trentmkelly/LessWrong-43k huggingface dataset:
Beyond Bayesians and Frequentists
by jsteinhardt — 2012-10-31T07:03:00.818Z

(Note: this is cross-posted from my blog and also available in pdf here.)

If you are a newly initiated student into the field of machine learning, it won't be long before you start hearing the words "Bayesian" and "frequentist" thrown around. Many people around you probably have strong opinions on which is the "right" way to do statistics, and within a year you've probably developed your own strong opinions (which are suspiciously similar to those of the people around you, despite there being a much greater variance of opinion between different labs). In fact, now that the year is 2012 the majority of new graduate students are being raised as Bayesians (at least in the U.S.) with frequentists thought of as stodgy emeritus professors stuck in their ways.

If you are like me, the preceding set of facts will make you very uneasy. They will make you uneasy because simple pattern-matching -- the strength of people's opinions, the reliability with which these opinions split along age boundaries and lab boundaries, and the ridicule that each side levels at the other camp – makes the "Bayesians vs. frequentists" debate look far more like politics than like scholarly discourse. Of course, that alone does not necessarily prove anything; these disconcerting similarities could just be coincidences that I happened to cherry-pick.

My next point, then, is that we are right to be uneasy, because such debate makes us less likely to evaluate the strengths and weaknesses of both approaches in good faith. This essay is a push against that --- I summarize the justifications for Bayesian methods and where they fall short, show how frequentist approaches can fill in some of their shortcomings, and then present my personal (though probably woefully under-informed) guidelines for choosing which type of approach to use.

Before doing any of this, though, a bit of background is in order...

1. Background on Bayesians and Frequentists

1.1. Three Levels of Argument

As Andrew Critch [6] insightfully points out, the Bayesians vs. frequentists debate is really three debates at once, centering around one or more of the following arguments:

Whether to interpret subjective beliefs as probabilities

Whether to interpret probabilities as subjective beliefs (as opposed to asymptotic frequencies)

Whether a Bayesian or frequentist algorithm is better suited to solving a particular problem.

...
Things philosophers have debated
by Eliezer Yudkowsky — 2012-10-31T05:09:00.208Z

Straight from Wikipedia.

I just had to stare at this a while. We can have papers published about this, we really ought to be able to get papers published about Friendly AI subproblems.

My favorite part is at the very end.

Trivialism is the theory that every proposition is true. A consequence of trivialism is that all statements, including all contradictions of the form "p and not p" (that something both 'is' and 'isn't' at the same time), are true.[1]

[edit]See also

Dialetheism

Kevala Jnana

Paraconsistency

Principle of explosion

[edit]References

^ Graham Priest; John Woods (2007). "Paraconsistency and Dialetheism". The Many Valued and Nonmonotonic Turn in Logic. Elsevier. p. 131. ISBN 978-0-444-51623-7.

[edit]Further reading

Paul Kabay (2008). "A defense of trivialism". PhD thesis, School of Philosophy, Anthropology, and Social Inquiry, The University of Melbourne.

Paul Kabay (2010). On the Plenitude of Truth. A Defense of Trivialism. Lambert Academic Publishing. ISBN 978-3-8383-5102-5.

Luis Estrada-González (2012) "Models of Possibilism and Trivialism", Logic and Logical Philosophy, Volume 21, 175–205

Frederick Kroon (2004). "Realism and Dialetheism". In Graham Priest, J. C. Beall, and Bradley Armour-Garb. The Law of Non-Contradiction: New Philosophical Essays. Oxford University Press. ISBN 978-0-19-926517-6.

Paul Kabay (2010). Interpreting the divyadhvani: On Why the Digambara Sect Is Right about the Nature of the Kevalin. The Australasian Philosophy of Religion Association Conference

Bueno, O. V. (2007). "Troubles with Trivialism". Inquiry 50 (6): 655–667. doi:10.1080/00201740701698670. edit

Priest, G. (2000). "Could everything be true?". Australasian Journal of Philosophy 78 (2): 189–195. doi:10.1080/00048400012349471. edit

This logic-related article is a stub. You can help Wikipedia by expanding it.
[SEQ RERUN] Failure By Affective Analogy
by MinibearRex — 2012-10-31T04:53:10.551Z

Today's post, Failure By Affective Analogy was originally published on 18 November 2008. A summary (taken from the LW wiki):

Making analogies to things that have positive or negative connotations is an even better way to make sure you fail.

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Failure By Analogy, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
Question about application of Bayes
by RolfAndreassen — 2012-10-31T02:35:56.354Z

I have successfully confused myself about probability again.

