trentmkelly/LessWrong-43k huggingface dataset:
A Healthy News Diet
by mpr — 2020-12-31T22:19:58.103Z
Link: https://matthewroll.com/2020/12/31/a-healthy-news.html

[Note: This post is a reaction to my drastic increase in Twitter consumption this year. All uses of the second person "you" and "your" in this post are in fact me talking to myself.]

If you aren’t careful, you’ll spend too much of your life scrolling a feed. Might be Twitter, HN, Reddit, NYT, etc. Could be some app that doesn’t exist yet. Doesn’t matter. The result is the same. You don’t get as much done as you want, and your soul slowly drains out into that screen.

The problem is, scrolling through that feed can be useful. It’s not useful very often, but it's useful enough that you don't feel you should completely quit. Mostly you're just wasting time, but you need to know what's going on in the world somehow. It's just that the signal to noise ratio by default is so so so so bad.

Maybe there's a way to increase the signal and decrease the noise. Maybe there's a way to make news reading an intentional part of your life that takes exactly the amount of time you allow it. Maybe. That's what I want to work towards in this post, anyhow. Which leads us to my working hypothesis:

There exists a balanced news diet that results in learning useful things while allowing for enough time to be productive, and also keeping your soul.

Finding that diet will require work, and sticking to it will require work. No free lunch, and all that.

I feel like a first principles approach is called for.

Why read news? For me it’s a way to find out what’s happening in areas of the world that I care about. It’s a way of learning about opportunities for investment, whether that be in the form of money, like buying bitcoin, or in the form of time, like deciding to read up on or tinker with a technology that seems like it will be important. Reading news is also a way to see warning signs that tell me I should be preparing for something or changing my behavior, exactly what happened this year with coronavirus.

So what would happen if I didn’t read news? I would miss out on investments and possible career opportunities. And I might be caught wrong-footed in black swan events like a health crisis, a violent conflict, or an economic crisis.

I think there are plenty of people who for whatever reason think that being on the lookout for such disasters is overkill. I’m not one of those people. I want to be prepared.

Which I think logically dictates that I read some news, in some form that reliably sends me ...
Predictions for 2021
by Eric Neyman — 2020-12-31T21:12:47.184Z
Link: https://ericneyman.wordpress.com/2020/12/31/predictions-for-2021/

Just as I did last year, I have some probabilistic predictions for 2021. In January 2022 I will return to grade them, just as in a week or two I’ll grade my 2020 predictions. This year the predictions fall into four categories: U.S. politics (#1-17 below), COVID (#18-39), Miscellaneous (#40-53), and Personal (#54-100).

Note that these predictions are not necessarily well-informed. You should consider my U.S. politics predictions to be more or less reasonable, but it’s totally possible that my COVID predictions are crazy. This is just to say: if you think you have decent COVID information, don’t use my predictions to inform your decisions.

As last year, I’ll be assessing my predictions on calibration (the details are the same as last year, except that [0.9, 1) will be one bucket). I will also be evaluating the predictions on personal pessimism/optimism — whether I’m too optimistic, too pessimistic, or about right in my judgment of what I will get done next year — this time with weights proportional to the importance of each goal (see below).

I’d also like to advertise a challenge for my readers. You can email me with your predictions for a subset of my predictions with your own prediction. I’ll judge your predictions against mine using the logarithmic scoring rule. I have the advantage of having chosen the questions, but you have the advantage of having seen my predictions. In theory this gives you the upper hand: for example, you could put down my probabilities for every event except a few where you think I clearly messed up. Consider this an exercise in second-order knowledge: figuring out how much weight to put on my probabilities despite not knowing my reasoning behind them. Your score will be the sum, over all questions you choose, of your log score minus my log score.1 (So you can guarantee yourself a score of 0 by sending me an empty list.) [Edit: the “choose at least 40 predictions” requirement has been removed.]

Please make your predictions by January 4th at 11:59pm ET and send them to me as a text file where each line has the format “[event #], [probability as a decimal]”. I will throw out any predictions that resolve (or nearly resolve) by January 4th.

Finally, a note: many of the probabilities I give are numbers like 34% (not multiples of 5%). Don’t take this to mean that I somehow precisely calculated the probabilities or that I’m really knowledgeable...
Some end-of-year media recommendations
by mingyuan — 2020-12-31T20:10:17.516Z

Books

I've read about 150 books over the past five or so years, after a decade-long dry spell where 95% of the reading I did was for school. These are the ~20% of those books that have really made an impact on me, that I've continued to think about even long after finishing them. I'm not going to give you summaries because (a) you can find those elsewhere, and (b) for a lot of them, I don't even really remember the contents. What I do remember is why I liked them and what I took away from them, so that's what's here. Books in each section are roughly ordered by how important they've been for me.

