trentmkelly/LessWrong-43k huggingface dataset:
Bayesian Reasoning - Explained Like You're Five
by Satoshi_Nakamoto — 2015-07-24T03:59:34.901Z

(This post is not an attempt to convey anything new, but is instead an attempt to convey the concept of Bayesian reasoning as simply as possible. There have been other elementary posts that have covered how to use Bayes’ theorem: here, here, here and here)

Bayes’ theorem is about the probability that something is true given some piece or pieces of evidence. In a really simple form it is basically the equation below:

This will be explained using the following coin flipping scenario:

If someone is flipping two coins: one fair and one biased (has heads on both sides), then what is the probability that the coin flipped was the fair coin given that you know that the result of the coin being flipped was heads?

Let’s figure this out by listing out the potential states using a decision tree:

We know that the tail state is not true because the result of the coin being flipped was heads. So, let’s update the decision tree:

The decision tree now lists all of the possible states given that the result was heads.

Let's now plug in the values into the formula. We know that there are three potential states. One in which the coin is fair and two in which it is biased. Let's assume that each state has the same likelihood.

So, the result is: 1 / 1 + 2 which is 1 / 3 which equals 33%.Using the formula we have found out that there is 33% chance that the coin flipped was the fair one when we already know that the result of the flip was heads.

At this point you you may be thinking what any of this has to do with bayesian reasoning. Well, the relation is that the above formula is pretty much the same as Bayes’ theorem which in its explicit form is:

You can see that P(B|A) * P(A) (in bold) is on both the top and the bottom of the equation. It represents “expected number of times it’s true” in the generic formula above. P(B|~A) * P(~A) represents "expected number of times it's false".

You don’t need to worry about what the whole formula means yet as this post is just about how to use Bayesian reasoning and why it is useful. If you want to find out how to deduce Bayes' theorem, check out this post. If you want some examples of how to use Bayes' theorem see one of these posts: 1, 2, 3 and 4.

Let’s now continue on. This time

...
An example demonstrating how to deduce Bayes' Theorem
by Satoshi_Nakamoto — 2015-07-24T03:58:40.955Z

(This post is not an attempt to convey anything new, but is instead just an attempt to provide background context on how Bayes' theorem works by describing how it can be deduced. This is not meant to be a formal proof. There have been other elementary posts that have covered how to use Bayes’ theorem: here, here, here and here)

Consider the following example

Imagine that your friend has a bowl that contains cookies in two varieties: chocolate chip and white chip macadamia nut. You think to yourself: “Yum. I would really like a chocolate chip cookie”. So you reach for one, but before you can pull one out your friend lets you know that you can only pick one, that you cannot look into the bowl and that all the cookies are either fresh or stale. Your friend also tells you that there are 80 fresh cookies, 40 chocolate chip cookies, 15 stale white chip macadamia nut cookies and 100 cookies in total. What is the probability that you will pull out a fresh chocolate chip cookie?

To figure this out we will create a truth table. If we fill in the values that we do know, then we will end up with the below table. I have highlighted in yellow the cell that we want to find the value of.

Chocolate Chip

White Chip Macadamia Nut

Total

Fresh

80

Stale

15

Total

40

100

If we look at the above table we can notice that, like in Sudoku, there are some values that we can fill in based on the information that we already know. These values are coloured in grey and they are:

The number of stale cookies. We know that 80 cookies are fresh and that there are 100 cookies in total, so this means that there must be 20 stale cookies.

The number of white chip macadamia nut cookies. We know that there are 40 chocolate chip cookies and 100 cookies in total, so this means that there must be 60 white chip macadamia nut cookies

If we fill in both these values we end up with the below table:

Chocolate Chip

White Chip Macadamia Nut

Total

Fresh

80

Stale

15

20

Total

40

60

100

If we look at the table no...
Meetup : Washington, D.C.: Meta Meetup
by RobinZ — 2015-07-23T22:36:24.391Z

Discussion article for the meetup : Washington, D.C.: Meta Meetup

WHEN:
26 July 2015 03:00:00PM (-0400)

WHERE:
National Portrait Gallery

Crossposted from the mailing list:

As usual, we will be congregating in the courtyard from 3:00 to 3:30 p.m., to start at 3:30.

