trentmkelly/LessWrong-43k huggingface dataset:
play and spinning plates
by brown — 2018-10-23T23:17:54.500Z

There are four levels of ignorance [1]:

Known Knowns - Knowledge - Things you know and you know you know them.

Known Unknowns - Lack of Knowledge - Things you know you don’t know. - I don’t know how to juggle, piffle is probably a word, That feeling that there’s something you’ve forgotten.

Unknown Knowns - Lack of Awareness - Things you don’t know you know. Unconscious social knowledge, Things you assume everyone knows how to do, math intuitions.

Unknown Unknowns - Things you don’t know you don’t know. A fundamentally different way of experiencing reality, is cryptography important to economics. is having kids transformative. The edge of the map. Ontological updates.

It’s very important to have ways of bumping into Unknown Unknowns. A friend tells you about X. A chance meeting. You read a strange book. You notice you’ve been failing and don’t know why. Going for a walk. Attending University. New experiences.

Once strategy is serious play. Messing around with something seemingly unrelated. Following your interest and sense of fun where it leads even if it’s silly. Don’t let your societal schooling interfere with your weirdness education.

Play lets you explore your boundaries, you’re more comfortable failing so your reach extends. When you're in this mind-frame you notice the wobble of spinning plates in the cafeteria or leave photographic plates near your radium samples or think it strange that mold grew on your infected samples.

Reality is far stranger that it appears. You’re probably wrong about something and there are might crucial considerations that you’re woefully neglecting. But you can’t know which until after the fact. There’s always more to learn, go bump into the world.

Great experiments can be accidents, play will make you accident prone. Play is a biased random walk.

In that spirit, experiments:

say "rhubarb" over and over again untill it sounds like weird noises, look at the world upside down, open a “forbidden” door, write as many questions as you can about one particular tree.

Notes

[1] These are four levels that I know of. Might be more. who knows.

Armour, P. (2000). The five orders of ignorance. Communications of the ACM, 43(10), pp.17-20.

Title is inspired by more Feynman, spinning plates & serious play
Cognitive Enhancers: Mechanisms And Tradeoffs
by Scott Alexander — 2018-10-23T18:40:03.112Z

[Epistemic status: so, so, so speculative. I do not necessarily endorse taking any of the substances mentioned in this post.]

There’s been recent interest in “smart drugs” said to enhance learning and memory. For example, from the Washington Post:

When aficionados talk about nootropics, they usually refer to substances that have supposedly few side effects and low toxicity. Most often they mean piracetam, which Giurgea first synthesized in 1964 and which is approved for therapeutic use in dozens of countries for use in adults and the elderly. Not so in the United States, however, where officially it can be sold only for research purposes. Piracetam is well studied and is credited by its users with boosting their memory, sharpening their focus, heightening their immune system, even bettering their personalities.

Along with piracetam, a few other substances have been credited with these kinds of benefits, including some old friends:

“To my knowledge, nicotine is the most reliable cognitive enhancer that we currently have, bizarrely,” said Jennifer Rusted, professor of experimental psychology at Sussex University in Britain when we spoke. “The cognitive-enhancing effects of nicotine in a normal population are more robust than you get with any other agent. With Provigil, for instance, the evidence for cognitive benefits is nowhere near as strong as it is for nicotine.”

But why should there be smart drugs? Popular metaphors speak of drugs fitting into receptors like “a key into a lock” to “flip a switch”. But why should there be a locked switch in the brain to shift from THINK WORSE to THINK BETTER? Why not just always stay on the THINK BETTER side? Wouldn’t we expect some kind of tradeoff?

Piracetam and nicotine have something in common: both activate the brain’s acetylcholine system. So do three of the most successful Alzheimers drugs: donepezil, rivastigmine, and galantamine. What is acetylcholine and why does activating it improve memory and cognition?

Acetylcholine is many things to many people. If you’re a doctor, you might use neostigmine, an acetylcholinesterase inhibitor, to treat the muscle disease myasthenia gravis. If you’re a terrorist, you might use sarin nerve gas, a more dramatic acetylcholinesterase inhibitor, to bomb subways. If you’re an Amazonian tribesman, you might use curare, an acetylcholine receptor antagonist, on your blowdarts. If you’re a...
Mark Eichenlaub: How to develop scientific intuition
by Kaj_Sotala — 2018-10-23T13:30:03.252Z

Recently on the CFAR alumni mailing list, someone asked a question about how to develop scientific intuition. In response, Mark Eichenlaub posted an excellent and extensive answer, which was so good that I asked for permission to repost it in public. He graciously gave permission, so I’ve reproduced his message below. (He otherwise retains the rights to this, meaning that the standard CC license on my blog doesn’t apply to this post.)