Self-help

Replacing Guilt

Every single essay on Nate Soares's blog was earth-shattering for me when I read them in college, and most of all 'Half-assing it with everything you've got'. Unfortunately by the time I found the Replacing Guilt sequence it was too late for me to not waste my years in school, but I still carry his lessons with me always.

Bonds That Make Us Free

This book is about how to treat other people. It's got some strong Christian undertones which turned off my mom (who was raised Catholic) but didn't bother me that much (I was raised atheist). This book has helped me more than any therapist; specifically, it helped me move on from something I'd been having nightmares about for ten years, by reframing and really seeing things from the other person's perspective. That's damn magic is what that is.

The Life-Changing Magic of Tidying Up

Marie Kondo is the person I aspire to be, in roughly half the aspects of myself that I care about. She's so kind and at peace and knows exactly what she likes. I tidy all day and feel awful about it; she tidies people's lives and feels deeply fulfilled. She also has some hella cute children. I KonMaried my stuff at the end of 2019 and have continued to live by her philosophy and be inspired by her aesthetic. I also helped my mom and two of my housemates KonMari! There's also a Marie Kondo Netflix show which is my happy place.

Atomic Habits

A recent book and therefore one I haven't had much time to reflect on yet, but it presents a really solid framework for habit creation that I adopted upon reading. Also has some really riveting personal anecdotes that are maybe only tangentially related to the book, but dope nonetheless.

The 4-Hour Workweek

I didn't develop a passive income or become a digital nomad or start my own business or any ...
Luna Lovegood and the Chamber of Secrets - Part 12
by lsusr — 2020-12-31T20:02:33.759Z

"Stupefy," Professor Lockhart said.

Lord Voldemort spat a verbal non-somatic counterspell. He dodged too, just in case.

"Do not do that again," said Lord Voldemort.

Lord Voldemort cast another non-somatic spell. Animated skeleton hands grew from his wrists. He picked up Harry Potter's wand.

"Take a look at this map," Luna said, "Do you see this nargle? It will hunt you forever unless we work together to stop it."

Lord Voldemort's Legilimency tore through Luna's mind.

"It is almost here," said Professor Lockhart.

"Place your hands on my astrolabe," Luna said.

"Rennervate the boy to awaken after we leave," said Lord Voldemort to Professor Lockhart.

Headmistress Minerva McGonagall, Chief Warlock Amelia Bones and Auror Mad-Eye Moody burst into Harry Potter's office.

"What did you do this time?" said Auror Moody.

"Nothing," said Harry James Potter-Evans-Verres.

"Uh-huh," said Headmistress McGonagall.

The Forgotten Library was a regular heptagon centered around a giant Pensieve. Seven giant shelves radiated toward the corners. The shelves held brown tattered diaries, vials of silvery memories and endless stacks of stationary. Six desks were interlaced in six aisles between the seven shelves. The seventh aisle led to the entrance. Each desk was covered in parchment covered in Luna's handwriting. A flowchart on the far left desk said START HERE in giant letters.

Luna checked the Marauder's Map. The nargle moved faster than it had before.

According to Luna's notes, they had all three components of the ritual: the Sword of Gryffindor, the secrets of Slytherin and the Diadem of Ravenclaw. Lockhart wielded the sword. Lord Voldemort wielded the secrets. Luna placed Ravenclaw's Diadem on her head. The diadem forced Luna's thoughts into coherence.

There was an epic battle.

The vials had been smashed. The diaries had been burned. Something was missing.

Ravenclaw's horcrux continued to overwrite the Luna personality. Not yet. Luna had unanswered questions. The power of Gryffindor. What had destroyed Atlantis? The intellect of Ravenclaw. Where was Platform Nine and One Half? The secrets of Slytherin. What had happened to the third cauldron? The sacrifice of Hufflepuff.

Luna wondered where the Wrackspurt could have come from. It had to have been there before she shut the door. But the Wrackspurt had not immediately entered Luna's ears when she had first entered the compartment. There m

...
Crowd-Forecasting Covid-19 
by nikos — 2020-12-31T19:30:37.019Z

Forecasting is hard and many forecasting models do not do a particularly good job. The question is: can humans do better? And if so, how can we best use that? This is what I have tried to investigate in the last few months.

Many platforms like Metaculus, Good Judgement, or the Delphi Crowdcast Project collect human forecasts. Other platforms like the Covid-19 Forecast Hub (their data is used by the CDC) collect forecasts from computer models submitted by different teams around the world. As targets and forecasting formats often differ, human and computer forecasts are often hard to compare. Often, policy-makers want a visual representation of the forecast, which requires a format that is more suitable for computers than humans. Sometimes, we therefore would like to have something like a drop-in replacement for a computer model that reflects human judgement - which is exactly what I have been working on.