The meta meetup is intended to accomplish a number of purposes:

Get feedback on how we, the organizers, have been doing.

Solicit requests/suggestions for meetups people would like to have happen in the future.

With fall coming up, we have the possibility of planning outdoors meetups without the weather being wretched, so we'd like to gauge interest and plan them out.

For months I've wanted a code of conduct, since before I was a Defacto, but it's been stalling. I want something that the people who come to meetups are comfortable with and this requires community input.

If these sound of partial or no interest to you, you're invited to come anyway to hang out and start side conversations as usual.

Upcoming Meetups:

Aug 2: Optical Illusions

Aug 9: Fun & Games

Aug 16: Mini Talks

Discussion article for the meetup : Washington, D.C.: Meta Meetup
Steelmaning AI risk critiques
by Stuart_Armstrong — 2015-07-23T10:01:02.117Z

At some point soon, I'm going to attempt to steelman the position of those who reject the AI risk thesis, to see if it can be made solid. Here, I'm just asking if people can link to the most convincing arguments they've found against AI risk.

EDIT: Thanks for all the contribution! Keep them coming...
Self-improvement without self-modification
by Stuart_Armstrong — 2015-07-23T09:59:01.156Z

This is just a short note to point out that AIs can self-improve without having to self-modify. So locking down an agent from self-modification is not an effective safety measure.

How could AIs do that? The easiest and the most trivial is to create a subagent, and transfer their resources and abilities to it ("create a subagent" is a generic way to get around most restriction ideas).

Or it the AI remains unchanged and in charge, it could change the whole process around itself, so that the whole process changes and improves. For instance, if the AI is inconsistent and has to pay more attention to problems that are brought to its attention than problems that aren't, it can start to act to manage the news (or the news-bearers) to hear more of what it wants. If it can't experiment on humans, it will give advice that will cause more "natural experiments", and so on. It will gradually try to reform its environment to get around its programmed limitations.

Anyway, that was nothing new or deep, just a reminder point I hadn't seen written out.
(Rational) website design and cognitive aesthetics generally- why no uptake?
by TimothyScriven — 2015-07-23T05:32:06.769Z

So I'm working for a friend's company at the moment (friend is a small business owner who designs websites and a bit of an entrepreneur) anyway, I've persuaded him that we should research the empirical literature on what makes websites effective (which we've done a lot of now) and to advertise ourselves as being special by reason of doing this (which we're only just starting to do).
One thing that I found absolutely remarkable is how unfilled this space tends to be. Like a lot of things in the broad area of empirical aesthetics it seems like there are a lot of potentially useful results (c.f.http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3485842/ ), but they're simply not being applied- either as points of real practice or of marketing differentiation.
A fascinating gap.
Asymptotic Logical Uncertainty: Concrete Failure of the Solomonoff Approach
by Scott Garrabrant — 2015-07-22T19:27:51.000Z

This post is part of the Asymptotic Logical Uncertainty series. In this post, I give a concrete example of how the Solomonoff Induction Inspired Aproach fails to converge to the correct probabilities when predicting the output of a simple Turing machine.

Let f:N→{0,1} be a function such that (for large n) f(n) can be computed in time 2n, but there is no algorithm that can predict f(n) in time less than 2n consistently better than a coin which outputs 1 with probability 1/3.

Construct a Turing machine E as follows:

E(2k)=f(2k), and

E(2k+1)=f(2k+1) XOR f(10k).

Imagine trying to predict E(k) with an algorithm which takes time at most k2. If k is even, then it is determined by an event indistinguishable in time k2 from a 1/3 coin, so the predictor should output 1 or 0 with probability 1/3. If k is odd, then we have the XOR of two bits that are indistinguishable from independently 1 with probability 1/3, so the predictor should output 1 with probability 4/9.

