trentmkelly/LessWrong-43k huggingface dataset:
Alignment Newsletter #53
by Rohin Shah — 2019-04-18T17:20:02.571Z

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.

Cody Wild is now contributing summaries to the newsletter!

Highlights

Alignment Newsletter One Year Retrospective

(Rohin Shah): The Alignment Newsletter is one year old! I've written a retrospective of the newsletter's impact over the last year, with a lot of open questions about what the newsletter should look like in the future. Please help me figure out the answers by taking this 3-minute

survey

, and if you're feeling particularly generous with your time, read the retrospective and tell me your opinions in the comments!

Are Deep Neural Networks Dramatically Overfitted?

(Lilian Weng): The concepts of underfitting and overfitting, and their relation to the bias-variance tradeoff, are fundamental to standard machine learning theory. Roughly, for a fixed amount of data, there is an optimal model complexity for learning from that data: any less complex and the model won't be able to fit the data, and any more complex and it will overfit to noise in the data. This means that as you increase model complexity, training error will go down to zero, but validation error will go down and then start turning back up once the model is overfitting.

We know that neural networks are much more expressive than the theory would predict is optimal, both from theorems showing that neural networks can learn any function (including one that provides a rather tight bound on number of parameters), as well as a

paper

showing that neural nets can learn random noise. Yet they work well in practice, achieving good within-distribution generalization.

The post starts with a brief summary of topics that readers of this newsletter are probably familiar with: Occam's razor, the Minimum Description Length principle, Kolmogorov Complexity, and Solomonoff Induction. If you don't know these, I strongly recommend learning them if you care about understanding within-distribution generalization. The post then looks at a few recent informative papers, and tries to reproduce them.

The

first one

is the most surprising: they find that as you increase the model complexity, your validation error goes down and then back up, as expected, but then at some point it enters a new regime and goes down again. However, the a...
Liar Paradox Revisited
by Chris_Leong — 2019-04-17T23:02:45.875Z

A well-known brainteaser asks about the truth of the statement "this statement is false". My previous article on this topic, outlined common approaches to this problem and then argued that we should conceive of two distinct kinds of truth:

Statements about the world, where as per Tarski there is a natural interpretation: "Snow is white" is true iff and only iff "Snow is white"
Logical/Mathematical statements, where the notion truth is constructed to give us a convenient way of talking about our rules of inference within a particular system that normally excludes self-referential statements

I should add that some statements can only be defined using a combined notion of truth, ie. "The car is red and 1+1=2".

My point was that if we chose to extent logical/mathematical statements outside of their usual bounds, we shouldn't be surprised that it breaks down and that if we choose to patch it, there will be multiple possible ways of achieving this.

Patching with INFINITE-LOOP

So let's consider how we might attempt to patch it. Suppose we follow the Formalists (ht CousinIt) and insist that "true" or "false" or only applied to sentences that can be evaluated by running a finite computation process. Let's add a third possible "truth" value: INFINITE-LOOP.

Consider the following sentence:

The truth value of this sentence is not INFINITE-LOOP

This seems to be a contradiction because the sentence is infinitely recursive, but at the same time denies this.

In order to understand what is happening, we need to make our algorithm for assigning truth values more explicit:

if expansion terminates:
Resolve truth value by expanding expressions with references to other expressions
else:
Assign INFINITE-LOOP if expansion fails to terminate

What we see here is that if the sentence is not able to be expanded without ending up in an infinite loop, it is assigned the truth value INFINITE-LOOP without any regard to what the sentence asserts. So there isn't actually an inconsistency, at most, this system for assigning truth values just isn't behaving how we'd want.

In fact consider the following:

A: This sentence is false
B: Sentence A has a truth value of INFINITE-LOOP

According to the above algorithm, assigning INFINITE-LOOP to B is correct, when it seems like it should be FALSE. Further, this sys...
Episode 1 of "Tsuyoku Naritai!" (the 'becoming stronger' podcast/YT series).
by Bae's Theorem — 2019-04-17T21:58:30.591Z

The Kansas City Rationalists just had our first dojo meetup yesterday. It was a success as far as first meetings go; we had great attendance, including a couple non-rationalists. We are using the 'Hammertime' sequence as our content.