This post will give an overview of the crowd-forecasting project I created for my PhD in epidemiology. It will

present a crowd-forecasting web app developed
explain how we evaluate forecasts and present the necessary tools
discuss results and lessons learned
give a quick overview of future development

I wrote a post previously on my own blog that presents some preliminary evaluation and also goes into more detail on how to make a good forecast. If you like, check that out as well.

The crowd-forecasting app

To elicit human predictions, I created a R shiny web app. The app can be found here, the associated github repo is here (you need to navigate to human-forecasts). Here is what the app currently looks like:

User interface forecasting app

Starting from early October, participants were asked to provide one- to four-week-ahead forecasts for Covid-19 case and death numbers in Germany and Poland. Germany and Poland were chosen as a test ground, as we could submit the forecast to the German and Polish Forecast Hub, where our crowd forecasts would directly compete against a variety of computer-generated models.

In addition to the past observations, users could access some additional information, e.g. from Our World in Data through the app. Here is a screenshot of that:

Users were asked to provide a full predictive distribution. They would pick one of several variations of the normal distribution (the default is log-normal) and give a m...
Anti-Aging: State of the Art 
by JackH — 2020-12-31T19:07:03.430Z

Aging is a problem that ought to be solved, and most Less Wrongers recognize this. However, few members of the community seem to be aware of the current state of the anti-aging field, and how close we are to developing effective anti-aging therapies. As a result, there is a much greater (and in my opinion, irrational) overemphasis on the Plan B of cryonics for life extension, rather than Plan A of solving aging. Both are important, but the latter is under-emphasised despite being a potentially more feasible strategy for life extension given the potentially high probability that cryonics will not work.

Today, there are over

130 longevity biotechnology companies

and over

50 anti-aging drugs in clinical trials in humans

. The evidence is promising that in the next 5-10 years, we will start seeing robust evidence that aging can be therapeutically slowed or reversed in humans. Whether we live to see anti-aging therapies to keep us alive indefinitely (i.e. whether we make it to

longevity escape velocity

) depends on how much traction and funding the field gets in coming decades.

In this post, I summarise the state of the art of the anti-aging field (also known as longevity biotechnology, rejuvenation biotechnology, translational biogerontology or geroscience). If you feel you already possess the necessary background on aging, feel free to skip to Part V.

Part I: Why is Aging a problem?

Aging is the biggest killer worldwide, and also the

largest source of morbidity

. Aging kills

100,000 people per day

; more than twice the sum of all other causes of death. This equates to 37 million people - a population the size of Canada - dying per year of aging. In developed countries, 9 out of 10 deaths are due to aging.

Aging also accounts for more than

30% of all disability-adjusted life years lost (DALYs)

; more than any other single cause. Deaths due to aging are not usually quick and painless, but preceded by 10-15 years of chronic illnesses such as cancer, type 2 diabetes and Alzheimer’s disease. Quality of life typically deteriorates in older age, and the

highest rates of depression worldwide are among the elderly

.

To give a relevant example of the effects of aging, consider that aging is primarily responsible for almost all COVID-19 deaths. This is observable in the strong association of COVID-19 mortality with age (below, middle panel):

T...
A Simplified Version of Perspective Solution to the Sleeping Beauty Problem
by dadadarren — 2020-12-31T18:27:14.349Z
Link: https://www.sleepingbeautyproblem.com

This post attempts to give a more intuitive explanation of my perspective-based solution to anthropic paradoxes. By using examples, I want to show how perspectives are axiomatic in reasoning.

For each of us, our perceptions and experience originate from a particular perspective. Yet when reasoning we often remove ourselves as the first-person and present our logic in an objective manner. There are many names for this way of thinking, e.g. “take the third-person view”, “use an outsider’s perspective”, “think as an impartial observer”, “the god’s eye view”, “the view from nowhere”, etc.

I think the best way to highlight the differences between god’s-eye-view reasoning and first-person reasoning is to use examples. Imagine during Alex’s sleep some mad scientist placed him in a maze. After waking up, he sees the scientist left a map of the maze next to him. Nonetheless, he is still lost. Here we can say, given the map, the maze is known. Alex is lost because he doesn’t know his location.

Figure 1. From a god’s eye view, the maze’s location is known. But Alex’s location is unknown. The colored dots show some possibilities given Alex’s immediate surroundings.

In contrast, an alternative interpretation is to think in Alex’s shoes. I can say of course I know where I am. Right here. I can look down and see the place I am standing on. No location in the world is more clearly presented to me than here. I’m lost because I don’t know how is the maze located relative to me, even with the map.

Figure 2. Here is known. The maze’s location is unknown. The colored parts show some possibilities.

