trentmkelly/LessWrong-43k huggingface dataset:
Notes on Actually Trying
by NoSignalNoNoise — 2015-09-23T02:53:36.275Z

These ideas came out of a recent discussion on actually trying at Citadel, Boston's Less Wrong house.

What does "Actually Trying" mean?

Actually Trying means applying the combination of effort and optimization power needed to accomplish a difficult but feasible goal. The effort and optimization power are both necessary.

Failure Modes that can Resemble Actually Trying

Pretending to try

Pretending to try means doing things that superficially resemble actually trying but are missing a key piece. You could, for example, make a plan related to your goal and diligently carry it out but never stop to notice that the plan was optimized for convenience or sounding good or gaming a measurement rather than achieving the goal. Alternatively, you could have a truly great plan and put effort into carrying it out until it gets difficult.

Trying to Try

Trying to try is when you throw a lot of time and perhaps mental anguish at a task but not actually do the task. Writer's block is the classic example of this.

Sphexing

Sphexing is the act of carrying out a plan or behavior repeatedly despite it not working.

The Two Modes Model of Actually Trying

Actually Trying requires a combination of optimization power and effort, but each of those is done with a very different way of thinking, so it's helpful to do the two separately. In the first way of thinking, Optimizing Mode, you think hard about the problem you are trying to solve, develop a plan, look carefully at whether it's actually well-suited to solving the problem (as opposed to pretending to try) and perhaps Murphy-jitsu it. In Executing Mode, you carry out the plan.

Executing Mode breaks down when you reach an obstacle that you either don't know how to overcome or where the solution is something you don't want to do. In my personal experience, this is where things tend to get derailed. There are a few ways to respond to this situation:

Return to Optimizing Mode to figure out how to overcome the obstacle / improve your plan (good),

Ask for help / consult a relevant expert (good),

Take a break, which could lead to a eureka moment, lead to Optimizing Mode or lead to derailing (ok),

Sphex (bad),

Derail / procrastinate (bad), or

Punt / give up (ok if the obstacle is insurmountable).

The key is to respond constructively to obstacles. This usually means getting back to Optimizing Mode, either directly or after a break. The...
Meetup : LessWrong Hamburg 2015 Q4
by Gunnar_Zarncke — 2015-09-22T20:54:35.430Z

Discussion article for the meetup : LessWrong Hamburg 2015 Q4

WHEN:
16 October 2015 07:00:00PM (+0200)

WHERE:
Hamburg, Steindamm 80

The next large LW Hamburg Meetup as planned three months after this started big in June. We hope to meet you again.

We have a fine location near Hamburg main station. Reachable via U1 Lohmühlenstraße. You should arrive between 6 and 7 PM (or else contact me) to be let in (the janitor will be informed).

Topics will be announced later. See alse the announcement on Meetup.com.

Discussion article for the meetup : LessWrong Hamburg 2015 Q4
Meetup : LessWrong Hamburg
by Gunnar_Zarncke — 2015-09-22T20:43:30.553Z

Discussion article for the meetup : LessWrong Hamburg

WHEN:
02 October 2015 07:00:00PM (+0200)

WHERE:
Fehnweg 4, Hamburg

Some LessWrongers from all over northern Germany have banded together and found a date for another LessWrong Hamburg Meetup: Friday, Oct. 2nd 7 PM CET.
I'd be happy to host you at my place in Fehnweg 4, 2215 Hamburg.

Google Maps route from nearby underground station U1 Langenhorn Markt.

No specific topic. We will mostly get to know each other an exchange interests and such. Books and games will be there though.

Discussion article for the meetup : LessWrong Hamburg
Help me test out my Bayes Academy game
by Kaj_Sotala — 2015-09-22T20:02:35.740Z

You may remember my earlier posts on a game that would teach you Bayesian networks. As might be expected, the original plans turned out to be way too ambitious and I ended up cutting a lot of the story content and turned into more of a simple puzzle game, but here it is regardless.

I need people to test this out to get material for my thesis. So if you could fill out one brief questionnaire, play the game for a bit, and then fill out another questionnaire, that'd help a lot. You may also learn a bit in the process - at least for me, I've found that it made concepts like the conjunction fallacy and value of information clearer. Here's a link to the first questionnaire, and you'll get the link to the actual game once you fill out the form. Thanks a lot! [Java required.]

Also please let me know if you're having any trouble with it.

ETA: Known issues: Some Mac users have reported an issue where the game info panel vanishes after a few steps. I've updated the JAR with a version that has the info buttons at the top; this won't help you complete the tutorial, but at least it might be possible to play the actual game (though I'm not sure of how easy it will be to understand without the tutorial). When you've hit "done", the graph will change colors to show which variables you guessed correctly; at that point you need to hit space which *hopefully* should work even though you can't see the text at the bottom.