I am debugging an intermittent crash; it doesn't happen every time I run the program. After much confusion I believe I have traced the problem to a specific line (activating my debug logger, as it happens; irony...) I have tested my program with and without this line commented out. I find that, when the line is active, I get two crashes on seven runs. Without the line, I get no crashes on ten runs. Intuitively this seems like evidence in favour of the hypothesis that the line is causing the crash. But I'm confused on how to set up the equations. Do I need a probability distribution over crash frequencies? That was the solution the last time I was confused over Bayes, but I don't understand what it means to say "The probability of having the line, given crash frequency f", which it seems I need to know to calculate a new probability distribution.

I'm going to go with my intuition and code on the assumption that the debug logger should be activated much later in the program to avoid a race condition, but I'd like to understand this math.
Group rationality diary, 10/29/12
by cata — 2012-10-30T18:01:56.568Z

This is the public group instrumental rationality diary for the week of October 29th. It's a place to record and chat about it if you have done, or are actively doing, things like:

Established a useful new habit

Obtained new evidence that made you change your mind about some belief

Decided to behave in a different way in some set of situations

Optimized some part of a common routine or cached behavior

Consciously changed your emotions or affect with respect to something

Consciously pursued new valuable information about something that could make a big difference in your life

Learned something new about your beliefs, behavior, or life that surprised you

Tried doing any of the above and failed

Or anything else interesting which you want to share, so that other people can think about it, and perhaps be inspired to take action themselves. Try to include enough details so that everyone can use each other's experiences to learn about what tends to work out, and what doesn't tend to work out.

Thanks to everyone who contributes!

Previous diary; archive of prior diaries.
Proofs, Implications, and Models
by Eliezer Yudkowsky — 2012-10-30T13:02:20.077Z

Followup to: Causal Reference

From a math professor's blog:

One thing I discussed with my students here at HCSSiM yesterday is the question of what is a proof.

They’re smart kids, but completely new to proofs, and they often have questions about whether what they’ve written down constitutes a proof. Here’s what I said to them.

A proof is a social construct – it is what we need it to be in order to be convinced something is true. If you write something down and you want it to count as a proof, the only real issue is whether you’re completely convincing.

This is not quite the definition I would give of what constitutes "proof" in mathematics - perhaps because I am so used to isolating arguments that are convincing, but ought not to be.

Or here again, from "An Introduction to Proof Theory" by Samuel R. Buss:

There are two distinct viewpoints of what a mathematical proof is. The first view is that proofs are social conventions by which mathematicians convince one another of the truth of theorems. That is to say, a proof is expressed in natural language plus possibly symbols and figures, and is sufficient to convince an expert of the correctness of a theorem. Examples of social proofs include the kinds of proofs that are presented in conversations or published in articles. Of course, it is impossible to precisely define what constitutes a valid proof in this social sense; and, the standards for valid proofs may vary with the audience and over time. The second view of proofs is more narrow in scope: in this view, a proof consists of a string of symbols which satisfy some precisely stated set of rules and which prove a theorem, which itself must also be expressed as a string of symbols. According to this view, mathematics can be regarded as a 'game' played with strings of symbols according to some precisely defined rules. Proofs of the latter kind are called "formal" proofs to distinguish them from "social" proofs.

In modern mathematics there is a much better answer that could be given to a student who asks, "What exactly is a proof?", which does not match either of the above ideas. So:

Meditation: What distinguishes a correct mathematical proof from an incorrect mathematical proof - what does it mean for a mathematical proof to be good? And why, in the real world, would anyone ever be interested in a mathematical proof of this type, or obeying whatever goodness-ru...
Checking Kurzweil's track record 
by Stuart_Armstrong — 2012-10-30T11:07:50.989Z

Predictions are cheap and easy; verification is hard, essential, and rare. For things like AI, we seem to be restricted to nothing but expert predictions - but expert predictions on AI are not very good, either in theory or in practice. If we are some experts who stand out, we would really want to identify them - and there is nothing better than a track record for identifying true experts.

So we're asking for help to verify the predictions of one of the most prominent futurists of this century: Ray Kurzweil, from his book "The Age of Spiritual Machines". By examining his predictions for times that have already come and gone, we'll be able to more appropriately weight his predictions for times still to come. By taking part, by lending your time to this, you will be directly helping us understand and predict the future, and will get showered in gratitude and kudos and maybe even karma.

