trentmkelly/LessWrong-43k huggingface dataset:
Impossible moral problems and moral authority
by Charlie Steiner — 2019-11-18T09:28:28.766Z

The Tails Come Apart As Metaphor For Life, but with an extra pun.

Suppose you task your friends with designing the Optimal Meal. The meal that maximizes utility, in virtue of its performance at the usual roles food fills for us. We leave aside considerations such as sourcing the ingredients ethically, or writing the code for an FAI on the appetizer in tiny ketchup print, or injecting the lettuce with nanobots that will grant the eater eternal youth, and solely concern ourselves with arranging atoms to get a good meal qua meal.

So you tell your friends to plan the best meal possible, and they go off and think about it. One comes back and tells you that their optimal meal is like one of those modernist 30-course productions, where each dish is a new and exciting adventure. The next comes back and says that their optimal meal is mostly just a big bowl of their favorite beef stew, with some fresh bread and vegetables.

To you, both of these meals seem good - certainly better than what you've eaten recently. But then you start worrying that if this meal is important, then the difference in utility between the two proposed meals might be large, even though they're both better than the status quo (say, cold pizza). In a phrase, gastronomical waste. But then how do you deal with the fact that different people have chosen different meals? Do you just have to choose one yourself?

Now your focus turns inward, and you discover a horrifying fact. You're not sure which meal you think is better. You, as a human, don't have a utility function written down anywhere, you just make decisions and have emotions. And as you turn these meals over in your mind, you realize that different contexts, different fleeting thoughts or feelings, different ways of phrasing the question, or even just what side of the bed you got up on that morning, might influence you to choose a different meal at a point of decision, or rate a meal differently during or after the fact.

You contain within yourself the ability to justify either choice, which is remarkably like being unable justify either choice. This "Optimal Meal" was a boondoggle all along. Although you can tell that either would be better than going home and eating cold pizza, there was never any guarantee that your "better" was a total ordering of meals, not merely a partial ordering.

Then, disaster truly strikes. Your best friend asks you "So,...
The Power to Draw Better
by Liron — 2019-11-18T03:06:02.832Z

This is Part X of the Specificity Sequence

Cats notoriously get stuck in trees because their claws are better at climbing up than down. Throughout this sequence, we’ve seen how humans are similar: We get stuck in high-level abstractions because our brains struggle to unpack them into specifics. Our brains are better at climbing up (concrete→abstract) than down (abstract→concrete).

If you’ve ever struggled to draw a decent picture, you know what being stuck at a high level of abstraction feels like in the domain of visual processing. I know I do. My drawing skills are nonexistent. I can draw kindergarten-quality stick figures, but I don’t even know where to begin drawing something that looks the least bit realistic.

Despite how pathetic a stick figure looks, it’s worth marveling at our brain’s formidable power to distill a mess of light and dark stimuli into a few geometric parts.

A stick figure is a mental representation of an animal which is great for practical tasks like throwing a spear at it.

The question is just, why can people like me only draw a pathetic stick figure even when we’re trying to draw a nice picture?

The visual system was only under selective pressure to evolve a processing pathway from sensing visual features to building an abstract representation of those features, not a processing pathway to transform a high-level mental representation into a low-level pencil strokes representation. I draw stick figures because my conscious mind thinks in stick figures.

But we know that the unconscious part of our visual brains isn’t one-way. When we first recognize that we’re seeing a cow, our brain propagates visual information from abstract mental representations down toward lower-level visual feature recognition.

Consider this image:

Most people have trouble identifying what they’re seeing in different parts of the image, until they realize it’s a cow, and then all the parts snap into focus. Their interpretation of low-level features of the image, e.g. that the white dots within the black foreground region on the leftmost part of the image are “furry”, is influenced top-down by their abstract mental representation of a cow.

How do skilled artists navigate down the ladder of visual abstraction consciously?

In Drawing on the Right Side of the Brain, Betty Edw...
Do we know if spaced repetition can be used with randomized content?
by Kinrany — 2019-11-17T18:01:54.337Z

Disclaimer: there may be major flaws in the way I use words. Corrections are welcome.

Suppose I want to memorize all the software design patterns.

I could use spaced repetition and create a new deck of flashcards. Each card would have the name of the pattern on one side and the definition on the other.

This would help me understand references to patterns without opening Wikipedia every time. This would probably help me recognize patterns by descriptions, as long as they're close enough to the definitions.

But this wouldn't help me recognize patterns just by looking at their implementations. I'd have to actively think about each pattern I remember and compare the definition and the code.

I could create a second deck, with names and examples. But then I'd just memorize those specific examples and maybe get better at recognizing similar ones.

