trentmkelly/LessWrong-43k huggingface dataset:
Help Ukraine
by ubik — 2022-03-07T20:40:50.115Z

Hi, my name is Dmytro. I'm from Ukraine, Kyiv. You've probably heard about what's going on here now. It's not "crisis" or "conflict" it's a WAR.
People are dying, thousands, cities are bombed, millions of refugees, millions left their homes. The government is trying to control the situation and it's doing well. But we have tons of needs in many places: the front is a thousand kilometers long, soldiers need a lot of stuff. We are equipping new soldiers, in many places, simultaneously. We lack equipment. And of course humanitarian crisis: millions of people have left war zones and moved to relatively safe cities. They need places to live, food, etc. The government is trying its best, but it's slow, lacks effective management + situation is extremely difficult.
We have networks of volunteers. Many of them emerged during the war in 2014. They are trying to help. But they need money NOW. Every day, every hour is important.
Here is a list of verified organizations you can donate to:
https://supportukrainenow.org/donate
I'm asking for your help. Do it now. We need money now. In a month Ukraine will not be as hot a topic as now, people in the West will get used to this war, unfortunately. So I'm asking you for help NOW.
A couple of additional things:
1. Helping Ukraine is morally right. But it's also rational for you - the world without Putin will be a safer place.
2. Prefer helping the military. It's the most important now. If we win or achieve some sort of peace - fewer people will die, fewer people lose their homes. And our children will have a future.
3. Prefer smaller organizations. Big ones are usually slower. I've donated a big part of my money to "Democratic Axe". They are pretty efficient.

4. Share this post with your friends, Facebook, Twitter, etc please.
5. If you have contacts of some bloggers, podcasters, etc - text me. I will try to use every channel
If you have some questions I will gladly answer.
Thank you!
The First-Person Perspective Is Not A Random Sample
by dadadarren — 2022-03-07T16:31:59.088Z

This is another attempt to promote my solution to anthropic paradoxes (perspective-based reasoning, PBR).

Quick Recap

In a previous post, I suggested the problem in anthropics is treating it as an observation selection effect (OSE). I.E. considering the first-person perspective as a random sample. Both major schools, SSA and SIA, follow this line of reasoning, only disagreeing on the correct sampling process. In contrast, I purpose the first-person perspective should be considered a primitive axiomatic fact. This is plausible prima facie, "I naturally know I am this person, and there seems to be no underlying reason of explanation to it. I just am." Recognizing it solves anthropic paradoxes and more.

This leads to double-halving in the sleeping beauty problem. (Probability of head is 1/2 when waking up, and remains at 1/2 after learning it is the first awakening). It does not cause paradoxes such as the Doomsday Argument or the Presumptuous Philosopher. It leads to complete agreement of Bayesian and frequentist interpretations in anthropics. And gives justification for perspective disagreement required for Halfers. For the complete argument, check out my website.

The Fission Problem

I think the best way to show the difference between my solution and the traditional camps is to use an example.

Imagine during tonight's sleep, an advanced alien would split you into 2 halves right through the middle. He will then complete each part by accurately cloning the missing half onto it. By the end, there will be two copies of you with memories preserved, indiscernible to human cognition. After waking up from this experiment, and not knowing which physical copy you are, how should you reason about the probability that "my left side is the same old part from yesterday?"

(For easier expression let L be the copy with the same left half as yesterday, and R be the copy with the same right half yesterday. So the question can also be stated as "How to reason about the probability that I am L.")

Quite an exotic thought experiment, I know. Some may think it has an uncontroversial answer of 1/2. But I find it highlights the key feature of anthropic problems rather well.

A Question Based on Perspective

The interesting thing about the Fission Problem is how it is asked. The experiment produces two near-identical copies, and the probability in question is about a particular one. But ...
An Intuitive Introduction to Functional Decision Theory
by Heighn — 2022-03-07T16:07:54.298Z

In the last two posts, we looked at both Causal (CDT) and Evidential Decision Theory (EDT). While both theories have their strength, CDT fails on Newcomb's Problem and EDT performs poorly on Smoking Lesion. Both problems do have a correct answer if you want to get as much utility as possible: one-boxing results in earning $1,000,000, and smoking results in an extra $1,000.

Functional Decision Theory (FDT) is a decision theory that manages to answer both problems correctly. Where CDT looks at the causal effects of the available actions, FDT considers the effects of its decision. It asks:

Which output of this decision procedure causes the best outcome?

Where the decision procedure is simply the reasoning or "thinking" done to determine what action to do. This might seem like only a superficial difference with CDT, but that really isn't the case. The crucial point is that the same decision procedure can be implemented by multiple physical systems. It's best to explain this using an example.

