trentmkelly/LessWrong-43k huggingface dataset:
Velocity-sensitive electronic drum pedals
by jefftk — 2020-12-08T20:20:04.762Z

For years, I've been making my own drum trigger pedals, for playing
drums with my feet while playing mandolin or piano. Recently, however,
I've started looking at commercial ones. It turns out there's quite
a range of options, depending on what you're looking for.

At the very low end, there are pedals that are just switches. I'm not
interested in these: I want velocity sensitivity. This means you can
play louder or quieter depending on circumstances, and can configure
one pedal to trigger additional drums when triggered especially
forcefully.

As you get to fancier pedals, there tend to be several things that go
together:

More consistent velocity sensitivity

Wider dynamic range

Sturdier materials

Better imitation of traditional kick drum action

I care a lot about the first, some about the second two, and not an all
about the fourth because I don't already play a kick drum. This makes
reading reviews tricky: someone may pan a pedal for feeling "fake" or
having the wrong level of springiness when (I think) it isn't
something I would mind at all.

Aside: this is also how I feel about keyboards: I want it to be 88
keys and velocity sensitive, but it doesn't need to reproduce the
feeling of playing on a real piano. You can do it, it just costs a
lot and weighs a lot.

The most standard option here is typically to use a standard kick
pedal with a trigger pad:

This provides a pretty realistic feeling, but it's much too bulky for
me. I want something where I can take four of these on a plane, along
with whatever else I'm going to be playing for the gig.

Pedals without a beater are called, not to surprisingly, "beaterless".
They all have the same basic design: a hinged pedal that comes down
onto a velocity sensitive pad, with a spring to bring it back up
again:

DrumTech FAT
Pedal (~$200):

Roland KT-9 (~$190):

Yamaha KU-100
(~$70)

Roland also makes an entry-level kick pedal as part of their TD-1K set, but
there's apparently no way to buy it as an individual unit.

If you look around online there are some things that appear to be even
cheaper, but it looks to me like they are actually just switches.

I decided to order the Yamaha pedal, since it's the cheapest and seems
like it's good enough for my purposes. Comparing it to my DIY pedals,
it does have a wider dynamic range, and is more consistent....
Conservatism in neocortex-like AGIs
by Steven Byrnes — 2020-12-08T16:37:20.780Z

(Related to

Stuart Armstrong’s post last summer on Model Splintering

.)

Let’s say I have a human personal assistant. Call him Ahmed. Ahmed is generally trying to help me. Sometimes a situation comes up where Ahmed feels surprised and confused. Other times, a situation comes up where Ahmed feels torn and conflicted about what to do next. In both those types of situations, I would want Ahmed to act conservatively (only do things which he considers clearly good in all respects)—or better yet, stop and ask me for help. Maybe Ahmed would say something to me like "On the one hand ... On the other hand...". That would be great. I would be very happy for him to do that.

So, we should try to instill a similar behavior in AGIs!

I think something like this is likely to be feasible, at least in the neocortex-like AGIs that I’ve been thinking about.

More specifically: OK sure, maybe we need new breakthroughs in interpretability tools in order to understand what an AGI is confused or conflicted about, and why. But it seems quite tractable to develop methods to recognize that the AGI is confused or conflicted, and then to run some special-purpose code whenever that happens. Or more simply, just have it not take any action about which it feels confused or conflicted.

To start with, I’ll elaborate on what I think it would look like to have a circuit that detects when an AGI is confused or conflicted. Start with confusion.

What are the algorithmic correlates of feeling confused?

Confusion is pretty straightforward

in my picture

. You have a strongly-held generative model—one that has made lots of correct predictions in the past—and it predicts X under some circumstance. You have another strongly-held generative model which predicts NOT-X under the same circumstance. The message-passing algorithm flails around, trying in vain to slot these two contradictory expectations into the same composite model, or to deactivate one of them.

That’s confusion.

(I guess in

logical induction

, confusion would look like two very successful traders with two very different assessments of the value of an asset, or something like that, maybe. I don't know much about logical induction.)