This difference also exists in examples involving time. Imagine a thunder wakes Alex up at night. Here we can say he doesn’t know what time it is. Maybe he went to bed at 10 pm and his alarm clock rings at 6 am. There is no information to place his awakening on the time axis.

Figure 3. From a god’s eye view, it is known when Alex went to bed and when the alarm clock is going to ring. But the time when the thunder wakes him up is not known. Colored lines show some possibilities.

Alternatively, we can think from Alex’s perspective as he wakes up. I can say of course I know what time it is. It is now. It is the time most immediate to my perception and experience. No other moment is more clearly felt. What I don’t know is how long ago was 10 pm when I went to bed, and how far in the fu...
[AN #131]: Formalizing the argument of ignored attributes in a utility function
by Rohin Shah — 2020-12-31T18:20:04.835Z

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

Consequences of Misaligned AI (Simon Zhuang et al) (summarized by Flo): One intuition for why powerful AI systems might lead to bad consequences goes as follows:

1) Humans care about many attributes of the world and we would likely forget some of these when trying to list them all.

2) Improvements along these attributes usually require resources, and gaining additional resources often requires sacrifices along some attributes.

3) Because of 1), naively deployed AI systems would only optimize some of the attributes we care about, and because of 2) this would lead to bad outcomes along the other attributes.

This paper formalizes this intuition in a model, identifies conditions for when deploying AIs can reduce true utility within the model and proposes two mitigation strategies, impact minimization and interactivity.

We assume that the world state consists of L attributes, all of which the human cares about having more of, that is, true utility is strictly increasing in each of the attributes. Each attribute has some minimum value, and can be increased from that minimum value through the use of a fixed, finite resource (which you could think of as money, if you want); this allows us to formalize (2) above. To formalize (1), we assume that the proxy utility optimized by the AI is only allowed to depend on J<L of the attribute dimensions.

Given this setup, the paper proves that if the AI maximizes the proxy utility, then all attributes that were omitted in the proxy utility will be set to their minimal value. This will be worse than not using the AI system at all if 1) the minimum values of attributes are sufficiently small (allowing the AI to cause damage), 2) the resource cost (resp. gain in true utility) for increasing an attribute is independent of the other attributes' level, 3) it always costs at least K resources to get a unit increase in any attribute, for some K > 0, and 4) utility has diminishing marg...
Covid 12/31: Meet the New Year
by Zvi — 2020-12-31T17:20:01.393Z

Same as the old year.

It’s deja vu all over again. It’s not good.

We’re focused on political fights over priority and concerns someone might jump the line, and took off for Christmas. Doctors get forty-six dollars for administering two vaccine shots, and aren’t allowed to bill for anything involved, including either office visit. The vaccine rollout is sufficiently botched that we now have to be worried about millions of doses spoiling and going unused.

It’s that bad.

Meanwhile, here we go again. The evidence from this week points to the baseline scenario laid out last week being correct. The new strain is here. It is almost certainly more infectious, probably as much more infectious as feared, and has been identified in multiple states. This is going to happen and there is nothing left capable of stopping it.

Eventually, some day, hopefully even some time in 2021, one way or another, this all will end.

Alas, it will not be tonight at midnight.

Let’s run the numbers.

The Numbers

Predictions

Last week’s prediction: 13.6% positive rate on 10.1 million tests, and an average of 2,500 deaths.

Results: 13.8% positive rate on 9.1 million tests, and an average of 2,287 deaths.

That looks like a straightforward case of not taking the drop-off from Christmas seriously enough, with deaths and tests both down 10% more than predicted, and the positive rate what you’d expect accordingly. These are temporary slash artificial drops.

But it’s hard to know when we will be back on a normal track, because things still weren’t back to normal today, and New Year’s Eve is upon us, and also the real effects of Christmas are about to arrive and the last two days positive rates are super scary.

Prediction: 14.3% positive rate on 9.7 million tests, and an average of 2,500 deaths, again with wide error bars.

Deaths

Date	WEST	MIDWEST	SOUTH	NORTHEAST
Oct 29-Nov 4	956	1977	2309	613
Nov 5-Nov 11	1089	2712	2535	870
Nov 12-Nov 18	1255	2934	2818	1127
Nov 19-Nov 25	1761	4169	3396	1714
Nov 26-Dec 2	1628	3814	2742	1939
Dec 3-Dec 9	2437	5508	4286	2744
Dec 10-Dec 16	3278	5324	4376	3541
Dec 17-Dec 23	3826	5158	5131	3772
Dec 24-Dec 30	3363	3668	4171	3640

The Northeast generally seems to be better about reporting deaths on holidays and weekends, so the relative increase here is likely exaggerated, but also likely real. The Midwest drop likewise is too large to not be largely a function of re...