trentmkelly/LessWrong-43k huggingface dataset:
Three car seats?
by jefftk — 2020-10-01T14:30:06.216Z

I recently was linked to

Car
Seats as Contraception, which argues that car seat laws have led
to parents choosing to have fewer children because "physical
limitations of many cars precludes the use of three car seats in the
back seat." Now, this is a correlational study, so I'm

skeptical that this is
a real effect, but the premise here is also strange. If the only
way to have three children in your car was to buy a larger car, I
see why that could be a major issue for a lot of people. On the other
hand, buying a narrow car seat is pretty straightforward, and three
narrow seats fit across the back of almost anything.

We have several Cosco
seats that are 17" wide. They are one of the cheapest
seats out there ($50), and they fit three-across in almost anything.
Manufacturers publish "rear shoulder room" measurements for their
cars, so roughly we're talking about cars with at least 51".
Alternatively, if you're willing to spend 4x as much ($200, still
small compared to the cost of a car or of raising a child) there are 16" seats which
would only need 48" across the back. Small cars I checked:

Toyota Corolla: 52"

Honda Fit: 53"

Chevy Sonic: 53"

Ford Focus: 54"

Honda Civic: 55"

Kia Forte: 55"

Nissan Sentra: 55"

Hyundai Elantra: 55"

Subaru Impreza: 56"

Of these, I think we've put our seats three-across in both the Toyota
Corolla and Honda Fit, the two smallest on the list, without issue.
It's more annoying than just putting in two, but if you own your car
(unlike us) you
can leave them installed. There are tons
of blog
posts
on
this.
This mild hassle doesn't seem like it could be large enough to cause a
substantial shift in whether or when people choose to have a third
child?

Comment via: facebook
Forecasting Newsletter: September 2020. 
by NunoSempere — 2020-10-01T11:00:54.354Z

Highlights

Red Cross and Red Crescent societies have been trying out forecast based financing, where funds are released before a potential disaster happens based on forecasts thereof.

Andrew Gelman releases Information, incentives, and goals in election forecasts; 538's 80% political predictions turn out to have happened 88% of the time.

Nonprofit Ought organizes a forecasting thread on existential risk, where participants display and discuss their probability distributions for existential risk.

Index

Highlights

Prediction Markets & Forecasting Platforms

In The News

Hard To Categorize

Long Content

Sign up here or browse past newsletters here.

Prediction Markets & Forecasting Platforms

Metaculus updated their track record page. You can now look at accuracy across time, at the distribution of brier scores, and a calibration graph. They also have a new black swan question: When will US metaculus users face an emigration crisis?.

Good Judgement Open has a thread in which forecasters share and discuss tips, tricks and experiences. An account is needed to browse it.

Augur modifications in response to higher ETH prices. Some unfiltered comments on reddit

An overview of PlotX, a new decentralized prediction protocol/marketplace. PlotX focuses on non-subjective markets that can be programmatically determined, like the exchange rate between currencies or tokens.

A Replication Markets participant wrote What's Wrong with Social Science and How to Fix It: Reflections After Reading 2578 Papers. See also: An old long-form introduction to Replication Markets.

Georgetown's CSET is attempting to use forecasting to influence policy. A seminar discussing their approach Using Crowd Forecasting to Inform Policy with Jason Matheny is scheduled for the 19th of October. But their current forecasting tournament, foretell, isn't yet very well populated, and the aggregate isn't that good because participants don't update all that often, leading to sometimes clearly outdated aggregates. Perhaps because of this relative lack of competition, my team is in 2nd place at the time of this writting (with myself at #6, Eli Lifland at #12 and Misha Yagudin at #21). You can join foretell here.

There is a new contest on Hypermind, The Long Fork Project, which aims to predict the impact of a Trump or a Biden victory in November, with $20k in prize money. H/t to user ChickCounterfly.

The Univer...
Babble challenge: 50 ways of sending something to the moon
by Bird Concept — 2020-10-01T04:20:24.016Z

This is an exercise, and as such is a bit different from your ordinary question post...

What?

Come up with 50 ways of sending something to the moon. In less than 1 hour.

I don’t care how stupid they are. My own list included “Slingshot”, “Massive trampoline” and “Bird with spacesuit”.

What matters is that you actually hit 50. I want you to have the experience of thinking that you’re out of ideas, but nonetheless deciding to push yourself, and finding your best idea thus far.

This regularly happens to me when I do this exercise. I’ll feel stuck. I’ll feel like giving up. But I force myself to say three more stupid things… “mega tall tree”, “super boomerang”, “railgun” ... and, all of sudden, I have a fourth idea that’s actually not that shabby.

