trentmkelly/LessWrong-43k huggingface dataset:
[AN #60] A new AI challenge: Minecraft agents that assist human players in creative mode
by Rohin Shah — 2019-07-22T17:00:01.759Z

Find all Alignment Newsletter resources

here

. In particular, you can

sign up

, or look through this

spreadsheet

of all summaries that have ever been in the newsletter. I'm always happy to hear feedback; you can send it to me by replying to this email.

Highlights

Why Build an Assistant in Minecraft?

(Arthur Szlam et al): This position paper proposes a new challenge for AI research: building a bot that can provide assistance in

Minecraft

(creative mode). A

companion paper

presents an initial setup for such an agent.

The main goal here is to advance natural language understanding, intent inference and instruction following. As a result, there is no formal specification like a reward function -- in their own words, "the ultimate goal of the bot is to be a useful and fun assistant in a wide variety of tasks specified and evaluated by human players". They chose Minecraft in particular partly because it has a very rich space of tasks, even though the execution of any given task is relatively straightforward. They script many low level policies to automate this execution in order to make learning easier (for example, they have policies to navigate to a location or to build specified structures) and focus the learning challenge on figuring out what the user wants.

The current version of the bot takes dialogue from the user and uses a neural model to parse it into an action dictionary that unambiguously specifies what the agent should do -- I think this neural model is the main thing to be learned. There are a bunch of details on how the rest of the modules work as well. They have also released three datasets: a semantic parsing dataset that associates instructions with action dictionaries, a house dataset that has trajectories where a human builds a house, and a semantic segmentation dataset that labels various parts of houses.

Rohin's opinion: I'm really excited to see a project that is very directly aimed at inferring end user intent in a complex environment. This seems like a great direction for the field to move towards. I think Minecraft would also be great as a test bed for the setting in which researchers or engineers (as opposed to end users) are trying to get an agent to do something: we can assume more expertise and knowledge here. Ideally, this would allow us to solve more complex tasks than can be accomplished with natural language from...
Solving for X instead of 3 in love triangles?
by Eruvnal — 2019-07-22T16:35:45.533Z

I was rereading HPMOR and got to the math problem about love triangles (4^3 possible combinations but you need to remove the combinations where 1 person is not loved or in love with someone) and solved it by counting all the combinations that did not work (10) and subtracted it from the 64 possibility's to get 54 then did the same sort of counting strategy for if there were four people and fond that knowing the answer for 3 people made counting here easy (4 nodes (4^6) = 4096 won't include everyone (54x4)+(3x6)+1 = 235 so answer is 3861) but then, And here is my question forward is there a mathematical formula so solve for X people instead of laboriously solving by adding 1 person at a time and counting the permutations that don't include everyone?
Self-experiment Protocol: Effect of Chocolate on Sleep
by NoSignalNoNoise — 2019-07-22T03:32:19.106Z

Epistemic Status

Preregistering an experiment, not very confident in the design.

Because it is important to decide how the data will be analyzed before collecting it, I'm posting my planned experiment here, may modify the design based on feedback (I'll post an update if I do), and then I will start the experiment, and finally I'll post the results when it's finished.

All feedback is appreciated!

Introduction and Goals

I often suffer from fatigue in the early evening that leads to anxiety later at night, which makes it difficult to sleep. The purpose of this experiment is to determine whether eating a small amount of chocolate to counteract fatigue in the early evening affects my sleep, and if so, how. I could easily imagine the effect on sleep being either beneficial (counteract the pattern of fatigue and anxiety that makes it hard to sleep) or harmful (the theobromine might keep me awake).

Because I'd rather not add the fat and sugar that comes with chocolate to my diet, the effect on sleep needs to be appreciable for it to be worthwhile. I have fairly arbitrarily decided that the chocolate is worth it iff it causes me to fall asleep at least 15 minutes earlier than I otherwise would.

