trentmkelly/LessWrong-43k huggingface dataset:
Dishwasher Filter
by jefftk — 2020-05-15T20:30:02.779Z

A few months ago our dishwasher started building up food inside the
sprayer arms. It happened pretty suddenly: within a few days I was
needing to pick things out daily. I initially thought the
dishwasher had been run with the filter not properly seated and food
had gotten into the water recirculation system. I reseated the filters
and waited a while, but it wasn't getting better.

It all still mostly worked, but only if we scraped our dishes well and if
I spent a frustratingly large fraction of my time picking at the
sprayer arms with a bent paperclip. Still less work than handwashing,
though not by much. I read the manual, forums, etc; couldn't figure
it out. Eventually we called the manufacturer and they sent someone
out. They pulled the filter, and saw that the macro filter (left) was
present but the micro filter (right) just wasn't there.

As soon as the tech pointed out that the filter was missing this very
clearly explained what we'd been seeing. I ordered a new one,
installed it, and the dishwasher worked perfectly again. What I
really don't understand, though, is how the micro filter could have
gone missing?
Craving, suffering, and predictive processing (three characteristics series)
by Kaj_Sotala — 2020-05-15T13:21:50.666Z

This is the third post of the "a non-mystical explanation of insight meditation and the three characteristics of existence" series. I originally intended this post to more closely connect no-self and unsatisfactoriness, but then decided on focusing on unsatisfactoriness in this post and relating it to no-self in the next one.

Unsatisfactoriness

In the previous post, I discussed some of the ways that the mind seems to construct a notion of a self. In this post, I will talk about a specific form of motivation, which Buddhism commonly refers to as

craving

(taṇhā in the original Pali). Some discussions distinguish between craving (in the sense of wanting positive things) and aversion (wanting to avoid negative things); this article uses the definition where both desire and aversion are considered subtypes of craving.

My model is that craving is generated by a particular set of motivational subsystems within the brain. Craving is not the only form of motivation that a person has, but it normally tends to be the loudest and most dominant. As a form of motivation, craving has some advantages:

People tend to experience a strong craving to pursue positive states and avoid negative states. If they had less craving, they might not do this with an equal zeal.

To some extent, craving looks to me like a mechanism that shifts behaviors from

exploration to exploitation

.
In an earlier post,

Building up to an Internal Family Systems model

, I suggested that the human mind might incorporate mechanisms that acted as priority overrides to avoid repeating particular catastrophic events. Craving feels like a major component of how this is implemented in the mind.

Craving tends to be automatic and visceral. A strong craving to eat when hungry may cause a person to get food when they need it, even if they did not intellectually understand the need to eat.

At the same time, craving also has a number of disadvantages:

Craving superficially looks like it cares about outcomes. However, it actually cares about positive or negative feelings (

valence

). This can lead to behaviors that are akin to

wireheading

in that they suppress the unpleasant feeling while doing nothing about the problem. If thinking about death makes you feel unpleasant and going to the doctor reminds you of your mortality, you may avoid doctors - even if this actually increases your risk of dying.
Craving na

...
How should AIs update a prior over human preferences?
by Stuart_Armstrong — 2020-05-15T13:14:30.805Z

I've always emphasised the constructive aspect of figuring out human preferences, and the desired formal properties of preference learning processes.

A common response to these points is something along the line of "have the AI pick a prior over human preferences, and update it".

However, I've come to realise that a prior over human preferences is of little use. The real key is figuring out how to update it, and that contains almost the entirety of the problem.

I've shown that you cannot deduce preferences from observations or facts about the world - at least, without making some assumptions. These assumptions are needed to bridge the gap between observations/facts, and updates to preferences.

For example, imagine you are doing cooperative inverse reinforcement learning[1] and want to deduce the preferences of the human H. CIRL assumes that H knows the true reward function, and is generally rational or noisily rational (along with a few other scenarios).

So, this is the bridging law:

H knows their true reward function, and is noisily rational.

Given this, the AI has many options available to it, including the "drug the human with heroin" approach. If H is not well-defined in the bridging law, then "do brain surgery on the human" also becomes valid.

