trentmkelly/LessWrong-43k huggingface dataset:
Classical vs MWI Probability Nomenclature
by DataPacRat — 2013-11-23T16:23:11.085Z

"I estimate 5% odds of X happening" can mean at least two things:

* I have about 1-in-20 confidence that all future timelines from this point contain X, and about 19-in-20 confidence that none do.

* I estimate about 1-in-20 future timelines contain X, and 19-in-20 future timelines don't.

Looked at this way, the usual way of quantifying probability seems to be a lot like quantifying area - the first bullet-point by having a 1x20 rectangle, the second by having a 20x1 one. (This also seems valid for having, say, 50% confidence that 1-in-10 future timelines contain X.) It seems like it might be worth having an easy and understandable way to differentiate between these different forms of '5% odds', but any easy way I've been able to think of is barely understandable, and vice versa. Are there any existing standard ways to do this that I'm unaware of? If not, does anyone reading this have any decent answers?

I'm not opposed to coming up with a new word for personal use to help get in the habit of thinking in certain ways; such as bei'e in Lojban to remind myself to think of probability logarithmically. I don't mind doing the same with a word meaning 'such-and-such a fraction of future MWI branches', if that's the best solution, or even just a useful tool; I'd just like to know what the full range of useful approaches really are, first, and any potential loopholes therein or drawbacks thereof.
A Limited But Better Than Nothing Way To Assign Probabilities to Statements of Logic, Arithmetic, etc.
by alex_zag_al — 2013-11-22T21:14:18.021Z

If we want to reason with probability theory, we seem to be stuck if we want to reason about mathematics.

You can skip this pararaph and the next if you're familiar with the problem. But if you're not, here's an illustration. Suppose your friend has some pennies that she would like to arrange into a rectangle, which of course is impossible if the number of pennies is prime. Let's call the number of pennies N. Your friend would like to use probability theory to guess whether it's worth trying; if there's a 50% chance that Prime(N), she won't bother trying to make the rectangle. You might imagine that if she counts them and finds that there's an odd number, this is evidence of Prime(N); if she furthermore notices that the digits don't sum to a multiple of three, this is further evidence of Prime(N). In general, each test of compositeness that she knows should, if it fails, raise the probability of Prime(N).

But what happens instead is this. Suppose you both count them, and find that N=53. Being a LessWrong reader, you of course recognize from recently posted articles that N=53 implies Prime(N), though she does not. But this means that P(N=53) <= P(Prime(N)). If you're quite sure of N=53—that is, P(N=53) is near 1—then P(Prime(N)) is also near 1. There's no way for her to get a gradient of uncertainty from simple tests of compositeness. The probability is just some number near 1.

In general, conditional on the axioms, mathematical theorems have probability 1 if they're true, and 0 if they're false. Deriving these probabilities is exactly as difficult as deriving the theorems themselves.

A way of assigning actual probabilities to theorems occurred to me today. I usually see this problem discussed by folks that want to develop formal models of AI, and I don't know if this'll be helpful at all for that. But it's something I can imagine myself using when I want to reason about a mathematical conjecture in a basically reasonable way.

The basic idea is just, don't condition on the axioms. Condition on a few relevant things implied by the axioms. Then, maximize entropy subject to those constraints.

Like, if you're trying to figure out whether a number N is prime. Don't condition on any properties of numbers, any facts about addition or multiplication etc., just treat Prime(N) as a predicate. Then condition on a f...
New LW Meetups: Amsterdam, Jacksonville, Milwaukee
by FrankAdamek — 2013-11-22T17:01:35.844Z

This summary was posted to LW main on November 15th. The following week's summary is here.