This problem is similar to that of testing software. (There must be a more straightforward analogy, but I couldn't find one.) Individual tests can only prevent individual errors. Formal verification is better, but not always possible. The next best thing is fuzzing: using random inputs and heuristics like "did it crash?".

So I wonder if I could generate new examples on the fly. (More realistically, pull hand-labeled examples from a database.)

The idea is that a skill like recognizing a pattern in the code should also be a form of memory. Or at least the parts of it that do not change between the examples. So using spaced repetition with randomized examples would be like JIT-compilation in brains.

There was an LW post about genetic programming working better when the environment was modular. Maybe something similar would happen here.

But I couldn't find anything on the internet. Has anybody seen any research on this?
How common is it for one entity to have a 3+ year technological lead on its nearest competitor?
by Daniel Kokotajlo — 2019-11-17T15:23:36.913Z

I'm writing a follow-up to my blog post on soft takeoff and DSA, and I am looking for good examples of tech companies or academic research projects that are ~3+ years ahead of their nearest competitors in the technology(ies) they are focusing on.

Exception: I'm not that interested in projects that are pursuing some niche technology, such that no one else wants to compete with them. Also: I'm especially interested in examples that are analogous to AGI in some way, e.g. because they deal with present-day AI or because they have a feedback loop effect.

Even better would be someone with expertise on the area being able to answer the title question directly. Best of all would be some solid statistics on the matter. Thanks in advance!
AGI safety and losing electricity/industry resilience cost-effectiveness
by rosstieman — 2019-11-17T06:48:34.063Z

Cross posted on Effective Altruism Forum https://forum.effectivealtruism.org/posts/XA8QSCL7wZ973i6vr/agi-safety-and-losing-electricity-industry-resilience-cost

Below is a paper about to be submitted. The focus is on

interventions

that could improve the long-term outcome given catastrophes that disrupt electricity/industry, such as solar storm, high-altitude electromagnetic pulse (HEMP), narrow AI computer virus, and extreme pandemic. Work on these interventions is even more neglected than interventions for

feeding everyone if the sun is blocked

. Cost-effectiveness is compared to a modified AGI safety cost-effectiveness

model

posted earlier on the EA forum. Two different cost-effectiveness estimates for losing industry interventions were developed: one by Denkenberger and a poll at EA Global San Francisco 2018, and the other by Anders Sandberg at Future of Humanity Institute. There is great uncertainty in both AGI safety and interventions for losing industry. However, the models have ~99% confidence that funding interventions for losing industry now is more cost effective than additional funding for AGI safety beyond ~$3 billion. This does not take into account model or theory uncertainty, so the confidence would likely decrease. However, in order to make AGI safety more cost effective, this required changing four variables in the Sandberg model to the 5th percentile on the pessimistic end simultaneously. For the other model, it required changing seven variables. Therefore, it is quite robust that a significant amount of money should be invested in losing industry interventions now. There is closer to 50%-88% confidence that spending the ~$40 million on interventions for losing industry is more cost effective than AGI safety. Overall, AGI safety is more important and more total money should be spent on it. The modeling concludes that additional funding would be justified on both causes even for the present generation.

Long Term Cost-Effectiveness of Interventions for Loss of Electricity/Industry Compared to Artificial General Intelligence Safety

David Denkenberger 1,2, Anders Sandberg 3, Ross Tieman *1, and Joshua M. Pearce 4,5

1. Alliance to Feed the Earth in Disasters (ALLFED), Fairbanks, AK 99775, USA

2. University of Alaska Fairbanks, Fairbanks, AK 99775, USA

3. Future of Humanity Institute, University of Oxford, Oxford, UK

4. Department of Material Science ...
Matthew Walker's "Why We Sleep" Is Riddled with Scientific and Factual Errors
by habryka — 2019-11-16T20:27:57.039Z
Link: https://guzey.com/books/why-we-sleep/#the-full-discussion-of-sleep-deprivation-therapy-from-chapter-7

This was one of the most thought-provoking posts I read this month. Mostly because I spent a really large number of hours of my life sleeping, and also significantly increased the amount that I've been sleeping over the past three years, and this has me seriously considering reducing that number again.

The opening section of the article:

Matthew Walker (a) is a professor of neuroscience and psychology at the University of California, Berkeley, where he also leads the Center for Human Sleep Science.

His book Why We Sleep (a) was published in September 2017. Part survey of sleep research, part self-help book, it was praised by The New York Times (a), The Guardian (a), and many others. It was named one of NPR’s favorite books of 2017. After publishing the book, Walker gave a TED talk, a talk at Google, and appeared on Joe Rogan’s and Peter Attia’s podcasts. A month after the book’s publication, he became (a) a sleep scientist at Google.

