trentmkelly/LessWrong-43k huggingface dataset:
FHI paper published in Science: interventions against COVID-19
by SoerenMind — 2020-12-16T21:19:00.441Z

This is a linkpost for https://science.sciencemag.org/lookup/doi/10.1126/science.abd9338

Summary: this is basically a Bayesian regression of the COVID-19 cases and deaths in 41 countries against the measures taken by those countries. It requires a rough model of how infections spread which means you have to be careful about choosing models, validate them by predicting the epidemic, and try to deal with confounding. The results support closing schools and universities, gatherings, and some types of face-to-face businesses.

It's been great to work on this with a large team of talented researchers from many universities, most of whom are rationalists and EAs. The lead authors and the senior author are employees and affiliates of FHI:

Jan M. Brauner*, Sören Mindermann*, Mrinank Sharma*, David Johnston, John Salvatier, Tomáš Gavenčiak, Anna B. Stephenson, Gavin Leech, George Altman, Vladimir Mikulik, Alexander John Norman, Joshua Teperowski Monrad, Tamay Besiroglu, Hong Ge, Meghan A. Hartwick, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal, Jan Kulveit

We're also grateful to Tim Telleen-Lawton and BERI who funded and operationally supported the Epidemic Forecasting project which in turn incubated this project.

Paper: Inferring the Effectiveness of Government Interventions Against COVID-19

There's also a closely related NeurIPS paper.

The story of this paper is easy to tell with its figures so I'll briefly to that.

Data:

Main results:

Fig. 2 NPI effectiveness under default model settings.Posterior percentage reductions in Rt with median, 50% and 95% prediction intervals shown. Prediction intervals reflect many sources of uncertainty, including NPI effectiveness varying by country and uncertainty in epidemiological parameters. A negative 1% reduction refers to a 1% increase in Rt. “Schools and universities closed” shows the joint effect of closing both schools and universities in conjunction; the individual effect of closing just one will be smaller (see text). Cumulative effects are shown for hierarchical NPIs (gathering bans and business closures) i.e., the result for “Most nonessential businesses closed” shows the cumulative effect of two NPIs with separate parameters and symbols—closing some (high-risk) businesses, and additionally closing most remaining (non-high-risk, but nonessential) businesses given that some businesses are

...
[AN #129]: Explaining double descent by measuring bias and variance
by Rohin Shah — 2020-12-16T18:10:04.840Z

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

Rethinking Bias-Variance Trade-off for Generalization of Neural Networks (Zitong Yang et al) (summarized by Nicholas): A fundamental result in ML theory shows that the squared error loss function can be decomposed into two components: bias and variance. Suppose that we train a model f to predict some ground truth function y. The bias measures how incorrect the model will be in expectation over the training process, while the variance measures how different the model’s output can be over different runs of the training process. More concretely, imagine that we run a training process N times, each with a different training set drawn iid from the same underlying training distribution, to get N different models. Bias is like taking the average of these N models, and asking how far away it is from the truth. Meanwhile, variance is like the average distance from each of the N models to the average of all of the N models.

Classical ML predicts that larger models have lower bias but higher variance. This paper shows that instead, the variance of deep NNs first increases but then decreases at larger model sizes. If the bias tends to be much larger than variance, then we see monotonically decreasing total error. If the variance tends to be much larger than the bias, then loss will also look bell-shaped, initially increasing as models get bigger and then decreasing. Finally, if the bias starts high, but over time is overshadowed by the variance, we get double descent (AN #77) curves; this explains why previous work needed to add label noise to get double descent curves (as higher label noise should lead to higher variance).

In order to estimate the variance, the authors split their data into two subsets and use these to create an unbiased estimator of the variance (effectively following a similar procedure to the one described in the first paragraph). The bias estimate can then be determined from the test loss and the est...
Beta test GPT-3 based research assistant
by jungofthewon — 2020-12-16T13:42:50.432Z

Ought is working on building Elicit, a tool to

automate and scale open-ended reasoning about the future

. To date, we’ve collaborated with LessWrong to

embed interactive binary predictions

,

share AGI timelines

and the

assumptions driving them

,

forecast existential risk

, and much more.

We’re working on adding GPT-3 based research assistant features to help forecasters with the earlier steps in their workflow. Users create and apply GPT-3 actions by providing a few training examples. Elicit then scales that action to thousands of publications, datasets, or use cases.