New meetups (or meetups with a hiatus of more than a year) are happening in:

Amsterdam/Netherlands: 23 November 2013 02:00PM
Jacksonville, FL: 24 November 2013 04:00PM
Milwaukee: 19 November 2013 06:30PM

Other irregularly scheduled Less Wrong meetups are taking place in:

[Atlanta GA] November Meetup (Second of Two): 23 November 2013 06:00PM
Berlin: 01 January 2019 01:30PM
Frankfurt: 24 November 2013 02:00PM
Princeton NJ Meetup: 16 November 2013 01:00PM
Saint-Petersburg: Game Event: 24 November 2013 04:00PM
Urbana-Champaign: Thinking Fast and Slow Discussion November 17: 17 November 2013 02:00PM

The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:

Austin, TX: 16 November 2019 01:30AM
Brussels monthly meetup: time!: 14 December 2013 01:00PM
Durham/RTLW HPMoR discussion, ch. 97-98: 16 November 2013 12:00PM
London social meetup - New venue: 17 November 2013 02:00PM
West LA—In Apprehending Hard Stuff: 20 November 2013 07:00AM

Locations with regularly scheduled meetups: Austin, Berkeley, Brussels, Cambridge, MA, Cambridge UK, Columbus, London, Madison WI, Melbourne, Mountain View, New York, Philadelphia, Research Triangle NC, Salt Lake City, Seattle, Toronto, Vienna, Washington DC, Waterloo, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers.

If you'd like to talk with other LW-ers face to face, and there is no meetup in your area, consider starting your own meetup; it's easy (more resources here). Check one out, stretch your rationality skills, build community, and have fun!

In addition to the handy sidebar of upcoming meetups, a meetup overview will continue to be posted on the front page every Friday. These will be an attempt to collect information on all the meetups happening in the next weeks. The best way to get your meetup featured is still to use the Add New Meetup feature, but you'll now also have the benefit of having your meetup mentioned in a weekly overview. These overview posts will be moved to the discussion section when the new post goes up.

Please note that for your meetup to appear in the weekly meetups feature, you need to post your meetup before t...
The sun reflected off things
by polymathwannabe — 2013-11-22T14:59:53.630Z

An insight I had a while ago:

When I'm out in the daylight, and I see a tree, what I actually see is not the tree itself. What I see is the sun reflected off the tree. Likewise with rocks, grass and birds: it's always the sun I'm seeing reflected off them. This is possible because the sun emits all visible colors (or rather, our eyes evolved to perceive almost all EM frequencies that almost all solid matter deflects). I'm not seeing the things. I'm seeing the light. We live surrounded by the sun.

Is this too obvious? Inconsequential? Redundant?
Meetup : Mumbai Meetup
by Ronak — 2013-11-22T10:14:00.678Z

Discussion article for the meetup : Mumbai Meetup

WHEN:
15 December 2013 03:00:00PM (+0530)

WHERE:
Gloria Jeans Coffee, S P Mukherji Chowk, Colaba, Mumbai

I haven't actually seen anyone else here who lives in India.
If you live in Mumbai, turn up and we can talk.
If you live elsewhere in India, message me - I do travel at least once a year and we may (or may not, as the case may be) be able to meet some time.

Discussion article for the meetup : Mumbai Meetup
2013 Less Wrong Census/Survey
by Scott Alexander — 2013-11-22T09:26:38.606Z

It's that time of year again.
If you are reading this post, and have not been sent here by some sort of conspiracy trying to throw off the survey results, then you are the target population for the Less Wrong Census/Survey. Please take it. Doesn't matter if you don't post much. Doesn't matter if you're a lurker. Take the survey.
This year's census contains a "main survey" that should take about ten or fifteen minutes, as well as a bunch of "extra credit questions". You may do the extra credit questions if you want. You may skip all the extra credit questions if you want. They're pretty long and not all of them are very interesting. But it is very important that you not put off doing the survey or not do the survey at all because you're intimidated by the extra credit questions.
It also contains a chance at winning a MONETARY REWARD at the bottom. You do not need to fill in all the extra credit questions to get the MONETARY REWARD, just make an honest stab at as much of the survey as you can.
Please make things easier for my computer and by extension me by reading all the instructions and by answering any text questions in the simplest and most obvious possible way. For example, if it asks you "What language do you speak?" please answer "English" instead of "I speak English" or "It's English" or "English since I live in Canada" or "English (US)" or anything else. This will help me sort responses quickly and easily. Likewise, if a question asks for a number, please answer with a number such as "4", rather than "four".
Last year there was some concern that the survey period was too short, or too uncertain. This year the survey will remain open until 23:59 PST December 31st 2013, so as long as you make time to take it sometime this year, you should be fine. Many people put it off last year and then forgot about it, so why not take it right now while you are reading this post?
Okay! Enough preliminaries! Time to take the...