On page 8 of the book, Walker writes:

> [T]he real evidence that makes clear all of the dangers that befall individuals and societies when sleep becomes short have not been clearly telegraphed to the public … In response, this book is intended to serve as a scientifically accurate intervention addressing this unmet need [emphasis in this quote and in all quotes below mine]

In the process of reading the book and encountering some extraordinary claims about sleep, I decided to compare the facts it presented with the scientific literature. I found that the book consistently overstates the problem of lack of sleep, sometimes egregiously so. It misrepresents basic sleep research and contradicts its own sources.

In one instance, Walker claims that sleeping less than six or seven hours a night doubles one’s risk of cancer – this is not supported by the scientific evidence. In another instance, Walker seems to have invented a “fact” that the WHO has declared a sleep loss epidemic. In yet another instance, he falsely claims that the National Sleep Foundation recommends 8 hours of sleep per night, and then uses this “fact” to falsely claim that two-thirds of people in developed nations sleep less than the “the recommended eight hours of nightly sleep” – a myth that spread like wildfire after the book’s publication.

Walker’s book has likely wasted thousands of hours of life and worsened the health of people who read it and too

...
[Productivity] Task vs. time delimitation
by aaq — 2019-11-16T16:27:30.380Z

A draft from my personal-productivity journal.

TL;DR

A natural 2x2 falls out when you organize tasks by how clear you are on their start/end times (or the amount of time they'll actually take), and how clear it is in a non-time sense that you've started or finished it. I call these two axes time and task delimitation.

Time delimitation and task delimitation defined

Time delimited

Things which are highly time-delimited have clear, natural start times and end times. Things can also be considered highly time-delimited if you know to a good deal of accuracy about how long they will take. Things which are less time-delimited are fuzzy as to when they start and end.

Task delimited

Things which are highly task-delimited have clear, natural non-temporal start and end states. Things which are not highly task-delimited are fuzzier as to what their starting and ending states are.

The four kinds of labor

Here's how I generally name and think through these categories.

Factory Labor / Serfdom (high time, high task)

Some things are both highly time- and task-delimited. The example par excellence is the academic test. In a typical test, you have a specific amount of time, and a specific start and end state you want to leave the test in. Walk in -> take test -> hand in -> walk out. The preparation for the test is more like Salary Labor (low time/high task, measured against internal confidence that you've studied enough), but the test itself is Factory Labor.

Another example might be the manufacturing work done after a designer has gone through a few mockups and is ready to begin building a prototype. The goal: Create a prototype given blueprints and materials. You have a limited number of workshop hours you can spend in an average day to get them done. This task feels more task- than time-delimited to me, because you can run overtime, but it's pretty high on both axes.

I call this Factory Labor, or Serfdom if I'm feeling ornery. You have a specific number of widgets you have to crank out in your shift, so you work to accomplish that. Most people will end up cranking out about the same, average number of widgets, for the same, average amount of time.

Wage Labor / Meetings (High time; low task)

I work IT support as a part time job at my college. I have a set number of hours I have to be in for, and while some of the tasks contained within that time are...
EA Forum AMA - MIRI's Buck Shlegeris
by Rob Bensinger — 2019-11-15T23:27:07.238Z
Link: https://forum.effectivealtruism.org/posts/tDk57GhrdK54TWzPY/i-m-buck-shlegeris-i-do-research-and-outreach-at-miri-ama

Buck Shlegeris is doing an AMA on the Effective Altruism Forum:

I'm going to do an AMA on Tuesday next week. Below I've written a brief description of what I'm doing at the moment. Ask any questions you like; I'll respond to as many as I can on Tuesday.

Although I'm eager to discuss MIRI-related things in this AMA, my replies will represent my own views rather than MIRI's, and as a rule I won't be running my answers by anyone else at MIRI. Think of it as a relatively candid and informal Q&A session, rather than anything polished or definitive.
----
I'm a researcher at MIRI. At MIRI I divide my time roughly equally between technical work and recruitment/outreach work.
On the recruitment/outreach side, I do things like the following:
- For the AI Risk for Computer Scientists workshops (which are slightly badly named; we accept some technical people who aren't computer scientists), I handle the intake of participants, and also teach classes and lead discussions on AI risk at the workshops.
- I do most of the technical interviewing for engineering roles at MIRI.
- I manage the AI Safety Retraining Program, in which MIRI gives grants to people to study ML for three months with the goal of making it easier for them to transition into working on AI safety.
- I sometimes do weird things like going on a Slate Star Codex roadtrip, where I led a group of EAs as we travelled along the East Coast going to Slate Star Codex meetups and visiting EA groups for five days.
On the technical side, I mostly work on some of our nondisclosed-by-default technical research; this involves thinking about various kinds of math and implementing things related to the math. Because the work isn't public, there are many questions about it that I can't answer. But this is my problem, not yours; feel free to ask whatever questions you like and I'll take responsibility for choosing to answer or not.
----
Here are some things I've been thinking about recently:
- I think that the field of AI safety is growing in an awkward way. Lots of people are trying to work on it, and many of these people have pretty different pictures of what the problem is and how we should try to work on it. How should we handle this? How should you try to work in a field when at least half the "experts" are going to think that your research direction is misguided?
- The AIRCS workshops that I'm involved with contain a variet