And not only are those approaches valid; if the AI wants to maximise the reward function, according to how this is defined, then these are the optimal policies, as they result in the most return, given that bridging law.

Note that the following is not sufficient either:

H has a noisy impression of their true reward function, and is noisily rational.

Neither of the "noisy" statements are true, so if the AI uses this bridging law, then, for almost any prior, preference learning will come to a bad end.

Joint priors

What we really want is something like:

H has an imperfect impression of their true reward function, and is biased.

And yes, that bridging law is true. But it's also massively underdefined. We want to know how H's impression is imperfect, how they are biased, and also what counts as H versus some brain-surgeried replacement of them.

So, given certain human actions, the AI can deduce human preferences. So this gives a joint prior p over R×ΠH, the possible human reward functions and possible the human's policies[2]. Given that joint prior, then, yes, an AI can start deducing preferences from observations.

So ins

...
Subspace optima
by Chris van Merwijk — 2020-05-15T12:38:32.444Z

The term "global optimum" and "local optimum" have come from mathematical terminology and entered daily language. They are useful ways of thinking in every day life. Another useful concept, which I don't hear people talk about much is "subspace optimum": A point maximizes a function not in the whole space, but in a subspace. You have to move along a different dimension than those of the subspace in order to improve. A subspace optimum doesn't have to be a local optimum either, because even a small change along the new dimension might yield improvements. If you're in a subspace optimum, this requires a different attitude to get to a global optimum, than if you're in a local optimum, which makes me think it's good for the term to be part of every day language.

When you're in a local optimum, you have to do something quite different from what you're doing to improve.
When you're in a subspace optimum, you have to notice dimensions along which you could be doing things differently that you didn't even notice before, but small changes along those new dimensions might already help. You're applying constraints to yourself that you could let go.

Regarding how it looks subjectively:

The phrase: "am I in a local optimum?" generates curiosity about whether you maybe should undertake a quite different plan from the one you're taking now. (Should I do a different project, rather than make local changes to the project I'm taking?)
The phrase: "am I in a subspace optimum?" generates curiosity about whether you maybe are not noticing (possibly small) changes you could be making across dimensions you haven't been considering. (Should I optimize/adjust the way I'm doing my project across different dimensions/variables than the ones I've been optimizing over so far?)

My impression is that somewhat often when people informally use the term local optimum, they are in fact talking about a subspace optimum.

Bonus for the theoretically inclined: A local subspace optimum is one where you can improve by temporarily doing things differently along dimension X, moving around in a bigger space, while eventually ending up on a different, better, point in the same subspace.
Distinguishing logistic curves
by Stuart_Armstrong — 2020-05-15T11:38:04.516Z

This post will attempt to formalise the intuition that "it's hard to figure out the turning point of a logistic curve, at least until after that turning point". Ashort visual "proof" can also be found here.

Logistic curves

The logistic curves look like this:

Logistic curves can be specified by three parameters, c, l>0, and k>0. Their equation is then:

Fc,l,k(x)=lekxek(x−c)+1.

Note that this l is different from that in this article. The turning point of this curve is at x=c (where it takes the value of lekc/2) while its supremum is lekc; it tends to this value as x→∞. Take the limit as c→∞ as being the exponentials:

F∞,l,k(x)=lekx.

Figuring out the right curve

We'll imagine a simple Bayesian setup. An analyst of logistic curves is seeing data from one distribution, and has two hypotheses about it: FC,L,K, for values C, L, and K, and Fc,l,k with values c, l, and k. We'll designate FC,L,K by F and Fc,l,k by f.

Now, the true distribution is F, but the analyst doesn't know that. The question we're asking is thus:

Starting from an equal prior on F and f, how much of what kind of observation will the analyst need to establish that F is the true underlying distribution?

Noisy Sampling

If the analyst can sample noiselessly from the curve, then three samples should generally suffice to fully establish F, and one sample should generally suffice to distinguish F from f. So we'll consider the (realistic) situation where there is noise in the samples.