I am making a series of vlog posts/podcast episodes detailing my personal journey through the sequence, also for the benefit of others. I plan to expand it as I learn and experience more.

https://www.youtube.com/watch?v=JmRr7Op6sfQ&feature=youtu.be

https://pca.st/26cQ
Crypto quant trading: Intro
by Alexei — 2019-04-17T20:52:53.279Z

I’m going to write a few posts on quant trading. Specifically trading crypto, since that’s what I know best. Here’s a few reasons why I’m doing this:

I think I can benefit a lot from writing about my approach and methodology. Hopefully this will make the ideas and assumptions more clear.
I’d love to get input from other people in the community on their approaches to model building, data analysis, time series analysis, and trading.
There’s been a lot of great content on this website, and I’d love to contribute. This is the topic I currently know best, so I might as well write about it.
My company (Temple Capital) is also looking to hire quants and we believe the rationalist way of thinking is very conducive to successful quant trading.

My goal here isn’t to make you think that “Oh gosh, I can become a millionaire by trading crypto!” or “Here’s the strategy that nobody else has found!” Instead, I want to give you a taste of what quant trading looks like, and what thinking like a quant feels like.

EAs have been talking about earning to give for a while

, and it’s well known that quant trading is a very lucrative career. I’ve known about it for a while, and several of my friends have done quant (e.g. at Jane Street) or worked at a hedge fund. But, I never thought that it was something I could do or would find enjoyable. Turns out that I can! And it is!

I’m going to be sharing the code and sometimes the step by step thinking process. If you’re interested in learning this on a deeper level, definitely download the code and play with the data yourself. I’ve been doing this for just over a year, so in many ways I’m a novice myself. But the general approach I’ll be sharing has yielded good results, and it’s consistent with what other traders / hedge funds are doing.

Setup

Note: I’ve actually haven’t gone through these install steps on a clean machine. I think they’re mostly sufficient. If you run into any issues, please post in the comments.

Make sure you have

Python

3.6+ and

pip

`pip install pandas numpy scipy matplotlib ipython jupyter`
`git clone

https://github.com/STOpandthink/temple-capital.git

`
`cd temple-capital`
`jupyter notebook`
Open `blog1_simple_prediction_daily.ipynb`

If you’re not familiar with the tools we’re using here, then the next section is for you.

Python, Pandas, Matplotlib, and Jupyter

We’re going to be writing P...
StrongerByScience: a rational strength training website
by ryan_b — 2019-04-17T18:12:47.481Z
Link: https://www.strongerbyscience.com/

StrongerByScience is a website I have been using to inform my recently-renewed weightlifting habit. I think it will be of interest here because the writers are experts in a practice (strength training, particularly powerlifting), and their modus operandi is to do careful literature reviews of the latest strength research and use that to inform their practice.

I recommend all of the Guides on the Big Three page (now technically four), which cover the big three lifts from the biomechanics on up, as well as one on strength training. They have monthly reviews of the latest papers in the field. They occasionally do deep reviews of particular subjects (I am reading the metabolism article on and off now). They're a good source of references for other literature in the field.

Stuff you would normally expect to be unpleasant, but isn't: the mailing list is just the articles, not spam; the articles are written in a way similar to how posts are often written here, and are not particularly marketing-y; the articles and guides can mostly be downloaded as PDFs in addition to being read on the website.

The specific benefits I got came from the Strength Training Guide and from the article on high-volume training, which defined my current set structure. I have a new lifetime personal best in dumbbell press, and I am close to recovering my previous deadlift. I have not purchased any of the books or trainings, however.
No Safe AI and Creating Optionality
by SebastianG  — 2019-04-17T14:08:41.843Z

[I am working on some formal arguments about the possibility of safe AI, and what realistic alternatives might be. I wrote the following to get imaginative feedback before I continue my arguments. I strongly believe that before developing formal argumentation, it is very helpful to play around with imaginative stories so that possibilities that otherwise would not have been considered can surface. Stories are like epistemic lubricant.]

Within a span of 3 months two formal proofs were published showing:

1) There is no learning algorithm of complexity X or greater such that humans can prove there are no unknown nonlinear effects. And then…

2) Any learning algorithm which interacts with agents which are not itself cannot have rigorously bounded effects.

Most people understood these proofs, which were far more technical and caveated than these summaries, to mean, in shortest form, that no safe AI is even possible. Some pointed out that Google Maps qualified as “unsafe” by this definition. The reactions to the Google Maps example spawned two camps. The one said that intuition and experience tells us Google Maps is safe, so need to worry, the other side said Google Maps is not safe, but the problems it causes are minimal and so we put up with them. This argument was just window dressing on a march darker and more dire arguments happening all over the world.

Deep inside the hallways of American policy Pentagon generals postulated that although these systems are not safe, we must build some and test them in foreign countries. “The only way through this labyrinth of technology was trial and error, and so the U.S. should be on the forefront of the trials.”

In China, Xi Jinping’s government reasserted control over all technology companies and began pulling all electronic systems unnecessary for party rule out of the hands of consumers and promoted a more natural world-oriented China.