Therefore, if we let T(k)=k3 and R(k)=k2, if SIA worked correctly, then we would have that the limit of the probability that SIA(E,T,R) outputs 1 on the even bits would be 1/3.

However, this is not the case. In particular, the the probability that the 10kth bit output by SIA(E,T,R) is a 1 will not converge to 1/3, but rather switch between 4/9 and 5/9.

This is because at the time that a predictor needs to predict E(10k), it will have already outputted a 1 with probability 4/9 for E(2k+1), and had enough time to compute f(2k+1). Therefore, if f(2k+1)=0, then any predictor that does make its prediction for E(10k) match its prediction for E(2k+1) will contradict itself.

The machines that will do well when sampled in SIA(E,T,R) will be the ones that output 1 independently with probability 1/3 for all even bits and 1 with probability 4/9 for all odd bits, EXCEPT when predicting a bit in a location that is a power of 10, in which case, it will copy (or negate) a previous bit, and output 1 with probability either 4/9 or 5/9, both of which are greater than the desired 1/3.

Further, a machine that gives a 1 with probability of 1/3 on E(10k) for all k will have a likelyhood going to 0 relative to the machines which just copy or negate their old answers.

Note that the correct probability with which to believe E(10k)=1 really is 1/3, not 4/9 or 5/9. The prediction given to E(2k+1) was calculated from a state of ignorance about f(2n

...
Welcome to Less Wrong! (8th thread, July 2015) 
by Sarunas — 2015-07-22T16:49:46.765Z

If you've recently joined the Less Wrong community, please leave a comment here and introduce yourself. We'd love to know who you are, what you're doing, what you value, how you came to identify as an aspiring rationalist or how you found us. You can skip right to that if you like; the rest of this post consists of a few things you might find helpful. More can be found at the FAQ.

A few notes about the site mechanics

To post your first comment, you must have carried out the e-mail confirmation: When you signed up to create your account, an e-mail was sent to the address you provided with a link that you need to follow to confirm your e-mail address. You must do this before you can post!

Less Wrong comments are threaded for easy following of multiple conversations. To respond to any comment, click the "Reply" link at the bottom of that comment's box. Within the comment box, links and formatting are achieved via Markdown syntax (you can click the "Help" link below the text box to bring up a primer).

You may have noticed that all the posts and comments on this site have buttons to vote them up or down, and all the users have "karma" scores which come from the sum of all their comments and posts. This immediate easy feedback mechanism helps keep arguments from turning into flamewars and helps make the best posts more visible; it's part of what makes discussions on Less Wrong look different from those anywhere else on the Internet.

However, it can feel really irritating to get downvoted, especially if one doesn't know why. It happens to all of us sometimes, and it's perfectly acceptable to ask for an explanation. (Sometimes it's the unwritten LW etiquette; we have different norms than other forums.) Take note when you're downvoted a lot on one topic, as it often means that several members of the community think you're missing an important point or making a mistake in reasoning— not just that they disagree with you! If you have any questions about karma or voting, please feel free to ask here.

Replies to your comments across the site, plus private messages from other users, will show up in your inbox. You can reach it via the little mail icon beneath your karma score on the upper right of most pages. When you have a new reply or message, it glows red. You can also click on any user's name to view all of their comments a

...
Oracle AI: Human beliefs vs human values
by Stuart_Armstrong — 2015-07-22T11:54:26.832Z

It seems that if we can ever define the difference between human beliefs and values, we could program a safe Oracle by requiring it to maximise the accuracy of human beliefs on a question, while keeping human values fixed (or very little changing). Plus a whole load of other constraints, as usual, but that might work for a boxed Oracle answering a single question.

