trentmkelly/LessWrong-43k huggingface dataset:
Order and Chaos
by ExCeph — 2019-11-28T21:27:22.979Z

Follows from:

Map and Territory

Babble and Prune

Warning: I strongly recommend not using the concepts in this sequence to try and build a generalized artificial intelligence. These concepts describe how humans function as conscious entities, and humans are not known for being safe or friendly to human values. Human limitations currently provide the only check on human abuse of power, and building something with human cognitive abilities but without those limitations would be ill-advised. Human.

Introduction

This series of articles is about applied metacognition, laying the groundwork for developing the skills to approach and effectively handle any type of problem or situation.

The concepts in this particular article may already be familiar. I’m presenting them here because we will use them in later articles to derive the different types of skills and explain how those skills work and how they fit into our toolbox.

The Map and the Territory

A previous article,

No Really, Why Aren’t Rationalists Winning?

, established that skills are highly compressed procedural information. In our sequence premier,

The Foundational Toolbox for Life: Introduction

, we looked at them from a different angle: skills start as paradigms which filter out information. We develop our paradigms into skills by calibrating them with experience to produce useful answers to problems in a practical time frame.

Now we will look at the map/territory distinction and how we use it to define the basic building blocks of cognition itself. Once we’ve done that, we can finally move on to what we can build with those blocks.

Every skillset, from science to art to athletics to management, requires an explicit or implicit mental model of certain aspects of the world: a map. Every person has at least one map in their brain, which

represents the territory

that is the real world, or at least the part they deal with. The map lets a person predict the outcomes of their actions, and thereby allows them to effectively navigate the territory and change it in pursuit of their desires. Without the map, there would be no way for a person to predict which options lead to desirable outcomes.

Even primitive life forms have evolved rudimentary maps. Their instincts represent the effects of their potential responses to specific stimuli on the probability that they will survive and reproduce. The correl...
What are the requirements for being "citable?"
by Raemon — 2019-11-28T21:24:56.682Z

A sort of... "stretch goal", for the 2018 Review, is developing a system wherein LessWrong has proven itself credible enough for some posts to actually be citable by other institutions.

I'm not sure how much of this has to do with "just actually do a good job ensuring accuracy/relevance", and how much has to do with "jumping through arbitrary hoops", and how hard those hoops are to jump through.

Two obvious things to shoot for might be:

Being considered a valid source by wikipedia
Being considered a valid source by google scholar.

I'm curious if anyone is familiar with how either of those work, in detail, and whether this is an achievable goal.
Kansas City Dojo meetup 11-12-19
by Bae's Theorem — 2019-11-28T19:11:23.129Z

I.) POST MORTEM OF PREVIOUS MEETING

We discuss the situation from last meeting, when a newcomer and non-rationalist showed up and got into a testy argument with Life Engineer. We concluded that it was primarily a matter of conflicting expectations; the newcomer was expecting a more "salon"-type, open-ended, intellectual conversation like the kind we have on our casual Sunday lunches. Additionally, he has probably never been asked such probing questions as was asked of him; in fact, most people probably haven’t.

We decided that we need to be more clear on setting expectations. "A genuine desire to change" will be an explicit requirement for anyone who attends, in the same way that "a desire to not be an alcoholic" is a requirement for AA.

Since it wasn't quite time to start yet, and only core members were present, we allowed ourselves to follow a short tangent from Life Engineer about a comic he read recently about the failure modes of the United States government; specifically how political parties and the internet have thrown a wrench into the system that the Founders created. Since we weren't seriously entertaining the problem we didn't allocate much brainpower to it; the only obvious solution is a factory reset of our government, which would likely only happen if something else Very Bad happens.

With the arrival of our 4th and final RSVP for the evening, we began the Dojo.

II.) ROUND TABLE

"W", one of our founding members, began by reporting that his adventures in weight loss continue to go positively. His conscious effort to abstain from Entertainment Eating has become habitual now, requiring little to no effort, after about 2-3 months of implementing it. He spoke about his aspirations; how his ideal self is an "impossible person", something to constantly strive for. In addition to a lovely tangent on "Positive Social Butterflies" (the butterfly effect mixed with an improved variant of the Golden Rule), W talked about how one of his favorite techniques is a particular kind of reframing where instead of seeing what actions he can take to accomplish his goals, he first checks to see if there's anything he can *stop* doing in order to accomplish his goals.