Either way, confusion seems like it should be straightforwardly detectable by monitoring the algorithm.

What are the algorithmic correlates of feeling conflicted?

In broad outline, the brain has a world-model, and it's stored mai...
How long does it take to become Gaussian?
by Maxwell Peterson — 2020-12-08T07:23:41.725Z

The central limit theorems all say that if you convolve stuff enough, and that stuff is sufficiently nice, the result will be a Gaussian distribution. How much is enough, and how nice is sufficient?

Identically-distributed distributions converge quickly

For many distributions d, the repeated convolution d∗d∗⋯∗d looks Gaussian. The number of convolutions you need to look Gaussian depends on the shape of d. This is the easiest variant of the central limit theorem: identically-distributed distributions.

The uniform distribution converges real quick:

The result of uniform(1, 2) * uniform(1, 2) * ... * uniform(1, 2), with 30 distributions total. This plot is an animated version of the plots in the previous post. The black curve is the Gaussian distribution with the same mean and variance as the red distribution. The more similar red is to black, the more Gaussian the result of the convolutions is.

The numbers on the x axis are increasing because the mean of f∗g is the sum of the means of f and g, so if we start with positive means, repeated convolutions shoot off into higher numbers. Similar for the variance - notice how the width starts as the difference between 1 and 2, but ends with differences in the tens. You can keep the location stationary under convolution by starting with a distribution centered at 0, but you can't keep the variance from increasing, because you can't have a variance of 0 (except in the limiting case).

Here's a more skewed distribution: beta(50, 1). beta(50, 1) is the probability distribution that represents knowing that a lake has bass and carp, but not how many of each, and then catching 49 bass in a row. It's fairly skewed! This time, after 30 convolutions, we're not quite Gaussian - the skew is still hanging around. But for a lot of real applications, I'd call the result "Gaussian enough".

beta(50, 1) convolved with itself 30 times.

A similar skew in the opposite direction, from the exponential distribution:

exp(20)

I was surprised to see the exponential distribution go into a Gaussian, because Wikipedia says that an exponential distribution with parameter θ goes into a gamma distribution with parameters gamma(n, θ) when you convolve it with itself n times. But it turns out gamma(n,θ) looks more and more Gaussian as n goes up.

How about ...
Why are delicious biscuits obscure?
by KatjaGrace — 2020-12-08T06:10:01.241Z

Crossposted from world spirit sock puppet.

I saw a picture of these biscuits (or cookies), and they looked very delicious. So much so that I took the uncharacteristic step of actually making them. They were indeed among the most delicious biscuits of which I am aware. And yet I don’t recall hearing of them before. This seems like a telling sign about something. (The capitalist machinery? Culture? Industrial food production constraints? The vagaries of individual enjoyment?)

Why doesn’t the market offer these delicious biscuits all over the place? Isn’t this just the kind of rival, excludable, information-available, well-internalized good that markets are on top of?

Some explanations that occur to me:

I am wrong or unusual in my assessment of deliciousness, and for instance most people would find a chocolate chip cookie or an Oreo more delicious.
They are harder to cook commercially than the ubiquitous biscuits for some reason. e.g. they are most delicious warm.
They are Swedish, and there are mysterious cultural or linguistic barriers to foods spreading from their original homes. This would also help explain some other observations, to the extent that it counts as an explanation at all.
Deliciousness is not a central factor in food spread. (Then what is?)

If you want to help investigate, you can do so by carrying out the following recipe and reporting on the percentile of deliciousness of the resulting biscuits. (I do not claim that this is a high priority investigation to take part in, unless you are hungry for delicious biscuits or a firsthand encounter with a moderately interesting sociological puzzle.)

Kolakakor

(Or Kolasnittar. Adapted from

House & Garden’s

account of a recipe in Magnus Nilsson’s “

The Nordic Baking Book

”. It’s quite plausible that their versions are better than mine, which has undergone pressure for ease plus some random ingredient substitutions. However I offer mine, since it is the one I can really vouch for.)