Experimental Protocol

Every work day evening when I go directly home from work and do not have a social event planned, I will get home, make any necessary adjustments to the air conditioning (temperature affects my sleep a lot, and I want to make sure I don't accidentally bias the results by setting the AC differently depending on whether I have chocolate), and then with probability 1/2 (decided by a coin flip or die roll) eat a square of dark chocolate.

I will use a spreadsheet to track which nights I did and did not eat a square of chocolate, and I will use sleep data from a Fitbit Blaze smartwatch to record what time I fall asleep each night. The Blaze uses heart rate and movement data to decide what time I fall asleep (I don't need to actively tell it when I go to bed). In my experience, the time it says I fell asleep generally matches my subjective memory of when I fell asleep (not when I went to bed).

Statistical Analysis

I care about the magnitude of the effect, not just the direction, and my goal is to make the correct decision rather than to get published, so testing whether the observed effect is statistically significantly different from 0 is not very useful. Whil

...
How Much Do Different Users Really Care About Upvotes?
by Evan_Gaensbauer — 2019-07-22T02:31:47.273Z

For the last several years, I've known people who've submitted articles to the EA Forum or LessWrong, and found that the culture on these sites is pretty hostile to the kinds of views they're presenting (different people have different opinions of the patterns of hostility to different views on the EA Forum and LW, respectively). What the particular views are doesn't matter, because it's been all kinds of views. What's common between them is a perception an article they wrote they believe to be quite good was "poorly received" on the site, while still having a positive and significant number of upvotes. Now, none of the articles I'm thinking of had what I would call a high number of upvotes, but it was enough that at least several people had read the article, and a majority of them had upvoted the post. As a proportion of the people who read the article, it tells us a very significant minority, typically between 30-45%, disagreed with or disliked it enough to downvote it.

So, they're articles which are not very well-received. Unless I'm missing something, an article having a positive number of upvotes should be interpreted as one's article being at least somewhat well-received by the readers. If someone thinks that an article on the EA Forum or LW has received too many downvotes, or not enough upvotes, because there is something wrong with the general culture of the respective membership bases of these sites, that is one argument. Yet that is a different one than the arguments I see typically made by those who complain about the proportion of upvotes:downvotes they receive on the EA Forum or LessWrong. They just say that based on the appearance of not receiving strong, consistent, vocal support for their articles that they would like to have seen, that the reception to their article was overwhelmingly and uniformly negative. This is in spite of the fact they received at least a higher proportion of positive:negative feedback, even if only on the measure of karma. In other words, it's my observation a lot of people who complain as if they've gotten uniformly, overwhelmingly, and inappropriately negative feedback on their articles have false impressions of how their articles were received, and are wrong.

The common tendency I see in articles like this is that there is often a high proportion...
Should I wear wrist-weights while playing Beat Saber?
by Raemon — 2019-07-21T19:56:54.102Z

I recently started playing the VR game Beat Saber as a form of exercise. It involves waving your arms around a bunch.

I *also* got 1.5 pound wrist weights to use while playing, to increase the degree of workout.

Since then, someone made a vague claim about this potentially being damaging for joints, or something. I'm curious if anyone has a clear sense of whether and why wrist-weights would be beneficial or harmful.
Cross-Validation vs Bayesian Model Comparison
by johnswentworth — 2019-07-21T18:14:34.207Z

Suppose we need to predict the outcomes of simulated dice rolls as a project for a machine learning class. We have two models: an "unbiased" model, which assigns equal probability to all outcomes, and a "biased" model, which learns each outcome frequency from the data.

In a machine learning class, how would we compare the performance of these two models?

We'd probably use a procedure like this:

Train both models on the first 80% of the data (although training is trivial for the first model).
Run both models on the remaining 20%, and keep whichever one performs better.

This method is called cross-validation (along with its generalizations). It's simple, it's intuitive, and it's widely-used.

So far in this sequence, we've talked about Bayesian model comparison: to compare two models, calculate the posterior P[model|data] for each model. How do cross-validation and Bayesian model comparison differ?