Psychological Twin Prisoner's Dilemma

The Psychological Twin Prisoner's Dilemma (PTPD) runs as follows:

An agent and her twin must both choose to either “cooperate” or “defect.” If both cooperate, they each receive $1,000,000. If both defect, they each receive $1,000. If one cooperates and the other defects, the defector gets $1,001,000 and the cooperator gets nothing. The agent and the twin know that they reason the same way, using the same considerations to come to their conclusions. However, their decisions are causally independent, made in separate rooms without communication. Should the agent cooperate with her twin?

Looking at this in the most selfish way possible, the best outcome for the agent is the one where she defects and her twin cooperates, in which case she gets $1,001,000. However, note that this situation is impossible: the agent and her twin reason the same way. If the agent wants the $1,001,000 outcome, so does her twin; in that case, they both defect and both get $1,000, while nobody gets the $1,001,000.

The reader might have noticed there are only two outcomes possible here: either both the agent and her twin cooperate, or they both defect. The agent prefers the first outcome: that's the one where she gets $1,000,000, whereas the defect-defect scenario only gets her $1,000. CDT'ers, however, defect: since the agent's decision doesn't causally effect that of the ...
An Intuitive Introduction to Evidential Decision Theory
by Heighn — 2022-03-07T16:06:26.355Z

In the last post, we discussed Causal Decision Theory (CDT). We learned how purely looking at the causal effects of your actions makes you lose at Newcomb's Problem. Evidential Decision Theory (EDT) is an alternative decision theory that tells us to take the action that, conditional upon it happening, gives the best outcome. This means EDT looks at the evidence each action provides for the different outcomes. It's best to explain what this means exactly by looking at a few known problems. But first, what is "evidence", anyway?

What is evidence, anyway?

"Evidence" may sound like a complicated term, but really, it's extremely simple. Imagine we have a big box with 1000 items in it. 600 of these items are balls; 400 are cubes. Of the balls, 200 are red, whereas 400 are green. Furthermore, 300 cubes are red, and 100 are green. I draw a random item from our big box. What is the probability that the item is a ball?

Well, there are 1000 items, of which 600 are balls. That gives us a probability of 600 / 1000 = 0.6 that a random item is a ball. We write P(B) = 0.6, where the B stands for an item being a ball.

Now, suppose I putt the ball back and again draw a random item from our box. I tell you that it is red. What is the probability that it is a ball?

In total, there are 500 red items (200 red balls + 300 red cubes). Of those red items, 200 are balls. 200 / 500 = 0.4; so that's a 0.4 probability that a red item is a ball. We write P(B | R) = 0.4: the probability that an item is a ball given that or conditional on the fact that it is red equals 0.4. P(C | R) - the probability of an item being a cube given that it is red - equals 300 red cubes / 500 red items = 0.6. Note that P(B | R) + P(C | R) = 0.4 + 0.6 = 1 (or 100%). P(B | R) and P(C | R) adding up to 1 makes sense - a red item must either be a ball or a cube.

P(B | R) = 0.4 tells us the evidence redness gives us about the item being a ball. That evidence is quite weak: the evidence redness gives for the item being a cube is bigger (0.6). Therefore, we would usually simply say that redness is evidence for the item being a cube. This is simply because there are more red cubes than red balls.

We can easily turn things around. P(R | B) is the probability that an item is red given that it is a ball. There are 600 balls, 200 of which are red. Then P(R | B) = 200 / 600 = 13. P(G) = 500 / 1000 = 0.5 - the probability a ...
An Intuitive Introduction to Causal Decision Theory
by Heighn — 2022-03-07T16:05:44.644Z

Like any decision theory, Causal Decision Theory (CDT) aims to maximize expected utility; it does this by looking at the causal effects each available action in a problem has. For example, in Problem 1, taking box A has the causal effect of earning $100, whereas taking box B causes you to earn $500. $500 is more than $100, so CDT says to take box B (like any decision theory worth anything should). Similarly, CDT advices to take box A in Problem 2.

CDT's rule of looking at an action's causal effects make sense: if you're deciding which action to take, you want to know how your actions change the environment. And as we will see later, CDT correctly solves the problem of the Smoking Lesion. But first, we have to ask ourselves: what is causality?

What is causality?

A formal description of causality is beyond the purpose of this post (and sequence), but intuitively speaking, causality is about stuff that makes stuff happen. If I throw a glass vase on concrete, it will break; my action of throwing the vase caused it to break.

