trentmkelly/LessWrong-43k huggingface dataset:
[Link] On the paradox of tolerance in relation to fascism and online content moderation – Unstable Ontology
by Kenny — 2022-07-01T16:43:50.739Z

On the paradox of tolerance in relation to fascism and online content moderation – Unstable Ontology

I found the post to be wonderful. It expresses my own thoughts and feelings extremely well.

Some comments I made on the ACX post that shared this:

YESSSSS! Tolerate all speech! Persecute the initiation of violence or coercion (e.g. the threat of violence)! Ayn Rand had this down but this post is wonderful too. I like the thoughts on when, i.e. under what circumstances, to 'de-platform' someone: "they make enough absurd arguments that are easy to demonstrate false"; that "enough" is doing a LOT of the work tho! Daryl Davis and similar are what I think of as an 'existence proof' that "enough" is, for many of the relevant people, QUITE a lot actually!
Trends in GPU price-performance
by Marius Hobbhahn — 2022-07-01T15:51:10.850Z
Link: https://epochai.org/blog/trends-in-gpu-price-performance

Executive Summary

Using a dataset of 470 models of graphics processing units (GPUs) released between 2006 and 2021, we find that the amount of floating-point operations/second per $ (hereafter FLOP/s per $) doubles every ~2.5 years. For top GPUs, we find a slower rate of improvement (FLOP/s per $ doubles every 2.95 years), while for models of GPU typically used in ML research, we find a faster rate of improvement (FLOP/s per $ doubles every 2.07 years). GPU price-performance improvements have generally been slightly slower than the 2-year doubling time associated with Moore’s law, much slower than what is implied by Huang’s law, yet considerably faster than was generally found in prior work on trends in GPU price-performance. Our work aims to provide a more precise characterization of GPU price-performance trends based on more or higher-quality data, that is more robust to justifiable changes in the analysis than previous investigations.

Figure 1. Plots of FLOP/s and FLOP/s per dollar for our dataset and relevant trends from the existing literature

Trend	2x time	10x time	Metric
Our dataset
(n=470)	2.46 years
[2.24, 2.72]	8.17 years
[7.45, 9.04]	FLOP/s per dollar
ML GPUs
(n=26)	2.07 years
[1.54, 3.13]	6.86 years
[5.12, 10.39]	FLOP/s per dollar
Top GPUs
(n=57)	2.95 years
[2.54, 3.52]	9.81 years
[8.45, 11.71]	FLOP/s per dollar
Our data FP16 (n=91)	2.30 years
[1.69, 3.62]	7.64 years
[5.60, 12.03]	FLOP/s per dollar
Moore’s law	2 years	6.64 years	FLOP/s
Huang’s law	1.08 years	3.58 years	FLOP/s
CPU historical (

AI Impacts, 2019

)	2.32 years	7.7 years	FLOP/s per dollar

Bergal, 2019

4.4 years	14.7 years	FLOPs/dollar

Table 1. Summary of our findings on GPU price-performance trends and relevant trends in the existing literature with the 95% confidence intervals in square brackets.

In future work, we intend to build on this work to produce projections of GPU price-performance, and investigate how our findings inform us about the growth in dollar-spending on computing hardware in Machine Learning.

We would like to thank Alyssa Vance, Ashwin Acharya, Jessica Taylor and the Epoch team for helpful feedback and comments.
How to deal with non-schedulable one-off stimulus-response-pair-like situations when planning/organising projects?
by mikbp — 2022-07-01T15:22:39.160Z

When working in projects it often happens that one task (A) must be performed after something else (B) happens. If you know B is going to happen at a particular time in the future, this is straightforward to plan --one just needs to add a reminder of A in the appropriate time slot in the calender. If it is something that happens often, one could try to use TAP (Trigger-Action Planning) if s/he is able to train enough. However, when B is a one-off situation that will happen in an unknown time in the future, particularly if it is weeks or months in the future, it is much less obvious how to deal with this. At least for me.

So, how do you guys deal with situations like "when the paper gets published, I need to do X, Y and Z"?