I went next. My book reading is going slowly but surely. Chapter a day on average. My review of A Way of Being is positive ...
SSC Meetups Everywhere Retrospective
by Scott Alexander — 2019-11-28T19:10:02.028Z

Slate Star Codex has regular weekly-to-monthly meetups in a bunch of cities around the world. Earlier this autumn, we held a Meetups Everywhere event, hoping to promote and expand these groups. We collected information on existing meetups, got volunteers to create new meetups in cities that didn’t have them already, and posted times and dates prominently on the blog.

During late September and early October, I traveled around the US to attend as many meetups as I could. I hoped my presence would draw more people; I also wanted to learn more about meetups and the community and how best to guide them. Buck Shlegeris and a few other Bay Area effective altruists came along to meet people, talk to them about effective altruism, and potentially nudge them into the recruiting pipeline for EA organizations.

Lots of people asked me how my trip was. In a word: exhausting. I got to meet a lot of people for about three minutes each. There were a lot of really fascinating people with knowledge of a bewildering variety of subjects, but I didn’t get to pick their minds anywhere as thoroughly as I would have liked. I’m sorry if I talked to you for three minutes, you told me about some amazing project you were working on to clone neuroscientists or eradicate bees or convert atmospheric CO2 into vegan meat substitutes, and I mumbled something and walked away. You are all great and I wish I could have spent more time with you.

I finally got to put faces to many of the names I’ve interacted with through the years. For example, Bryan Caplan is exactly how you would expect, in every way. Also, in front of his office, he has a unique painting, which he apparently got by asking a Mexican street artist to paint an homage to Lord of the Rings. The artist had never heard of it before, but Bryan described it to him very enthusiastically, and the completely bonkers result is hanging in front of his office. This is probably a metaphor for something.

Philadelphia hosted their meetup in a beautiful room that looked like a Roman temple, and had miniature cheesesteaks for everybody. Chicago held theirs in a gym; appropriate, given this blog’s focus on BRUTE STRENGTH. Berkeley’s was in a group house with posters representing the Twelve Virtues Of Rationality hanging along the staircase. In Fairbanks, a person who had never read the blog showed up to get a story and an autograph for his brother who...
Transparent Newcomb's Problem and the limitations of the Erasure framing
by Chris_Leong — 2019-11-28T11:32:11.870Z

One of the aspects of the Erasure Approach that always felt kind of shaky was that in Transparent Newcomb's Problem it required you to forget that you'd seen that the the box was full. Recently come to believe that this really isn't the best way of framing the situation.

Let's begin by recapping the problem. In a room there are two boxes, with one-containing $1000 and the other being a transparent box that contains either nothing or $1 million. Before you entered the room, a perfect predictor predicted what you would do if you saw $1 million in the transparent box. If it predicted that you would one-boxed, then it put $1 million in the transparent box, otherwise it left the box empty. If you can see $1 million in the transparent box, which choice should you pick?

The argument I provided before was as follows: If you see a full box, then you must be going to one-box if the predictor really is perfect. So there would only be one decision consistent with the problem description and to produce a non-trivial decision theory problem we'd have to erase some information. And the most logical thing to erase would be what you see in the box.

I still mostly agree with this argument, but I feel the reasoning is a bit sparse, so this post will try to break it down in more detail. I'll just note in advance that when you start breaking it down, you end up performing a kind of psychological or social analysis. However, I think this is inevitable when dealing with ambiguous problems; if you could provide a mathematical proof of what an ambiguous problem meant then it wouldn't be ambiguous.

As I noted in Deconfusing Logical Counterfactuals, there is only one choice consistent with the problem (one-boxing), so in order to answer this question we'll have to construct some counterfactuals. A good way to view this is that instead of asking what choice should the agent make, we will ask whether the agent made the best choice.

Now, in order to construct these counterfactuals we'll have to consider situations with at least one of the above assumptions missing. Now we want to consider counterfactuals involving both one-boxing and two-boxing. Unfortunately, it is impossible for a two-boxer to a) see $1 million in a box if b) the money is only in the box if the predictor predicts the agent will one-box in this situation and c) the predictor is ...
Updating a Complex Mental Model - An Applied Election Odds Example
by Davidmanheim — 2019-11-28T09:29:56.753Z

There are probabilities, and there are probabilities about probabilities. How do these get updated? I've had the same discussion several times, and have tried to describe this, but it is hard without going into the math. The formal model is clear, but I have found that the practical implications are hard to describe concretely. I just ran into a great concrete example, however, and I wanted to work through the logic of how I'm updating as a way to show what should happen.

