trentmkelly/LessWrong-43k huggingface dataset:
Food manufacturers are out to get you
by jwray — 2022-03-13T19:36:06.273Z

[epistemic status: n=1, but the theory seems fine. Mixture of theory and personal narrative. Trigger warnings: body image issues, money, and info-dumping without a great narrative flow. I am not a good rationalist yet, haven't even finished reading the sequences yet, and this is my first long post on here.]
The obesity epidemic emerges from processed food manufacturers competing to get you to eat more of their product by any means necessary. They try to make their product be digested as quickly as possible so you will be hungry again sooner (largely by pulverizing biomass before forming it into products). They add salt/sugar/fat to make their product so delicious that it is literally addictive. Both of these make you eat more of the product and enrich the food manufacturer, with a side effect of making you fat.
After years of failed attempts at calorie restriction without categorically changing which foods I eat, my solution was to boycott all these manufacturers completely.
As of six weeks ago, and continuing indefinitely, I only eat intact solid biomass, and I only stock my kitchen with what I am allowed to eat. I do not eat any liquids, particles <5mm, or derivatives thereof. The emphasis is on a wide variety of fruits, vegetables, and meats. The intactness of the fibrous cellular matrix of the plant requires me to chew more and slows digestion. Slowing digestion has two benefits: staying full longer, and preventing insulin spikes which cause leptin-resistance in the brain which makes you hungrier. Leptin is the hormone that your fat cells make to tell the brain that you're fat so you should reduce appetite and increase energy expenditure. Leptin is a really good hormone to have if you're trying to lose weight. High insulin blocks the brain's ability to see leptin.
I can't stress enough how important the intactness of plants is. If the carbs are located inside intact cellulosic cell walls, which are clustered together as an entire intact tissue of cell walls, it takes longer for the enzymes and bacteria in your gut to penetrate those cell walls and liberate the carbs. This keeps insulin low, which makes the brain more sensitive to leptin. Intactness also forces you to slow down and chew more, which allows time for other lagging signals of satiety to reach ...
Game theory, sanctions, and Ukraine
by Dumbledore's Army — 2022-03-13T17:45:05.433Z

Epistemic status: I am doing a master's degree in Economics and know quite a lot about game theory but have no expertise in war. I can recommend Bret Devereaux's blog on nuclear deterrence 101 for a perspective from someone who does have more military knowledge than me.

Most discussion around the war in Ukraine, and the Western response, including Zvi's otherwise excellent post, misses what I think one of the most important goals the West should have.[1] (For the rest of this post, I will use 'we' and 'the West' loosely to mean the US/EU/UK/NATO and allies.) That goal is deterring the next war.

There is a concept in game theory called backward induction. You look at possible future decision nodes, and ask what is the expected value of being at that decision node. For example, imagine that China invades Taiwan tomorrow. What should the West do then? 1) Fight to defend Taiwan, with extremely high risk of escalation and causing World War Three? 2) Let millions of people fall to tyranny, demonstrate that the value of Western protection is worthless, encourage even more future invasions, and watch as every medium-sized country scrambles to build nukes to defend itself?[2] If we get to the point that someone has to make this decision, we have already lost. There are no positive-expected-value choices left.

You can make the same argument for a world where Russia invades a NATO member like the Baltic states. What do we do then? Start World War Three? Abandon huge parts of Europe to be invaded until Russian tanks are rolling through Germany?

We do not ever want to end up in these scenarios. Therefore the main goal of Western strategy should be to never get to that point. That means that our response to the Russian invasion of Ukraine must credibly signal to Russia, China and any other potential invaders that the cost of invading another country is too high. Remember that their leaders are making comparable calculations: what is the best strategy for China/Russia to take given the expected response of the West.[3] If China thinks that invading Taiwan will result in World War Three - or even that there is a 50:50 chance of World War Three - then it is not in their interests. If they think that invading Taiwan will result in a slap on the wrist then they will do it, and the West faces its terrible choice.

What does this mean for our response to the Russian ...
How to Lumenate (UK Edition)
by chanamessinger — 2022-03-13T13:43:42.226Z

When my partner and I decided to move in together, I promised I would make our home bright and lumenated and optimized in ways that matter. So early on I bought three lamps (and fairy lights for the bedroom) and bought the brightest possible lightbulbs in lumens I could get on Amazon and made a lot of mistakes because it turns out in the UK there are like 4 kinds of standard lightbulbs including like mini ones with mini little threads that made me feel like I had bought a toy and tried to install it into a real life electronic device.

And I asked him if it was good, and he said it was.

And then four months later, I thought, "but couldn't it be a lot brighter in here?" and he said "yes" and I realized I had not held to my promise at all, so I doubled the number of lamps in the living room, and I asked him if it was good, and he said it was.