ETA2: The game has now been updated with a version that lets you choose a lower resolution setting, which should help for people who were having problems with some nodes going off the screen / the text being too large.
Publication on formalizing preference utilitarianism in physical world models
by Caspar Oesterheld — 2015-09-22T16:46:54.934Z

About a year ago I asked for help with a paper on a formalization of preference utilitarianism in cellular automata. The paper has now been published in the Springer journal Synthese and is available here. I wonder what you think about it and if you are interested would like to discuss it with you.
Optimiser to Oracle
by Stuart_Armstrong — 2015-09-22T10:27:59.000Z

It would be useful to have a mechanism for getting an AI to provide an honest answer to a specific question. This post presents such a mechanism.

The question is: what is the impact of X on the expected utility of a utility function v? Here X is some well-defined binary event. Formally, for a constant c (positive or negative), we want to query the AI as to whether Q(v,X,c) is true, where

Q(v,X,c) = {E(v|X=1) - E(v|¬X=0) > c}.

How can we do that? Well, for a start, let M(u) be a u-maximising agent. We'll assume we can feed M() any utility function to maximise. We'll further assume that M() is boxed, that it has a single output bit Y, and that we have cut any acausal trade possibilities. We'll add another binary process Z, which, if Z=1, ensure that Y/¬Y is never recorded. In actual fact, P(Z=0)≈1.

The remaining assumptions are on the operations we can perform on v (without needing any further understanding of v). We replace v by v#, defined in the following way. First of all, we make v# indifferent to worlds in which X≠Y or Z=0. Indifference to Z=0 can proceed in the usual way; indifference to X≠Y means that the value of worlds with X≠Y are changed to the value of worlds where X is set to the value of Y.

Therefore any v# maximiser will behave as if it lived in a world where X=Y (its output and the outcome of X were the same) and Z=1 (its output was never recorded). Then we add a special, one-off penalty of -c for outputting Y=1 (there is no effect for outputting Y=0). This defines v#, and we give our boxed AI v# to maximise.

So, how will M(v#) behave? Because it acts as if X=Y with certainty, it behaves as if it can determine the value of X by outputting Y. Because it acts as if Z=0 with certainty, it behaves as if the output of Y is never read, so it can determine the value of X without affecting anything else about the universe. It will now behave as if it was a v maximiser with control over X. So if it outputs Y=0, it expects to get utility E(v|X=0). If it outputs Y, it expects to get utility E(v|X=1)-c (because of the -c penalty).

Therefore (ignoring ties) M(v#) will output Y=1 iff it estimates Q(v,X,c) to be true. And, since in actual fact Z=0 almost certainly, we get to see its output, hence its conclusion. Voila!

Note: the advantage of this method is to allow us to get the truth out of an AI, without knowing how to define "truth".
Meetup : Cambridge Less Wrong Meetup - Book Recommendations
by NoSignalNoNoise — 2015-09-22T02:53:31.734Z

Discussion article for the meetup : Cambridge Less Wrong Meetup - Book Recommendations

WHEN:
04 October 2015 03:30:00PM (-0400)

WHERE:
98 Elm Street, Apartment 1, Somerville, MA

Come to give, listen to and discuss book recommendations. People will be giving 1-minute pitches for books they think may be of interest to the rationalist community. All comers are encouraged to offer their recommendations.

Phase 1: Arrival, greetings, unstructured conversation.

Phase 2: Presentations. This starts promptly at 4:00, and lasts 30-60 minutes (until we run out of book recommendations).

Phase 3: Further discussion. We'll explore the ideas raised in phase 2, often in smaller groups.

Phase 4: Dinner. It's about a five minute walk to the usual restaurant.

Discussion article for the meetup : Cambridge Less Wrong Meetup - Book Recommendations
Fixing Resource Bounded Solomonoff Induction (draft)
by Scott Garrabrant — 2015-09-21T17:51:16.000Z

This post is very sketchy, but it is a big idea that I have been wanting to talk with a lot of people about. I wanted to get a post out with all the key ideas. I promise that either there is an error in this post, or I will write it up much more clearly, so if you are not in a rush, you can wait for that. (I already found an error, I will see if I can patch it.)

The rough idea of this algorithm is that the Solomonoff induction inspired approach fails because sampled Turing machines have an incentive to double down on past mistakes when trying to correlate their behavior with past weaker versions of themselves.

This algorithm gets past that by only requiring that sampled machines do well on a very sparse training set. This training set is so sparse that when a machine has to choose what to do for one input, it has enough time to actually compute the correct answer to all previous values of the training set.

However, we cant just use any sparse sequence. If we did, we would have a positive proportion of sampled Turing machines do well on the training sequence, but identify but identify inputs that it knows are not in the training set, and have bad behavior on those inputs it knows it wont be judged on.