Brussels took the most drastic steps. Three times they tried to pass legislation, one banning microprocessors, another banning certain classes of algorithms, another banning private or public AI research and funding. None of these laws passed, but Poland and Finland left the EU over the controversy.

Far from the centers of power other stews were brewing. Large bands of people on the internet were arguing for the disestablishment of the internet. The NYT wondered whether more education could hav...
Reflections on the Mythic Invitational
by Zvi — 2019-04-17T11:50:00.315Z

Previously / Compare and Contrast To: Reflections on the 2017 Magic Online Championship

Previously: Speculations on Duo Standard

Compare To (Frank Karsten at Channel Fireball): The Mythic Invitational Wasn’t Perfect—And It Was Still a Smashing Success

And Remember, Guys, You Asked For It: The “And” of MTG Arena

Two years ago we were treated to a virtuoso performance on all fronts at the 2017 Magic Online Championship. All the players on the Sunday stage played the best Magic we’ve ever seen. The commentary drew us into exactly that which made the games, and the game of Magic in general, great. I called upon the game to bottle that lightning, and build upon it to create our future.

At the Invitational we experienced a very different digital tournament.

Some stuff was great. We made giant leaps forward in some areas.

Including viewers. We’re playing in a different league now.

We turned ourselves into a real e-sport! Woo-hoo!

Other areas, not so much. We mustn’t let the good distract from fixing the bad.

I won’t speak of the minor technical difficulties, as this is already far too long and they are doubtless being addressed. Frank Karsten mentions them in his article, along with good suggestions for the Arena client.

This is a case of ‘I should get this out there one way or another’ so I’m doing that. I hope it helps.

The Great

You can’t argue with a million dollars in prizes. Competitive Magic’s biggest flaw has always been the size of its prize pools. There’s still room to improve, but I think it is safe to say: Problem solved. This is no longer the lowest hanging fruit.

Physical production values were off the charts. Game play on Arena is much easier to follow even for invested veterans like me. For new and casual players, it’s a transformation. The game looks and feels exciting and fast paced.

No doubt the stage looked like we wanted it to look, and we had the commentary teams we wanted to have, no matter the expense.

Things felt instinctively like they were supposed to feel. High stakes, high tension, high drama. The money and stage did their jobs quite well.

Becca Scott, Brian Kibler and the other commentators were (someone please create the supercut so I can link to it here, and also watch it multiple times) very excited. That latest draw (again, supercut please) was huge. If Richard Hagon had been on site, I would have worried if he could have survived.

Viewership foll...
Open Problems in Archipelago
by Raemon — 2019-04-16T22:57:07.704Z

Over a year ago, I wrote about Public Archipelago and why it seemed important for LessWrong. Since then, nothing much has come of that. It seemed important to acknowledge that. There are more things I think are worth trying, but I've updated a bit that maybe the problem is intractable or my frame on it might be wrong.

The core problems Public Archipelago was aiming to solve are:

By default, public spaces and discussions force conversation and progress to happen at the lowest common denominator.
This results in a default of high-effort projects happening in private, where it is harder for others to learn from.
The people doing high-effort projects have lots of internal context, which is hard to communicate and get people up to speed on in a public setting. But internally, they can talk easily about it. So that ends up being what they do by default.
Longterm, this kills the engine by which intellectual growth happens. It's what killed old LessWrong – all the interesting projects were happening in private, (usually in-person) spaces, and that meant that:

newcomers couldn't latch onto them and learn about them incidentally
at least some important concepts didn't enter the intellectual commons, where they could actually be critiqued or built upon

The solution was a world of spaces that were public, but with barriers to entry, and/or the ability to kick people out. So people could easily have the high-context conversations that they wanted, but newcomers could slowly orient around those conversations, and others could either critique those ideas in their own posts, or build off them.

Since last year, very few of my hopes have materialized.

(I think LessWrong in general has done okay, but not great, and Public-Archipelago-esque things in particular have not happened, and there's continued to be interesting discussion in private areas that not everyone is privy to)

I think the only thing that came close is some discussion on AI Alignment topics, which benefited from being technical enough to automatically have a barrier to entry, and created a discussion shaped in such a way that it was harder to drag it into Overton Window Fights.

The core problem is that maintaining a high-context space requires a collection of skills that few people have, and even if they do, it requires effort to maintain.

The moderation tools we built last year still require: a l...
Robin Hanson on Simple, Evidence Backed Models
by Raemon — 2019-04-16T22:22:19.784Z
Link: http://www.overcomingbias.com/2019/04/publish-tax-returns.html

This is link to a Hanson post that is primarily about tax returns, and whether they should be public. It was an interesting foray into the considerations that bear on that topic.

