trentmkelly/LessWrong-43k huggingface dataset:
Does anyone know of a good overview of what humans know about depression?
by Eli Tyre — 2019-08-30T23:22:05.405Z

If I wanted to spend 1 to 5 hours getting oriented on everything that we collectively know about depression (starting with whatever pieces are settled, and then moving onto the confusing mess of conflicting data that's left), what resources should I start with?

Textbooks, sections of textbooks, web articles, blogposts by experts, youtube videos, and other formats that I haven't thought of, are all admissible.

Thanks!
September Bragging Thread
by Raemon — 2019-08-30T21:58:45.918Z

Thought I'd try reviving an old LessWrong experiment: the Bragging Thread. (See this old one for some context)

LessWrong mostly rewards people for writing blogposts. That's cool and all, but is not the only thing worth doing. What's an awesome thing you've done lately?

You are encouraged to be as blatantly proud as you inwardly feel. :)

(Note that this is not a thread for progress, or for things you might do. This is for celebrating things you have actually done lately)
2-D Robustness
by Vlad Mikulik — 2019-08-30T20:27:34.432Z

This is a short note on a framing that was developed in collaboration with Joar Skalse, Chris van Merwijk and Evan Hubinger while working on Risks from Learned Optimization, but which did not find a natural place in the report.

Mesa-optimisation is a kind of robustness problem, in the following sense:

Since the mesa-optimiser is selected based on performance on the base objective, we expect it (once trained) to have a good policy on the training distribution. That is, we can expect the mesa-optimiser to act in a way that results in outcomes that we want, and to do so competently.

The place where we expect trouble is off-distribution. When the mesa-optimiser is placed in a new situation, I want to highlight two distinct failure modes; that is, outcomes which score poorly on the base objective:

The mesa-optimiser fails to generalise in any way, and simply breaks, scoring poorly on the base objective.
The mesa-optimiser robustly and competently achieves an objective that is different from the base objective, thereby scoring poorly on it.

Both of these are failures of robustness, but there is an important distinction to be made between them. In the first failure mode, the agent's capabilities fail to generalise. In the second, its capabilities generalise, but its objective does not. This second failure mode seems in general more dangerous: if an agent is sufficiently capable, it might, for example, hinder human attempts to shut it down (if its capabilities are robust enough to generalise to situations involving human attempts to shut it down). These failure modes map to what Paul Christiano calls benign and malign failures in Techniques for optimizing worst-case performance.

This distinction suggests a framing of robustness that we have found useful while writing our report: instead of treating robustness as a scalar quantity that measures the degree to which the system continues working off-distribution, we can view robustness as a 2-dimensional quantity. Its two axes are something like “capabilities” and “alignment”, and the failure modes at different points in the space look different.

Unlike the 1-d picture, the 2-d picture suggests that more robustness is not always a good thing. In particular, robustness in capabilities is only good insofar is it is matched by robust alignment between the mesa-objective and the base objective. It ...
How to Make Billions of Dollars Reducing Loneliness
by John_Maxwell — 2019-08-30T17:30:50.006Z

Loneliness Is a Big Problem

On Facebook, my friend Tyler writes:

Lately, I've been having an alarming amount of conversations arise about the burdens of loneliness, alienation, rootlessness, and a lack of belonging that many of my peers feel, especially in the Bay Area. I feel it too. Everyone has a gazillion friends and events to attend. But there's a palpable lack of social fabric. I worry that this atomization is becoming a world-wide phenomenon – that we might be some of the first generations without the sort of community that it's in human nature to rely on.

And that the result is a worsening epidemic of mental illness...

Without the framework of a uniting religion, ethnicity, or purpose, it's hard to get people to truly commit to a given community. Especially when it's so easy to swipe left and opt for things that offer the fleeting feeling of community without being the real thing: the parties, the once-a-month lecture series, the Facebook threads, the workshops, the New Age ceremonies. We often use these as "community porn" – they're easier than the real thing and they satisfy enough of the craving. But they don't make you whole.

I've had some thoughts about experiments to try. But then I think about how hard it is (especially in this geographic area) to get people to show up to something on at least a weekly basis. Even if it's for something really great. I see many great attempts at community slowly peter out.

Young people are lonely. Old people are lonely.
Loneliness is bad for your health. It's bad for society's health.

Having a smartphone that keeps you entertained all day, and enough money to live by yourself, might sound like first world problems. But they are likely contributors to loneliness. And as developing countries get richer, they'll start having first world problems too. So I think addressing loneliness could be very high-leverage for the world.