...
Hard to find factors messing up experiments: Examples?
by Hazard — 2019-11-15T17:46:03.762Z

In Richard Feynman's talk on cargo cults he mentions a story about experiments with rat mazes (skip if you recognize it)

All experiments in psychology are not of this type, however. For example, there have been many experiments running rats through all kinds of mazes, and so on—with little clear result. But in 1937 a man named Young did a very interesting one. He had a long corridor with doors all along one side where the rats came in, and doors along the other side where the food was. He wanted to see if he could train the rats to go in at the third door down from wherever he started them off. No. The rats went immediately to the door where the food had been the time before.

The question was, how did the rats know, because the corridor was so beautifully built and so uniform, that this was the same door as before? Obviously there was something about the door that was different from the other doors. So he painted the doors very carefully, arranging the textures on the faces of the doors exactly the same. Still the rats could tell. Then he thought maybe the rats were smelling the food, so he used chemicals to change the smell after each run. Still the rats could tell. Then he realized the rats might be able to tell by seeing the lights and the arrangement in the laboratory like any commonsense person. So he covered the corridor, and, still the rats could tell.

He finally found that they could tell by the way the floor sounded when they ran over it. And he could only fix that by putting his corridor in sand. So he covered one after another of all possible clues and finally was able to fool the rats so that they had to learn to go in the third door. If he relaxed any of his conditions, the rats could tell.

I also recall some experiment where it was eventually discovered that the control of some microbiology experiment was screwed because the paper towels used in some process were made with wood that was treated with a particular chemical before it was turned into the paper towel (was this linked by gwern at some point?).

I'm interested in collecting any and all examples you have of these events; through painstaking effort someone realized that XYZ was interfering with an inten...
Lazy Compost is Worse Than Landfill
by jefftk — 2019-11-15T16:20:01.629Z

Growing up we kept a compost pile. We'd keep food scraps in a bucket
in the kitchen, and dump them into a fenced-in pile in the backyard
along with yard waste. When that pile was full we'd start a new one,
and after a year or two the old pile would have turned into dirt we
could use in the garden. While it smelled kind of bad, and would have
bugs, we liked that we were doing our part for the planet.

Unfortunately, in retrospect I think our piles were mostly decomposing
anaerobically, and it would have been better to just throw things out.

There are two main kinds of decomposition you can get in a compost
pile: aerobic or anaerobic. Most advice you find is for aerobic ("with
air") composting: water your pile, turn it every couple weeks with a
fork, include branches and similar things that keep air pockets.
Aerobic composting gives off carbon dioxide (CO2). If you don't do
these things and just let it sit, however, you get anaerobic ("without
air") composting, which gives off methane (CH4) instead.

Methane is a much more potent greenhouse gas than carbon dioxide; in
calculating "carbon dioxide equivalents" (CO2e) people usually count
it as 25x worse. Landfills decompose mostly anaerobically, but modern
landfills capture and burn much of the methane, converting it to
carbon dioxide. Even if your trash currently goes to a landfill
without capture technology, fixing a smaller number of centralized
sourced is much more practical than many backyard piles.

Industrial composting doesn't have these problems: it's generally well
managed. Either they make sure to give it enough air to facilitate
aerobic decomposition, or they run anaerobic digestion sealed to
capture the methane.

If you don't have industrial compost at home, though, could the
benefit of not using landfill space make backyard composting worth it?
There was a period when environmentalists were raising awareness
around the idea that we would run out of space for trash:

Garbage, garbage, garbage, garbage

What will we do when there's no place left

To put all the garbage

—Garbage,
Bill Steele, 1969

This wasn't actually a serious issue, however. While there have been
regional shortages, the problem is one of needing to open new
landfills, not a lack space. The actual amount of space you need for
trash is tiny. The US produces about 260
million tons of trash per year...