You may have heard that correlation doesn't necessarily imply causality, which is true. For example, I'd bet hand size and foot size in humans strongly correlate: if we'd measure the hands and feet of a million people, those with larger hands will - on average - have larger feet as well, and vice versa. But hopefully we can agree hand size doesn't have a causal effect on foot size, or vice versa: your hands aren't large or small because your feet are large or small, even though we might be able to quite accurately predict your foot size using your hand size. Rather, hand size and foot size have common causes like genetics (determining how large a person can grow) and quality and quantity of food taken, etc.

Eliezer Yudkowsky describes causality in a the following very neat way:

There's causality anywhere there's a noun, a verb, and a subject.

"I broke the vase" and "John kicks the ball" are both examples of this.

With the hope the reader now has an intuitive notion of causality, we can move on to see how CDT handles Smoking Lesion.

Smoking Lesion

An agent is debating whether or not to smoke. She knows that smoking is correlated with an invariably fatal variety of lung cancer, but the correlation is (in this imaginary world) entirely due to a common cause: an arterial lesion that causes those afflicted with it to love smoking and also (99% of the

...
Basic Concepts in Decision Theory
by Heighn — 2022-03-07T16:05:10.570Z

Imagine I show you two boxes, A and B. Both boxes are open: you can directly observe their contents. Box A contains $100, while box B contains $500. You can choose to receive either box A or box B, but not both. Assume you just want as much money as possible; you don't care about me losing money or anything (we will assume this pure self-interest in all problems in this sequence). Let's call this Problem 1. Which box do you choose?

I hope it's obvious picking box B is the better choice, because it contains more money than box A. In more formal terms, choosing B gives you more utility. Just like temperature is a measure for how hot something is, utility measures how much an outcome (like getting box B) satisfies one's preferences. While temperature has units like Celsius, Fahrenheit and Kelvin, the "official" unit of utility is the util or utilon - but often utility is expressed in dollars. This is how it's measured in Problem 1, for example: the utility of getting box A is $100, while getting B gives $500 utility. Utility will always be measured in dollars in this sequence.

Now imagine I again show you two boxes A and B - but now, they are closed: you can't see what's inside them. However, I tell you the following: "I flipped a fair coin. If it was heads, I filled box A with $100; otherwise, it contains $200. For box B, I flipped another fair coin; on heads, box B contains $50; on tails, I filled it with $150." Assume I am honest about all this. Let's call this one Problem 2. Which box do you pick?

Problem 2 is a bit harder to figure out than Problem 1, but we can still calculate the correct answer. A fair coin, by definition, has 0.5 (50%) probability of coming up heads and 0.5 probability of coming up tails. So if you pick box A, you can expect to get $100 with probability 0.5 (if the coin comes up heads), and $200 also with probability 0.5 (if the coin comes up tails). Integrating both into a single number gives an expected utility of 0.5 x $100 + 0.5 x $200 = $50 + $100 = $150 for picking box A. For picking box B, the expected utility equals 0.5 x $50 + 0.5 x $150 = $25 + $75 = $100. The expected utility associated with choosing box A is higher than that of choosing B; therefore, you should pick A in Problem 2. In other words, picking A is the rational action: it maximizes expected utility.

Note that we could have calculated the expe...
How to Best Use Twitter
by Zvi — 2022-03-07T15:40:01.086Z

[Note: While I do intend to write more about Russia’s invasion of Ukraine, this post is intended to address this only indirectly rather than directly, by helping illustrate how to find other sources of information, and you are once again implored to rely on other news sources. I need to get inside the war’s OODA loop to write about it usefully, which requires more dedicated time than I’ve had this past week.]

It is high time for me to talk about the only practical way I know about to follow developments in the world in real time, whether they be a war, a pandemic or something else entirely, which is Twitter and in particular Twitter Lists.

I do not know of any practical alternative. One can of course watch or read the usual news reports, which are mostly effectively State Media of various quality, for various different States. When you’re reading about an actual war, the State part of State Media becomes more prominent and harder to miss.

The best TV sources for international events like the war that I have are Bloomberg (as far as I can tell, the closest thing to non-state media) for the economic side of things and the BBC World News for the more general side. Occasionally I’ll take a glance at CNN or Fox News or various other networks to get a sense of what they are focusing on. I have not attempted to directly watch any Russian broadcasts but am curious what is the best option for that.

For domestic American events, there are no non-obvious TV sources I have found, and TV is essentially useless other than to know what the Narratives are saying or to cover discrete events like debates, elections or the State of the Union. Any kind of #Analysis is strictly fuhgeddaboudit.

