trentmkelly/LessWrong-43k huggingface dataset:
Notes on Robert McIntyre’s Brain Preservation Talk at the Long Now Foundation
by JohnGreer — 2021-04-28T20:47:41.325Z

Update: I later ended up interviewing Robert.
This is cross-posted from my blog and is written more for a general audience rather than LessWrong people who will be more familiar with some of the relevant concepts.

These are my notes on Robert McIntyre’s talk at the Long Now Foundation:
Engram Preservation: Early Work Towards Mind Uploading | Robert McIntyre

https://youtu.be/FCK6Yrx_PSQ

I stumbled across the Long Now Foundation back in 2011 and heard about their 10,000 year clock, a project to design a clock to keep time for 10,000 years (and bring media attention to their project) and it’s cool seeing they’re still doing stuff. (Ten years ago isn’t that far back even in normal time so I hope their foundation lasts longer than that.)

Robert McIntyre is the CEO of a company called Nectome. Nectome’s goal is to try to better understand human memory and preserve brains. I know what you’re thinking... the human memory part seems normal enough but preserving brains? For who? Zombies? Stick with me.

I remember hearing something about brain preservation and a thing called the “Large Mammal Brain Preservation Prize” being won and a company called Nectome doing it but didn’t look too much into it.

I re-stumbled across Nectome in reading the writings of fellow cryonics and life extension supporter, Mati Roy.

Let’s dive in:

“I consider myself an archivist. And what I work on archiving are human memories.”

Stages of Information Transmission in History

Robert talks about dividing human history into different stages. We advance from one stage to another by developing technologies that allow better transmission and preservation of information. Every time we invent tech that does this, it radically catapults our society to new heights. Note: Regular people often think about technology as just gadgets like a TV or iPhone but technology (of course, depending on semantics) does include things like language and writing.

This is a similar paradigm to the transition from the hunter-gather stage to the agriculture stage to the industrial revolution. It cannot be overestimated how massive these changes were.

This video discusses this briefly in a nice way:

https://www.youtube.com/watch?v=mrWPSP35gUk

He calls the pre-language stage intuitive and talks about how weird this must have been. It is a trip to think about what our qualia would have been like in a pre-language era. L...
Is sitting in the sun much better than sitting in the shade? 
by just_browsing — 2021-04-28T18:48:48.622Z

How much worse is sitting outside in the shade compared to the sun, for the purpose of experiencing enough lux to feel alert?

Making the difference between shade and sun a bit more extreme than it probably is in practice, we can take "sun" to mean "10,000 lux" and "shade" to mean "1,000 lux". Data to back this up:

Wikipedia gives these lux values for different conditions:

20,000 lux "Shade illuminated by entire clear blue sky, midday"
1,000–2,000 lux "Typical overcast day, midday"
<200 lux "Extreme of thickest storm clouds, midday"

NOAO (National Optical Astronomy Observatory) says:

10,000 lux "Full Daylight"
1,000 lux "Overcast Day"
100 lux "Very Dark Day"

[Side question: What does Wikipedia exactly mean with "Shade illuminated by entire clear blue sky"? Is this a strange way of describing sunlight, or are they referring to the lighting experienced standing in the shade of an object during an otherwise sunny day? The words sound like the latter to me, but the former fits better with NOAO's description. I don't think this distinction breaks my question either way.]

Same orders of magnitude, just seems like NOAO is averaging throughout the day and Wikipedia is looking at midday.

Meanwhile, this LW post discusses how to install indoor lighting to get about 1,000 lux inside. If 1,000 lux is enough to keep a human alert and non-SAD, then it seems like shade is no worse than sun. Or, maybe people installing lumenators in their homes just stop at 1,000 lux because it's impractical to get an order of magnitude more lux than that.

One way of phrasing the argument in favor of lumenators is that going from 100 lux to 1,000 lux provides huge benefits. Can the same thing be said about going from 1,000 lux to 10,000 lux, or are there diminishing returns? In other words, should I sit in the sun instead of the shade?
[AN #148]: Analyzing generalization across more axes than just accuracy or loss
by Rohin Shah — 2021-04-28T18:30:03.066Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer.

HIGHLIGHTS

Distributional Generalization: A New Kind of Generalization (Preetum Nakkiran and Yamini Bansal) (summarized by Rohin): Suppose you train a classifier to distinguish between CIFAR-10 classes, except each airplane has a 30% chance of being mislabeled as a car. If you then train a model to achieve perfect accuracy on this badly labeled dataset, it will get 100% accuracy on the training set, and 97% of those labels will actually be correct (since 3% are mislabeled airplanes). Under the current paradigm, if we say that the model “generalizes”, that means that it will also get 97% accuracy at test time (according to the actually correct labels). However, this doesn’t tell us anything about what mistakes are made at test time -- is it still the case that 30% of airplanes are mislabeled as cars, or does the model also make mistakes on e.g. deer?