And then two months after that, I thought "but couldn't it be a lot brighter in here?" and he said "yes" and I realized I had not held to my promise at all and I decided to Actually Try and apply as much Dakka as it took and follow the instructions here but they weren't that helpful because it's the wrong Amazon, so I found equivalents and made mistakes and did a bunch of arts and crafts and this is what I now have in my apartment. It's likely nowhere near ideal, and I would be thrilled to get to add better options.

Important Information:

Lightbulb thread types:

check the specs when you buy

E27/E26: which is what I consider "normal" and is what you'd expect if you bought a standard lightbulb in the US. E26/27 are apparently interchangeable?

Googled it: E26 vs E27, what's the difference? E26 stands for 26 mm and the E27 for 27 mm in diameter. These two standards are interchangeable, meaning a US E26 will fit in a European E27 base, and E27 will fit in a E26 base. The only difference is the voltage (for light bulbs).

B22: No, don't buy these for anything I suggest below, they have two stick out pieces on either side of the base and no thread, some lights in my apartment take these but nothing I suggest below.
E14: weirdly small cylinder as a base, with threading

General information:

Lumens are brightness, the more the better for this purpose. I aimed for 2000 lumens with each light and rarely found it
Lighting color temperature is measured in Kelvin, the higher the number the cooler and more like daylight. I aimed for as hig

...
New GPT3 Impressive Capabilities - InstructGPT3 [1/2]
by simeon_c — 2022-03-13T10:58:46.326Z

Summary

InstructGPT3 (hereafter IGPT3), a better version of GPT3 has recently been released by OpenAI. This post explores its new capabilities.
IGPT3 has new impressive capabilities and many potential uses. Among others, it can help users:

Brainstorm
Summarize the main claims made by a scientific field, an author or a school of thought.
Find an analogy or a metaphor for something hard to explain

I emphasize some of IGPT3's limits, especially situations where It provides very plausible fake answers.

A Twitter (more entertaining) version of the summary: https://twitter.com/Simeon_Cps/status/1503005935534366722?s=20&t=-LMu4jqg55_u2IQ2rTKj4g

Introduction

This post has been crossposted from the EA Forum.

Epistemic status: I spent about ~12h with IGPT3. So I’d say that I now have a pretty good sense of some of its key features. I tried several examples to ensure that I was not overfitting on a single example for the most important claims I made. That said, this is a huge model so there is probably a lot more to be discovered. FYI I had spent a decent amount of time playing with the past GPT3, especially with the Davinci (175b params) and the Curie (6b params) models, so I had a clear idea of "what it is like to try to get nice completions from GPT3". That may be one reason why I’m so amazed by this one.

Here's the first post of a series of 2 blog posts exploring some of the IGPT3 (I) new capabilities and (II) epistemic biases.

This first post will focus on some interesting uses I had of IGPT3. It also gives a sense of how good it is in various domains. Let me tell you: I'm amazed by its new capabilities. I find it really impressive that most of the time, the first result I get, without any tuning (either of the parameters or of the prompt) is great. You can try it yourself here.

The blogpost is organized as follow:

A few general observations
2 mains parts (Examples of Potential Uses / Limits)
The last part entitled “Many more prompts than you wanted to read” where I put robustness checks, I test how sensitive IGPT3 is to unique words variations, I compare IGPT3 and GPT3 and I show how you can have fun with IGPT3.

Acknowledgment: Thanks to JS and Florent Berther for the proofreading, to Ozzie Gooen for the suggestion to make a post out of my comments on his post and to Charbel-Raphaël Segerie for some nice ideas.

General Feat

...
Anecdotes Can Be Strong Evidence and Bayes Theorem Proves It
by FCCC — 2022-03-13T04:06:23.717Z

Anecdotes can be weak evidence

We all know that a lot of people overvalue their N=1 self-study: “I started eating only meat and my arthritis was cured!” Unfortunately, the weakness of this data is often attributed to the fact that it is an anecdote, and so, anecdotes are wrongly vilified as “the bottom of the evidential hierarchy.” In the case of a diet change, the remission of arthritis could be due to any number of reasons: time (perhaps it would have healed on its own, diet change or not), perhaps the person had a reduction in stress leading to lower inflammation, perhaps the person acquired a higher pain tolerance, perhaps the person even lied.

Anecdotes can be strong evidence

Anecdotes can actually be incredibly strong evidence, and Bayes Theorem tells us as much. An anecdote is strong evidence for a reasonable hypothesis when there is no alternative reasonable hypothesis. That said, sometimes, the apparent lack of alternative reasonable hypotheses is due to one’s lack of knowledge or imagination. I try to counter this, to some degree, by coming up with a mechanistic hypothesis.

