trentmkelly/LessWrong-43k huggingface dataset:
Muting on Group Calls
by jefftk — 2020-10-02T20:30:11.280Z

Ben recently wrote a post with a lot of advice on how to have

better video calls, and reading
the

HN
discussion one of the

most

controversial
suggestions was that you should figure out a mic setup where you can
stay unmuted.

I see where this is coming from. We've all been on calls with people
who had all sorts of extraneous background noise. It's distracting and
frustrating, especially in large groups or when combined with call
software that focuses whichever participant is the loudest. If you're
just using your laptop's speakers and microphone, then yes, muting is
good.

On the other hand, video calls are famously draining. They don't feel
natural, and conversation doesn't quite work properly. A lot of this
is latency (No Bluetooth! Run
Ethernet! Wireless is
a Trap!) but every barrier to conversational flow makes things
worse. If you have to choose, muting yourself when you're not
speaking is generally better than everyone hearing your background
noise, but we have the technology so that you don't have to choose:
point a directional microphone at your mouth.

This works even in relatively noisy environments: my kids can be
playing loudly, even shouting, right outside my door and my coworkers
don't hear anything. Compare my voice at the
mic(mp3) to 5ft away
(mp3). I have a Jabra
Evolve 40 (per the Wirecutter)
and it works well. My guess is that anything else (wired!) with a
boom mic should be pretty good, but I haven't tested.

Since it doesn't look like remote work will be ending anytime soon,
if you have a job where are you do a lot of calls, making them feel
natural is worth solid investment.
Attention to snakes not fear of snakes: evolution encoding environmental knowledge in peripheral systems
by Kaj_Sotala — 2020-10-02T11:50:05.327Z

Sinking In: The Peripheral Baldwinisation of Human Cognition. Cecilia Heyes, Nick Chater & Dominic Michael Dwyer. Trends in Cognitive Sciences, 2020.

Some theories have proposed that humans have evolved to experience some stimuli (e.g. snakes, spiders) as more potentially frightening, so that a fear for these entities is learned faster than a fear for more neutral things. In evolutionary psychology, there has been talk about modules for a fear of snakes, for example. However, research suggests that rather than “the fear system” itself having innate biases towards picking up particular kinds of fears, humans are evolutionarily biased towards paying extra attention to things like spiders and snakes. Because of these stimuli being more attended than others, it also becomes more probable that a fear response gets paired with them.

The authors call the attention system “peripheral” and the fear system “central”, in that the attention system brings in information for the fear system to process. (This is in analogy to the peripherals of a computer, where e.g. the keyboard and mouse are used to deliver information to the central processor.) They argue that in general, while it is possible for responses to specific environmental stimuli to become genetic as sensitivty for those stimuli is selected for, this learning will be more likely to get encoded into “peripheral” than “central” systems.

One of their other examples is that the central mechanisms of language learning seem theoretically and empirically unlikely to be affected by the environment – there are no genes for learning English grammar better than Chinese grammar. However, there are indications that the peripheral mechanisms of language have been more affected. E.g. some languages use lexical tone (where word identities are partly defined by pitch contours), and genes that seem to make lexical tone easier to perceive seem to be more common among speakers of those languages.

Seligman’s account suggested that specialised, central mechanisms of fear learning more readily connect aversive events, such as electric shock, with fear-relevant stimuli, such as snakes – which presented genuine threats to our evolutionary ancestors – than with ‘fear-irrelevant’ stimuli such as geometric shapes or flowers. This account predicts that fear of fear-relevant objects should be learned faster, and be extinguished more slowl

...
Math That Clicks: Look for Two-Way Correspondences
by TurnTrout — 2020-10-02T01:22:18.177Z

Andrew Critch made this explicit to me.

Suppose you want to formalize "information". What does it mean to "gain information about something"?

You know about probability distributions, and you remember that Shannon came up with information theory, but you aren't allowed to look that up, because you're stranded on a desert island, and your "rescuers" won't take you to safety until you can formalize this.

Don't you hate it when this happens?

Anyways, you're starving, and so you think:

Suppose I'm trying to figure out the bias θ on a coin someone is flipping for me. I start with a uniform prior over θ∈[0,1], and observe the process for a while. As I learn more, I become more confident in the true hypothesis,[1] and so my credence increases.

So maybe, I gain information about a hypothesis when the Bayesian update increases the hypothesis's probability!

You sanity-check your guess by checking this against remembered instances of information gain: memorizing facts in history class, a friend telling you their address, and so on.

Your stomach rumbles. This guess should be good enough. You tell your would-be rescuers your final answer...

🚨 No! Wrong! 🚨 That's not how you should discover new math. True, you formalized a guess that was prompted by examples of the thing-in-question (information gain) – there was a one-way correspondence from intuitive concept ⟹ math. That's not enough.