Here’s a demo of how someone applies existing actions:

And a demo of how someone creates their own action (no coding required):

Some actions we currently support include:

Find relevant publications from think tanks
Find relevant datasets
Find forecasting questions from Metaculus, PredictIt, Foretell
Decompose a vague query into more concrete subquestions or factors

There’s no better community than LessWrong to codify and share good reasoning steps so we’re looking for people to contribute to our action repository, creating actions like:

Suggest reasons for / against
Suggest a potential analysis someone can do in natural language (a different take on converting a natural language question into a SQL query)
Find key players in an industry
Suggest hypothesis
Apply Occam’s razor / come up with the simplest explanation

If you’re interested in becoming a beta tester and contributing to Elicit, please fill out

this form

! Again, no technical experience required.
Machine learning could be fundamentally unexplainable
by George3d6 — 2020-12-16T13:32:36.105Z
Link: https://cerebralab.com/Machine_learning_could_be_fundamentally_unexplainable

I’m going to consider a fairly unpopular idea: most efforts towards “explainable AI” are essentially pointless. Useful as an academic pursuit and topic for philosophical debate, but not much else.

Consider this article a generator of interesting intuitions and viewpoints, rather than an authoritative take-down of explainability techniques.

That disclaimer aside:

What if almost every problem for which it is desirable to use machine learning is unexplainable?

At least unexplainable in an efficient-enough way to be worth explaining. Whether it is an algorithm or a human that is doing the explanation.

Let’s define “explainable AI” in a semi-narrow sense, inspired by the DARPA definition, as an inference system that can answer the questions:

Why was that prediction made as a function of our inputs and their interactions?
Under what conditions would the outcome differ?
How confident can we be in this prediction and why is the confidence such?

Why might we be unable to answer the above questions in a satisfactory manner for most machine learning algorithms? I think I can name four chief reasons:

Some problems are just too complex to explain. Often enough, these are perfect problems for machine learning, it’s exactly their intractability to our brains that makes them ideal for equation-generating algorithms to solve.
Some problems, while not that complex, are really boring and no human wants or should be forced to understand them.
Some problems can be understood, but understanding in itself is different for every single one of us, and people’s culture and background often influence what “understanding” means. So explainable for one person is not explainable for another.
Even given an explanation that everyone agrees on, this usually puts us no closer to most of what we want to achieve with said explanation, things like gathering better data or removing “biases” from our models.

I - Unexplainable due to complexity

Let’s say, physicists, take in 100 PetaBytes of experimental data, reduce them using equations, and claim with a high probability that there exists this thing called a “Higgs Boson” with implications for how gravity works, among other things.

The resulting Boson can probably be defined within a few pages of text via things such as mass, the things it decays into, its charge, its spin, the various interactions it can have with other particles, and so on.

But if a luddite lik...
Incentivizing forecasting via social media
by David Althaus — 2020-12-16T12:15:01.446Z
Link: https://forum.effectivealtruism.org/posts/842uRXWoS76wxYG9C/incentivizing-forecasting-via-social-media

Daniel Kokotajlo and I published a post on Incentivizing forecasting via social media on the EA Forum, perhaps of interest to some readers here.

Summary

Most people will probably never participate on existing forecasting platforms which limits their effects on mainstream institutions and public discourse.
Changes to the user interface and recommendation algorithms of social media platforms might incentivize forecasting and lead to its more widespread adoption. Broadly, we envision i) automatically suggesting questions of likely interest to the user—e.g., questions related to the user’s current post or trending topics—and ii) rewarding users with higher than average forecasting accuracy with increased visibility.
In a best case scenario, such forecasting-incentivizing features might have various positive consequences such as increasing society’s shared sense of reality and the quality of public discourse, while reducing polarization and the spread of misinformation.
Facebook’s Forecast could be seen as one notable step towards such a scenario and might offer lessons on how to best proceed in this area.
However, various problems and risks would need to be overcome—e.g., lack of trust or interest, cost, risks of politicization, and potential for abuse.
While recommendation algorithms seem to present a particularly high-leverage point, there are other ways of promoting truth-seeking.
Similar ideas might be applied to fact-checking efforts.
Why I love stand up comedy
by Adam Zerner — 2020-12-16T09:34:22.198Z

Good stand up comedy is one of my favorite things in the world, and I think that this joke really demonstrates why:

We all act like Louis and his New Yorker friends. When we see a bum on the street, we don't think twice. And if we're with someone new to town who stopped to help them, we'd probably do what Louis did and "correct" their behavior.

No, no. He needs you desperately. We just... don't do that here.

There's something very wrong with that. If you and I had a conversation about this and you explained to me why exactly it is unethical, maybe I'd follow your logic and agree. But I wouldn't feel it. Stand up comedy makes you feel it.

Of course, it's not the only art form that makes you feel it. Books, movies, music, paintings — they can all have the same effect. I guess. But for me, stand up comedy has always stood out as a tool that does a better job at this.

Well, books and movies sometimes do a great job too. But stand up comedy is concice, and there's something that I find elegant about that. Jokes have such little room for error. If you change just a word or two, the joke is often times sensitive to that.