Biased/Unbiased Die Simulation

Let's run a simulation. We'll roll a 100-sided die N times, using both a biased die and an unbiased die. We'll apply both cross-validation and Bayesian model comparison to the data, and see which model each one picks. Specifics:

We'll use N-fold cross-validation with log likelihood loss: for each data point xi, we learn the maximum-likelihood parameters p∗i based on all data except xi. To get the a final metric, we then sum log likelihood over all points: ∑ilnP[xi|p∗i]
For Bayesian model comparison, we'll compute lnP[model|data] for an unbiased model, and a model with uniform prior on the biases, just like we did for Wolf's Dice
The simulated biased die has probability 1/200 on half the faces and 3/200 on the other half

We'll plot the difference in score/evidence/whatever-you-want-to-call-it assigned to each model by each method, as the number of data points N ranges from 1 up to 10000.

Here are the results from one run:

First and most important: both cross-validation and Bayesian model comparison assign more evidence to the biased model (i.e. line above zero) when the die is biased, and more evidence to the unbiased model (i.e. line below zero) when the die is unbiased.

The most striking difference between the methods is in the case of an unbiased die: Bayesian model comparison assigns lower and lower probability to the biased model, whereas cross-validation is basically...
Learning to Learn and Navigating Moods
by calebo — 2019-07-21T15:53:06.667Z

Moods are important to learning. Clearly, if you’re upset, it will be harder to learn JavaScript, how to cook a fine pesto, or surf. If you’re curious and filled with wonder, it will be much easier. Because your mood can be either promote or hamper your learning success, learning how to navigate moods usefully is an important metaskill for skill acquisition. Knowing about the importance of moods is useful for teaching, students or colleagues, in addition to learning.

Those are the main claims of Gloria Flores’

Learning to Learn and the Navigation of Moods: The Meta-Skill for the Acquisition of Skills

. In this post I’ll summarize the book. I’ve also created an audio for an exercise that she suggests using AWS Polly that you can

access here

.

What are the components of this meta-skill?

First, a learner knows about the varieties of different moods and whether they are useful or not.
Second, a learner knows which judgements give rise to their specific moods.
Third, a learner knows which moods tend to arise at which learning stage. They know what to watch out for and what to promote.
Finally, a learner knows how to interact with these judgements to nudge their mood into a different direction.

The Varieties of Moods

A quick definition, what are moods? In Flores’ words:

“Moods are like the coloring of how we encounter the world around us, what it says to us or how it appears to us” (21-22)

I think of moods as been made up of two components:

dispositional: dispositions to act or think in a particular way
experiential: a mood has an experiential quality, one is disposed to feeling a particular way in addition to acting or thinking a particular way

If someone is in a blue mood, they may skip a party they would have otherwise gone to and enjoyed. If someone is annoyed, they will be more likely to lash out at others. If someone is feeling joyous, they’ll be far less likely to lash out and more likely to play with and help others. Each of these moods have a particular experiential quality to them, there’s something it’s like to have a blue, annoyed, or joyous mood.

Flores provides a number of different moods that may productively impact our learning. Here are a list of them with associated thoughts and feelings:

Wonder

I don’t know what’s going on and that’s exciting! I want to know!

Perplexity

I am totally confused, but I’ve got to know what’s going on. I’m going to persevere until I

...
Bayes' Theorem in three pictures
by Sunny from QAD — 2019-07-21T07:01:45.068Z

Epistemic status: Just another visualization of Bayes' Theorem (and some light introductory text). Tl;dr.

Let's say you have a belief — I'll call it "B" for "belief" — that you would assign 2:1 odds to. In other words, you consider it twice as likely that B is true than that B is false. If you were to sort all possible worlds into piles based on whether B is true, there would be twice as many where it's true than not.

Then you make an observation O, which you estimate to have had different probabilities depending on whether B is true. In fact:

If B is true, then O had a 1/8 chance of occurring.
If B is false, then O had a 3/4 chance of occurring.

