embedded in a system with well-defined boundaries subjected to simple selection rules (e.g. an organism that must survive and reproduce), and go back down to understand what role the focal object plays in that selection.

- look back: observe its sequence of past states and identify simple historical accidents, largely unpredictable from either its instantaneous composition or its instantaneous context, that caused it to jump between states (e.g. phylogeny or etymology).

I will call these three approaches reductionism, telism and historicism. Unfortunately, only reductionism has developped a native mathematical language. The other two approaches are still deeply verbal, and mainly produce just-so stories, where mathematics may occur incidentally to justify some steps, but are never the source of the concepts themselves.

My aim is to take baby steps toward a mathematical formalization of telism (for a start!)

1. Objectives: what would success look like?

Since I believe in reiterating things to death, a simple parallel to clarify the basic concepts:

- Reductionism starts by positing (microscopic) ingredients and assembles them, with dynamical laws being what relates emergent higher-level behaviors to the imposed microscopic ones.

- Telism starts by positing (macroscopic) constraints and dissects them, with functional architecture being what relates emergent lower-level constraints to the imposed macroscopic ones.

Thus, in telism, the basic objects we want to define and manipulate are constraints (= selection rules e.g. is an organism alive?), and the basic phenomena we want to see emerge from these assumptions are functional rol...
Open & Welcome Thread - January 2023
by DragonGod — 2023-01-07T11:16:18.646Z

If it’s worth saying, but not worth its own post, here's a place to put it.

If you are new to LessWrong, here's the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are invited. This is also the place to discuss feature requests and other ideas you have for the site, if you don't want to write a full top-level post.

If you're new to the community, you can start reading the Highlights from the Sequences, a collection of posts about the core ideas of LessWrong.

If you want to explore the community more, I recommend reading the Library, checking recent Curated posts, seeing if there are any meetups in your area, and checking out the Getting Started section of the LessWrong FAQ. If you want to orient to the content on the site, you can also check out the Concepts section.

The Open Thread tag is here. The Open Thread sequence is here.
Anchoring focalism and the Identifiable victim effect: Bias in Evaluating AGI X-Risks
by Remmelt — 2023-01-07T09:59:52.120Z

Anchoring focalism

The tendency to rely too heavily, or "anchor", on one trait or piece of information when making decisions – usually the first piece of information that we acquire on that subject.

Examples:

Judging the contents of a book by its cover.
Judging the strength of an argument by its conclusion.
The need to consider and understand the contents of a message purely on the basis of how and when it was delivered and/or who delivered it.

ie. 'If the courier is well dressed and the timing is right, then the message must be important' (and vice versa).

Unfortunately, perhaps due to the prior actions of others in your same social group, a deceptive frame of interpretation is more likely to be encountered first, effectively 'inoculating' everyone else in the group against an unbiased receipt of any further information.

Roughly parallel to the 'Identifiable Victim', particularly as an instantiation.

- link Wikipedia: Anchoring
- an item on Forrest Landry's compiled list of biases in evaluating extinction risks.

Identifiable victim effect

The tendency to respond more strongly to a single identified person at risk than to a large group of people at risk.

This is parallel to the 'base rate fallacy', the 'normalcy bias', and 'scope insensitivity' effects.

It represents another attempt to substitute intuition (fast/easy) in place of real analysis (hard, abstract, and slow).

It is an example of a compensatory effect wherein concrete and visible/identifiable specifics are treated in place of abstract concepts (the possibility of catastrophic events in the future, many aspects and follow on effects of which will be fully unknown – ie. via the 'Ambiguity effect')

Another way in which this effect has been observed to occur is when mentioning various x-risk concerns to intelligent peers. There is an immediate tendency for each one to consider the meaning of the concern in terms of their own lives only. Ie. how they would prefer to die, etc, usually with some element of obligatory moral fatalism included.

This is effect is ignoring the ethical considerations of the degree to which their own actions (or inaction) may be contributory to impacts on others, on other life, etc.

- link Wikipedia: Identifiable victim effect
- an item on Forrest Landry's compiled list of biases in evaluating extinction risks.
Can ChatGPT count?
by p.b. — 2023-01-07T07:57:20.949Z

A few month back there was a question on lesswrong about what the least impressive thing is going to be that GPT4 can't do. At the time I thought maybe counting could be a good candidate, because I had noticed that this seems to be a problem for all big transformer models.

Dall-E and other image generation models can't reliably get cardinalities right beyond something like 3.

Luminous/Magma the multi-modal AlephAlpha language model that can take pictures as input, cannot count items on these pictures with any reliability.

All pure language models I had checked, weren't able to count the number of occurrences of a specific word in a sentence or a specific character in a string.