***

2013 Less Wrong Census/Survey

***

Thanks to everyone who suggested questions and ideas for the 2013 Less Wrong Census/Survey. I regret I was unable to take all of your suggestions into account, because of some limitations in Google Docs, concern about survey length, and contradictions/duplications among suggestions. I think I got most of them in, and others can wait until next year.
By ancient tradition, if you take the survey you may comment saying ...
Probabilistic self-defeat arguments
by KatjaGrace — 2013-11-21T20:25:10.000Z

Alvin Plantinga‘s ‘evolutionary argument against naturalism‘ (EAAN) goes like this:

If humans were created by natural selection, and also not under the guidance of a creator (‘naturalism’), then (for various reasons he gives) the probability that their beliefs are accurate is low.

Therefore believing in natural selection and naturalism should lead a reasonable person to abandon these beliefs (among others). i.e. belief in naturalism and natural selection is self defeating.

Therefore a reasonable person should not believe in naturalism and natural selection.

Naturalism is generally taken to imply natural selection, so a reasonable person should not believe in naturalism.

This has been attacked from many directions. I agree with others that what I have called point 1 is dubious. However even on accepting it, it seems to me the argument fails.

Let us break down the space of possibilities under consideration into:

A: N&T: naturalism and true beliefs

B: N&F: naturalism and false beliefs

C: G&T: God and true beliefs

The EAAN says conditional on N, B is more likely than A, and infers from this that one cannot believe in either A or B, since both are included in N.

But there is no obvious reason to lump A and B together. Why not lump B and C together? Suppose we believe ‘natural selection has not produced true beliefs in us’. Then either natural selection has produced false beliefs, or God has produced true beliefs. If we don’t assign very high credence to the latter relative to the former, then we have a version of the EAAN that contradicts its earlier incarnation: ‘natural selection has not produced true beliefs in us’ is self-defeating. So we must believe that natural selection has produced true beliefs in us*.

What if we do assign a very high credence to C over B? It seems we can just break C up into smaller parts, and defeat them one at a time. B is more likely than C&D, where D = “I roll 1 on my n sided dice”, for some value of n. So consider the belief “B or C&D”. This is self-defeating. As it would seem “B or C&E” is, where E is “I roll 2 on my n sided dice”. And so on.

By this reasoning, if there is any possible world that is self-defeating, pretty much any other possible world can be sucked into the defeat. This depends a bit ...
Meetup : Saskatoon - Gauging the strength of evidence and Bayesian reasoning.
by Nicholas_Rutherford — 2013-11-21T19:33:18.300Z

Discussion article for the meetup : Saskatoon - Gauging the strength of evidence and Bayesian reasoning.

WHEN:
23 November 2013 01:00:00PM (-0600)

WHERE:
2318 8th St E, Saskatoon, SK

Another Saskatoon meetup at the same place and time as the last one: Broadway Roaster on 8th street (not on broadway!) at 1:00 in the afternoon.

A lot of the time beliefs are thought to be either true or false [1]. However this is rarely the case in practice! Instead of assigning beliefs a boolean value of true or false we can think of beliefs on a scale which goes from true to false and all possible values in between. This week we'll go over how to use this idea of continuous beliefs along with Bayesian reasoning to 'update' our beliefs when new evidence presents itself.

More info here: http://www.meetup.com/Saskatoon-Rationalists/

Hope to see you there!

[1] Hopefully this statement made your “false dichotomy detector” start ringing!

Discussion article for the meetup : Saskatoon - Gauging the strength of evidence and Bayesian reasoning.
Book Review: Computability and Logic
by So8res — 2013-11-21T13:52:02.094Z

I'm reviewing the books on the MIRI course list. After putting down Model Theory partway through I picked up a book on logic. Computability and Logic, specifically.