Takes about fifteen minutes of making, and fifteen further minutes of waiting. Makes enough biscuits for about five people to eat too many biscuits, plus a handful left over. (Other recipe calls it about 40 ‘shortbreads’)

Ingredients

200 g melted butter (e.g. microwave it)
180 g sugar
50 g golden syrup
50g honey
300 g flour, ideally

King Arthur gluten free flour

, but wheat flour will also do
1 teaspoon bicarbonate of soda (baking

...
White Sphere Hypothesis
by James S. Eriee — 2020-12-08T05:56:18.349Z

Imagine an existence where the only perspective for your entire life (40 years) has been from the inside of a white sphere. In this white sphere, you have no physical body, and there is nothing else in the white sphere with you. You only exist in this white sphere as a brain with perfectly working eyes attached. The gravity inside creates a perfect equilibrium which forces you to float in a single spot, but you can rotate in any direction. You can't see your own brain, or what is powering your brain. Your brain sleeps for 8 hours a day, and it's capable of dreaming. What do you think or dream about inside this white sphere?

I can't imagine you would be able to think about anything at all. You can't imagine, "What is this place?", because you don't know those words and what they mean. Any language happening in your conscious would be impossible. You've never seen an object or angle, so your concept of reality is confined within the white sphere, which, as far as you know, is infinite in every direction. My assumption would be that you'd have the cognitive ability of fetus and nothing more. On the other hand, could it be possible that evolutionary biology would force concepts in your conscious, provoking thought? If so, what would those concepts be? Would they be profound enough to make your conscious question its existence?

Besides sight, could it ever be possible to achieve sentience in this white sphere? And, if not, is it subjectivity that creates the sentience that drives us?
An elegant proof of Laplace’s rule of succession
by Eric Neyman — 2020-12-07T22:43:33.593Z
Link: https://ericneyman.wordpress.com/2020/11/29/an-elegant-proof-of-laplaces-rule-of-succession/

(Already know what Laplace's rule of succession is and just want the proof? Scroll down.)

Suppose I give you a bag of marbles and tell you that all the marbles are either green or black. You repeatedly reach in, pick out a random marble, and then put it back. You find that out of 100 draws, 70 of the marbles you took out of the bag were green. What's the probability that the next marble you'll draw will be green? Or, to put it another way, what's your best guess (expected value) of the fraction of marbles in the bag that are green?

A decently sensible answer is: 70%. After all, 70 of the 100 marbles you've seen were green, and you don't really have more information than that.

Okay, fair enough. But let's back up a bit. Suppose that you draw just one marble from the bag, and it's green. What's the probability that the next marble will be green too? The same reasoning as before would make you say 100%, but that doesn't make sense. It's not like you know, after just that one draw, that all the marbles in the bag are for sure green.

Really, I haven't given you enough information answer the question. What you need is some sort of prior information about the marbles in the bag. For example, here are two different assumptions you could make that lead to wildly different answers:

The marbles in the bag are either all green or all black. (In this case, 100% is the right answer.)
The marbles in the bag are chosen to be all green, all black, or 50/50, each with probability 1/3. (Under this assumption, the right answer turns out to be 5/6.)

Okay, but my question still stands. Suppose that, in real life, I give you a bag of marbles, tell you that they're all green or black, let you drawn one marble (it turns out green), and you have to guess the probability that the next marble you draw is green. What should you guess?

One reasonable assumption you could make is that, when filling the bag with marbles, I chose the fraction of green marbles uniformly at random. That is, I picked a random number between 0% and 100% and decided to make that fraction of all the marbles green. So your prior over the fraction of marbles that are green is uniformly distributed between 0 and 1. Another way to think about this assumption is that it's the simplest one you could make: you don't know what your prior distribution should be, but this seems like the simplest one.1

This assumption, called the un...
Please Take the 2020 Effective Altruism Survey
by Peter Wildeford — 2020-12-07T22:18:38.213Z

The 2020 Effective Altruism Survey is now live at the following link: https://www.surveymonkey.com/r/EAS2020LW

If you would like to share the EA Survey with others, please share this link: https://www.surveymonkey.com/r/EAS2020Share

The survey will close at the end of the year

-

What is the Effective Altruism Survey?