Recovering the intuition from the math

Don't think for a second that having math representing your thoughts means you've necessarily made progress – for the kind of problems I'm thinking about right now, the math has to sing with the elegance of the philosophical insight you're formalizing.

~ How I Do Research

I claim that you should create math such that the math suggests the intuitive concept it's formalizing. If you obfuscate any suggestive notation and show the math to someone who already knows the intuitive concept, would the concept jump back out at them?

Above, information gain was formalized as increase in credence; roughly,

Info-gain(hypothesis, observation):=P(h∣o)−P(h).

Assuming we already know about probability and arithmetic and function notation, what does the obfuscated version bring to mind?

met32vfs(e1ht, jt3n):=P(e1ht∣jt3n)−P(e1ht).

When this value is high, you're just becoming more confident in something. What if you gain information that makes you confused?

What if you th...
A simple device for indoor air management
by Richard Korzekwa  — 2020-10-02T01:02:42.471Z

Note: I wrote this in much more of a hurry than I normally write things, because I have noticed that I get way too meticulous, so it takes forever and I eventually give up. I hope it is still readable/useful!

Like many of us living in the Bay Area and other parts of the Western US, I have been dealing with smoke, and like most people who live and work indoors, I sometimes deal with stuffy, high-CO2 air. Smoke is bad for health, mood, productivity, sleep, and comfort. CO2 is bad for all the same things except perhaps health. Even people who live in places without seasonal wildfires might benefit from being able to filter out pollution or allergens without closing off their house and building up CO2.

The standard way to handle smoke is to buy air purifiers that pull air from inside the room, run it through a HEPA filter to remove particulates, and push it back out into the room. These work well for keeping particulate levels low when the air is not too smoky. However, if the air is very smoky, the only way they can keep up is if only a small amount of air is entering the house from outside. This means either closing the windows or buying more air purifiers. The trouble with closing all the windows is that CO2 created by people in the house will build up, and the trouble with buying more air purifiers is that it is expensive and impractical for high levels of particulates.

The solution is to filter air as it enters the house. An air filter that is pulling in smoky air from outside removes more particulates per minute for the same throughput than one that is filtering less particulate-filled air. An additional benefit of this is that it serves the purpose of actively cycling air into the house, rather than allowing it to passively happen through an open window. I spent some time searching for filters that work this way, and I was unable to find one. I also looked a bit for an in-window air conditioner that included a HEPA filter, but did not find one. So I built one myself. It was actually quite easy and inexpensive.

The filter consists of a cardboard box, a HEPA filter, an in-line duct fan, a short length of flexible ducting, and a cardboard flap. The box has a large rectangular hole cut into one side and a 6" circular hole cut into another side. The fan is attached to the 6" hole using duct tape (I used "all-weather" duct tape, since some of it would be exposed to...
Sunday Meetup: Workshop on Online Surveys with Spencer Greenberg 
by Raemon — 2020-10-02T00:34:35.586Z

Alternate title: "Geez LessWrong peeps do more empiricism please".

At this Sunday's LW meetup, special guest Spencer Greenberg will be running a workshop on how to run a psychology study using online tools.

The LessWrong community raises a lot of interesting questions. I've long wished we did more empirical answering of those questions. In the past year I've become excited about several of the tools Spencer has created, which help make it easier to run surveys and studies over mechanical turk. I think it'd be valuable if a number of LessWrong users developed a basic affordance doing that.

So this Sunday at noon PT, we'll be hosting a 2 hour workshop, with two phases:

In the first half, Spencer will walk us through how to use Positly (a wrapper for mechanical turk that gives you lots of demographic data and streamlines the process), and GuidedTrack, which can be used to create flexible surveys and online experiments. He'll also talk about practices for good study design.

In the second half, people will work to create a simple project in Positly and Guided Track. Spencer will answer questions that come up. At the end, people are welcome to briefly share what they ended up working on.

The meetup will be held in Gather Town – we'll be experimenting with using their Presenter Mode functionality so that everyone can hear Spencer give his talk, and then chat in small groups as they work on their separate projects. Users will be able to "walk up to the microphone" to ask questions that the whole room can hear.