The example I'm using is my expectations about the 2020 election, how accurate various models are1, and how important the inputs are. This type of problem is fairly common - I have both an object level prediction about the winner, and a prediction about / model of how accurate different sources of information will be.

So, what do I do when information comes in that seems surprising? Two things; I update in the direction the information indicates, and I update against the reliability of the data. The second may seem counter-intuitive, but the example makes it clearer.

The economy is doing well - recent news is that it's better than expected. Presidents with great economies tend to get re-elected. Trump is also unpopular. Unpopular presidents tend not to get re-elected2. How do we balance these two, and how do they interact? My model of whether he will win is fairly uncertain, and my model of the sources of data is also uncertain. They are also related in complex ways3. For instance, if Trump's popularity plummets because, for instance, the impeachment inquiries find something shocking and horrible even to his base, I expect that GDP matters far less for his reelection chances. Other data sources also constrain how far I will update4 - no level of GDP growth alone will make me say he's certain5 to win.

So I updated towards Trump's reelection based on the economic data, but my underlying model is telling me that it is decreasingly relevant. That means I'm very slightly down-weighting the importance of economic factors compared to approval rating, since he's seemingly not getting credit for the growth (or the growth isn't helping most voters.) The net impact is that I have updated slightly towards Trump's reelection.

1) For long term forecasts of presidential elections, forecasts based on fundamentals do just OK. But forecasts based on polls do ...
How to make TensorFlow run faster
by rmoehn — 2019-11-28T00:28:21.099Z

I quickly put this together for a fellow AI alignment researcher/engineer, so I thought I'd share it here.

The main recommendations are from Intel: https://software.intel.com/en-us/articles/maximize-tensorflow-performance-on-cpu-considerations-and-recommendations-for-inference

I implement these recommendations by setting the following in the code:

tf.config.threading.set_inter_op_parallelism_threads(2)
tf.config.threading.set_intra_op_parallelism_threads(6) # Number of physical cores.

And setting the environment variables in the PyCharm run configuration and Python Console settings.

As a result, the per-epoch training time dropped by 20 % with a small multi-layer perceptron on Fashion MNIST.

Another important point is to use a TensorFlow binary that uses all available CPU capabilities. Ie. it should display something like this:

2019-11-27 17:26:42.782399: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA

And not something like this:

2019-11-28 09:18:26.315191: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

One way to achieve this is to install TensorFlow with Conda instead of Pip.

Here is more information about that:
https://software.intel.com/en-us/articles/intel-optimization-for-tensorflow-installation-guide

If you're on a Mac and the Conda-TensorFlow crashes with an OMP error, here's the solution: https://stackoverflow.com/a/53692707/5091738
Getting Ready for the FB Donation Match
by jefftk — 2019-11-27T19:20:02.156Z

Facebook is again going to be

matching
donations on Giving Tuesday:

$7M total

$20k max per donor

$100k max per organization

First-come first-served

Processing fees covered by FB

8AM Eastern, Tuesday 12/3

I participated last year, and
while the match ran out in seconds I was able to direct the full counterfactual
$20k by being (a) prepared and (b) lucky not to get any declines. As
with last year, I'm planning to donate more than $20k across several
cards, so even if I get declines this year I'll still have a good
chance of getting my donation matched.

There are good instructions on the EA Giving Tuesday site.
One way things are different this year is that by confirming your
identity ahead of time you can get FB to raise your per-donation
limit. This means instead of twelve $2,499 transactions I'm planning
to do three $9,999 ones.

I did one test $2,501 donation a few days ago to verify that
confirming my identity worked, and three $9,999 donations yesterday to
check if I got declines and practice. My guess is that this year the
donation amount runs out in 1-2s, which means there's probably only
time for one $9,999 donation before the $7M is gone. Very curious how
it goes though!

Comment via: facebook
[AN #75]: Solving Atari and Go with learned game models, and thoughts from a MIRI employee
by Rohin Shah — 2019-11-27T18:10:01.332Z

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.

Audio version

here

(may not be up yet).