People are starting businesses to address loneliness: you can pay someone to call you periodically or take you for a walk. But I'd argue these services are a band-aid in the same sense that parties, workshops, and ceremonies are. They don't solve the underlying problem: You're still alone by default instead of together by default.

Roommates Could Be a Great Solution

Sociologists think there are three conditions necessary for making friends: proximity; repeated, unplanned interacti...
A Game of Giants [Wait But Why]
by kilotaras — 2019-08-30T05:48:00.035Z
Link: https://waitbutwhy.com/2019/08/giants.html

Key insights

Let's look at emergence tower (atom -> molecule -> .. -> cell -> organ system -> animal -> pack/tribe) and consider where independent lifeforms live

amoebas are on a cell level
ants live on a pack level
spiders live on single animals level
humans live somewhere between ants and spiders and can move up and down as a response to external threat

Evolutionary pressure prefers inner tribe kindness and outer kind ruthlessness

Totally ruthless people die to inner tribe competition
Totally kind people die to intertribe competition
The Crisis of Evidence Use
by Ian David Moss — 2019-08-30T04:50:21.000Z

We aspire to let evidence drive our actions, but in practice it rarely does.

Photo by Patrick Tomasso on Unsplash

In 2015, the Center for Evaluation Innovation and the Center for Effective Philanthropy surveyed evaluation and program executives at 127 US and Canadian foundations with at least $10 million in annual giving. The result was the report “Benchmarking Foundation Evaluation Practices,” and it contains one of the most amazing facts I’ve encountered in nearly two decades of working in the social sector.

In response to a question about the challenges they encounter in their work, more than three-quarters of respondents said they have a hard time commissioning evaluations that yield meaningful insights for their own foundations. To put that in plainer language: most of the folks who commission evaluations of social programs have trouble getting people — including even their own colleagues — to use them. And this is coming from the people whose job it is to commission those evaluations, so if anything we’d expect them to be biased against admitting there’s any problem. Not only that, each of the top four challenges identified by foundation evaluation professionals were all related to use, and all experienced by more than 7 in 10 respondents.

— @iandavidmoss

Some might suppose that this has something to do with the choice of study design, but foundations that commission randomized controlled trials (RCTs) didn’t seem to fare much better. Of the foundations that funded RCTs, only 38% reported that they actually drove grantmaking decisions. And just 25% found them useful for developing strategy.

It’s no surprise, then, that survey respondents hoped to see substantial changes to the way the field designs and uses evaluations going forward.

— @iandavidmoss

These numbers are eye-popping, but you could be forgiven for wondering how much stock to put in them. After all, this is only one study. What if it was just a fluke?

Sadly, there is plenty of other research that demonstrates we are experiencing a crisis of evidence use in our field. Taken together, they strongly reinforce the point that people with influence over social policy simply don’t read or use the vast majority of the knowledge we produce, no matter how relevant it is.

One of my favorite factoids of all time comes from a study the World Bank conducted on its own policy papers several years ago. The methodol...
AI Alignment Writing Day Roundup #1
by Ben Pace — 2019-08-30T01:26:05.485Z

Here are some of the posts from last week's writing day. Due to the participants writing 34 posts in less than 24 hours (!), I'm re-airing them to let people have a proper chance to read (and comment) on them, in roughly chronological order.

1) Markets are Universal for Logical Induction by John Swentworth

A discussion and proof of the following.

We want to show that any possible logical inductor can be represented by a market of traders - i.e. there is some market of traders which produces exactly the same prices.

2) Intentional Bucket Errors by Scott Garrabrant

Bucket errors are normally thought of as a bad thing. It has "errors" right in the name. However, I want to argue that bucket errors can sometimes be useful, and you might want to consider having some bucket errors on purpose.

3) Logical Counterfactuals and Proposition graphs, Part 1 by Donald Hobson

Within this sequence of posts I will outline a procedure for logical counterfactuals based on something similar to proof length. In this post I present a reimagining of propositional logic in which proving a theorem is taking a walk around a graph of equivalent proposititions.

The post shows two intuitive models of proving propositional logic:

...a directed acyclic graph, as shown above. Under this interpretation, all we have to do is test node identity.

and another graph where:

...all statements that are provably equivalent in propositional logic will be within the same connected component of the graph. All statements that can't be proved equivalent are in different components, with no path between them.

Finding a mathematical proof becomes an exercise in navigating an infinite maze.