Gather Town instance:

https://gather.town/app/Dgohwk4BUg5HjGZE/lesswrong-clubhouse
Linkpost: Choice Explains Positivity and Confirmation Bias
by Gunnar_Zarncke — 2020-10-01T21:46:46.289Z

https://www.nature.com/articles/s41562-020-0919-5

Abstract:

The valence of new information influences learning rates in humans: good news tends to receive more weight than bad news. We investigated this learning bias in four experiments, by systematically manipulating the source of required action (free versus forced choices), outcome contingencies (low versus high reward) and motor requirements (go versus no-go choices). Analysis of model-estimated learning rates showed that the confirmation bias in learning rates was specific to free choices, but was independent of outcome contingencies. The bias was also unaffected by the motor requirements, thus suggesting that it operates in the representational space of decisions, rather than motoric actions. Finally, model simulations revealed that learning rates estimated from the choice-confirmation model had the effect of maximizing performance across low- and high-reward environments. We therefore suggest that choice-confirmation bias may be adaptive for efficient learning of action–outcome contingencies, above and beyond fostering person-level dispositions such as self-esteem.

(emphasis mine)
Open & Welcome Thread – October 2020
by Ben Pace — 2020-10-01T19:06:45.928Z

If it’s worth saying, but not worth its own post, here's a place to put it.

If you are new to LessWrong, here's the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are invited.

If you want to explore the community more, I recommend reading the Library, checking recent Curated posts, seeing if there are any meetups in your area, and checking out the Getting Started section of the LessWrong FAQ. If you want to orient to the content on the site, you can also check out the new Concepts section.

The Open Thread tag is here.
Hiring engineers and researchers to help align GPT-3
by paulfchristiano — 2020-10-01T18:54:23.551Z

My team at OpenAI, which works on aligning GPT-3, is hiring ML engineers and researchers. Apply

here

for the ML engineer role and

here

for the ML researcher role.

GPT-3 is similar enough to "prosaic" AGI that we can work on key alignment problems without relying on conjecture or speculative analogies. And because GPT-3 is already being deployed in the OpenAI API, its misalignment matters to OpenAI’s bottom line — it would be much better if we had an API that was trying to help the user instead of trying to predict the next word of text from the internet.

I think this puts our team in a great place to have an impact:

If our research succeeds I think it will directly reduce existential risk from AI. This is not meant to be a warm-up problem, I think it’s the real thing.
We are working with state of the art systems that could pose an existential risk if scaled up, and our team’s success actually matters to the people deploying those systems.
We are working on the whole pipeline from “interesting idea” to “production-ready system,” building critical skills and getting empirical feedback on whether our ideas actually work.

We have the real-world problems to motivate alignment research, the financial support to hire more people, and a research vision to execute on. We are bottlenecked by excellent researchers and engineers who are excited to work on alignment.

What the team does

In the past Reflection focused on fine-tuning GPT-3 using a reward function learned from human feedback. Our most recent results are

here

, and had the unusual virtue of simultaneously being exciting enough to ML researchers to be accepted at NeurIPS while being described by Eliezer as “directly, straight-up relevant to real alignment problems.”

We’re currently working on three things:

[20%] Applying basic alignment approaches to the API, aiming to close the gap between theory and practice.
[60%] Extending existing approaches to tasks that are too hard for humans to evaluate; in particular, we are training models that summarize more text than human trainers have time to read. Our approach is to use weaker ML systems operating over shorter contexts to help oversee stronger ones over longer contexts. This is conceptually straightforward but still poses significant engineering and ML challenges.
[20%] Conceptual research on domains that no one knows how to oversee and empirical work on debates between hum

...
Covid 10/1: The Long Haul
by Zvi — 2020-10-01T18:00:00.848Z

If you were watching the so-called ‘presidential debate’ on Tuesday night, first off, you have my sympathies. It was the day after the Day of Atonement. If you watched, no matter what wrongs you may have committed this past year, and no matter who you intend to vote for, no one can deny that you have atoned. Your slate is clean.

Alas, the country is not so lucky. Getting away clean is not a near-term prospect on any level.

What did we learn from the Covid-19 portion of the debate?

Very little. The focus was on Biden blaming Trump for things being terrible, and Trump saying things are great and blaming Biden claiming that with Biden in charge things would have been worse. No one said anything about any of the real issues except for masks. On masks, Trump decided to dispute that there was agreement on masks, to point out that people changed their mind about masks, and so on, in case his supporters were in danger of protecting themselves or others by wearing one.

Biden’s criticisms of Trump left out most of the worst things Trump did regarding Covid-19. Biden’s plans, as stated, didn’t provide the help we need to solve Covid-19. Mostly, what we learned is what we already knew. Biden has little interest in talking about the ways to actually solve the problem, and mostly does correct symbolic actions like supporting PPE or small business or wearing masks while blaming Trump for not doing so. Whereas Trump actively gets in the way of solving the problem and lies about, well, basically everything. Not where one hopes the choices to be, but hopefully an easy choice nonetheless.

Biden repeated in the debate the general expectation that another wave of infections and deaths is coming Real Soon Now, and that deaths may double over the next few months, as the rate goes up by a factor of five or more. That’s the new Very Serious Person position.