This is a reason to suspect it will not be easy to distinguish human beliefs and values ^_^
Meetup : Rationality Meetup Vienna
by AnnaLeptikon — 2015-07-22T09:05:49.742Z

Discussion article for the meetup : Rationality Meetup Vienna

WHEN:
15 August 2015 03:00:00PM (+0200)

WHERE:
Kaisermühlenstraße 24/2, Wien

FB event: https://www.facebook.com/events/670741589694482/
(join the "Rationality Vienna" group to see it!)

Location
http://web.student.tuwien.ac.at/~e0326238/rationality_meetup/directions.html

Discussion article for the meetup : Rationality Meetup Vienna
Base your self-esteem on your rationality
by ThePrussian — 2015-07-22T08:54:32.757Z

Some time ago, I wrote a piece called "How to argue LIKE STALIN - and why you shouldn't". It was a comment on the tendency, which is very widespread online, to judge an argument not by its merits, but by the motive of the arguer. And since it's hard to determine someone else's motive (especially on the internet), this decays into working out what the worst possible motive could be, assigning it to your opponent, and then writing him off as a whole.

Via Cracked, here's an example of such arguing from Conservapedia:

"A liberal is someone who rejects logical and biblical standards, often for self-centered reasons. There are no coherent liberal standards; often a liberal is merely someone who craves attention, and who uses many words to say nothing."

And speaking as a loud & proud rightist myself, there is more than a little truth in the joke that a racist is a conservative winning an argument.

I've been puzzling over this for a few years now, and trying to work out what lies underneath it. What always struck me was the heat and venom with this kind of argument gets made. One thing has to be granted - the people who Argue Like Stalin are not hypocrites; this isn't an act. They clearly do believe that their opponents are morally tainted.

And that's what's weird. Look around online, and you'll find a lot of articles on the late Christopher Hitchens, asking why he supported the second Iraq war and the removal of Saddam Hussain. Everything is proposed, from drink addling his brain, to selling out, to being a willful contrarian - everything except the obvious answer: Hitchens was a friend to Kurdish and Iraqi socialists, saw them as the radical and revolutionary force in that part of the world, and wanted to see the Saddam Hussain regime overthrown, even if it took George Bush to do that. No wishing to revist the arguments for and against the removal of Saddam Hussain, but what was striking is this utter unwillingness to grant the assumption of innocence or virtue.

I think that it rests on a simple, and slightly childish, error. The error goes like this: "Only bad people believe bad things, and only good people believe good things."

But even a basic study of history can find plenty of examples of good - or, anyway, ordinary - chaps supporting the most apallingly evil ideas and actions.&nb...
How to grow faster
by BayesianMind — 2015-07-22T04:01:52.796Z

The most impressive feature of our brain is its capacity to learn. All of our most celebrated cognitive, linguistic, social, artistic, perceptual, emotional, and motor skills are the product of learning. Your brain's amazing plasticity means that you can learn almost anything. No matter how bad you are at something, you too can become an expert at it. Learning, however, does not happen automatically. To learn something new we have to confront a new challenge. Early in life everything is new and challenging and consequently we learn amazingly fast. But as we transition into adulthood we are given a choice between reaping the rewards for the skills we have already mastered or learning something new. We make this choice many times every day, but most of the time we are not even aware of it. What do you think you usually choose? Rewarding activities that you excel in or difficult challenges that make you look stupid? Most of us choose the former most of the time. This is why our cognitive development plateaus in adulthood and we get stuck in doing the same things in the same ways for many years, even though we are still capable of so much more. So what can we do to get unstuck, accelerate our personal growth, and unleash our full potential? I organized a discussion on this very topic at the Consciousness Hacking Meetup, and it inspired me to propose the following eight steps as a starting point for our personal and scientific exploration of interventions to promote cognitive growth:

1. Develop a growth mindset (Dweck, 2006): Convince yourself that you will learn virtually any cognitive skill if you invest the necessary hard work. Even talent is just a matter of training. Each failure is a learning opportunity and so are your little successes along the way.