4) Why so much variance in human intelligence? by me

In this question I ask a rambling question about why the difference between peak and median human performance is so much larger than for other species, and Vaniver and Carl Shulman give some fascinating answers.
LW Team Updates - September 2019
by Ruby — 2019-08-29T22:12:55.747Z

To better communicate site updates, we’re going to experiment with a once-monthly updates post. We’ll pin this post pin on the homepage for roughly a week and then continue to post updates here throughout the month.

Please also feel free to use the comments section on this post as a Schelling point to give feedback, file bug reports, or ask questions you have about the site. (You can also email us, ask a question, or use Intercom.)

Recent Features

Shortform

The major feature announcement for August was the launch of the Shortform beta (aka www.lesslong.com).

Shortform is for:

Writing that is short in length, or written in a short amount of time. Includes off-the-cuff thoughts and brainstorming.

Shortform content can be seen in Recent Discussion on the homepage, on www.lesslong.com/shortform, and on the All Posts page using the Daily view.

Anyone can create their own shortform by clicking New Shortform in their dropdown menu, clicking the New Shortform Post button on the homepage above Recent Discussion, or via the text box on the Shortform page.

See the announcement post here with full explanation of the philosophy behind Shortform and full instructions on how to use it. We’ll also be having a party on September 7 in Berkeley to celebrate the launch of Shortform. Details.

Upcoming Features

Heads up on new features which should arrive in September:

Subscriptions Overhaul

We have a planned overhaul of LessWrong’s subscription system which will allow you to subscribe to posts, comments, users, and private messages thereby receiving notifications and/or emails.

Link Previews [DONE]

Soon, when you hover over a green link on a LessWrong page that leads to another LessWrong page, you’ll see a pop-up preview of what’s on the other side plus extra info like the author and karma of the post being linked to.

New Editor Option, including Collaborative Editing and In-Line Comments

Currently, you can write comments and posts using either the Draft.js or Markdown editor. Soon, we'll enable a new experimental editor that should be more powerful and include features like collaborative editing and in-line comments in the style of Google Docs.

Convert Comments to Posts

Sometimes, you write a comment, and then realize it turned out to be a self-contained essay. Soon comments will have a create draft post option that will create a copy of it in draft form. You can make any additional edits you’d...
Why study perturbative adversarial attacks ?
by Yuxi_Liu — 2019-08-29T20:15:59.364Z

This post summarizes and comments on Motivating the Rules of the Game for Adversarial Example Research

Summary of paper

Despite the amount of recent work done, human-inperceptible perturbation adversarial attacks (Example: One Pixel Attack) are not as useful as the researchers may think, for two reasons:

They are not based on realistic attacks against these AI systems.

we were unable to find a compelling example that required indistinguishability.

... the best papers on defending against adversarial examples carefully articulate and motivate a realistic attack model, ideally inspired by actual attacks against a real system.

There are much better attack methods where a real adversary could use:

Test-set attack. Just keep feeding it natural inputs until it gets an error. As long as the system is not error-free this will succeed.

It's suggested that such attacks can be used to fool speeding cameras, by adding human-invisible dots to a license plate. But if one is actually caught speeding, the failure of the algorithm would simply prompt a human review. Much more usefully, one can use clear sprays that are intended to overexpose any photographed image of the licence plate.

Similarly, it's suggested that a perturbation attack could threaten to make a self-driving car to misidentify a STOP sign. But any robust self-driving car must deal with situations much worse than that, such as the stop sign not existing, not visible, and people breaking the traffic rules.

Against security cameras which automatically identify blacklisted people, one can wear a good mask. Since there probably are humans monitoring the camera feed, it's necessary to fool both the camera and the humans. Imagine a monitoring system that automatically displays people's real faces on camera and the best match faces. A human monitor would immediately notice the discrepancy.

They are not very fruitful for improving robustness.

In practice, the best solutions to the l_p problem are essentially to optimize the metric directly and these solutions seem not to generalize to other threat models.

My speculations

If so much work has been done for such dubious gains, I have two bitter questions:

Why did they work on the perturbation attacks so much?

Why are these works so fun to read?

The second question partially answers the first: because they are fun. But that can't be the only explanation. I thi

...
AI Forecasting Resolution Council (Forecasting infrastructure, part 2)
by Bird Concept — 2019-08-29T17:35:26.962Z

UPDATE October 18, 2020: The AI Resolution Forecasting Council has been retired due to lack of demand for its services. I'm keeping this post up for historical value.