The Effective Altruism (EA) Survey provides valuable information about the demographics of the EA community, how people get involved, how they donate, what causes they prioritize, their experiences of EA, and more. LessWrong users are an important part of this community and we want to ensure they are represented.

The estimated average completion time for the main section of this year’s survey is 20 minutes. There is also an ‘Extra Credit’ section at the end of the survey, if you are happy to answer some more questions.

What's new this year?

There are two important changes regarding privacy and sharing permissions this year:
1) This year, all responses to the survey (including personal information such as name and e-mail address) will be shared with the Centre for Effective Altruism unless you opt out on the first page of the survey.

2) Rethink Priorities (the authors of the survey) will not be making an anonymised data set available to the community this year. We will, however, consider requests for us to provide additional aggregate analyses which are not included in our main series of posts.

Also the Centre for Effective Altruism has generously donated a prize of $500 USD that will be awarded to a randomly selected respondent to the EA Survey, for them to donate to any of the organizations listed on EA Funds. Please note that to be eligible, you need to provide a valid e-mail address so that we can contact you.

We would like to express our gratitude to the Centre for Effective Altruism for supporting our work.
Notes on Fairness
by David Gross — 2020-12-07T18:52:46.649Z

This post examines the virtue of fairness. It is meant mostly as an exploration of what others have learned about this virtue, rather than as me expressing my own opinions about it, though I’ve been selective about what I found interesting or credible, according to my own inclinations. I wrote this not as an expert on the topic, but as someone who wants to learn more about it. I hope it will be helpful to people who want to know more about this virtue and how to nurture it.

Fairness may seem like a things-I-learned-in-kindergarten virtue, but experimental evidence shows that it can be difficult for mature adults to wrap their heads around. To summarize: you can set up an experiment in such a way that a subject declares that being fair is very important, expresses that A is more fair than B, does B anyway, and then congratulates themself for their fairness, all seemingly with eyes wide open. More about that later…

What is fairness?

I struggled to define fairness. It seems to be different things, or at least to emphasize different things, in different contexts. (See also Eliezer Yudkowsky’s The Bedrock of Fairness and Is Fairness Arbitrary, and the comments to those.) I’ll stick to being descriptive rather than prescriptive:

One aspect of fairness is impartiality. A process tainted by favoritism, self-dealing, invidious discrimination, or nepotism is unfair. (Why am I still wearing these rags while my wicked stepsisters get all gussied up for the ball?)

A decision can be unfair if it was based on things that are not justly relevant, for instance if prejudice was involved. A decision can also be unfair if it is not based on anything at all: if it is arbitrary.

Fairness can be connected to merit. It is fair to give the trophy to the winner. It would be unfair to give it to the second-place finisher instead. (Such a travesty would probably be a case of prejudice, partiality, or arbitrariness, so maybe it’s subsumed by the previous cases.)

That said, the trophy might fairly be withheld from the winner if it was not won by means of fair play. Fair in that sense means by-the-rules (or sometimes by the unwritten rules of sportsmanship). Being unjust in general is sometimes also called unfair: it’s unfair to go back on your word, or to counterfeit money, or to defraud your customers, for example.

You are supposed to be best able to make fair decisions if you can adopt an obje...
Announcing the Technical AI Safety Podcast
by Quinn — 2020-12-07T18:51:58.257Z
Link: https://technical-ai-safety.libsyn.com/

Please comment if you use an obscure podcast app and I'll try to set it up for my feed. I'll have at least spotify, apple podcasts, and stitcher up shortly.
EDIT: spotify, pocketcasts, stitcher, and apple podcasts are live. google podcasts is simply timegated at this point unless something goes wrong.