Distributional generalization aims to make claims about situations like these. The core idea is to make claims about the full distribution of classifier outputs, rather than just the single metric of test accuracy.

Formally, we assume there is some distribution D, from which we can sample pairs of points (x, y), which generates both our train and test sets. Then, the train (resp. test) distribution of classifier outputs is (x, f(x)), with x coming from the train (resp. test) set. The train and test distributions of classifier outputs are the objects of study in distributional generalization. In particular, given a [0,1]-valued function on distributions (called a test T), we say that the classifier generalizes w.r.t T if T outputs similar values on the train and test distribution. (W.r.t means “with respect to”.) For example, given a distribution, the accuracy test checks how often the classifier’s output is correct in expectation over that distribution. Generalization w.r.t the accuracy test is equivalent to the canonical notion of generalization.

Let’...
Facebook is Simulacra Level 3, Andreessen is Level 4
by Ben Pace — 2021-04-28T17:38:03.981Z

A passage I just read in The Hard Thing About Hard Things by Ben Horowitz:

Andreessen vs Zuckerberg: How Big Should the Titles Be?

Should your company Vice President the top title or should you have Chief Marketing Officers, Chief Revenue Officers, Chief People Officers, and Chief Snack Officers? There are two schools of thoughts regarding this, one represented by Marc Andreessen and the other by Mark Zuckerberg.

Andreessen argues that people ask for many things from a company: salary, bonus, stock options, span of control, and titles. Of those, title is by far the cheapest, so it makes sense to give the highest titles possible. The hierarchy should have. Presidents, Chiefs, and Senior Executive Vice Presidents. If it makes people feel better, let them feel better. Titles cost nothing. Better yet, when competing for new employees with other companies, using Andreessen's method you can always outbid the competition in at least one dimension.

At Facebook, by contrast, Mark Zuckerberg purposely deploys titles that are significantly lower than the industry standard. Senior Vice Presidents at other companies must take title haircuts down to Directors or Managers at Facebook. Why does he do this? First, he guarantees that every new employee gets releveled as they enter his company. in this way, he avoids accidentally giving new employees higher titles and positions than better-performing existing employees. This boost morale and increases fairness. Second, it forces all the managers of Facebook to understand and internalize Facebook's leveling system, which serves the company extremely well in their own promotion and compensation processes.

He also wants titles to be meaningful and reflect who has influence in the organization. As a company grows quickly, it's important to provide organizational clarity wherever possible and that gets more difficult if there are fifty VPs and ten Chiefs.

Next, he finds that businesspeople often carry inflated titles versus their engineering counterparts. While he recognizes that big titles help them out externally with getting meetings, he still wants to have an organization where the product people and engineers form the cultural core, so he strives to keep this in check as well.

Does Facebook ever miss out on a new hire due to its low titles? Yes, definitely. But one might argue that they miss out on precisely the employees they don't

...
When Should the Fire Alarm Go Off: A model for optimal thresholds
by peterbarnett — 2021-04-28T12:27:20.031Z
Link: https://peterbarnett.org/2021/04/28/optimal-thresholds/

In this post I consider a model where there is a possible bad outcome, and the chance of it happening is related to something we can measure. We need to be able to set a threshold for these measurements, such that if a measurement exceeds a threshold then we decide to act; to prepare for or treat the bad outcome. This seems like a pretty basic idea, so I expect that either most results will be trivial, or that someone has already done this before.

A basic example of this would be a disease, where if a patient reaches a threshold for the number and severity of symptoms then the doctor decides to prescribe a medicine. Or the current pandemic, where in February 2020 if governments had a more accurate view of both the chance and severity of a global pandemic they may have decided to put in place seemingly drastic measures to stop worse outcomes in the future.
This should also apply to important risks in the future, where it is important to know the probabilities and magnitudes so that we can adequately prepare. We need to know where to sensibly set the threshold which tells us when to act.

A helpful metaphor I will use here is a building with a sprinkler system. We want the sprinklers to turn on when there is a fire (there is a huge cost if the building burns down), but also we don't want the sprinklers constantly going off whenever there's dust in the air because this also has a cost associated with it.

When deciding whether something is going to happen, and whether to act there are 4 options:

True Positive (TP), where the event happens but luckily we decided to act

The building is on fire, but the sprinklers turned on and saved everything

True Negative (TN), where nothing happens and we didn't do anything

No fire, no sprinklers

False Positive (FP), where nothing happens and we act unnecessarily

The building is not on fire, but the sprinklers turned on anyway

False Negative (FN), where the event happens and we didn't act

The building is on fire, but the sprinklers didn't turn on and the building burned down

The chance of each of these options will depend on where we set our threshold. If the threshold is very low then we will have a lot of True Positives, but will also have to incur the costs of more False Positives. If the threshold is very high then we will have less False Positives, but will have more False Negatives.