---------------

This post introduces the

AI Forecasting Resolution Council

, a group of researchers with technical expertise in AI who will allow us to expand the space of effectively forecastable questions. It is the second part in a series of blog posts which motivate and introduce pieces of infrastructure intended to improve our ability to forecast novel and uncertain domains like AI.

The Council is currently in beta, and we're launching early to get feedback from the community and quickly figure out how useful it is.

Background and motivation

A key challenge in (AI) forecasting is to write good questions. This is tricky because we want questions which both capture important uncertainties, and are sufficiently concrete that we can resolve them and award points to forecasters in hindsight.

Here are some example questions within AI that make this especially difficult:

Counterfactual questions

Suppose in 2000 you use “superhuman Othello from self-play” as a benchmark of a certain kind of impressive AI progress, and forecast it to be possible by 2020. It seems you were correct -- very plausibly the AlphaZero architecture should work for this. However, in a strict sense your forecast was wrong -- because no one has actually bothered to build a powerful Othello agent without relying on handcrafted evaluation functions (EDIT: thanks to Vanessa Kosoy for pointing out that otherwise superhuman Othello systems exist).

So if a calibrated forecaster faces this question in 2000, considerations regarding who will bother to pursue what project “screen off” considerations regarding fundamental drivers of AI progress and their gradients. Yet the latter concern is arguably more interesting.

This problem could be solved if we instead forecasted the question “If someone were to run an experiment using the AI technology available in 2020, given certain resource constraints, would it seem with >95% confidence, that they’d be able to create a superhuman Othello agent that learnt only from self-play?”

Doing so requires a way of evaluating the truth value of that counterfactual, such as by asking a group of experts.

Similarity questions

Suppose we try to capture...
Why are the people who could be doing safety research, but aren’t, doing something else? 
by Adam Scholl — 2019-08-29T08:51:33.219Z

Imagine the set “the 50 people who would most helpfully contribute to technical alignment research, were they to be working on it, yet who are working on something else instead.” If you had to guess—if you had to make up a story which seems plausible—why are they working on something else instead? And what is it they’re working on?
[Link] Book Review: Reframing Superintelligence (SSC)
by ioannes — 2019-08-28T22:57:09.455Z

https://slatestarcodex.com/2019/08/27/book-review-reframing-superintelligence/

Drexler asks: what if future AI looks a lot like current AI, but better?

For example, take Google Translate. A future superintelligent Google Translate would be able to translate texts faster and better than any human translator, capturing subtleties of language beyond what even a native speaker could pick up. It might be able to understand hundreds of languages, handle complicated multilingual puns with ease, do all sorts of amazing things. But in the end, it would just be a translation app. It wouldn’t want to take over the world. It wouldn’t even “want” to become better at translating than it was already. It would just translate stuff really well.

...

In this future, our AI technology would have taken the same path as our physical technology. The human body can run fast, lift weights, and fight off enemies. But the automobile, crane, and gun are three different machines. Evolution had to cram running-ability, lifting-ability, and fighting-ability into the same body, but humans had more options and were able to do better by separating them out. In the same way, evolution had to cram book-writing, technology-inventing, and strategic-planning into the same kind of intelligence – an intelligence that also has associated goals and drives. But humans don’t have to do that, and we probably won’t. We’re not doing it today in 2019, when Google Translate and AlphaGo are two different AIs; there’s no reason to write a single AI that both translates languages and plays Go. And we probably won’t do it in the superintelligent future either. Any assumption that we will is based more on anthropomorphism than on a true understanding of intelligence.

These superintelligent services would be safer than general-purpose superintelligent agents. General-purpose superintelligent agents (from here on: agents) would need a human-like structure of goals and desires to operate independently in the world; Bostrom has explained ways this is likely to go wrong. AI services would just sit around algorithmically mapping inputs to outputs in a specific domain.

A takeaway:

I think Drexler’s basic insight is that Bostromian agents need to be really different from our current paradigm to do any of the things Bostrom predicts. A paperclip maximizer built on current technology would have to eat gigabytes of training data

...
I think I came up with a good utility function for AI that seems too obvious. Can you people poke holes in it?
by litten8 — 2019-08-28T22:33:39.084Z

Basically, the AI does the following:

Create a list of possible futures that it could cause.

For each person and future at the time of the AI's activation:

1. Simulate convincing that person that the future is going to happen.

2. If the person would try to help the AI, add 1 to the utility of that future, and if the person would try to stop the AI, subtract 1 from the utility of that future.

Cause the future with the highest utility