A gross example (I apologise)

For example, I recently heard that armpit odour is caused by bacteria. Bacteria can be killed if exposed to an environment that strongly deviates from the one in which they thrive. So, I scrubbed my armpits with concentrated vinegar, which is acidic (I read that armpit bacteria are basic). I smelled like a salad, so took a shower, and my underarm odour–which I’d had for over a decade–was entirely gone.

Even at this point, I was extremely confident that, even if I got the mechanism wrong, this remedy worked. Why the confidence off of so little evidence? Well, what are the chances that the smell just happened to go away on its own, right at that exact moment? Basically zero. Are there any other reasonable causes for the smell to disappear at the exact moment I used the vinegar. Not that I can think of. Why would anything else have been the cause now, but at no point before? Is the data reliable? I think so, vinegar has never affected my ability to detect odours before.

A few days later, my body odour returned a little, so I did the same thing, and it was gone again.

If my mechanistic hypothesis was right, I should expect the bacteria to evolve towards getting used to these new shocks to their environment. To minimise this chance, I decided to switch back...
Compute Trends — Comparison to OpenAI’s AI and Compute
by lennart — 2022-03-12T18:09:55.039Z

This is a slightly modified version of Appendix E from our paper "Compute Trends Across Three Eras of Machine Learning". You can find the summary here and the complete paper here.

After sharing our updated compute trends analysis a common question was: "So how are your findings different from OpenAI’s previous compute analysis by Amodei and Hernandez?". We try to answer this question in this short post.

Comparison to OpenAI’s AI and Compute

OpenAI’s analysis shows a 3.4 month doubling from 2012 to 2018. Our analysis suggests a 5.7 month doubling time from 2012 to 2022 (Table 1, purple). In this post, we investigate this difference. We use the same methods for estimating compute for the final training run. Our methods are described in detail in Estimating training compute of Deep Learning models.

Our analysis differs in three points: (I) number of samples, (II) extended time period, and (III) the identification of a distinct large-scale trend. Of these, either the time period or the separation of the large-scale models is enough to explain the difference between our results.

To show this, we investigate the same period as in the OpenAI dataset. The period starts with AlexNet in September 2012 and ends with AlphaZero in December 2018.

As discussed, our work suggests that between 2015 and 2017 a new trend emerged — the Large-Scale Era. We discuss two scenarios: (1) assuming our distinction into two trends and (2) assuming there is a single trend (similar to OpenAI’s analysis).

Period

Data

Scale (FLOPs)

Slope

Doubling time

R²

AlexNet to AlphaZero

09-2012 to 12-2017

All models (n=31)

1e+16 / 1e+21

1.0 OOMs/year

[0.6 ; 1.0 ; 1.3]

3.7 months

[2.8 ; 3.7 ; 6.2]

0.48

Regular scale (n=24)

2e+16 / 1e+20

0.8 OOMs/year

[0.5 ; 0.8 ; 1.1]

4.5 months

[3.2 ; 4.3 ; 7.8]

0.48

AlphaGo Fan to AlphaZero

09-2015 to 12-2017

Large scale

(n=7)

2e+17 / 3e+23

1.2 OOMs/year

[1.0 ; 1.3 ; 1.8]

3.0 months

[2.1 ; 2.9 ; 3.5]

0.95

AlphaZero to present

12-2017 to 02-2022

All models

(n=62)

5e+19 / 1e+23

0.8 OOMs/year

[0.5 ; 0.8 ; 1.1]

4.5 months

[3.3 ; 4.4 ; 7.1]

0.36

Regular scale

(n=47)

2e+19 / 3e+22

0.9 OOMs/year

[0.6 ; 0.9 ; 1.2]

4.2 months

[3.1 ; 4.2 ; 6.0]

0.46

Large scale

(n=15)

1e+22 / 6e+23

0.4 OOMs/year

[0.3 ; 0.4 ; 0.7]

8.7 months

[5.4 ; 8.7 ; 14.6]

0.68

AlexNet to present

09-2012 to 02-202

...
Experience Restarting Contra
by jefftk — 2022-03-12T15:00:02.491Z

BIDA, the contra dance that I
help organize, has now had two dances since resuming. These have been
with masks, proof of vaccination, boosters if eligible, email
addresses for contact tracing, and as much ventilation as possible,
which isn't that much in our hall. There were a few surprises for us,
so I wanted to write up a bit about how it went.

Overall, it's been really good. People are happy to see each
other and happy to be dancing again.

Attendance has been much higher than before
the pandemic (~215 vs ~130), with lots of people who used to attend
occasionally. I expect this will fall off as people return to
thinking of dances as something they can go to whenever they want, and
when other area dances restart.

With checking proof of vaccination and getting people to put
down their emails for contact tracing, we expected to need additional
people at the door, but even with three people there was sometimes a
line. This is a lot slower than just collecting money.

No one has informed us that they were infectious at the dance,
or that they think they got sick at a dance. I'm not sure when it
would make sense to stop collecting emails for contact tracing; maybe
when we expect that attending a dance is only a small portion of
someone's weekly exposure and that most other sources aren't contact
tracing?