Along with the old Very Serious Person position that herd immunity is of course ending Real Soon Now, probably last week.

Do we see any signs of any of that? Is it justified?

Let’s run the numbers.

Positive Test Counts

Date	WEST	MIDWEST	SOUTH	NORTHEAST
Aug 6-Aug 12	93042	61931	188486	21569
Aug 13-Aug 19	80887	63384	156998	20857
Aug 20-Aug 26	67545	66540	132322	18707
Aug 7-Sep 2	55000	75401	127414	21056
Sep 3-Sep 9	47273	72439	106408	21926
Sep 10-Sep 16	45050	75264	115812	23755
Sep 17-Sep 23	54025	85381	127732	23342
Sep 24-Sep 30	55496	92932	106300	27214

Th...
Words and Implications
by johnswentworth — 2020-10-01T17:37:20.399Z

Professor Quirrell didn't care what your expression looked like, he cared which states of mind made it likely.

- HPMOR, Ch. 26

Words should not always be taken at face value. Presumably you know this. You probably have some heuristics about specific situations or claims in which a person’s words should not be taken literally. But I think most peoples’ heuristics here are far too narrow - that is, most people take words literally far too often.

The

sequences

talk about habitually asking, in everyday life, “What do I think I know, and how do I think I know it? What physical process produced this belief?”. I suggest a similar habit for words in everyday life: “What is being said, and why is it being said? What physical process produced these words?”.

This post is a bunch of examples, in an attempt to goad your system-1 into looking past surface-level meanings more often.

Dishes

Once or twice a week, I’ll hear my girlfriend yell from the kitchen “Joooooohn! Why are there so many dirty dishes in the sink?”. Going to wash the dishes is not the correct response to this.

If I go wash the dishes, then she will quite consistently find something else to complain about in the meantime - floor needs sweeping, nothing to eat, neighbors are noisy, etc. Usually multiple other things. It was never really about the dishes in the first place, after all. Really, she’s stressed and looking for an outlet.

A hug fixes the problem much more effectively than washing the dishes would.

The general mental motions required to notice this are something like:

Stop. Don’t just go wash the dishes.
Ask why this is coming up, and in particular why it’s coming up right now specifically. Is there any particular reason the dishes are relevant right now? (Sometimes the answer is “yes”, and then it’s time to go do the dishes.)
If there isn’t a reason why the dishes are relevant right now, then I need to figure out the actual reason for the complaint.

This scene

from the movie Limitless is a similar example. It’s a bit over-the-top, but it’s one of the few examples of a supposedly-intelligent person in a Hollywood movie actually doing something intelligent (as opposed to

technobabble

).

Designers and Engineers

If you work in software, one problem you’ve probably encountered on the job is “the thing they literally ask for is only very loosely correlated with the thing they actually want”.

A designer or product manag...
Your Standards are Too High
by Neel Nanda — 2020-10-01T17:03:31.969Z
Link: https://neelnanda.io/blog/35-standards

Introduction

I’m a perfectionist, and a pretty neurotic person, so a common experience for me is feeling dissatisfied and guilty. Some part of me is deeply convinced that everything should be easy, and fast. That if it’s not, I am failing. That I could have done better.

When I do an exam, no matter how well I actually did, this part is always drawn to the marks missed and the dumb screw ups. When I'm thinking about something hard and find an inspired idea, it always feels obvious in hindsight and I kick myself for missing it. When I'm tired and spend the first hour of a day procrastinating, even if I completely pull it around from there, I'll compare what I do to what could have been.

I think this is an extremely common pattern, and one that I see in many of my friends. And the crux of it is the word could have, the standards we hold ourselves to. This mix of guilt, insecurity and unrealistic standards is a major loss of productivity, and a major source of unhappiness. And this pisses me off.

I am going to spend this post trying to argue exactly why this is so terrible, and how I personally think about doing something about it. I expect that some lucky people reading this won't relate at all to these problems, and more power to you! But for those who resonate, I hope my thoughts can add a useful perspective!

The key framework I will argue for is as follows:

My coulds push me towards things that are fundamentally unrealistic, and my intuitions are badly calibrated. Hindsight bias, planning fallacy, far mode thinking all combine to create a fundamentally wrong set of expectations
The point of standards is to push me to be better. They are an instrumental tool, not a terminal goal. There are ways my standards do not help me to become better, and so my desire to be better means I need to change them

The impulse to become better is not the problem, it's the execution

It's hard to change your standards just by willing it, or by realising they're unhealthy. My main tool here is to become better calibrated, and try to ground my standards in the true goal of becoming better.

There is also a problem of having bad goals, and standards tying me to goals I no longer ultimately care about. This is a very real problem, but I won't focus on it here - my earlier post on prioritisation somewhat gives my thoughts there.

It's ...