I've already made an attempt at this (if you are interested in taking part in this project, avoid clicking on that link for now!). But you cannot trust a single person's opinions, and that was from a small (albeit random) sample of the predictions. For this project, I've transcribed his predictions into 172 separate (short) statements, and any volunteers would be presented with a random selection among these. The volunteers would then do some Google research (or other) to establish whether the prediction had come to pass, and then indicate their verdict. More details on what exactly will be measured, and how to interpret ambiguous statements, will be given to the volunteers once the project starts.

If you are interested, please let me know at stuart.armstrong@philosophy.ox.ac.uk (or in the comment thread here), indicating how many of the 172 questions you would like to attempt. The exercise will probably happen in late November or early December.

This will be done unblinded, because Kurzweil's predictions are so well known that it would be infeasible to find large numbers of people who are technologically aware but ignorant of them. Please avoid sharing your verdicts with others; it is entirely your own individual assessment that we are interested in having.
[SEQ RERUN] Failure By Analogy
by MinibearRex — 2012-10-30T04:07:22.855Z

Today's post, Failure By Analogy was originally published on 18 November 2008. A summary (taken from the LW wiki):

It's very tempting to reason that your invention X will do Y, because it is similar to thing Z, which also does Y. But reality very often ignores this justification for why your new invention will work.

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Logical or Connectionist AI?, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
An anecdote about names
by Manfred — 2012-10-30T03:40:56.686Z

I've always been bad at names. But this semester, as part of my duties as a physics teacher, I tried to learn the names of 100 students. It went alright - two months later and there are still some I have trouble with. At the start, I was a bit worried. What if I've just read to many books and things, and used up most of my abnormally small allotment of memorized names? Then wasting names on students would be a really bad idea.

But things turned out not to work that way (as might be expected from how big the human brain is, viz people with eidetic memories). I've started remembering peoples' names after just one introduction, sometimes two. And the reasonable culprit just seems to be practice. You practice learning names, you get better at doing so. Before, I made occasional conversational detours if I couldn't remember someone's name. Now, I ask them again, because I'm confident that I'll remember it without tons of further awkwardness. If I'm really having trouble, sometimes I've written down a name with a short description, and that usually cements it. Remembering names isn't the most important social skill I've learned, but it's a surprisingly dramatic one - it makes me feel closer to people, and vice versa. And it only took having to learn the names of 100 people to get started.

The lesson from this is not necessarily just about learning names, though that might be useful to you. The lesson is about how ordinary people get a lot of practice at doing things like remembering names - if you can't do something that someone else can do, practice may be an effective place to start. What seems like a property of yourself ("bad at names") may turn out to be quite mutable with the kind of practice those other people are doing.
Incentives to Make Money More Effectively, Should We List Them?
by diegocaleiro — 2012-10-30T00:28:15.767Z

There is a bit of nice and recent discussion going on about money here, "money" in the sense of the common peasant "I want more money" not in the senses of "Unit of caring" "Utilon buyer" "Trade-able entity unavailable for acausal trade" etc...

There is also, on the web, and in your local store, hundreds of thousands of advices on how to make money: fast, more, passively, selling your body, or brain, or virtual structures.

People who have money (entrepreneurs, owners), study money (economists) or focus on money (stockbrokers) have a lot to tell you about something closely related. Incentives. They studied and understand when incentives work and when they don't. Some have become masters of creating the right incentives, for employees, customers, CEOs, everyone.

I wont lie, usually, the incentive is money. Pure, abstract, trade-able, feeling-of-power causing, money. Sometimes (Citibank, Safra Bank) also travelling.

What I don't see around a lot, and would like to ask you to help me brainstorm, and list (later I'll edit by adding suggestions and maybe commenting) are the incentives to make money themselves that work, and those that don't.

There are a lot of LWers, I know, who excel at domain X and are emotionally averse to making money, out of X, or sometimes, at all. Money-making is, in a word, impure! Item 6 will deal with that.

I'll say some random unconventional things and open the discussion for those with more experience:

1)Belonging to a society that lacks money and money making altogether is a very powerful incentive for wanting money. The Indians of Brazil (my homeland) frequently engage in juridic battles and bureaucracy to obtain more money from governmental organs, more time than they would dedicate to get medical attention, for instance.

2)I've heard that having a lot of money is an incentive to make more, by making it easier to do so. Spiteful Billionaire meme makes fun of this hypothesis

3)Also heard that not having money is a great incentive to make more. This seems more logical. When you see the empty plate in the horizon, or being thrown in the streets, some sort of alarm must trigger. (If you have experience with this, relate it please)

Yet, I find that even though there is a strong correlation (within the brazilian cultural elite I hang around with) between having a job and not having money at ages 18-28, there is no correlation at all between n...
Original Research on Less Wrong
by lukeprog — 2012-10-29T22:50:35.671Z

Hundreds of Less Wrong posts summarize or repackage work previously published in professional books and journals, but Less Wrong also hosts lots of original research in philosophy, decision theory, mathematical logic, and other fields. This post serves as a curated index of Less Wrong posts containing significant original research.