Mathematical Model

We can decide where to put the...
Better air is the easiest way not to die
by digital_carver — 2021-04-28T10:24:37.344Z
Link: https://dynomight.net/air/

It’s hard to prioritize health advice. I’m told I should limit salt and eat cruciferous vegetables and do cardio and sleep well and limit alcohol and reduce stress and go for regular checkups. But how much do each of these matter? If you’re a fallible hairless ape, what should you do first?

You should put the stuff with the highest return on effort first, and that’s air.
Naming and pointer thickness
by Adam Zerner — 2021-04-28T06:35:08.865Z

Occam’s Razor is often phrased as “The simplest explanation that fits the facts.” Robert Heinlein replied that the simplest explanation is “The lady down the street is a witch; she did it.”

− Occam's Razor, Eliezer Yudkowsky

The point here is that people have confused ideas about what "simple" means. The sentence "The lady down the street is a witch; she did it." seems simple, but in actuality, it is very complex. It seems simple because it's kinda short, but the shortness is deceiving.

Don't see what I mean? Let me ask you a question. What if we took the first letter of each word and abbreviated the sentence as "tldtsiawsdi"? Is it simpler? No, of course not. But why?

Well, it's still pointing to the same underlying thing. We just used different symbols to do the pointing. When we talk about simplicity and complexity in Occam's Razor, we're talking about the substance, not the symbols.

In this particular example, there's a big issue with the word "witch".

“Witch,” itself, is a label for some extraordinary assertions—just because we all know what it means doesn’t mean the concept is simple.

There's a lot of fun places to take this conversation. If you're interested in things like rationality and philosophy, I'd really recommend checking out A Humans Guide to Words. Here, I want to talk about how this relates to programmig.

In programming, we also have symbols and substances.

var adam = {
firstName: 'adam',
lastName: 'zerner',
email: 'adamzerner@protonmail.com',
birthday: '11/03/1992'
};

adam is the symbol, and { firstName: 'adam'... } is the substance. The way I like to think of it, the substance gets compressed, or squished into the symbol. You have this big complicated thing, and you pack it up nicely into a symbol that represents it. Then, elsewhere in the codebase, you can refer to adam instead of { firstName: 'adam'... }. I think that conceptually, this is the same thing that is happening with "witch" and 🧙‍♀️, as well as "apple" and 🍎, "bear" and 🐻, and "carrot" and 🥕. (If 🧙‍♀️→ "witch" doesn't look like compression to you, read about thingspace and you'll see how much is packed into 🧙‍♀️.)

In writing as well as speaking, it is important to communicate clearly. One way to do that is to choose simple words. To speak in plain english. Eg. saying "start" instead of "commence".

Why? Think about it in terms of the symbol and the substance. Both ...
Talkers and doers
by Amir Bolous — 2021-04-28T04:26:28.269Z

This is a linkpost for https://amirbolous.com/posts/doers

In this world, there are two types of people: the talkers and the doers. The talkers, self-evidently, talk about things. They talk about building. They talk about solving problems. They talk about starting a project. Most people are talkers.

But some special people are doers. They build. They solve problems. They draw. They write. They code. They paint. They do stuff. They don't need to talk because their work speaks for itself.

It's easy to be a talker. It's hard to be a doer because it's uncomfortable on many levels. Uncomfortable to acknowledge that you're a noob in the beginning. Uncomfortable to not know the map from the territory. Uncomfortable to not have the the cheap validation of people around you. But uncomfortable is good, the only way to learn is the hard way (that is getting outside your comfort zone) which is by definition uncomfortable.

Whenever I find myself talking and not doing, I try to do myself and everyone else a huge favor.

I shut the hell up.

I hope that others strive to do this too.
A casual intro to Geometric Algebra
by Orfeas — 2021-04-28T00:00:46.221Z

(casual but not informal, prerequisites are trigonometry, vectors, and complex numbers, subject as digested by a non-expert in an unrelated field of science)

Some background

How I got interested in geometric algebra

One evening I stayed after class to speak with my mechanics professor. He showed me a formulation of Maxwell's equations that looked like

∇F=J

"Wait, what? Aren't there supposed to be four equations?" is a question I should have asked, had I known what a Maxwell was. A year later I had forgotten the exchange, but I happened to come across a swift introduction to geometric algebra on youtube. A comically dramatic flashback ensued and after a brief email correspondence, I ended up with Alan Macdonald's fantastic Linear and Geometric Algebra.

Motivation: representing rotations.

Hermione's hand shot up wildly from the desk right in front of her professor's.

"I know this! In Linearibus Essentia Mathematica by Grant Sanderson, chapter three, minute 3, second 48, he says that the ending coordinates of the basis vectors after a linear transformation uniquely describe the entire transformation! Put the coordinates of rotated basis vectors in a matrix, define the matrix product, and you're all set!"