2. Find a good target and motivate yourself by mental contrasting (Oettingen, et al., 2009): Which skills do you want to develop and why? Imagine how wonderful it will be to have perfected the skill you seek to develop and visualize the benefits. Next, think about the obstacles to developing that skill. Visualize the biggest obstacle. Do you think you will be able to overcome it? If the answer is yes, then go for it! If the answer is no, then think of a similar goal that may be easier to attain and repeat the exercise. Finally, make yourself aware that you and the world around you will benefit from any progres...
[Repost] CFAR, or The Art of Winning at Life
by Ben Pace — 2015-07-21T23:00:00.000Z

I wrote this in the summer of 2015, in the week after I attended a CFAR workshop and met the in-person community for the first time. I no longer have the blog I wrote this to at the time, so I've reposted it to my new blog here for posterity.

For my first blog post, I’d like to write about my recent CFAR workshop, and what I’ve learned. But before that, an anecdote:

On the first night, I made friends with Matt O’Brien. We had a conversation which I think encapsulates the ideals of CFAR quite nicely. The situation was this: we had been speaking for about ten minutes, and I had told Matt about my place in life; about to start at university, great uncertainty about my future, not sure what to do about it. Matt asked for how long I was staying in the Bay Area after the workshop, and I said I was flying out the day after. Matt said that the Bay Area was probably the ideal place for me to answer all of the questions that I had, due to all of the awesome people I could meet, and so I should change my plans to stay longer.

Me: “But I can immediately think of two things that will get in the way of me doing that. So it’s not going to happen.”

Matt: “Have you asked yourself this question: ‘If I made it my goal to solve those two problems, could I?’ ”

Me: “Er… No. I have not asked myself that question.”

Matt: “So do that.”

Me: “…If I made it my goal to solve those two problems, could I…”

*pause*

Me: “Yes!”

And then I did.

—

CFAR uses a concept which I think is really useful. I’ll give the name later, but I’ll say that the concept includes various minor life problems such as

things that aren’t working and you don’t know why (e.g. repeatedly not getting things done on time, there’s always issues with your finished product at work)
things that just don’t feel right (e.g. your daily schedule, personal relationships)
things where you want to do x, but you also don’t want to do x (e.g. exercise, doing your taxes)
various other internal conflicts
things you want to improve and don’t know how to (e.g. getting a good night’s sleep, social skills)
plans you think are going to fail (e.g. getting your class assignment in two months from now)
other dissatisfactions and inefficiencies

CFAR calls all of these ‘bugs’. I think this concept gets you a lot, actually. Let me explain like this:

I am as stupid now as I was before the workshop, except for in two important ways:

I now see these problems as thi

...
Meetup : LW Scotland August meetup
by cjb230 — 2015-07-21T17:58:21.978Z

Discussion article for the meetup : LW Scotland August meetup

WHEN:
09 August 2015 02:00:00PM (+0100)

WHERE:
125 Dalry Road, EH11 2DR Edinburgh, United Kingdom

As usual, this note is a mirror of the canonical meetup note, which is here: https://www.facebook.com/events/862671740491601/

But at the time of posting, these are the details:

The August meetup will be on the ninth, in Edinburgh. We'll start at two at the Clock Cafe, on Dalry Street (http://www.clockdiner.co.uk/).
At three thirty we'll move to The Haymarket, a few minutes walk away on West Maitland Street (http://www.nicholsonspubs.co.uk/haymarketedinburgh/).

On the agenda for this month will be - at least - this:

1 - Intros. What's the most (or least!) rational thing you did in the last month?

2 - A short Fermi estimation intro and game, by me

3 - Discussion of the next chunk of the Rationality book. That will be the interlude ("The Simple Truth"), the intro to Book II ("Rationality: an Introduction"), and then Part E ("Overly Convenient Excuses"), which is chapters 46 to 56.

Also, please note that the venues for this meetup are closer to Haymarket station than they are to Waverley station.

Discussion article for the meetup : LW Scotland August meetup