Aside from making insightful points and allowing you to feel it, there are other things I absolutely love about stand up comedy. One is just the expert usage of words. No, of language. Verbal and nonverbal.

"That smelly hole of a place."

"And we pass this homeless guy and she sees him. I mean we all passed him, but she saw him." Really cool how he emphasized seeing the homeless guy.

"He was one of those high octane homeless..."

"She takes a knee. I mean I'm not even taking a knee now that's how little I give a shit."

Maybe I have a particular appreciation of this because... one of my dark secrets is that I try to write my own stand up comedy sometimes and would feel highly incomplete if I never did a successful bit at some point in my life. Anyway, I realize how hard it is to hit the head of the nail with the wording and make the joke actually come to life. So when I see others hit it, it makes me want to fist pump. "Yes! That's it, that was a perfect way to word that!"

Another thing I love about stand up comedy is just how much balls it takes to actually do it. Getting up on stage and bombing sucks. You're up there, vulnerable. Often times you're also afraid of public speaking. You spent all this time writing your jokes, trying to use the right wor...
What is it good for? But actually?
by KatjaGrace — 2020-12-16T09:20:08.255Z

I didn’t learn about history very well prior to my thirties somehow, but lately I’ve been variously trying to rectify this. Lately I’ve been reading Howard Zinn’s People’s History of the United States, listening to Steven Pinker’s the Better Angels of Our Nature, watching Ken Burns and Lynn Novick’s documentary about the Vietnam War and watching Oversimplified history videos on YouTube (which I find too lighthearted for the subject matter, but if you want to squeeze extra history learning in your leisure and dessert time, compromises can be worth it.)

There is a basic feature of all this that I’m perpetually confused about: how has there been so much energy for going to war?

It’s hard to explain my confusion, because in each particular case, there might be plenty of plausible motives given–someone wants ‘power’, or to ‘reunite their country’, or there is some customary enemy, or that enemy might attack them otherwise–but overall, it seems like the kind of thing people should be extremely averse to, such that even if there were plausibly good justifications, they wouldn’t just win out constantly, other justifications for not doing the thing would usually be found. Like, there are great reasons for writing epic treatises on abstract topics, but somehow, most people find that they don’t get around to it. I expect going to some huge effort to travel overseas and die in the mud to be more like that, intuitively.

To be clear, I’m not confused here about people fighting in defense of things they care a lot about—joining the army when their country is under attack, or joining the Allies in WWII. And I’m not confused by people who are forced to fight, by conscription or desperate need of money. It’s just that across these various sources on history, I haven’t seen much comprehensible-to-me explanation of what’s going on in the minds of the people who volunteer to go to war (or take part in smaller dangerous violence) when the stakes aren’t already at the life or death level for them.

I am also not criticizing the people whose motives I am confused by–I’m confident that I’m missing things.

It’s like if I woke up tomorrow to find that half the country was volunteering to cut off their little finger for charity, I’d be pretty surprised. And if upon inquiring, each person had something to say—about how it was a good charity, or how suffering is brave and valiant, or how their...
How to Compliment
by less_penguiny — 2020-12-16T08:25:58.186Z

As part of a social skills sequence I'm writing, I did a deep dive on how compliments, covering areas like
— what topics are most often complimented
— in what situations are they most often exchanged
— how are they amplified or diminished
— what are the purposes behind complimenting behavior

The results — all 40 pages of them — are on my website:
How to Compliment
Bad reductionism
by Adam Zerner — 2020-12-16T08:21:33.944Z

I've been becoming more of a nature person recently.

I moved into an apartment about a mile from the mountains and there's something really cool to me about being on the outskirts of town, crossing the line from "society" into "nature".

A few weeks ago I went on a trip to the Grand Canyon. We also saw the Hoover Damn. I was surprised at how awe inspiring I found each of them to be.

I went on a bike ride along the River Mountains Loop Trail. It was awesome. I only did 1/4 of it but I find myself watching YouTube videos to see what the rest of the trail looks like. I really want to get to Lake Mead!

However, all of this has been making me feel some sort of dissonance. I think of myself as not a nature person. There's a particular story that comes to mind.

Growing up, my family would go to the beach once in a while during the summer. I thought it was alright. I liked to dig big holes, go boogey boarding, and maybe play catch if I had someone to play with. But after a few hours of that I'd be ready to go home. My parents on the other hand liked to get there early and stay the entire day. Just sitting there in their chairs doing nothing.

I couldn't understand this. How can you not get bored sitting there all day? I spent the day running around doing various things and even I got bored of that and was ready to go home!

So I would ask my mom: why do you even like going to the beach in the first place? The conversation would go something like this:

Adam: Why do you like going to the beach in the first place?