Why do this?

1) Becoming more creative.

Coming up with ideas is a bottleneck for me personally.

I want to become stronger

.

I have a very simple model for how to improve. My brain will start generating more ideas if I A) force myself to have ideas, even if they’re bad, and B) reward myself for having them.

The act of filtering for actually good ideas is a second, different step. First you

babble

. And only then you prune. I claim that you can train each of those “muscles” separately.

I think that in the past my creativity has been held back by excessive self-criticism. I now think it should be possible to improve by separating the creative and the evaluative step -- at least for practice purposes.

2) Building a culture of practice on LessWrong

LessWrong currently feels like an unusually intellectual bar. You pop in and grab a drink; instead of watching a stand-up comedian someone does a PowerPoint presentation; and the ensuing conversation is great. That’s a fine thing.

But what if part of LessWrong was more like a gym? Or

a dojo

?

You come in, bow to the sensei, and then you start practicing. Together. Each of you focusing all your attention on your movements, pushing yourselves to your limits, and trying deliberately to become stronger.

I want us to have that, but for rationality.

Rules

50 answers or nothing.

That’s the babble challenge. We’re here to work hard.

Post your answers inside of spoiler tags! (

How do I do that?

)
Celebrate other’s answers.

This is really important. Sharing babble in public is a scary experience. I don’t want people to leave th...
AGI safety from first principles: Alignment
by Richard_Ngo — 2020-10-01T03:13:46.491Z

Parts of this section were rewritten in mid-October.

In the previous section, I discussed the plausibility of ML-based agents developing the capability to seek influence for instrumental reasons. This would not be a problem if they do so only in the ways that are aligned with human values. Indeed, many of the benefits we expect from AGIs will require them to wield power to influence the world. And by default, AI researchers will apply their efforts towards making agents do whatever tasks those researchers desire, rather than learning to be disobedient. However, there are reasons to worry that despite such efforts by AI researchers, AIs will develop undesirable final goals which lead to conflict with humans.

To start with, what does “aligned with human values” even mean? Following Gabriel and Christiano, I’ll distinguish between two types of interpretations. Minimalist (aka narrow) approaches focus on avoiding catastrophic outcomes. The best example is Christiano’s concept of intent alignment: “When I say an AI A is aligned with an operator H, I mean: A is trying to do what H wants it to do.” While there will always be some edge cases in figuring out a given human’s intentions, there is at least a rough commonsense interpretation. By contrast, maximalist (aka ambitious) approaches attempt to make AIs adopt or defer to a specific overarching set of values - like a particular moral theory, or a global democratic consensus, or a meta-level procedure for deciding between moral theories.

My opinion is that defining alignment in maximalist terms is unhelpful, because it bundles together technical, ethical and political problems. While it may be the case that we need to make progress on all of these, assumptions about the latter two can significantly reduce clarity about technical issues. So from now on, when I refer to alignment, I’ll only refer to intent alignment. I’ll also define an AI A to be misaligned with a human H if H would want A not to do what A is trying to do (if H were aware of A’s intentions). This implies that AIs could potentially be neither aligned nor misaligned with an operator - for example, if they only do things which the operator doesn’t care about. Whether an AI qualifies as aligned or misaligned obviously depends a lot on who the operator is, but for the purposes of this report I’ll focus on AIs which are clearly misaligned with respect to mo...
How to not be an alarmist
by DirectedEvolution — 2020-09-30T21:35:59.285Z

"I apologize. I didn't mean-- may I express my concern as calmly and respectfully as I can?"

- Prof. Legasov on the consequences of an RBMK reactor meltdown (HBO's Chernobyl)

"This is not going to end well."

"There is no evidence of that-"

"This is not going to end well."

- Donald Trump and Joe Biden on voter fraud (first presidential debate)

Winning an argument often requires making a case that some X is a problem. X is bad. Here's how an avid practitioner of the dark arts can gather soldiers for their cause, no matter what X is or how bad the issue is in context. I think this is how most people argue for their cause. Craft the strongest argument in each category, and if that argument gets defeated, move to another one.

Current levels of X. No matter how small X is, it could always be smaller.
Projected levels of X. Sometimes, it's not the current, but the projected level of X at some point in the future that's bad.
The rate of change in X. If X is low and getting lower, it might not be declining fast enough. Or in the context of an overall good trend, there might be a tiny recent bad turn.
Past levels of X. Even if X no longer exists, it can be blamed for current problems.
Uncertainty in X. There might be uncertainty whether a low level, declining trend, planned solution, or potential technological breakthrough will happen or continue. Alternatively, some unpredictable circumstance or tail risk might cause a spike in X or a reversal of the declining trend in X.
Side effects of solutions to X. If the reason we're beating X is Y, then focus on bad aspects of Y.