What I usually do is setting reminders at whatever time interval that seems adequate for the specific situation. However, this has downsides: 1) if the interval is too short, I may get used to the reminder and just disregard it without thinking. 2) if the interval is too long, task A may be performed too late.
What Is The True Name of Modularity?
by CallumMcDougall — 2022-07-01T14:55:12.446Z

TL;DR

Modularity seems like an important feature of neural networks, but there is currently no canonical way of properly defining or measuring it which is properly theoretically motivated and doesn’t break down in some cases - in other words, we haven’t yet found a

True Name

for it. Most modularity measures used in experiments are based on ad-hoc methods from graph-theory or network theory, and don’t seem to capture the kind of modularity we care about.

In this post, we explore these existing ways of measuring modularity in neural networks, and their limitations. We also outline our ideas for a new modularity metric, and in particular the important role we think two branches of mathematics will play:

Information theory, because neural networks are fundamentally information-processing devices, and we want to measure how this information is being used, exchanged and transmitted. In particular, we expect mutual information to be at the core of any metric we use.
Causal inference, because correlation (and indeed mutual information) does not automatically equal causation, and we need a way of distinguishing “modules X and Y fire with similar patterns” from the more specific claim “modules X and Y are directly sharing information with each other”. In particular, we expect counterfactuals to play an important role here.

Introduction

A first-pass definition for modularity might go something like this:

A system is modular to the extent that it can be described in terms of discrete modules, which perform more intra-modular communication than inter-module.

In previous posts, we’ve talked about some

causes of modularity in biological systems

, reasons we should

care about modularity in the first place

, and unanswered questions about modularity which

require experimentation

. In this post, we will return to a more theoretical question - how can we develop a yardstick for measuring modularity, which is built on sound mathematical principles?

First off, why is this an important thing to do? Because most current ways of measuring modularity fall pretty short. As

recently discussed

by John Wentworth, if all you have is a weak proxy for the concept you’re trying to measure, then your definition is unlikely to generalise robustly in all the cases you care about. As a simple example, most architectural ways of...
Defining Optimization in a Deeper Way Part 1
by J Bostock — 2022-07-01T14:03:18.945Z

My aim is to define optimization without making reference to the following things:

A "null" action or "nonexistence" of the optimizer. This is generally poorly defined, and choices of different null actions give different answers.
Repeated action. An optimizer should still count even if it only does a single action.
Uncertainty. We should be able to define an optimizer in a fully deterministic universe.
Absolute time at all. This will be the hardest, but it would be nice to define optimization without reference to the "state" of the universe at "time t".

Attempt one

First let's just eliminate the concept of a null action. Imagine the state of the universe at a time t.

Let's divide the universe into two sections and call these A and B. They have states SA and SB. If we want to use continuous states we'll need to have some metric D(s1,s2) which applies to these states, so we can calculate things like the variance and entropy of probability distributions over them.

Treat SA and SB as part of a Read-Eval-Print-Loop. Each SAt produces some output OAt which acts like a function mapping SBt→SBt+1, and vice versa. OAt can be thought of as things which cross the Markov blanket.

Sadly we still have to introduce probability distributions. Let's consider a joint probability distribution PABt(sA,sB), and also the two individual probability distributions PAt(sA) and PBt(sB).

By defining distributions over O outputs based on the distribution PABt, we can define PABt+1(sA,sB) in the "normal" way. This looks like integrating over the space of sA and sB like so:

PABt+1(sAt+1,sBt+1)=∫PABt(sAt,sBt) δ(OAt(sBt)−sBt+1) δ(OBt(sAt)−sAt+1) dsAtdsBt

What this is basically saying is that to define the probability distribution of states sAt+1 and sBt+1, we integrate over all states sAt and sBt and sum up the states where the OAt corresponding to sAt maps sBt to the given sBt+1.

Now lets define an "uncorrelated" version of PABt+1, which we will refer to as P′ABt+1.