Professor Vector smiled kindly, "That's spot on, Miss Granger. 10 points to Gryffindor! However, it is known that algorithmagically cheaper methods exist."

--

By the end of high school, it had been essentially imprinted upon me that there were two flavours of rotation: rotation matrixes and complex numbers.

Here's how to rotate the vector [1, 0] by 30° anti-clockwise using a matrix.

Notice the coordinates of the basis vectors (read vertically) after a rotation by θdegrees must be

[cosθ−sinθsinθcosθ]

Replace θ by 30° and perform matrix left-multiplication on the vector

[√3/2−1/21/2√3/2][10]=[√3/21/2]

And here's the same rotation, now using complex numbers.

Throw the vector [1, 0] on the complex plane like so: 1+0i
Multiply by eiπ/6 like this :

eiπ/6(1+0i)=eiπ/6=cosπ/6+isinπ/6=√3/2+i/2

Hey presto, retrieve the vector [√3/2,1/2] from the complex plane

Looking back on these notations, I feel like they do not capture the essence of what it means to rotate a vector. Matrix notation is really more adapted for robots than for humans. The issue is that unless you sit down and draw the resulting positions of the resulting basis vectors, you cannot...
Rationality Cardinality
by jimrandomh — 2021-04-27T22:27:27.412Z

Rationality Cardinality is a humorous card game, built out of rationality concepts and vocabulary and designed to teach them. I have recently finished an online-game implementation, which can be embedded into gather.town worlds. Try it out by joining the Rationality Cardinality gather.town server! It helps to have other people to play with, so I suggest Saturday May 1, 11am PST as a Schelling meetup time.

Rationality Cardinality is a two-for one combined bet on dick jokes and the Sapir-Whorf hypothesis. Sapir-Whorf is the hypothesis that many people are bottlenecked on what thoughts they can think, on having vocabulary for the relevant concepts. I've been curating a set of cards (concepts, explanations, and jokes) over the years, and I'm quite proud of it. (Except for the cards about concepts that fell to the replication crisis, of course.)
Pitfalls of the agent model
by Alex Flint — 2021-04-27T22:19:30.031Z

This is independent research. To make further posts like this possible, please consider supporting me.

Outline

I study the agent model as a frame for understanding the phenomenon of entities that exert influence over the future.

I focus on one implication of the agent model: the assumption of a fixed agent policy that is unchanging over time.

I study the consequences of this assumption in a number of scenarios. I consider single- and multi-agent scenarios, scenarios consisting of humans, machines, and combinations of humans and machines, and scenarios in which the agent model is being used by an entity to model itself versus other entities. I show that all combinations of these can be covered by six basic cases. For each of the six cases I identify pitfalls in which certain aspects of reality are abstracted away by the application of the agent model, and we examine the consequences of each.

I draw connections to related work in embedded agency, partial agency, and embodied cognition.

I conclude that the agent model has significant shortcomings as a frame for the engineering of advanced AI systems.

Introduction

Yesterday I wrote about the agent model as a frame for understanding the real-world phenomenon of entities in the world that exert influence over the future.

I said that we should be cautious about over-using any one frame as a means for understanding any phenomena, because when we only have one frame it is easy to forget about the boundary between the phenomena that we’re looking at and the frame that we’re looking at it with, which is a problem no matter how powerful or accurate our preferred frame is. This is indeed one reason to consider frames other than the agent model when studying entities that exert influence over the future. A second reason is that there are specific shortcomings of the agent model. I want to examine some of those today.

All frames have shortcomings. A frame is precisely that which gives us a way of seeing that is simpler than reality itself, so the whole point of a frame is that it does not include every aspect of reality. We can always point to ways in which any given frames fail to capture all of reality, and that is no reason on its own to discard a frame. Nevertheless, we may want to de-emphasize frames whose shortcomings are severe enough. In this post I will present the shortcomings of the agent model as I ...
Covid-19 in India: Why didn't it happen earlier?
by Sherrinford — 2021-04-27T19:13:00.798Z

When I look at the numbers of Covid-19 cases and Covid-19 deaths among G20 countries, India still has a relatively low number per capita. The arguments for why the current wave is particularly catastrophical in India (like population density or poverty) applied all the time, however, except for the mutation. How did India manage to have relatively low case rates up to now?
Jaan Tallinn's 2020 Philanthropy Overview
by jaan — 2021-04-27T16:22:08.120Z
Link: https://jaan.online/philanthropy/#2020-results-success

to follow up my philantropic pledge from last year, i've updated my philanthropy page with 2020 results.

TL;DR: i made $4.3MM worth of endpoint grants in 2020, mostly via SFF's grant rounds, beating my minimum commitment by about 2x.