Corollary: if the reason we're beating X is that we're afraid of X, then admitting that X is low and/or declining reduces to case (5).

Stated in the abstract, it's easy to see how arbitrary this process is. It's a recipe for looking like an alarmist, even if we're telling the truth about a serious issue. And, of course, you might not just look like, but actually be an alarmist.

But we do have to confront the problems in our world. Bad things are, in fact, bad.

The answer is to have a sense of priorities. Your own, as well as those of your debate partner and audience. Show that you respect them as people. Make a clear statement of what you think their priorities are. Look for fair, win/win solutions.

If you can't reach that state of mutual understanding and reciprocity no matter how hard you try - and you have to...
Competence vs Alignment
by kwiat.dev — 2020-09-30T21:03:01.826Z

Is there a reliable way of determining whether an agent is misaligned, as compared to just incompetent? And can we talk of such distinction at all? (I suppose the orthogonality thesis implies that we can)

Let's say I give you an agent with suboptimal performance. Without having insights inside its "brain", and only observing its behavior, can we determine whether it's trying to optimize the correct value function but failing, or maybe it's just misaligned?
"Zero Sum" is a misnomer.
by abramdemski — 2020-09-30T18:25:30.603Z

This could have been a relatively short note about why "zero sum" is a misnomer, but I decided to elaborate some consequences. This post benefited from discussion with Sam Eisenstat.

"Zero Sum" is a misnomer.

The term intuitively suggests that an interaction is transferring resources from one person to another. For example, theft is zero-sum in the sense that it cannot create resources only transfer them. Elections are zero-sum in the sense that they only transfer power. And so on.

But this is far from the technical meaning of the term.

In order for the standard rationality assumptions used in game theory to apply, the payouts of a game must be utilities, not resources such as money, power, or personal property. Zero-sum transfer of resources is often far from zero-sum in utility.

But I'm getting ahead of myself. Let's examine the technical meaning of "zero sum" more precisely.

It's used to mean "constant sum".

The term "zero sum" is often used as a technical term, referring to games where the payouts for different players always sums to the same thing.

For example, the game rock-paper-scissors is zero sum, because it always has one winner and one loser.

More generally, constant-sum means that if you add up the utility functions of the players, you get a perfectly flat function.

"Constant sum" doesn't really make sense as a category.

It makes sense to conflate "zero sum" and "constant sum" because utility functions are equivalent under additive and positive multiplicative transforms, so we can always transform a constant-sum game down to a zero-sum game. However, by that same token, the concept of "constant sum" is meaningless: we can multiply the utility of one side or the other, and still have the same game. If you have good reflexes, you should hear "zero sum"/"constant sum" and shout "Type error! Radiation leak! You can't sum utilities without providing extra assumptions!"

Let's look at the "zero sum" game matching pennies as an example. In this game, two players have to say "heads" or "tails" simultaneously. One player is trying to match the other, while the other player is trying to be different from the one. Here's one way of writing the payoff matrix (with Alice trying to match):

Bob
Alice	Heads	Tails
Heads	Alice: 1
Bob: 0	Alice: 0
Bob: 1
Tails	Alice: 0
Bob: 1	Alice: 1
Bob: 0

In that case, the game has a constant sum of 1. We can re-scale it to have a constant sum ...
Evaluating Life Extension Advocacy Foundation
by emanuele ascani — 2020-09-30T18:04:51.346Z

(Crossposted from the EA Forum)

Summary

In this post, I try to evaluate Life Extension Advocacy Foundation, and I flesh out interview questions to ask them. LEAF has many foci, including crowdfunding, conference organizing, investor advisement, social media advocacy, news, YouTube shows and a podcast, partnering with YouTubers and content creators such as Kurzgesagt and Life Noggin, creating useful resources on aging research, and advocating for policy change, including communication with international organizations, such as the World Health Organization.

I think that LEAF's ratio of donations to money-brought-to-the-field is high. This organization's most easily measurable impact comes from its crowdfunding campaigns, which have brought $384k to other organizations (with LEAF's fee accounted for). I estimated its collaboration with Kurzgesagt to have brought to LEAF at least $3k per month for a couple of years from donors who were previously oblivious to the aging-research cause.