But I was particularly interested in the opening paragraph, which had a useful lens:

Our simplest model of an economy is: supply and demand. This model has many simple implications for policy. Now we know of many much more complicated economic models, which often have quite different policy implications. But often we are not sure which more complex models actually apply well to any given situation. So we have to worry that people favor more complex models mainly to justify their preferred policies. Knowing this pushes me toward recommending the policies implied by supply and demand, unless I see unusually clear evidence to support a different economic model. (FYI, the evidence that fixed costs exists seems plenty clear, so I really mean supply & demand with fixed costs.)

In our simplest modes of information, people are better off when they have more information, and also when information is distributed more symmetrically. There is a vast world of much more complicated models, but it is often hard to tell which more complex models apply well to given situations, and many probably favor particular models to justify preferred policies. So as with supply and demand, this uncertainty pushes me to favor the simplest info models, and their policy recommendations favoring more info and more symmetric info.
Conditional revealed preference
by jessicata — 2019-04-16T19:16:55.396Z
Link: https://unstableontology.com/2019/04/04/conditional-revealed-preference/

In the linked post, I discuss revealed preference analysis and argue that claims about someone's actual values should predict not only what they do in their current situation, but what they would do in substantially different situations, given e.g. different information and different expectations for how others behave.

(LessWrongers may ask "what does this add over CEV?" CEV is one possible hypothetical where people are more informed, but is impossible to compute and also not predictive of what people do in actual situations of having somewhat more information while still being under cognitive limitations. Thus, CEV "analysis", unlike conditional/counterfactual analysis, ends up being largely spurious.)
Notes from Literature Review: Distributed Teams
by Elizabeth — 2019-04-16T17:58:55.300Z

My disorganized, unformatted notes for Literature Review: Distributed Teams.

https://www.researchgate.net/publication/256005419_Understanding_Conflict_in_Geographically_Distributed_Teams_The_Moderating_Effects_of_Shared_Identity_Shared_Context_and_Spontaneous_Communication

Understanding Conflict in Geographically Distributed Teams: The Moderating Effects of Shared Identity, Shared Context, and Spontaneous Communication

Pamela J. Hinds, Mark Mortensen 2005

“t shared identity moderated the effect of distribution on interpersonal conflict and that shared context moderated the effect of distribution on task conflict”
“spontaneous communication was associated with a stronger shared identity and more shared context, our moderating variables. Second, spontaneous communication had a direct moderating effect on the distribution-conflict relationship, mitigating the effect of distribution on both types of conflict. W”

https://www.researchgate.net/profile/Vathsala_Wickramasinghe/publication/286524750_Diversity_in_team_composition_relationship_conflict_and_team_leader_support_on_globally_distributed_virtual_software_development_team_performance/links/5961ed54458515a35732894f/Diversity-in-team-composition-relationship-conflict-and-team-leader-support-on-globally-distributed-virtual-software-development-team-performance.pdf

Diversity in team composition, relationship conflict and team leader support on globally

distributed virtual software development team performance

Wickramasinghe, V.

Nandula, S.

University of Moratuwa, Sri Lanka.

“diversity in team composition leads to relationship conflict, relationship conflict leads to team performance, and team leader support moderates the latter relationship”
Never meeting in person is bad
Diversity -> relationship conflict, but that’s probably not the problem here
This is offshoring, which has class and quality implications
“, a mean value of 3.67 suggests a considerably high level of conflict between team members. “
Leadership is good

https://books.google.com/books?hl=en&lr=&id=GQUBDQAAQBAJ&oi=fnd&pg=PA259&ots=Oktuw9B7MA&sig=e8jFVYBt8Ub8nLAfj3dTp2-DO_0#v=onepage&q&f=false

(PDF available on request)

Insights for Culture and Psychology from the Study of Distributed Work Teams

Catherine Cramton

Screw you google books and your disabling of copy/paste and hiding of pages
The mutual knowledge problem: people act on dispara

...
Slack Club
by quanticle — 2019-04-16T06:43:22.442Z
Link: http://www.thelastrationalist.com/slack-club.html

This is a post from The Last Rationalist, which asks, generally, "Why do rationalists have such a hard time doing things, as a community?" Their answer is that rationality selects for a particular smart-but-lazy archetype, who values solving problems with silver bullets and abstraction, rather than hard work and perseverance. This archetype is easily distractible and does not cooperate with other instances of itself, so an entire community of people conforming to this archetype devolves into valuing abstraction and specialized jargon over solving problems.