From: Mark Eichenlaub

Date: Tue, Oct 23, 2018 at 9:34 AM

Subject: Re: [CFAR Alumni] Suggestions for developing scientific intuition

Sorry for the length, I recently finished a PhD on this topic. (After I wrote the answer kerspoon linked, I went to grad school to study the topic.) This is specifically about solving physics problems but hopefully speaks to intuition a bit more broadly in places.

I mostly think of intuition as the ability to quickly coordinate a large number of small heuristics. We know lots of small facts and patterns, and intuition is about matching the relevant ones onto the current situation. The little heuristics are often pretty local and small in scope.

For example, the other day I heard this physics problem:

You set up a trough with water in it. You hang just barely less than half of the trough off the edge of a table, so that it balances, but even a small force at the far end would make it tip over.

You put a boat in the trough at the end over the table. The trough remains balanced.

Then you slowly push the boat down to the other end of the trough, so that’s it’s in the part of the trough that hangs out from the table. What happens? (I.E. does the trough tip over?)

The answer is (rot13) Gur gebhtu qbrf abg gvc; vg erznvaf onynaprq (nf ybat nf gur zbirzrag bs gur obng vf fhssvpvragyl fybj fb gung rirelguvat erznvaf va rdhvyvoevhz).

I knew this “intuitively”, by which I mean I got it within a second or so of understanding the question, and without putting in conscious effort to thinking about it. (I wasn’t certain I was right until I had consciously thought it out, but I was reasonably confident within a second, and my intuition bore out.) I don’t think this was due to some sort of general intuition about problem solving, science, physics, mechanics, or even floating. It felt like I could solve the problem intuitively specifically because I had seen sufficiently-similar things that led me to the specific he

...
Unikernels: No Longer an Academic Exercise
by Martin Sustrik — 2018-10-23T11:40:00.926Z

Introduction

I've been following the unikernel area for years and I really liked the idea, but I was unconvinced about the possibility of the wide-scale adoption of the technology.

The cost was just too high. It required you to forget everything you knew, to drop all the existing code on the floor, to rewrite all your applcations and tools and start anew. (I am exaggerating, but not by much.) If microkernels never made it, the unikernels are not going to either.

Whatever the benefits, the cost was prohibitive.

Unikernels as processes

Recent "Unikernels as Processes" paper by Koller, Lucina, Prakash and Williams (free download) turns the situation on its head. It proposes to run unikernels as good old boring OS processes. The idea is that most of the stack that's currently in the kernel will be in libraries linked directly to the application. Only few calls would cross the user/kernel boundary.

One-click security

Assuming that libraries implementing POSIX APIs are available (and they are; see e.g. rumpkernel) it should be possible to take your existing application and just recompile it as a unikernel. The application would work as it did before, but it would use only a few system calls.

So, on one hand, any vulnerability in the kernel outside of those few functions won't affect your application.

On the other hand, any vulnerability in, say, TCP or filesystem implementation would compromise your application — but the problem would be at the same time kept at bay by separation between OS processes (different address spaces etc.) It wouldn't result in compromising other applications running on the same machine.

Now think about that from economic point of view.

Vendors are finally forced to take security seriously. But all the options they face are rather unpalatable.

They can keep the status quo and pray that nobody bothers to hack them.

Or they have to fix security flaws which, likely, means security audit of the entire stack and then rewriting most of the legacy code. That's prohibitively expensive. Only few, if any, companies are able to afford that.

Unikernels-as-processes model is no panacea, it won't fix a SQL-injection vulnerability for you, but it addresses a broad class of highly dangerous security flaws (for estimates, see, for example, this paper).

More importantly though, it's a once-click solution! It does improve security at close to zero cost.

That, in turn,

...
Possible Dangers of the Unrestricted Value Learners
by avturchin — 2018-10-23T09:15:36.582Z

TL;DR: AI which is learning human values may act unethically or be catastrophically dangerous, as it doesn’t yet understand human values.

The main idea is simple: a young AI which is trying to learn human values (which I will call a “value learner”) will have a “chicken and egg” problem. Such AIs must extract human values, but to do it safely, they should know these values, or at least have some safety rules for value extraction. This idea has been analyzed before (more on those analyses below); here, I will examine different ways in which value learners may create troubles.

It may be expected that the process of young AI learning human values will be akin to a nice conversation or perfect observation but it could easily take the form of a witch trial if appropriate constraints are not specified. Even a human child between ages 2 and 18 could do many stupid things just because she doesn’t understand all societal rules; however, some experimentation with such rules is necessary to learn them, and a (good) school provides a safe sandbox for such active learning.