Computability and Logic

This book is not on the MIRI course list. It was recommended to me by Luke along with a number of other books as a potential way to learn provability logic.

Computability and Logic is a wonderful book. It's well written. It's formal, but pulls off a conversational tone. It demonstrates many difficult concepts with ease. It even feels nice — it's got thick pages, large text, and a number of useful diagrams.

That said, I didn't find it very useful to me personally.

This book is a wonderful introduction to computability, incompleteness, unsatisfiability, and related concepts. It masterfully motivates the connection between computability and logic (a subject near and dear to my heart). It could be an invaluable resource for anyone in computer science looking to branch out into logic. It starts with the basic concept of enumeration and takes you all the way through Löb's theorem: quite an impressive feat, for one textbook.

For me, though, it was on the easy side. I already knew all the computability stuff quite well, and skimmed over much of it. The logic sections were a good refresher, though they were somewhat rudimentary by comparison to Model Theory. (Actually, this book would have been a great precursor to Model Theory: It spent quite a bit of time motivating and fleshing out concepts that Model Theory dumps on your head.)

Still, while this book was not exactly what I needed, I highly recommend it for other purposes. Its contents are summarized below.

Contents

Enumerability

Diagonalization

Turing Computability

Uncomputability

Abacus Computability

Recursive Functions

Recursive Sets and Relations

Equivalent Definitions of Computability

A Précis of First-Order Logic: Syntax

A Précis of First-Order Logic: Semantics

The Undecidability of First-Order Logic

Models

The Existence of Models

Proofs and Completeness

Arithmetization

Representability of Recursive Functions

Indefinability, Undecidability, and Incompleteness

The Unprovability of Consistency

Normal Forms

The Craig Interpolation Theorem

Monadic and Dyadic Logic

Second-Order Logic

Arithmetical Decidability

Decidability of Arithmetic without Multiplication

Nonstandard Models

Ramsey's Theorem

Modal Logic a

...
The dangers of zero and one
by PhilGoetz — 2013-11-21T12:21:23.684Z

Eliezer wrote a post warning against unrealistically confident estimates, in which he argued that you can't be 99.99% sure that 53 is prime. Chris Hallquist replied with a post arguing that you can.

That particular case is tricky. There have been many independent calculations of the first hundred prime numbers. 53 is a small enough number that I think someone would notice if Wikipedia included it erroneously. But can you be 99.99% confident that 1159 is a prime? You found it in one particular source. Can you trust that source? It's large enough that no one would notice if it were wrong. You could try to verify it, but if I write a Perl or C++ program, I can't even be 99.9% sure that the compiler or interpreter will interpret it correctly, let alone that the program is correct.

Rather than argue over the number of nines to use for a specific case, I want to emphasize the the importance of not assigning things probability zero or one. Here's a real case where approximating 99.9999% confidence as 100% had disastrous consequences.

I developed a new gene-caller for JCVI. Genes are interpreted in units of 3 DNA nucleotides called codons. A bacterial gene starts with a start codon (usually ATG, TTG, or GTG) and ends at the first stop codon (usually TAG, TGA, or TAA). Most such sequences are not genes. A gene-caller is a computer program that takes a DNA sequence and guesses which of them are genes.

The first thing I tried was to create a second-order Markov model on codons, and train it on all of the large possible genes in the genome. (Long sequences without stop codons are unlikely to occur by chance and are probably genes.) That means that you set P = 1 and go down the sequence of each large possible gene, codon by codon, multiplying P by the probability of seeing each of the 64 possible codons in the third position given the codons in the first and second positions. Then I created a second Markov model from the entire genome. This took about one day to write, and plugging these two models into Bayes' law as shown below turned out to work better than all the other single-method gene-prediction algorithms developed over the past 30 years.

But what probability should you assign to a codon sequence that you've never seen? A bacterial genome might have 4 million base pairs, about half of which are in long possible genes and will be used for training. That means your tr...
Be Skeptical of Correlational Studies
by jefftk — 2013-11-20T22:19:28.281Z

People kept noticing that blood donors were healthier than non-donors. Could giving blood be good for you, perhaps by removing excess iron? Perhaps medieval doctors practicing blood-letting were onto something? Running some studies (1998, 2001) this does seem to be a real correlation, so you see articles like "Men Who Donate Blood May Reduce Risk Of Heart Disease."