P′ABt+1(sAt+1,sBt+1)=∫PAt(sAt) PBt(sBt) δ(OAt(sBt)−sBt+1) δ(OBt(sAt)−sAt+1) dsAtdsBt

This loosely represents what happens if we decorrelate ...
Safetywashing
by Adam Scholl — 2022-07-01T11:56:33.495Z

In southern California there’s a two-acre butterfly preserve owned by the oil company Chevron. They spend little to maintain it, but many millions on television advertisements featuring it as evidence of their environmental stewardship.[1]

Environmentalists have a word for behavior like this: greenwashing. Greenwashing is when companies misleadingly portray themselves, or their products, as more environmentally-friendly than they are.

Greenwashing often does cause real environmental benefit. Take the signs in hotels discouraging you from washing your towels:

My guess is that the net environmental effect of these signs is in fact mildly positive. And while the most central examples of greenwashing involve deception, I’m sure some of these signs are put up by people who earnestly care. But I suspect hotels might tend to care less about water waste if utilities were less expensive, and that Chevron might care less about El Segundo Blue butterflies if environmental regulations were less expensive.

The field of AI alignment is growing rapidly. Each year it attracts more resources, more mindshare, more people trying to help. The more it grows, the more people will be incentivized to misleadingly portray themselves or their projects as more alignment-friendly than they are.

I think some of this is happening already. For example, a capabilities company launched recently with the aim of training transformers to use every API in the world, which they described as the “safest path to general intelligence.” As I understand it, their argument is that this helps with alignment because it involves collecting feedback about people’s preferences, and because humans often wish AI systems could more easily take actions in the physical world, which is easier once you know how to use all the APIs.[2]

It’s easier to avoid things that are easier to notice, and easier to notice things with good handles. So I propose adopting the handle “safetywashing.”

^

From what I can tell, the

original source

for this claim is the book “The Corporate Planet: Ecology and Politics in the Age of Globalization,” which from my samples seems about as pro-Chevron as you’d expect from the title. So I wouldn’t be stunned if the claim were misleading, though the numbers passed my sanity check, and I did confirm the

preserve

and

advertisements

exist.

^

I haven’t talked with anyone who works at th

...
Open & Welcome Thread - July 2022
by Kaj_Sotala — 2022-07-01T07:47:22.885Z

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 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 new Concepts section.

The Open Thread tag is here. The Open Thread sequence is here.
What is the contrast to counterfactual reasoning?
by Dominic Roser — 2022-07-01T07:39:12.484Z

Looking for help: what's the opposite of counterfactual reasoning -- in other words: when effective altruists encourage counterfactual reasoning, what do they discourage?
I ask because I'm writing a chapter for a book about good epistemic practices and mindsets. I am trying to structure my writing as a list of opposites (scout mindset vs soldier mindset, numerical vs verbal reasoning, etc).
Would it be correct to say that in the case of counterfactual reasoning there is no real opposite? Rather, the appropriate contrast is: "counterfactual reasoning done well vs. counterfactual reasoning done badly"?
[This was first posted as shortform on the EA forum but I got no replies; hence trying it here, too. Thanks for any help you might provide!]
Meiosis is all you need
by Metacelsus — 2022-07-01T07:39:01.547Z
Link: https://denovo.substack.com/p/meiosis-is-all-you-need

Cross-posted from my Substack.

Note: this post is still a rough draft but I think this idea is important enough to publish it anyway. I intend to revise it in the future. Also if you are a biologist who wants to collaborate with me on this project, or a philanthropist who wants to provide funding, please email me: metacelsus at protonmail dot com

Gwern Branwen, Carl Shulman, and others have written about the concept of iterated embryo selection (IES). The idea of IES is to:

Take some embryos,
derive embryonic stem cell lines from them,
genotype the cells and select the best ones according to a polygenic score,
differentiate the stem cells to gametes,
generate a new population of embryos and repeat.

IES would allow a much more rapid improvement in genotype than would be possible under normal selective breeding.

But there may be a faster and better way to do this (and it might even be easier)!

The idea: iterated meiotic selection

Here is my proposal:

Take a diploid cell line (probably ESC or iPSC or PGCLC)
Induce meiosis and form many haploid cell lines.1
Genotype the haploid lines and select the best ones.
Fuse two haploid cells to re-generate a diploid cell line.
Repeat as desired. At the end, either differentiate the cells into oocytes or perform nuclear transfer into a donor oocyte.