Highlights

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

(Julian Schrittwieser et al) (summarized by Nicholas): Up until now, model-free RL approaches have been state of the art at visually rich domains such as Atari, while model-based RL has excelled for games which require planning many steps ahead, such as Go, chess, and shogi. This paper attains state of the art performance on Atari using a model-based approach, MuZero, while matching

AlphaZero

(

AN #36

) at Go, chess, and shogi while using less compute. Importantly, it does this without requiring any advance knowledge of the rules of the game.

MuZero's model has three components:

1. The representation function produces an initial internal state from all existing observations.

2. The dynamics function predicts the next internal state and immediate reward after taking an action in a given internal state.

3. The prediction function generates a policy and a value prediction from an internal state.

Although these are based on the structure of an MDP, the internal states of the model do not necessarily have any human-interpretable meaning. They are trained end-to-end only to accurately predict the policy, value function, and immediate reward. This model is then used to simulate trajectories for use in MCTS.

Nicholas's opinion: This is clearly a major step for model-based RL, becoming the state of the art on a very popular benchmark and enabling planning approaches to be used in domains with unknown rules or dynamics. I am typically optimistic about model-based approaches as progress towards safe AGI. They map well to how humans think about most complex tasks: we consider the likely outcomes of our actions and then plan accordingly. Additionally, model-based RL typically has the safety property that the programmers know what states the algorithm expects to pass through and end up in, which aids with interpretability and auditing. However, MuZero loses that property by using a learned model whose internal states are not constrained to have any semantic meaning. I would be quite excited to see follo...
Mental Mountains
by Scott Alexander — 2019-11-27T05:30:02.107Z

I.

Kaj Sotala has an outstanding review of Unlocking The Emotional Brain; I read the book, and Kaj’s review is better.

He begins:

UtEB’s premise is that much if not most of our behavior is driven by emotional learning. Intense emotions generate unconscious predictive models of how the world functions and what caused those emotions to occur. The brain then uses those models to guide our future behavior. Emotional issues and seemingly irrational behaviors are generated from implicit world-models (schemas) which have been formed in response to various external challenges. Each schema contains memories relating to times when the challenge has been encountered and mental structures describing both the problem and a solution to it.

So in one of the book’s example cases, a man named Richard sought help for trouble speaking up at work. He would have good ideas during meetings, but felt inexplicably afraid to voice them. During therapy, he described his narcissistic father, who was always mouthing off about everything. Everyone hated his father for being a fool who wouldn’t shut up. The therapist conjectured that young Richard observed this and formed a predictive model, something like “talking makes people hate you”. This was overly general: talking only makes people hate you if you talk incessantly about really stupid things. But when you’re a kid you don’t have much data, so you end up generalizing a lot from the few examples you have.

When Richard started therapy, he didn’t consciously understand any of this. He just felt emotions (anxiety) at the thought of voicing his opinion. The predictive model output the anxiety, using reasoning like “if you talk, people will hate you, and the prospect of being hated should make you anxious – therefore, anxiety”, but not any of the intermediate steps. The therapist helped Richard tease out the underlying model, and at the end of the session Richard agreed that his symptoms were related to his experience of his father. But knowing this changed nothing; Richard felt as anxious as ever.

Predictions like “speaking up leads to being hated” are special kinds of emotional memory. You can rationally understand that the prediction is no longer useful, but that doesn’t really help; the emotional memory is still there, guiding your unconscious predictions. What should the therapist do?

Here UtEB dives into the science on memory reconsoli...
Building awareness habits (cognitive)
by Teach — 2019-11-27T01:36:25.019Z

Something I struggle with (probably largely due to ADHD) is building habits/patterns of being aware of things I want to notice/practice cognitively.

For example, I find myself easily becoming somewhat thoughtless in social situations, in that I do not stop to observe, think, and measure my actions and responses at all. I have, for a couple years now, been intending to work on this, and not really doing anything to act on this intention. I often plan to step back and just observe in social situations, but never do.

Since I struggle with this, I figure that the best thing I can do now is try and pick up some strategies for both reminding myself to think back to cognitive patterns I want to build when I enter into the relevant situation, and for doing said things.

So, does anyone have suggestions on how one can:

More consistently remember something they want to watch out for in various situations (especially social/fast-paced situations)
Build new mental patterns/habits (in general)
Effectively observe social situations as they occur, in person, to learn from the interactions in order to develop social skills (bonus if the strategy allows for one to not appear/be awkward or violate social etiquette)
Somewhat less relevantly, banish aversions to human connection/intimacies that make things like eye contact difficult/overwhelming (the aversion is mostly habitual at this point)