Counting very much feels like a basic ability, I think my daughter could reliably count ten items when she was 2, and I feel like there should be a way to prompt ChatGPT that demonstrates counting ability.

In my tests so far, even ChatGPT as the most advanced LM to date is unable to count specific words or characters. However, people seem to disagree ...

For a short while I thought the correct hack is to ask it to write a python function to do the counting and then output what it thinks the function would output. But now I suspect that ChatGPT executes functions behind the scenes. So it would get the answer correct the very first time after actually writing the function and revert to being wrong in subsequent answer to the question what the function would output for certain inputs. [I just now noticed that in the answer below, even the first proposed function output is incorrect, so maybe there is no code execution?]

Here is one of my tries:

I want you to count the number of occurrences of the character "X" in a string. To do that I propose a simple algorithm, just repeat the string and write the current count of "X"s behind each "X" like this: "XGJKXZUXGULXXG" --> "X1GJKX2ZUX3GULX4X5G" : the final count is 5. Now you do this for "GLXXXJHJHXJHKXJHKX"

Sure, here is the implementation in Python:

def count_occurrences(string):
count = 0
result = ""
for ch in string:
if ch == "X":
count += 1
result += "X" + str(count)
else: result += ch
return count

string = "GLXXXJHJHXJHKXJHKX"

count = count_occurrences(string)

print(f"The count of X in {string} is {count}")

This will print "The count of X in GL...
An Ignorant View on Ineffectiveness of AI Safety
by Iknownothing — 2023-01-07T01:29:59.126Z

I am ignorant of AI and AI Safety to a very large degree. It is very likely that a lot of what I talk about here will be silly to an actual expert or is something that has been thought of a long time ago and tried and taken as far as they knew how. But I think that if there is anything here that could help, or could be turned into something else that could help, I would rather have spent time writing this than not.

AI Safety and alignment, as far as this ignorant one knows, has been very ineffective. The current progress is reckless, very very fast, with little to no restraint. With the main approach in the actual industry seeming to be to patch things up as they come along, but only really if they can affect the profit margin. And the experts who aren't in favor of this seem to have amounted to little more than educated picketers.

Something I don't understand is that unlike other areas such as climate change and urban planning, which although very different have a slightly similar dynamic of experts with little personal power trying to advocate for changes which are better for people generally, against larger, more powerful organizations who profit hugely from the way things are currently done, people in AI Safety seem to be doing little else. What they especially don't seem to be doing, which experts in climate change and urban planning(which, I appreciate are very different, I am using them as examples due to slightly similar power dynamics) are doing, is offering profitable alternatives. Or even offering alternative courses of action, perhaps to another powerful group. As a small scale example, if I offer solar panels to people in my village, I can appeal to how they'll save money and have self-governance. I can offer the quietness of it compared to a generator. Sympathizing that they might not have the climate as a high priority as I do, because they have other problems. An urban planning advocate might appeal to how good drivers would have to deal with a lot less poor drivers on the road if more of them were in buses, if buses didn't get stuck in traffic, or how there might be more parking free for drivers if there were better bike lanes and bike parking. Essentially, putting forth an idea so that it's not just appealing to themselves, but to others as well. And also obviously profitable to others.

I don't see this in AI Safety. The atti...
Optimizing Human Collective Intelligence to Align AI
by Shoshannah Tekofsky — 2023-01-07T01:21:25.328Z

Commitments are hard. Based on my Research Principles for these 6 months of self-study, I'm writing up all my ideas regardless of level of polish. So here is the rough sketch of the idea I'm currently working on. Forgive the slightly over-confident style -- It's closer to the raw form of my cognition and not necessarily the level of nuance I reflectively endorse.

Will alignment be solved by a handful of geniuses or by the collective efforts of hundreds of the merely gifted? My understanding is that we are already trying our best to draw every genius out of the woodwork. Yet how much optimization pressure have we exerted on the collective efforts of the remaining 99% of alignment researchers?

Not much, I'm guessing?

So this is a short essay on what optimizing human collective intelligence may look like.

Known and Unknown Knowledge Space

First, some framing.

If you presume the solution to the alignment problem exists somewhere in knowledge space then what we need is a method to correctly and quickly navigate toward that solution. If we conceptualize knowledge space as a graph, where each node represents a modular piece of knowledge and edges are the relationships between these pieces of knowledge, then we could theoretically encode all of known and unknown knowledge in this form. Next, there is the individual skill of navigating across knowledge space as a researcher as well the collective skill of efficiently and completely searching knowledge space as a group of researchers. My proposal is that we need to improve both to find a reliable solution to the alignment problem before the development of AGI.