So assume the analyst samples n points, at →x=(x1,x2,…,xn). In return, it gets n values, →y=(y1,y2,…,yn); these are sampled independently from N(F(xi),σ2i). This is a normal distribution with mean F(xi) and standard deviation σi.

The analyst is assumed to know the vector →σ=(σ1,…σn), and indeed everything about this setup, with one exception: whether the means of these normal distributions are F(xi) or f(xi).

Let Pa be the analyst's probability distribution. Their prior gives equal weight to both hypotheses: Pa(F)=Pa(f)=1/2. Let O→y→x be the analyst observing →y after sampling from →x; their posterior is then Pa(f∣O→y→x).

Note that, from our perspective, Pa(F∣→x) is a random variable whose distribution we know. Say that:

→x establishes the difference between F and f if the expectation of Pa(F∣→x) is less than 1/16.

We could choose other criteria, and this a relatively loose one. It only assumes three bits of informat

...
Distinguishing logistic curves: visual
by Stuart_Armstrong — 2020-05-15T10:33:08.901Z

I wrote a post about distinguishing between logistic curves, specifically for finding their turning points.

That post was highly mathematical; but here is a visual "proof" of the "theorem":

Figuring out the turning point of a logistic curve before hitting that turning point is bloody hard, mate.

"Proof": The following is a plot two curves:

The logistic curve 1/(1+e−x) up to its turning point at x=0.

The exponential curve 0.51e0.69x, which never has any turning points.

So, if the data was noisy, could you distinguish between the curve that's reached its turning point, and the one that will never have one?

Things get even worse if we stop before the turning point; here's the plot of the logistic curve up to x=−log(3)≈−0.48, with the y=0.25 being half of the value at the turning point. This is plotted against the exponential 0.64e0.85x:
Song for a Red Planet
by Slimepriestess — 2020-05-15T05:30:02.575Z

This is the first original filk song I’ve written, it’s not derived from anything. I’ll be recording myself singing it if I can find a decent microphone because I don’t really think my webcam mic is up to the task. ( I might do a webcam mic version anyway just to get the basic idea, we’ll see). It’s sung as a duet and features call and response, although it can also be sung alone.

Verse 1:

I am just a son of men, I walk upon the earth

I am just a boy trying to – prove – my – wo – rth

I am just a passenger aboard a ship without a berth

but if there’s one thing that I know it’s that I know i’m leaving earth

Ar – ca – dia – plan – i – ti- a – a – a

That’s where – I’ll lay my he – ad

Where sunsets are blue

And the domes are too – oo – oo

And the skies, are pai- n – t – ed re – ed

Chorus:

Ar – ca – dia – plan – i – ti- a – a – a

That’s where – I want to be

To red rock plains

I’ll sail my shi – i – i – p

Across – the highest se – as

Verse 2:

I am just a girl born to a ship without a har – bor

I am just a passenger please take – me – far – ther

I am just a messenger and I am here to say,

Although I was born upon the earth on earth I cannot stay

Ar – ca – dia – plan – i – ti- a – a – a

Is where – I’ll make – my – ho – o – me

Through jet black space

That highest pla -a – a – ce

Is where – I want – to – ro – o – am

Chorus:

Ar – ca – dia – plan – i – ti- a – a – a

That’s where – I want to be

To red rock plains

I’ll sail my shi – i – i – p

Across – the highest se – as

Bridge:

I don’t wanna go (don’t wanna go)

To hot bangkok (to hot bangkok)

I don’t want to go to the ei – f – fel – to – wer’s – top

I don’t wanna swim (don’t wanna swim)

In the gulfstream waters (in the gulfstream waters)

I want a land – not – of – our – fa – ther – s

I don’t wanna go (don’t wanna go)

To New York City (to New York City)

I want to be in the mariner valley.