In other words:

O occurs in 1/8 of the worlds where B is true.
O occurs in 3/4 of the worlds where B is false.

We know you're in a world where O occurred — you just saw it happen! So let's go ahead and fade out all the worlds where O didn't occur, since we know you're not in one of them. This will slash the "B is true" pile of worlds down to 1/8 of its original size and the "B is false" pile of worlds down to 3/4 of its original size.

Why did I write Evidence: 1:6 at the top? Let's ignore that for a second. For now, it's safe to throw away the faded-out worlds, because, again, we now know they aren't true.

I'm going to make a bold claim: having made observation O, you have no choice but to re-asses your belief B as having 1:3 odds (three times as likely to be false than true).

How can I say such a thing? Well, initially you assigned it 2:1 odds, meaning that you thought there were twice as many possible worlds where it was true than where it was false. Having made observation O, you must eliminate all the worlds where O doesn't happen. As for the other worlds, if you considered them possible before, and O doesn't contradict them, then you should still consider them possible now. So there is no wiggle room: an observation tells you exactly which worlds to keep and which to throw away, and therefore determines how the ratio of worlds changes — or, how the odds change.

What about "Evidence: 1:6"?

Well, first notice that it doesn't really matter how many worlds you're considering total — what matters is the ratio of worlds in one pile to the other. We can write 2:1 odds as the fraction 21 . The numerator ...
What questions about the future would influence people’s actions today if they were informed by a prediction market? 
by Mati_Roy — 2019-07-21T05:26:14.082Z

I'm looking for questions that would be useful to many individuals and would influence their decisions today (not questions for those working on shaping the long term future of Earth originating life).

I would also like if you specified how those questions might influence people's actions.

There's a couple of questions I would really like to have our current best collective prediction, and I really feel our civilisation is inadequate at providing those.

Edit: side question: where could I post those questions? long term (>5 years) bets need to invested on the stock market, otherwise there's no financial incentives in betting
Do you fear the rock or the hard place?
by Ruby — 2019-07-20T22:01:48.392Z

Epistemic status: fairly confident based on my accumulated experience of debates and disagreements. I wrote this for myself as much as others.

There is a conversational dynamic which I think is extremely common, a failure mode which is all too easy to fall into. Alice and Bob are debating some course of action, e.g. should they do X or Y? Alice thinks that X is very likely to result in terrible consequence R, so they should definitely opt for Y. Bob thinks that Y most definitely will cause horrific result H, so they should definitely do X.

The distilled conversation goes a bit like this:

Alice: “We can’t do X! That would lead to R, which is unacceptable.”

Bob: “I don’t think you get it, Y results in H. You can’t think that we could allow H, do you?”

Alice: “I feel like you’re not listening, we need to account for R!”

Bob: “H is definitely a much worse and more real danger than R . . .”

Alice is afraid of the rock (R) and Bob is afraid of the hard place (H).

Possible values of X, Y, R, and H:

X = more gun control; Y = less gun control; R = people unable to defend themselves and having their rights taken away; H = increased risk of mass shootings, suicides, and children shooting themselves or others.

X = raising minimum wage; Y = maintaining minimum wage; R = reduction in number of jobs causing people to be fired; H = people not earning enough from their jobs to subsist.

X = increase immigration; Y = reduce immigration; R = loss of jobs from local community, erosion of national culture and values, crime committed by migrants; H = humanitarian impact, loss of potential growth of the national economy.

The above exchange is actually relatively good. Alice and Bob each know what they’re afraid of and have expressed that clearly. Bob even acknowledges Alice’s concern about R, but states that he thinks it’s the lesser danger. They’re at a point where they might be able to usefully double crux [1].

What goes wrong?