Obviously, there is much fuzziness about what counts as "significant" or "original." I'll be making lots of subjective judgment calls about which suggestions to add to this post. One clear rule is: I won't be linking anything that merely summarizes previous work (e.g. Stuart's summary of his earlier work on utility indifference).

Update 09/20/2013: Added Notes on logical priors from the MIRI workshop, Cooperating with agents with different ideas of fairness, while resisting exploitation, Do Earths with slower economic growth have a better chance at FAI?

Update 11/03/2013: Added Bayesian probability as an approximate theory of uncertainty?, On the importance of taking limits: Infinite Spheres of Utility, Of all the SIA-doomsdays in the all the worlds...

Update 01/22/2014: Added Change the labels, undo infinitely good, Reduced impact AI: no back channels, International cooperation vs. AI arms race, Naturalistic trust among AIs: The parable of the thesis advisor’s theorem

General philosophy

Highly Advanced Epistemology 101 for Beginners. Eliezer's bottom-up guide to truth, reference, meaningfulness, and epistemology. Includes practical applications and puzzling meditations.

Seeing Red, A Study of Scarlet, Nature: Red in Tooth and Qualia. Orthonormal dissolves Mary's room and qualia.

Counterfactual resiliency test for non-causal models. Stuart Armstrong suggests testing non-causal models for "counterfactual resiliency."

Thoughts and problems with Eliezer's measure of optimization power. Stuart Armstrong examines some potential problems with Eliezer's concept of optimization power.

Free will. Eliezer's particular compatibilist-style solution to the free will problem from reductionist viewpoint.

The absolute Self-Selection Assumption. A clarification on anthropic reasoning, focused on Wei Dai s UDASSA framework.

SIA, conditional probability, and Jaan Tallinn s simulation tree. Stuart Armstrong makes the bridge between Nick Bostrom s Self-Indication Assumption (SIA) and Jann Tallinn s of superintelligence reproduction

...
[SEQ RERUN] Logical or Connectionist AI?
by MinibearRex — 2012-10-29T03:47:45.527Z

Today's post, Logical or Connectionist AI? was originally published on 17 November 2008. A summary (taken from the LW wiki):

The difference between Logical and Connectionist AIs is portrayed as a grand dichotomy between two different sides of the force. The truth is that they're just two different designs out of many possible ones.

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was The Nature of Logic, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
Constructing fictional eugenics (LW edition)
by Eliezer Yudkowsky — 2012-10-29T00:41:05.662Z

Yvain asked:

So if you had to design a eugenics program, how would you do it? Be creative.
I'm asking because I'm working on writing about a fictional society that practices eugenics. I want them to be interesting and sympathetic, and not to immediately pattern-match to a dystopia that kills everyone who doesn't look exactly alike.

My reply was too long for LiveJournal, so I'm posting it here:

1. The real step 1 in any program like this would be to buy the 3 best modern textbooks on animal breeding and read them. (My grandfather is a researcher in this field so I'm unusually aware that it exists.)

2. If you give me genetic selection on multiple possible embryos where I can read off the genome of each one, I can do much better, much faster, than if I'm only allowed to look at the mother and father's genome and predict on that basis. If I can only look at the mother and father's relatives and life achievements, I do worse, but modern tech is very rapidly advancing to be able to read off the parents' genome cheaply.

3. If society's utility has a large component for genius production, then you probably want a very diverse mix of different high-IQ genes combined into different genotypes and phenotypes. (Although some recent research suggests that the most important thing for IQ may be avoiding mutational load, i.e., the modal genome would be super-von-Neumann. Even so, we'd want a diverse mix of everything else cognitive that wasn't about modality.)

4. Doing a Bayesian value-of-information calculation on rare alleles and potentially interesting allele combinations will automatically include a value for diversity into your eugenic program, based on the value of promoting a gene / combo in much larger numbers if that gene or gene combo is found to be successful. You would get much *more* interesting diversity in the next generation automatically, as many previously low-frequency alleles were combined in greater numbers and greater diversity than before. *Not* doing a value-of-info calculation accounts for a lot of the dystopic load of alleged dystopias.

5. The obvious basic instrument in a society depicted as well-intentioned would be an economic policy of trying to internalize the externalities of a child, just like a well-intentioned society might try to internalize the externalities of e.g. carbon dio...