This would have several advantages.

Selection is more powerful at the haploid stage.
Meiosis is faster than gametogenesis.
This does not require in vitro gametogenesis technology, since the final generation of an embryo could be performed by somatic cell nuclear transfer into a donor oocyte.

In addition to enabling iterated selection, this would also allow “genetic crosses” for human cells. Genetic crosses are very useful for model organisms (e.g. crossing Cre/lox mouse lines to generate tissue-specific knockouts) and they would be similarly useful for human cell lines.

Technological challenges and prospects

The key piece of missing technology is a way to induce meiosis in a diploid cell line to generate haploid lines. Other pieces (such as culturing haploid human stem cells, or electrofusion of cells) are already in place. Therefore, meiosis is all you need.

Now, inducing meiosis won’t be easy. Meiosis normally takes place only during gametogenesis and it is unclear whether it is possible to activate meiosis separately. But I think that it has a good chance of being achievable, and might even be easi...
How to Navigate Evaluating Politicized Research?
by Davis_Kingsley — 2022-07-01T05:59:24.239Z

I want to look into the research literature on an issue that has unfortunately become extremely politicized and controversial. (It isn't gun ownership, but let's say for the sake of argument that it is.) If I do a search for, say, "effects of gun ownership on crime", it is quite predictable that I will find a bunch of studies that say gun ownership decreases crime and a bunch of studies that say gun ownership increases crime. Some of those studies may be legitimate efforts at truthseeking, while others may be cherrypicked or manipulated, studies where the authors' bottom line has already been written before they do any work (to one degree or another), etc.
This seems like a potentially difficult environment to evaluate things in compared to one with less of a charged atmosphere, and I'm curious how best to navigate it -- any recommendations for how to best do that?
One is (almost) normal in base π
by Adam Scherlis — 2022-07-01T04:05:22.497Z
Link: https://adam.scherlis.com/2022/06/30/one-is-almost-normal-in-base-%cf%80/

See previous posts on my blog if you’re confused:

Part 1: Normal numbers in base ten
Part 2: Numbers in base φ and π
Part 3: One is normal in base π

I'm only posting Part 3 (the punchline) here on LW; Parts 1 and 2 explain things in much more detail.

One is a normal number.

“No it’s not.”

In base π, I mean.

“No, it’s still just 1.“

Don’t be so greedy. Start with 0. and go from there.

“So it’s 0.222… or 0.333… or something?”

Nope, it’s 0.3011021110…

“That’s… bad. Does it repeat?”

I think it’s normal, but that seems hard to prove.

“So each digit occurs equally often?”

No, normal numbers in base π are about a 37%-30%-29%-4% split of 0, 1, 2, 3.

“Where did that distribution come from?”

Integrals of this weird function:

(Specifically, integrals over [0, 1/π], [1/π, 2/π], [2/π, 3/π], and [3/π, 1].)

“And where did that come from?”

It’s the distribution of remainders when computing a random number in base π.

“What are the x coordinates of those discontinuities?”

Sequence of remainders when computing 0.3011021110…

“um”

which are dense in [0, 1], by the way.

“Can you prove that?”

No, and neither can you.

“…And the y coordinates?”

The discontinuities get smaller by a factor of π each time.

“And almost all numbers have this distribution of remainders?”

Yup.

“Including 1, if you use the 0.3011021110… representation.”

Yup.

“So the histogram of x-coordinates of the N largest discontinuities in this function approaches… this function itself, as N goes to infinity.”

Yup.

“Which has derivative zero almost everywhere, but has a dense set of discontinuities.”

Yup!

“Any other neat facts about it?”

It’s a fractal.

“How?”

Stretch out the colored regions by a factor of π horizontally, shrink by π vertically, and add them to each other. You get the original function back:

(Some imagination required.)

"And this is all because π is normal, or transcendental, or something?"