Episode one should go up in a few weeks. We're doing two episodes on shielding in RL to get started.

Feedback form: https://forms.gle/4YFCJ83seNwsoLnH6

Request an episode: https://forms.gle/AA3J7SeDsmADLkgK9

Episode 0 script

The Technical AI Safety Podcast is supported by the Center for Enabling Effective Altruist Learning and Research, or CEEALAR. CEEALAR, known to some as the EA Hotel, is a nonprofit focused on alleviating bottlenecks to desk work in the effective altruist community. Learn more at ceealar.org

Hello, and welcome to the technical ai safety podcast. Episode 0: announcement.

This is the announcement episode, briefly outlining who i am, what you can expect from me, and why i’m doing this.

First, a little about me. My name is Quinn Dougherty, I’m no one in particular, not a grad student, not a high-karma contributor on lesswrong, nor even really an independent researcher. I only began studying math and CS in 2016, and I haven’t even been razor-focused on AI Safety most of the time since. However, I eventually came to thinking there’s a reasonable chance AGI poses an existential threat to the flourishing of sentient life, and I think it’s nearly guaranteed that it poses a global catastrophic threat to the flourishing of sentient life. I recently quit my job and decided to focus my efforts in this area. My favorite area of computer science is formal verification, but I think I’m literate enough in machine learning to get away with a project like this. We’ll have to see, ultimately, you the listeners will be the judge of that.

Second, what can you expect from me? My plan is to read the alignment newsletter (produced by Rohin Shah) every week, cold-email authors of papers I think are interesting, and ask them to do interviews about their papers. I’m forecasting 1-2 episodes per month, each interview will be 45-120 minutes, and there’s already a google form you can use to request episodes (just link me to a paper you’re interested in) as well as a general feedback form. Just look in the show notes.

Finally, why am I doing this? You might ask, doesn’t 80000 hour...
Traversing a Cognition Space
by Rafael Harth — 2020-12-07T18:32:21.070Z

(This post is part of a sequence that's meant to be read in order; see the preface.)

Post #1 was about developing and justifying a formalism for Factored Cognition. Now that we have this formalism, this post is about doing as much with it as possible.

1. Debate Trees

Recall that a Cognition Space is a pair (Sh,dh) where Sh is a set of statements, dh:Sh→R+ is a difficulty function, and h is a human.

So far, I've only shown examples of single transcripts. A single transcript corresponds to one path through dh that is dependent on choices from both agents: at every step, the first agent outputs an explanation (which is a sequence of statements), the second agent points at one element of this sequence, and the first agent continues the path from that element onward. However, given that we model Ideal Debate agents as maximally powerful, it is also coherent to ask about the object that results if we fix all of the first agent's actions in advance, such that she 'pre-commits' for any possible combination of choices from the second agent.

I call such an object a Debate Tree, and we can define it formally as a directed rooted tree[1] (V,E), where V⊂S∗h (so each node is a sequence of statements) and E⊂V×V, that satisfies the following three conditions:

The unique root of (V,E) is a one-element 'sequence' (s0).

∀(S,S′)∈E∃s∈S[2] :S′e→s.

∀(S,S′),(S,S′′)∈E:[∃s∈S:S′e→s∧S′′e→s]⟹[S′=S′′].

The first condition says that the root of the tree needs to be a single statement (this should be the answer to the input question). The second condition says that every edge (S,S′) needs to explain a statement in S; we don't have redundant edges in our tree. And the third condition says that each statement is only explained once; the formal way of saying this is that, if two explanations exist for the same statement, they're really the same. (Note that this restriction exists because the first agent has to choose one explanation during the game; it certainly doesn't imply that only one explanation exists.) There is no condition to demand that each statement needs to have an explanation – whenever there is none, it means that the first agent decides to end the debate when that statement is pointed at.[3]

In this definition, nodes are explanations, and each node has one outgoing edge for each [statement of the explanation that the first agent wants to explain further], which links to an expl...