Ar – ca – dia – plan – i – ti- a – a – a

A plea, – Please take me the – e -re

We’ll lay a course – We’ll board our shi – i – i – p

Soaring on a wing – and – a – pra – a – yer

Chorus:

Ar – ca – dia (Ar – ca – dia ) Plan – i – ti- a – a – a (Plan – i – ti- a – a – a)

That’s where (that’s where) – I want to be

To red rock plains (to red rock plains)

I’ll sail my shi (I’ll sail my shi – i – i – p)

Across – the highest se – as

Ar – ca – dia (Ar – ca – dia ) Plan – i – ti- a – a – a (Plan – i – ti- a – a – a)

That’s where (that’s where) – I want to b...
"It's Okay", Instructions, Focusing, Experiencing and Frames
by Ouroborus — 2020-05-15T00:49:08.593Z
Link: https://ouroborusdotblog.wordpress.com/2020/05/13/its-okay-instructions-focusing-experiencing-and-frames/

Epistemic Status: Interpreting someone else's work will always be speculative

Valentine has described his experience of a Kenshō (or moment of understanding) from which he took away a lesson that could roughly be summarised by "It's okay". I've read that post and the follow up comments far many times as there were elements of it that I struggled to understand. Kaj Sotala has already written a quite good explication, but I still feel there is more to explore there. If you have time, I strongly recommend reading Valentine's post first, even though you might be able to understand it quicker by just reading this post. I think there's value in grappling with confusion as you'll learn more about how to deal with confusion in the future.

Valentine describes his insight more fully as:

“I’m okay. You’re okay. Everything is fundamentally okay. Whatever happens, it will be fine and good. Even our worry and pain is okay. There is something deeply sad about someone dying… and their death is okay. Obliteration of humanity would be tragic, but the universe will go on, and it’s okay.”

He then highlights two possible misunderstandings:

1) Some thought I was saying that nothing matters and that all outcomes are equally good.
2) Some thought I was claiming that you’ll feel good no matter what if you’re enlightened.

Both these misunderstandings were propositional claims, but Valentine clarifies that he meant this as an instruction for practise. He instructs:

The world is real in your immediate experience before you think about it. Set aside your interpretations and just look.

However, even having said all these words, he still worries that "people's thinking systems can grab statements like this and try to interpret them". He states that we have misunderstood if we simply understand him to be saying that the map is not the territory; ie. that our mental model of the world doesn't necessarily directly respond to reality. He also clarifies that it isn't the same as Seeing with Fresh Eyes, which is thinking through an idea as though you are hearing it for the first time, without any assumptions that you've attached to it along the way.

Valentine repeatedly worries that people will misunderstand him by taking what he is saying and squeezing it into the closest ...
Covid 5/21: Limbo Under
by Zvi — 2020-05-14T22:30:01.183Z

Previous weekly reports: Covid-19 5/7: Fighting Limbo, Covid-19 4/30: Stuck in Limbo

Slowly, a nation partially reopens. Is it too much, too soon? It’s too early to know for sure, because of lags, but so far we’ve only seen extraordinary good news. If you don’t think what we saw this past week was good news, either we disagree about how to read the data quite a lot, or you had what I consider highly realistic expectations.

Remarkably little has happened in the last week. There are weeks when decades happen, and previous weeks have felt like that. This one didn’t. This felt like waiting for things to happen and nothing happening except good numbers. Yes, we got some other news, but did any of it matter or surprise much? I would say that it did not.

Last time I did a bunch of analysis and a bit of editorializing alongside the numbers. This time I’ll keep it brief. This is mostly to get the charts out. I’ll get the other stuff out there in distinct posts if it’s worth saying.

The Data

Deaths:

WEST
MIDWEST
SOUTH
NE ex-NY
NY

Mar 19-Mar 25
116
67
111
84
203

Mar 26-Apr 1
347
477
502
454
1340

Apr 2-8
639
1335
1150
1783
3939

Apr 9-15
895
2106
1472
3261
5345

Apr 16-22
1008
2369
1730
5183
3994

Apr 23-29
1135
2500
1684
4285
2810

Apr 30-May 6
991
2413
1737
5349
2007

May 7-May 13
1044
2344
1679
4014
~1500*

*- New York includes ~700 reclassified deaths that did not take place this past week, but were instead from past weeks and added to the death count on 5/7. I’ve subtracted them out for purposes of this chart.