The training set therefore has to diagonalize across all fast Turing machines, so that no Turing machine (within some runtime constraints) can identify elements that are consistently not in the training set. To do this, we end up having to add some randomness to the training set.

To make the proofs work out, we end up wanting our whole logical predictor to be in the set of Turing machines we diagonalize against, so that if the logical predictor has bad behavior, the places where it has bad behavior is guaranteed to be represented in the training set. We achieve this by making the randomness used in the training set available to all Turing machines we diagonalize against, but in a limited way so that they cannot use the access to the random bits to dodge the training set.

These special Turing machines with shared randomness are used in the algorithm, but in the end, we just have a normal randomized Turing machine which passes the generalized Benford test (and much more).

Let g be increasing function. Let A be a fast growing function.

I will be presenting an algorithm that will use a type of randomized Turing machine, I will call an RTM. These machines will have acces

...
Some thoughts on meta-probabilties
by iarwain1 — 2015-09-21T17:23:36.898Z

I often like to think of my epistemic probability assignments in terms of probabilities-of-probabilities, or meta-probabilities. In other words, what probability would I assign that my probability estimate is accurate? Am I very confident, am I only mildly confident, or do I only have a vague clue?

I often think of it as a sort of bell curve, with the x-axis being possible probability estimates and the y-axis being my confidence in those estimates. So if I have very low confidence in my estimate then the height of the bell will be very low, and if I have high confidence it'll be pretty high.

Here are a few issues and insights that have come up when discussing or thinking about this:

What would a meta-probability actually mean?

There's two ways I have for thinking about it:

1) The meta-probability is my prediction for how likely I am to change my mind (and to what extent) as I learn more information about the topic.

2) I know that I'm not even close to being an ideal Bayesian agent, and that my best shots at a probability estimate are fuzzy, imprecise, and likely mistaken anyway. The meta-probability is my prediction for what an ideal Bayesian agent would assign as the probability for the question at hand.

What's the point?

Primarily it's just useful for conveying how sure I am of the probability estimate I'm assigning. It's a way of conveying that a coin flip is 50% heads in a very different sense than me saying "I have not the slightest clue whether it'll rain tomorrow on the other side of the world, and if I need to bet on it I'd give it ~50% odds". I've seen other people convey related sentiments by saying things like, "well 90% is probably too low an estimate, and 99% is probably too high, so somewhere between those". I'd just view the 90% and 99% figures as maybe 95% confidence bounds on a bell curve.

Why not keep going and say how confident you are about your confidence estimates?

True, I could do this, and I sometimes will do this if needed by visualizing a bit of fuzziness in my bell curve. But in any case it's usually enough for my purposes.

Is there any use for such a view in terms of instrumental or utilitarian calculations?

Not sure. I've seen some relevant discussion by Scott Alexander and Holden Karnofsky, but I'm not sure I followed everything there. I also suspect that if you view it as a prediction of how your views might change if ...
People being controlled by what they can't perceive consciously
by NancyLebovitz — 2015-09-21T16:42:59.230Z

An audience is switched from clapping on the first and third beats to the second and fourth beats because the pianist added a fifth beat.

I admit I can barely hear what's going on-- the audience sounds better to me after the 40 second mark, but I'm taking what a lot of other people are saying about what happened on trust. Still, I think this gives a different angle on priming research. I'm willing to bet that priming research was based on looking for implausibly small and ridiculous influences so as to get interesting-sounding results rather than looking at what actually changes behavior.
Open thread, Sep. 21 - Sep. 27, 2015
by MrMind — 2015-09-21T07:19:25.692Z

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.
One model of understanding independent differences in sensory perception
by Elo — 2015-09-20T21:32:26.431Z

This week my friend Anna said to me; "I just discovered my typical mind fallacy around visualisation is wrong". Naturally I was perplexed and confused. She said;

“When I was in second grade the teacher had the class do an exercise in visualization. The students sat in a circle and the teacher instructed us to picture an ice cream cone with our favorite 0ice cream. I thought about my favorite type of cone and my favorite flavor, but the teacher emphasized "picture this in your head, see the ice cream." I tried this, and nothing happened. I couldn't see anything in my head, let alone an ice cream. I concluded, in my childish vanity, that no one could see things in their head, "visualizing" must just be strong figurative language for "pretending," and the exercise was just boring.”

Typical mind fallacy being; "everyone thinks like me" (Or A-typical mind fallacy – "no one thinks like me"). My good friend had discovered (a long time ago) that she had no visualisation function. But only recently made sense of it (approximately 15-20 years later). Anna came to me upset, "I am missing out on a function of the brain; limited in my experiences". Yes; true. She was. And we talked about it and tried to measure and understand that loss in better terms. The next day Anna was back but resolved to feeling better about it. Of course realising the value of individual differences in humans, and accepting that whatever she was missing; she was compensating for it by being an ordinary functional human (give or take a few things here and there), and perhaps there were some advantages.