Other important sources of impact are LEAF’s investor network, which has a great potential influence on their ratio, the capital and human resources that it brings to the field in ways that are difficult to quantify, their role in public education on the topic, and its influence on policy and international organizations such as WHO, which may drive faster adoption of new biotechnologies, increased funding, and better international decision-making.

I invite the reader to come up with questions or criticize the questions that I have proposed. Please use the comment section in the forum or private messages.

Introduction and Focuses

Life Extension Advocacy Foundation (LEAF) is an advocacy and crowdfunding organization founded in 2014. It is now the leading advocacy organization in the space of aging research. Its foci include:

Crowdfunding basic research projects.
Organizing conferences.
Advising investors and attracting capital.
Social media advocacy.
News and blogging about aging research.
YouTube channel with news about aging research.
YouTube show (soon with its own channel) and podcast about aging.
YouTube channel about the positive impact of scientific research and how to support it.
Partnering with YouTubers and content creators in order to bring more donors and talent to the field and drive public awareness. The major milestones that they reached in this area are their collaborations with Kurz

...
[AN #119]: AI safety when agents are shaped by environments, not rewards
by Rohin Shah — 2020-09-30T17:10:03.662Z

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).

HIGHLIGHTS

Shaping Safer Goals (Richard Ngo) (summarized by Nicholas): Much of safety research focuses on a single agent that is directly incentivized by a loss/reward function to take particular actions. This sequence instead considers safety in the case of multi-agent systems interacting in complex environments. In this situation, even simple reward functions can yield complex and highly intelligent behaviors that are only indirectly related. For example, evolution led to humans who can learn to play chess, despite the fact that the ancestral environment did not contain chess games. In these situations, the problem is not how to construct an aligned reward function, the problem is how to shape the experience that the agent gets at training time such that the final agent policy optimizes for the goals that we want. This sequence lays out some considerations and research directions for safety in such situations.

One approach is to teach agents the generalizable skill of obedience. To accomplish this, one could design the environment to incentivize specialization. For instance, if an agent A is more powerful than agent B, but can see less of the environment than B, A might be incentivized to obey B’s instructions if they share a goal. Similarly we can increase the ease and value of coordination through enabling access to a shared permanent record or designing tasks that require large-scale coordination.

A second approach is to move agents to simpler and safer training regimes as they develop more intelligence. The key assumption here is that we may require complex regimes such as competitive multi-agent environments to jumpstart intelligent behavior, but may be able to continue training in a simpler regime such as single-task RL later. This is similar to current approaches for training a language model via supervised learning and then finetuning with RL, but going in the opposite direction to increase safety rather than capabilities.

A third approach is specific to a collective AGI: an AGI that is composed of a number of separate general agents train...
Learning how to learn 
by Neel Nanda — 2020-09-30T16:50:19.356Z
Link: https://www.neelnanda.io/blog/34-learning

Introduction

The skill of learning things fast and well is phenomenally valuable. It serves as a force multiplier on almost everything you’ll do later in life, and I think it is worth invest a ton of effort into cultivating and developing. Getting better at learning is one of the most valuable things I’ve learned in my degree, and I think it’s a strong contender for my most useful and employable skill. And I think it’s significantly increased the chance that I can do something awesome with my life. I think that anyone who isn’t putting significant effort into improving at this, especially if you’re a student, is systematically screwing up.

This post is an attempt to outline my philosophy of learning, and the tactics/mindset which have worked best for me.

Learning is a useful skill in many different areas of life, with some general skills and some specific skills. I’ll outline many different techniques and approaches, and I recommend reading this post while keeping in mind your goals and contexts, and focusing on the ideas most relevant to you. A lot of my ideas are likely best suited to learning pure maths well, but they’ve seemed to generalise well for me, so I hope this post is of value! And many of my examples will revolve around students, since that’s my context, but the core ideas of effective learning generalise far beyond that. The world is complex and messy, and the process of becoming awesome is a constant process of getting better.

I’d also highly recommend the course Learning How to Learn as a usefully different perspective on good learning! (and from which I shamelessly stole the title of this post)

Iterate!

The most important insight, is that learning is a personal process. There are many possible strategies for how to learn, with different pros and cons. There isn’t a perfect killer app, to find your ideal learning strategy, you just need to try a lot of different stuff! Try something new, review it and see what worked well and didn’t, adapt it and repeat this process until it feels like it works well. This post is essentially a collection of my most successful experiments.

Expect to try many things, and expect most of them to fail. But this is totally fine! This is a textbook example of upside risk! You are going to spend the rest of your life learning new things. If...