List of dangers

There are several possible types of failures of AI value learners:

1) Paperclipping. The value-learning process starts to have a very large impact on the world, for example, AI creates too many computers for modelling values. The special case here is a value learning AI which will never end its work; in that case, the AI will have to take over the world and continue to try to find human values until the end of the universe. In any case, we expect that good value learning will take some reasonable amount of time, on the order of days to years, and some limited resources, like interaction with just a few people.

2) Human torture. A value learner may conduct unethical experiments on humans in order to extract information on values, perhaps including causing pain, brain surgeries, and putting humans in unpleasant situations. Mindcrime is a special case: AI may need to run enormous number of human simulations or at least to upload a person, in order to understand his preferences.

3) Human value manipulation. AI may manipulate human values in order to make them simpler or to gain some type of advantage. This may include wireheading, or pressing to make fake approval of AI’s actions.

4) Wrong value extraction. AI may make an error, perhaps based on wrong assumptions about what and how it should le...
look at the water
by brown — 2018-10-23T00:02:35.444Z

Occasionally I’ve noticed I’m handicapping myself. I don’t let knowledge I know from different contexts seep in. I’ve got to solve the assignment problem the “proper way”.

If this was a problem I’d stumbled upon in the wild I would throw any tool I had at it. I’m not trying to solve this math question instead I’m performing a social ritual. I need to discharge obligation so I can say “I’ve tried” which then lets me read some fiction guilt free.

To compartmentalise is to keep different bits of knowledge from influencing each other.

Common sense contains a lot of knowledge. Couple that with some rough guesses and a bit of reasoning can get you really far. [1] Enrico Fermi was jokingly considered a magician because of the kinds of inferences he could make.

Experiment:

A back of the envelope calculation or fermi-question is great for cutting through some of these types of errors. What is the mass of the moon? [2]

I've also noticed this pattern when I'm talking about the results of a recent study.

Me: "a study showed facebook causes low mood".

friend: "how did they get that".

me: ¯_(ツ)_/¯.

friend: "well if I was trying to show this, I'd try and find people who don't use facebook and give them happiness quizzes, maybe that's hard because most people use facebook. Hmm, maybe I'd get 1000 people and get half of them to stop using facebook for a month."

me: "... how did you do that?"

There's a little learned helplessness on my part here. It's as if "the conclusions" have been given to me by the authority that is scientists. eg: Turns out Science says facebook is bad.

It was nice for me to realise that studies are essays written by people who looked at some phenomena for a while. The smarter the person and the longer they looked the better, but people nonetheless.

Notes

[1] Carl Schulman has complained that people do not do enough fermi's coupled with wiki-look ups and googling. Research advice here.

[2] The mass of the moon is 7 x 10^22 kg. how'd you do it? can you get it from a different angle?

Street fighting mathematics is a wonderful course about generalised-fermi's.
It has a beautifully silly introductory video. A promo reads: "No epsilons or deltas are harmed by taking this course"

The title is taken from a Feynman anecdote about Brazilian students guessing the teachers passwords
Kalman Filter for Bayesians
by SatvikBeri — 2018-10-22T17:06:02.783Z

Summary: the Kalman Filter is Bayesian updating applied to systems that are changing over time, assuming all our distributions are Gaussians and all our transformations are linear.

Preamble - the general Bayesian approach to estimation: the Kalman filter is an approach to estimating moving quantities. When I think about a Bayesian approach to estimation, I think about passing around probability distributions: we have some distribution as our prior, we gather some evidence, and we have a new distribution as our posterior. In general, the mean of our distribution measures our best guess of the underlying value, and the variance represents our uncertainty.

In the Kalman filter, the only distribution we use is the normal/Gaussian distribution. One important property of this is that it can be parameterized completely by the mean and variance (or covariance in the multi-variate case.) If you know those two values, you know everything about the distribution.

As a result, people often talk about the Kalman filter as though it's estimating means and variances at different points, but I find it easier to think of it as outputting a distribution representing our current knowledge at any point.

The simplest case: taking multiple measurements of a fixed quantity with an accurate but imprecise sensor. For example, say we're trying to measure the temperature with a thermometer that we believe is accurate but has a variance of 5 degrees2.

We're very bad at estimating temperatures by hand, so let's say our prior distribution is that the temperature is somewhere around 70 degrees with a variance of 20, or N(70,20) . We take one readout from the thermometer, which (by assumption) yields a normal distribution centered around the true temperature with variance 5: N(t, 5). The thermometer reads 78. What's our new estimate?