Positive Tests:

WEST
MIDWEST
SOUTH
NE ex-NY
NY

Mar 19-Mar 25
5744
6293
7933
8354
28429

Mar 26-Apr 1
15684
20337
24224
34391
52901

Apr 2-8
19455
31148
39618
56772
65604

Apr 9-15
16291
29267
35570
61921
64463

Apr 16-22
20065
34130
33932
64669
43437

Apr 23-29
21873
42343
33773
62189
42475

Apr 30-May 6
23424
49205
37880
51693
24287

May 7-May 13
22615
43264
37591
40209
16683

Overall test counts:

USA tests
Positive %
NY tests
Positive %

Mar 19-Mar 25
347577
16.2%
88882
32.0%

Mar 26-Apr 1
728474
20.2%
117401
45.1%

Apr 2-8
1,067,220
19.8%
144273
45.5%

Apr 9-15
1,039,790
20.1%
160859
40.1%

Apr 16-22
1,253,535
15.7%
143970
30.2%

Apr 23-29
1,480,101
13.7%
202499
21.0%

Apr 30-May 6
1,733,601
10.6%
183446
13.2%

May 7-May 13
2,215,060
7.4%
202980
8.2%

The chart tell the story. We expanded testin...
Adding a Housemate Under Covid
by jefftk — 2020-05-14T21:30:02.518Z

In mid-March when we

decided to
isolate, one of our housemates was interested in having her
partner move in for the duration. The house talked about it and was
ok with that, but he ended up deciding to stay where he was. Two
months later, with this lasting longer than we were expecting at the
time, he decided he did want to switch houses. Here's how we decided
to handle it:

Before moving in, he made an appointment for a test. Starting
in late
April it's been possible to get a test here even if you don't have
symptoms.

We divided the unit in two with plastic sheeting. They would
have two rooms, one bathroom, and the back exit.

They had a microwave, cooler, and shelf stable food.

We would bring them a hot meal at dinner time, and other things
as needed. Things that came back out either sat in the basement for
three days or got washed with soap and water.

If the test came back negative, and no one in his former house
had started having symptoms, we would end the isolation. If not, we'd
deal with it then.

He moved in on Saturday, had his testing appointment on Monday, and
the test came back negative this morning. We had a celebratory
removal of the sheeting:

Since there can be false negatives the risk isn't zero, but it's a
level we feel comfortable with, and that we think is responsible.

It will be nice having another housemate!

Comment via: facebook
How to avoid Schelling points?
by cwg1g16 — 2020-05-14T19:37:27.467Z

After reading an introductory post on game theory, specifically regarding Schelling Points, I noticed a paradox and I wonder if anyone here can shed some light on the subject.

The scenario I will use to illustrate this is the inverse of a frequently used example to describe popular Schelling points.

The Scenario

Suppose, in a dystopian future, you are on a game show with a partner. A list of numbers is presented to you:

[2, 5, 9, 25, 69, 73, 82, 96, 100, 126, 150]

You and your partner have to pick one number from the list each, without communicating with each other. You both know that if you pick the same number, a terrible fate looms. If you pick different numbers, you are both set free and live long and happy lives.

Assume that you are both human and computer unaided and therefore cannot choose truly random positions on the list.

Naturally, you would want to avoid Schelling points (special numbers or numbers in special positions in the list) to minimise the chance of picking matching numbers. In this case, the Schelling points are numbers which you would think your partner would be more likely to pick, for whatever reason. However, if you both rule out Schelling points, you make the list of numbers to choose from smaller, thus increasing the chance of you both picking the same number significantly. Therefore, if you both actively pick numbers which you think your partner is least likely to pick, assuming you both think rationally, you inadvertently increase the chance of picking the same number. Thus the notion of the Schelling point has become the numbers that are especially insignificant, and the cycle continues. This is the paradox.

A real life example:

Which bar do i choose to drink at on a Friday night if I want to avoid my ex (assuming she's actively avoiding me too)?

The point of this post:

Using game theory, what logical strategy would you employ in two-player avoidance games similar to the one above?

The apology in advance:

I'm new to the site and if any of this is convoluted or has been covered before, I apologise in advance.