Together we set off down the road of evaluating the concept of the visualisation sense. So bearing in mind; that we started with "visualise an ice cream"... Here is what we covered.

Close your eyes for a moment, (after reading this paragraph), you can see the "blackness' but you can also see the white sparkles/splotches and some red stuff (maybe beige), as well as the echo-y shadows of what you last looked at, probably your white computer screen. They echo and bounce around your vision. That's pretty easy. Now close your eyes and picture an ice cream cone. So the visualisation-imagination space is not in my visual field, but what I do have is a canvas somewhere on which I draw that ice cream; and anything else I visualise. It’s definitely in a dif...
Meetup : LW Cologne meetup
by EGI — 2015-09-20T19:51:54.698Z

Discussion article for the meetup : LW Cologne meetup

WHEN:
26 September 2015 05:00:00PM (+0200)

WHERE:
Marienweg 43, 50858 Köln

The next cologne meetup will be next Saturday (9/26), which is incidentially Petrov day (http://lesswrong.com/lw/jq/926_is_petrov_day/), 5 pm at my house (Marienweg 43 50858).

Discussion article for the meetup : LW Cologne meetup
Meetup : Australia Online Hangout
by Calien — 2015-09-20T19:51:50.768Z

Discussion article for the meetup : Australia Online Hangout

WHEN:
07 October 2015 06:00:00PM (+1000)

WHERE:
Canberra, ACT, Australia

Come one, come all, to our latest Less Wrong Online Hangout!

We talk about anything and everything Less-Wrong-related: things you've learned recently, problems you want to combine rationalist skills on, stuff you've read, whatever.

The link to the Google Hangout will be up at 6:50pm (GMT+11). (And I know how to avoid last month's technical mistake now.)

Facebook event: https://www.facebook.com/events/1504700129823228/

Held more or less monthly.

Discussion article for the meetup : Australia Online Hangout
GiveDirectly, SCI and health outcomes
by  — 2015-09-20T09:15:35.463Z

**What GiveDirectly says:**
>This study documented large, positive, and sustainable impacts across a wide range of outcomes including assets, earnings, food security, ** mental health**, and domestic violence. It found no evidence of impacts on alcohol or tobacco use, crime, or inflation. It also examined a number of design questions such as how to size transfers and whether to give them to men or women.
Source: [GiveDirectly](https://www.givedirectly.org/research-at-give-directly.html)
**What the evidence says:**
*GiveDirectly*

>Overall, GiveDirectly increased households’ assets, consumption, and food security. The program also improved psychological well-being, especially among households with female recipients and households that received the large transfer. GiveDirectly had no impact on health or education measures.

>Psychological impacts: GiveDirectly households reported a 0.2 standard deviation increase (0.35 sd for large transfer recipients) on an index measuring psychological well-being. This improvement was largely driven by increases in happiness and life satisfaction, and reductions in stress and depression. There were no differences in self-reported measures between monthly-transfer and lump-sum recipients, but cortisol levels were significantly higher for monthly-transfer recipients. A potential explanation being that the monthly-transfer recipients seemed to have difficulty saving or investing the transfer, which may have led to increased stress.

Source: [Innovations for Poverty Action](http://www.poverty-action.org/project/0522)
*SCI*

>There is a very strong case that mass deworming is effective in reducing infections. The evidence on the connection to positive quality-of-life impacts is less clear, but there is a fairly strong possibility that deworming is highly beneficial.

>There is strong evidence that administration of the drugs reduces worm loads, but weaker evidence on the causal relationship between reducing worm loads and improved life outcomes.

>Evidence for the impact of deworming on short-term general health is thin, especially for soil-transmitted helminth (STH)-only deworming. Most of the potential effects are relatively small, the evidence is mixed, and different approaches have varied effects. We would guess that deworming populations with schistosomiasis and STH (combination deworming) does

...
ED and Surgery waiting times
by  — 2015-09-20T08:56:02.232Z

source: AIHW

According to the AIHW, the procedure with the lowest median waiting time in 2005 06 was coronary artery bypass graft (15 days); the procedure with the longest wait time was total knee replacement (178 days). There were also variations in waiting times for these procedures across jurisdictions; Queensland patients waited a median time of 41 days for cataract surgery, while those in Tasmania waited a median time of 389 days.

Source: Australian Parliamentary Library

How can hospital emergency department and surgery waiting times be cut?

Cut doctors pay and hire more?

Create specialised sub-professions without the comprehensive training costs?

Something else?