While this sounds good, and it's nice when helpful things turn out to be healthy, the evidence is not very strong. When you notice A and B happen together it may be that A causes B, B causes A, or some hidden C causes A and B. We may have good reasons to believe A might cause B, but it's very hard to rule out a potential C. Instead if you intentionally manipulate A and observe what happens to B then you can actually see how much of an effect A has on B.

For example, people observed (2003) that infants fed soy-based formula were more likely to develop peanut allergies. So they recommended that "limiting soy milk or formula in the first 2 years of life may reduce sensitization." Here A is soy formula, B is peanut allergy, and we do see a correlation. When intentionally varying A (2008, n=620), however, B stays constant, which kind of sinks the whole theory. A likely candidate for a third cause, C, was a general predisposition to allergies: those infants were more likely to react to cows-milk formula and so be given soy-based ones, and they were also more likely to react to peanuts.

To take another example, based on studies (2000, 2008, 2010) finding a higher miscarriage rate among coffee drinkers pregnant women are advised to cut back their caffeine consumption. But a randomized controlled trial (2007, n=1207) found people randomly assigned to drink regular or decaf coffee were equally likely to have miscarriages. [EDIT: jimrandomh points out that I misread the study and it didn't actually show this. Instead it was too small a study to detect an effect on miscarriage rates.] A potential third cause (2012) here is that lack of morning sickness is associated with miscarriage (2010) and when you're nauseated you're less likely to drink a morning coffee. This doesn't tell us the root of the problem (why would feeling less sick go along with miscarriages?) but it does tell us cutting back on caffeine is probably not helpful.

Which brings us back to blood donation. What if instead of blood donation making you...
Brain Preservation Foundation ask me anything on Reddit 7:00PM EST Thursday Nov 21
by aurellem — 2013-11-20T21:24:10.263Z

AMA is here : http://www.reddit.com/r/IAmA/comments/1r6exr/i_am_kenneth_hayworth_a_phd_neuroscientist_and/

The Brain Preservation Foundation's founder Ken Hayworth is going to be available on Reddit (http://www.reddit.com/r/IAmA) this Thursday at 7:00PM EST to answer your questions. We hope to have a very interesting discussion ranging from the technical aspects of plastination and cryopreservation to the social consequences of widespread adoption of brain preservation.
Hayworth is a Senior Scientist at the Janelia Farm Research Campus and is an expert in state of the art brain preservation and imaging.
From the Brain Preservation Site:

Kenneth Hayworth, President and Co-Founder of the Brain Preservation Foundation, is currently a Senior Scientist at the Howard Hughes Medical Institute’s Janelia Farm Research Campus (JFRC) in Ashburn, Virginia. JFRC is perhaps the leading research institution in the field of connectomics in the United States. At JFRC, Hayworth is currently researching ways to extend Focused Ion Beam Scanning Electron Microscopy (FIBSEM) imaging of brain tissue to encompass much larger volumes than are currently possible. For an overview of this work see his recent review paper and online presentation. Prior to moving to JFRC, Hayworth was a postdoctoral researcher at Harvard University. Hayworth is co-inventor of the Tape-to-SEM process for high-throughput volume imaging of neural circuits at the nanometer scale and he designed and built several automated machines to implement this process. Hayworth received a PhD in Neuroscience from the University of Southern California for research into how the human visual system encodes spatial relations among objects. Hayworth is a vocal advocate for brain preservation and mind uploading and a co-founder of the Brain Preservation Foundation which calls for the implementation of an emergency glutaraldehyde perfusion procedure in hospitals, and for the development of a whole brain embedding procedure which can demonstrate perfect ultrastructure preservation across an entire human brain.

Links:

- http://www.reddit.com/r/IAmA
Ask me anything page where the discussion will be held
- http://chronicle.com/article/article-content/132819/
Overview article explaining plastination and the Brain Preservation foundation
- http://hplusmagazine.com/2013/05/28/neuroscienc...