Well, it turns out there's a simple rule for combining Normal distributions with known variance: if our prior is N(μ0,σ20) and our observation is N(μ1,σ21) then the posterior has mean

(1) μ′=μ0+k(μ1−μ0)

(2) σ′2=σ20−kσ20 , where

(3) k=σ20σ20+σ21 is called the Kalman gain.

So if our first reading is 72, then k is 2025=.8, σ′2=20−.8∗20=4, and μ′=70+.8∗(78−70)=76.4 . If we take another reading, we'd apply the same set of calculations, except our prior would be N(76.4,4).

Some intuition: let's look at the Kalman gain. First, note ... ,
Alignment Newsletter #29
by Rohin Shah — 2018-10-22T16:20:01.728Z

Highlights

Deep Imitative Models for Flexible Inference, Planning, and Control (Nicholas Rhinehart et al): It's hard to apply deep RL techniques to autonomous driving, because we can't simply collect a large amount of experience with collisions in order to learn. However, imitation learning is also hard, because as soon as your car deviates from the expert trajectories that you are imitating, you are out of distribution, and you could make more mistakes, leading to accumulating errors until you crash. Instead, we can model the expert's behavior, so that we can tell when we are moving out of distribution, and take corrective action.

They split up the problem into three different stages. First, they generate a set of waypoints along the path to be followed, which are about 20m away from each other, by using A* search on a map. Next, they use model-based planning using an imitative model to generate a plan (sequence of states) that would take the car to the next waypoint. Finally, they use a simple PID controller to choose low-level actions that keep the car on target towards the next state in the plan.

The key technical contribution is with the imitative model, which is a probabilistic model P(s_{1:T}, G, φ), where φ is the current observation (eg. LIDAR), s_{1:T} is the planned trajectory, and G is a goal. We can learn P(s_{1:T} | φ) from expert demonstrations. The goal G can be anything for which you can write down a specification P(G | s_{1:T}, φ). For example, if you simply want to reach a waypoint, you can use the normal distribution on the distance between the final state s_T and the waypoint. You can also incorporate a hand-designed cost on each state.

They evaluate in simulation on a static world (so no pedestrians, for example). They show decent transfer from one map to a second map, and also that they can avoid artificially introduced potholes at test time (despite not seeing them at training time), simply by adding a cost on states over a pothole (which they can take into account because they are performing model-based planning).

Rohin's opinion: I really like this paper, it showcases the benefits of both model-based planning and imitation learning. Since the problem has been decomposed into a predictive model, a goal G, and a planner, we can edit G directly to get new behavior at test time without any retraining (as they demonstrate ...
Why do we like stories?
by joshux — 2018-10-22T05:23:03.276Z
Link: https://steemit.com/philosophy/@yushih/why-do-we-like-stories-part-1

Some people like music. Others like visual art. Yet regardless of difference in age, gender and cultural backgrounds, everyone likes hearing a story. Whether it’s fantasized ‘Harry Potter’, romantic ‘Pride and Prejudice’, tragic 'Hamlet', or a beloved classic 'Journey to The West', man and women around the world are enchanted by their spell. Story is one of the most universal art forms. Our cultures are built on myths, and religions are passed on through legends.

A curious person would ask: Why do we like stories?

What Is a Story?

Before we try to find the origin of stories, we might first want to know what a story is. A cogent definition of story is given by Randy Olson, a scientist turned filmmaker who now teaches other scientists how to tell stories. He created a simple method for telling stories, called ABT—which stands for “And, But, Therefore”. These three words capture the basic structure of a story. For example, we can tell the story of "The Wizard of Oz" : "A little girl living on a farm in Kansas AND her life is boring, BUT one day a tornado sweeps her away to the land of Oz, THEREFORE she must undertake a journey to find her way home." [1]

This is idea is not entirely new: Aristotle, the philosopher and one of the first story analyzers, recognized that every story contains a three-act structure: Beginning, Middle, and End. The structure roughly corresponds to Olson’s "And", "But" and "Therefore". However, the advantages of Olson's idea are that the words are simple (they are among the most used words in English) and that each word has a meaning signifying its function in the story. "AND" connects relevant information to introduce the story; "BUT" brings in conflict; "Therefore" resolves the conflict and concludes the story.

Science and Story

While teaching storytelling to scientists over the years, Olson recognized that there is a similarity between ABT and a scientific paper. Most scientific papers have a structure of: Introduction, Method, Results, and Discussion. If we compare this to the structure of a story (ABT), we will find that "And" roughly corresponds to Introduction, "But" to Method, "Therefore" to Results and Discussion. This shows that storytelling is related to problem-solving.

Hypothesis

We can now come back to the question of why we like stories. My hypothesis is that storytelling began as a means of transferring problem-solution kn

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