For written media, the usual suspects are what they are so choose your favorites. None of them seem able to keep up with the pace of play other than Bloomberg offering insight into markets, so they are mostly again useful for ‘how are things being presented and sold’ than insight into actual events.

For what is happening, Twitter is where it is at.

To use Twitter properly, there are four vital pieces of technology.

Tweetdeck or another similar alternative application.
Knowing who to follow and read.
Lists.
Unfollows, filters, mutes and blocks.

I’ll also give advice for how to start out, and some thoughts on regulating use.

Tweetdeck

Twitter by default will present posts using an ...
Projecting compute trends in Machine Learning
by Tamay — 2022-03-07T15:32:13.278Z

Summary

Using our dataset of milestone Machine Learning models, and our recent analysis of compute trends in ML, we project forward 70 years worth of trends in the amount of compute used to train Machine Learning models. Our simulations account for (a) uncertainty in estimates of the growth rates in compute usage during the Deep Learning (DL)-era and Pre-DL era, and (b) uncertainty over the ‘reversion date’, i.e. the date when the current DL-era compute trend (with a ~6 month doubling time) will end and revert to the historically more common trend associated with Moore’s law. Assuming a reversion date of between 8 to 18 years, and without accounting for algorithmic progress, our projections suggest that the median of Cotra 2020’s biological anchors may be surpassed around August 2046 [95% CI: Jun 2039, Jul 2060]. This suggests that historical rates of compute scaling, if sustained briefly (relative to how long these trends have been around so far), could result in the emergence of transformative models.

Our work can be replicated using this Colab notebook.

Note: we present projections, not predictions. Our post answers the question of: “What would historical trends over the past 70 years when naively extrapolated forward imply about the future of ML compute?” It does not answer the question: “What should our all-things-considered best guess be about how much compute we should expect will be used in future ML experiments?”

Introduction

Recently, we put together a dataset of over a hundred milestone Machine Learning models, spanning from 1952 to today, annotated with the compute required to train them. Using this data, we produce simple projections of the amount of compute that might be used to train future ML systems.

The question of how much compute we might have available to train ML systems has received some attention in the past, most notably in Cotra’s Biological Anchors report. Cotra’s report investigates TAI timelines by analyzing: (i) the training compute required for the final training run of a transformative model (using biological anchors), and (ii) the amount of effective compute available at year Y. This article replaces (ii) the compute estimate by projecting 70 years worth of trends in the amount of compute used to train Machine Learning models.

Cotra's amount of effective compute available at year Y is broken down into forecasts of (a) compute cost, ...
Powerpoint joy - How I got 1.7 million views on Unsplash by nourishing the souls of office workers
by tomdekan — 2022-03-07T15:29:33.404Z

(Cross-posted from my blog. Article and free audio download at: https://www.tomdekan.com/powerpoint-joy)

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A while ago, I was sitting at home wondering, in what area I could have the greatest impact during an afternoon?

I ate a chickpea wrap and a Frosta meal. Few thoughts came. Then, with a sense of tremendous majesty, the idea crashed down on me like a giant wave.

The answer: Corporate powerpoints.

I would help those suffering from low-grade powerpoints. I would salve the needs of those who couldn't find high-quality photos for their presentations. And, in a way that Michelangelo failed to do, my images would grace team meetings and lunchtime commercial awareness talks.

People have now viewed my photos on Unsplash over 1.7 million times. This is quite crazy.

Here are my thoughts about why choosing a niche increases the quality that you can offer, and how you too might enrich the lives of the poor people working in large, preferably multi-national, corporations.

Getting 1.7 million views

Like I said, my paintbrush was the niche of corporate presentations. My aim was to bring joy to the office-bound by getting my photos in as many company powerpoints as possible.

People visit Unsplash to get photos for their projects. They want focused, high-quality, licence-free images. I would nourish their souls.

To begin the nourishment, I searched through my photos for high-grade images that would brighten someone's powerpoint presentation. I imagined that the person was presenting to sleep-deprived workers, before lunch, on a sunless winter day, in a big city, in a grey room with plastic plants. Perhaps the people in the room were even accountants.

So, the images needed to be simple, uncluttered, and with one main subject. They needed to grab the viewers' attention before they went for their fourth coffee of the day, or visited the toilet yet again to secretly check their phone.

This worked. The images have received over 1.7 million views, with hundreds of thousands of uses.

Besides many office presentations, I am delighted to see my photos being used by massage parlours and wellness centres. This is great continuity.

Have a coherent channel

This worked because I focused on a narrow niche. In particular, publishing multiple photos in the corporate powerpoint niche was effective. While a single image might become popular, having a portfolio in a niche makes them all more useful.

Publi...