Discovering New Nodes

The search for new knowledge is what we refer to as 'learning'. You can discover a new knowledge node through three different methods:

Logic
Empiricism
Communication

Logic yields knowledge through interpolation and extrapolation from observations or assumptions. As such, it can be practiced entirely in theory. Mathematics is the foremost field that relies completely on logic, where I'm loosely using logic to refer to any form of theoretical reasoning.

Empiricism consists of observation. Experiments are controlled observations you perform to gather specific, generalizable knowledge from reality. It is challenging to run good experiments that tease out complex or precise relationships in reality, but it's generally easy to run small-scale expe...
[Discussion] How Broad is the Human Cognitive Spectrum?
by DragonGod — 2023-01-07T00:56:21.456Z

Epistemic Status

Discussion question.

Related Posts

Where The Falling Einstein Meets The Rising Mouse (Slatestarcodex)
The range of human intelligence (AI Impacts)

Preamble: Why Does This Matter?

This question is important for building intuitions for thinking about takeoff dynamics; the breadth of the human cognitive spectrum (in an absolute sense) determines how long we have between AI that is capable enough to be economically impactful and AI that is capable enough to be existentially dangerous.

Our beliefs on this question factor into our beliefs on:

The viability of iterative alignment strategies
Feasibility of attaining alignment escape velocity
Required robustness to capability amplification of alignment techniques

Introduction

The Yudkowsky-Bostrom intelligence chart often depicts the gap between a village idiot and Einstein as very miniscule (especially compared to the gap between the village idiot and a chimpanzee):

Challenge

However, this claim does not feel to me to track reality very well/have been borne out empirically. It seems that for many cognitive tasks, the median practitioner is often much[1] closer to beginner/completely unskilled/random noise than they are to the best in the world ("peak human"):

Mathematics
Theoretical physics
Theoretical research in general
Fictional writing
Writing in general
Programming

"10x programmers"

Music
Art
Creative pursuits in general
Starcraft
Games in general
Invention/innovation in general
Etc.

It may be the case that median practitioners being much closer to beginners than the best in the world is the default/norm, rather than any sort of exception.

Furthermore, for some particular tasks (e.g. chess) peak human seems to be closer to optimal performance than to median human.

I also sometimes get the sense that for some activities, median humans are basically closer to an infant/chimpanzee/rock/ant (in that they cannot do the task at all) than they are to peak human. E.g. I think the median human basically cannot:

Invent general relativity pre 1920
Solve the millennium prize problems
Solve major open problems in physics/computer science/mathematics/other quantitative fields

And the incapability is to the extent where they cannot usefully contribute to the problems[2].

Lacklustre Empirical Support From AI's History

I don't necessarily think the history of AI has provided empirical validation for the Yudkowsky...
Implications of simulators
by TW123 — 2023-01-07T00:37:45.799Z

In this post, I:

Explain the idea of language models as simulators in a way I find compelling.
Introduce the notion of separate simulator selectors and simulation infrastructure within a model, and provide some evidence for this.
Discuss what the theory means about the agency of present and future LLMs.
Provide examples of "simulatorception" and "simulator verse jumping."
Give some suggestions for alignment researchers.

Epistemic status: I’m not very confident about any of these claims, and I wouldn’t call myself extremely well versed in all discussions of language models as simulators on LessWrong. But I wrote some ideas out for a class paper so I thought they might be worth sharing, particularly if I can inject some independent ideas by virtue of me having thought about this on my own. I’m very interested in hearing criticisms.

Language Models Are Simulators

Language models are built to predict human text. Does this make them mere pattern-matchers? Stochastic parrots? While these descriptions have their merits, the best-performing explanation is that language models are simulators. By a simulator, I mean a model that has the ability to predict arbitrary phenomena similar to the phenomena that gave rise to its training data – not just the training data itself.

This is not the first work to suggest that present day language models are simulators.

Janus has argued for this conception

, and

David Chalmers suggested

that language models are “chameleons” that can inhabit different personalities (note: I’m deliberately not overly anchoring on Janus's post here, since I’ve been thinking about this in a slightly different way and started doing so before I read that post). As we will see, many researchers are also implicitly and sometimes explicitly treating language models as simulators.

The training data used by LLMs was written by humans, who have beliefs, goals, communicative intent, world models, and every other property associated with intelligent, thinking, conscious beings. This does not show that language models must themselves have any of these properties in order to imitate humans. Chalmers, for example, argues for the conceivability of “zombies” that behave exactly as conscious beings but lack subjective experience.[1]

Daniel Dennett

leaves open the possibility that humans do not really have beliefs in a fundamental sense; Hume,[2] Parfit,[3]&nb...