We are having people write their emails on paper, and if
someone did tell us they were infectious it would be a daunting amount
of work to email everyone: bad handwriting, lots of addresses. On the
other hand, we could split this among the board, so it isn't that much
per person.

We are including a place on the email sheet where you can mark
if you would like to be added to our mailing
list. I feel a bit conflicted about this, for the general reason
that you shouldn't mix security/health with promotion, but people
don't seem to mind.

We expected that callers would need to start with easier
dances, because after two years people might be a bit rusty, but it
was really the opposite: the balance of experience in the hall is
really shifted toward long-time dancers.

At our first dance about 2% of people hadn't noticed that we
would be requiring boosters for people who were eligible, and we did
have to turn a few people away. This was really hard. If you are
organizing a dance you should talk in advance with your co-organizers

...
Is there a good dataset for the moments of the income distribution throughout history?
by Ege Erdil — 2022-03-12T13:26:05.657Z

Obviously it's difficult to get a handle on the distribution of personal income even today, but I have some theoretical interest in this question for the following reason:

When people try to build models to predict how population size and gross domestic product correspond to military power, a popular index of "national strength" that's used is

population×GDP per capitaα=population×E[income]α

for some exponent α>1. The idea is that, all else equal, if two countries have the same GDP the one with the smaller population should be at an advantage in any conflict. This matches many historical episodes, such as the wars fought by European countries in Asia in the late 18th and 19th centuries.

However, there is a big flaw with this kind of measure: it is not monotone. As an example, suppose α=2 and we have two countries A and B. A has a GDP per capita of $100k and a population of 100 million, B has the same population but a GDP per capita of only $10k. Now, the union of A and B has GDP per capita equal to $55k and a total population of 200 million, and so for α=2 the union A∪B actually appears weaker than the country A alone.

There is a natural alternative which is monotone: we take expectations after exponentiating income by α rather than before. Since x→xα is a convex map, this gives us a larger answer. It's not only monotone but also extensional, because it's equal to

population×E[incomeα]=∑p(Ip)α

where the sum runs over all individuals in a country and Ip denotes the income of individual p. So this index has desirable theoretical properties and we can trust it more when trying to evaluate the strength of international alliances or coalitions.

However, computing it and fitting a model based on it to data requires knowledge of the higher moments of the income distribution across countries and across time. We have data that's at least adequate when it comes to population and mean income, but I haven't seen any data on the mean of income squared or cubed, say. I imagine someone has already looked into this problem, and I'd be happy enough just with data pertaining to the past 300 years or so.
Why Rome?
by Erich_Grunewald — 2022-03-12T11:29:19.957Z
Link: https://www.erichgrunewald.com/posts/why-rome/

Separate an individual from society, and give him an island or a continent to possess, and he cannot acquire personal property. He cannot be rich.

– Thomas Paine (1797)

Rome, once a mere collection of huts on a hilltop overlooking the Tiber, grew to become a vast and hugely influential empire. Why? That is, why Rome? Why not Alba Longa, by some accounts the oldest Latin city, the mother of Rome the daughter, and an important religious centre as well as the original head of the Latin League? Or Praeneste which we now know as Palestrina, or Tibur which we know as Tivoli, or Labici, or Gabii, Nomentum, Laurentum, Lavinium? Those other cities were situated in the same area, had similar soil and climate, spoke versions of the same Latin language and shared with Rome a common Latin culture (and were all, compared to the Greek city-states, fairly open to and connected with one another) (Boatwright, Gargola, and Talbert 2006).

Or why not one of the prosperous Etruscan cities in the north? Or one of the Greek colonies in the south? Or some or another city among one of the other Italic peoples, like the Umbrians or the Samnites?

Roman culture spread primarily through conquest. That made it different from Greek culture, which, while it certainly spread through the feats of Alexander the Great, also spread via trade relationships and the establishment of colonies around the Mediterranean. So if I want to know what caused Rome to dominate such a large area, I want to know what caused the Romans to conquer. But a million things caused the Romans to conquer. That is, there are a million things that needed to be true for the Romans to conquer. What I want is something rare – a necessary condition, but one also somewhat unique to the Romans.

Summary

Rome rose from obscurity to become a vast, prosperous and durable empire. It first outgrew the other Latin cities mainly due to its location as a nexus and its proximity to the wealthy Etruscans (75% confidence). If this is not the case, Rome does not get hegemony over Latium (70% confidence).

Roman culture then spread primarily through conquest (95% confidence). A key reason that this worked was the Romans' unusually expansive and inclusive notion of citizenship, which increased its labour force, suppressed rebellious tendencies and gave conquered peoples a stake in the Roman venture (70% confidence). If this is not the case, the ...