Nope, I think analogous statements are all true for base 3.5.
AI safety university groups: a promising opportunity to reduce existential risk
by mic — 2022-07-01T03:59:44.813Z

Cross-posted from the Effective Altruism Forum

Summary: AI safety university groups[1] are a promising way to grow the talent pool working to reduce existential risk from AI and can be fairly straightforward to set up successfully. Based on the experience of several student groups last semester, running

AGI Safety Fundamentals

or a similar AI safety reading group can attract dozens of participants and boost their likelihood to work on AI safety, while engaging many that wouldn’t have been reached by cause-neutral effective altruism community building. If you don’t have the time or experience to run an AI safety reading group, it can be valuable to broadly publicize a virtual program facilitated by another group (e.g.,

EA Cambridge

).

Overall, AI safety field-building is a new, exciting area with much low-hanging fruit. At the same time, there are risks from poorly executed field-building which should be thoughtfully handled, such as inducing a poor impression of AI safety or increasing interest in accelerating the development of (potentially unsafe) artificial general intelligence (AGI).[2]

If you’re interested in AI safety field-building, either at a university or in another setting (e.g., technical workplaces[3]),

fill out this form to get support from AI Safety Hub

! Even if you think you’d do fine without a call, I’d strongly recommend taking a few seconds to fill this out, so that they can connect you with resources and other group organizers.

Transformative AI

might pose considerable existential risk

within the coming decades or later this century. Trained to maximize an objective, AI systems often find

creative and undesirable ways to do so

. YouTube's recommendation algorithm, trained to suggest content to maximize user engagement, ended up recommending increasingly extreme content and proliferating misinformation. Language models such as GPT-3, trained to predict the next word in a piece of text,

can be prompted

to produce offensive content and leak personal information from the training data. As AI capabilities advance, we can expect the stakes of these risks from misaligned systems to

dramatically increase

.

Aligning deep learning systems with human values might be extremely challenging

, and approaches that work for making a simpler model safe

may fail for more advanced models

. At the moment, leading AI companies such as Google Brain and Meta AI are ...
Looking back on my alignment PhD
by TurnTrout — 2022-07-01T03:19:59.497Z

This post has been recorded as part of the LessWrong Curated Podcast, and can be listened to on Spotify, Apple Podcasts, Libsyn, and more.

My dissertation. It's long, so if you're going to read anything from it, read Chapter 0 (Introduction).

The funny thing about long periods of time is that they do, eventually, come to an end. I'm proud of what I accomplished during my PhD. That said, I'm going to first focus on mistakes I've made over the past four[1] years.

Mistakes

I think I got significantly smarter in 2018–2019, and kept learning some in 2020–2021. I was significantly less of a fool in 2021 than I was in 2017. That is important and worth feeling good about. But all things considered, I still made a lot of profound mistakes over the course of my PhD.

Social dynamics distracted me from my core mission

I focused on "catching up" to other thinkers

I figured this point out by summer 2021.

I wanted to be more like Eliezer Yudkowsky and Buck Shlegeris and Paul Christiano. They know lots of facts and laws about lots of areas (e.g. general relativity and thermodynamics and information theory). I focused on building up dependencies (like analysis and geometry and topology) not only because I wanted to know the answers, but because I felt I owed a debt, that I was in the red until I could at least meet other thinkers at their level of knowledge.

But rationality is not about the bag of facts you know, nor is it about the concepts you have internalized. Rationality is about how your mind holds itself, it is how you weigh evidence, it is how you decide where to look next when puzzling out a new area.

If I had been more honest with myself, I could have nipped the "catching up with other thinkers" mistake in 2018. I could have removed the bad mental habits using certain introspective techniques; or at least been aware of the badness.

But I did not, in part because the truth was uncomfortable. If I did not have a clear set of prerequisites (e.g. analysis and topology and game theory) to work on, I would not have a clear and immediate direction of improvement. I would have felt adrift.

But there is not yet any "rationality tech tree", no succession of well-defined rationality skills such that you can learn them in order and grow way stronger. Like, you can't just do the calibration exercises, and then the noticing-confusion exercises, and then other th...