Failure to identify and articulate the fears

If Carol has held the position that X is really bad for a long time, or if her position stems from deep System 1 models and frames, then she might struggle to articulate clearly what specifically she’s afraid that X will cause. She might find any attempts by others to clarify to be unsatisfying, and possibly threatening because any incorrect articulation of your fear is often worse than none at all. Dylan might come along and...
Bayesian Model Testing Comparisons
by johnswentworth — 2019-07-20T16:40:50.879Z

We've now seen three different methods for model comparison:

Exact calculation of posterior probabilities of each model via P[data|model]
Laplace approximation
Bayesian Information Criterion (BIC)

Based on the derivations, we can make some predictions:

Laplace is a second-order approximation of the exact integral around the maximum-likelihood estimate. It should work well when the integral is dominated by one very pointy peak. In English, it should work well when we have enough data to get a precise estimate of the unobserved parameters.
BIC is an approximation of Laplace as the number of data points N→∞. It ignores terms which don't scale with N (but do scale with number of parameters k), so it will do best when N is large and k is small.

Let's test these predictions out with some dice-rolling simulations.

The graphs below show the difference in bits of evidence (i.e. lnP[model|data] or its approximations) between a biased model and an unbiased model, as a function of the number of data points in each simulation, for each of the three methods. A positive difference indicates that the method favors the biased model; a negative difference indicates that the method favors the unbiased model.

First up, let's compare a simulation with a 60-40 biased coin to a simulation with an unbiased coin. Hopefully, our methods will favor the biased model in the biased coin simulation and the unbiased model in the unbiased coin simulation.

Here's what's going on in those plots:

All three methods agree very well. We can't see the exact method's line at all, because it's under the Laplace line. BIC does have a visible bias - it's usually a little below exact & Laplace - but the difference is small.
All three methods generally assign higher evidence to the biased model (line above zero) in the biased coin simulation, and higher evidence to the unbiased model (line below zero) in the unbiased simulation.
As the number of data points N increases, the evidence in favor of the biased model grows roughly linearly in the biased simulation. But in the unbiased simulation, the evidence in favor of the unbiased model grows much more slowly - logarithmically, in theory.

Because the BIC is a large-N approximation, ignoring terms which scale with k but not N, we'd expect BIC to perform worse as we crank up the number of parameters k. Let'...
Book review: The Technology Trap
by Richard_Ngo — 2019-07-20T12:40:01.151Z

I recently finished reading The Technology Trap, by Carl Frey. The book attempts to do two things: chronicle the role of technology in economic progress throughout history, and argue that automation in our own era parallels the first seven decades of the industrial revolution, during which the wealth from mechanisation failed to reach most of the population.

I particularly enjoyed the first component, because until now I’ve read much less about the industrial revolution than I should have. That also means that I’m not qualified to evaluate the book’s accuracy. However, it had interesting discussions of:

The technological prowess of the Romans, and why they were held back from industrialising both because of their slave-based economy, and also because of an implicit dismissal of the private economy.
The development of some surprisingly important technologies during the Middle Ages, such as wind- and water-mills, better ways of accessing horsepower (via improved horseshoes, harnesses and ploughs), and town clocks.
The fact that most of the key innovations of the early industrial revolution (steam engines excepted) would have been technologically possible a century or two earlier, but were blocked by the political power of guilds.
The importance of the Glorious Revolution in shifting England’s political climate to favour industrialisation; and more generally, the role of competition between nation-states in spurring government permissiveness towards innovation.
The mechanisation of the silk industry as a smaller-scale precursor to the mechanisation of cotton processing that would drive the early years of the industrial revolution.
The prevalence of child labour in the first factories run by Arkwright and others; and more generally how miserable the first few decades of the industrial revolution were for the poor, who were crammed into unsanitary cities on reduced wages, with severe health consequences.
The fact that it took many decades after Watt’s steam engine was patented in 1769 for railways to actually become widely significant.
The role of arms manufacturers like Colt as precursors to Ford’s assembly lines.

In its latter role, however, the book seems a little incomplete. From around 1770 to 1840, productivity rose while worker incomes stagnated, with the increased wealth primarily going to industrialists - a period known as “Engels’ pause”. Frey argues that today,

...