Mom: I don't know, I just do.

Adam: That's not an answer.

Mom: I find it relaxing.

Adam: Why?

Mom: I like to listen to the waves.

Adam: You could do that at home. You could sit in a chair in the backyard and listen to the sounds of the ocean on your MP3 player.

Mom: I like to see the waves too.

Adam: You could get a magazine and look at cool ocean photos.

Mom: I like the feel of the sand between my toes.

Adam: That ones a little harder but we could go to Home Depot or something and get a little sandbox. If we did that would you still want to go to the beach?

Mom: Yes.

Adam: Why?! What is it about the beach?

Mom: I don't know, I just like it.

Adam: Well you haven't been able to actually tell me anything you like about the beach that you can't do at home, so you have no business liking the beach so much more than doing this stuff at home.

I made a big mistake here.

I was trying...
Debugging the student
by Adam Zerner — 2020-12-16T07:07:09.470Z

I'd like to explain to you what differential equations are. Imagine that we have the following:

f''(x) + 2 * f'(x) = 3 * f(x)

It's saying that the second derivative of f(x) plus two times the first derivative of f(x) is equal to three times f(x).

Following me so far? No? Why not?

In order to understand differential equations, you have to understand what a second derivative is.

In order to understand what a second derivative is, you have to understand what a first derivative is.

In order to understand what a first derivative is, you have to understand what limits are.

In order to understand what limits are, you have to understand what functions are.

In order to understand what functions are, you have to understand algebra.

In order to understand algebra, you have to understand arithmetic.

In other words, there are dependencies. It's similar to how classes in college have prerequisites. You have to take Biology 101 before you take Biology 102.

Except "biology" isn't a single concept. Here I'm saying that at a more granular single concept level, there are dependencies just like there are with college classes.

It looks something like this:

Cool, let's jump back to the differential equations example. In that example, I just started to explain differential equations to you without first checking to see if you know what a second derivative is. Or what a first derivative is. Or what limits are. Or what functions are. Or how algebra works. Or how arithmetic works.

Was that reasonable of me? Well, it depends on the context. If we're in a college differential equations class, I know that the students have all already taken calculus and have the prerequisite knowledge for me to start explaining differential equations this way.

But what if I'm talking to a person off the street? Is it reasonable for me to assume that they know what a second derivative is? No. A first derivative? No. Most people have never taken calculus.

What about limits? That's part of a normal high school curriculum, but most people have forgotton it by now.

What about functions? Maybe that's more basic and would be something you can expect someone to remember? Well, I was recently explaining what a function in programming is to someone new to programming and I tried to use functions in math (eg. from high school) as an analogy, but he was rusty on how those work. So I'd say it'd probably be a good idea to...
Notes on Courtesy
by David Gross — 2020-12-16T04:08:31.448Z

This post examines the virtue of courtesy. It is meant mostly as an exploration of what other people have learned about this virtue, rather than as me expressing my own opinions about it, though I’ve been selective about what I found interesting or credible, according to my own inclinations. I wrote this not as an expert on the topic, but as someone who wants to learn more about it. I hope it will be helpful to people who want to know more about this virtue and how to nurture it.

What is courtesy?

The best concise definition of courtesy I have found is this one, from H.E. Norton, honorary secretary of the charmingly-named “Children’s National Guild of Courtesy”: “kindly and thoughtful consideration for others.”[1]

Courtesy is closely related to things like manners, etiquette, politeness, decorum, and propriety.[2] If you go hunting for advice about courtesy (as I did), much of what you find will be guides to polite manners for various occasions. Some of these are more nuanced than others, but many seem to be catering to a hope that courtesy can be reduced to a set of rules that, once you learn them, will keep you out of trouble. But heuristics, norms, and rules of thumb will only get you so far. “Kindly and thoughtful consideration for others” requires attentiveness, discernment, and creativity.

Etiquette often has more to do with behaving conventionally than thoughtfully. That said, such conventions sometimes have courtesy as a goal, and often seem meant to be safe defaults that approximate kindly and thoughtful consideration for others in common situations. Other conventions have other purposes, such as to show submission to people in dominant classes (“always doff your hat to a duke”), or to signal your membership in (or aspiration to) a class (“a well-bred gentleman never…”). The origins of some conventions are obscure,[3] but they can still be useful signals of kindly-and-thoughtful intentions. For some conventions, even if they have an element of the arbitrary to them, just that they are conventions makes it kindly and thoughtful to follow them (“slow drivers stay in the right lane”). However, you should be prepared to sacrifice conventional etiquette for actual kindly and thoughtful consideration (“I apologize for not shaking your hand; I’m trying to be especially cautious during the pandemic”).

“Honorifics and formal politeness provide lubricati

...