trentmkelly/LessWrong-43k huggingface dataset:
(Subjective Bayesianism vs. Frequentism) VS. Formalism 
by Ronny Fernandez — 2011-11-26T05:05:41.138Z

One of the core aims of the philosophy of probability is to explain the relationship between frequency and probability. The frequentist proposes identity as the relationship. This use of identity is highly dubious. We know how to check for identity between numbers, or even how to check for the weaker copula relation between particular objects; but how would we test the identity of frequency and probability? It is not immediately obvious that there is some simple value out there which is modeled by probability, like position and mass are values that are modeled by Newton's Principia. You can actually check if density * volume = mass, by taking separate measurements of mass, density and volume, but what would you measure to check a frequency against a probability?

There are certain appeals to frequentest philosophy: we would like to say that if a bag has 100 balls in it, only 1 of which is white, then the probability of drawing the white ball is 1/100, and that if we take a non-white ball out, the probability of drawing the white ball is now 1/99. Frequentism would make the philosophical justification of that inference trivial. But of course, anything a frequentist can do, a Bayesian can do (better). I mean that literally: it's the stronger magic.

A Subjective Bayesian, more or less, says that the reason frequencies are related to probabilities is because when you learn a frequency you thereby learn a fact about the world, and one must update one's degrees of belief on every available fact. The subjective Bayesian actually uses the copula in another strange way:

Probability is subjective degree of belief.

and subjective Bayesians also claim:

Probabilities are not in the world, they are in your mind.

These two statements are brilliantly championed in Probability is Subjectively Objective. But ultimately, the formalism which I would like to suggest denies both of these statements. Formalists do not ontologically commit themselves to probabilities, just as they do not say that numbers exist; hence we don't allocate probabilities in the mind or anywhere else; we only commit ourselves to number theory, and probability theory. Mathematical theories are simply repeatable processes which construct certain sequences of squiggles called "theorems", by changing the squiggles of other theorems, according to certain rules called "inferences". Inferences always take as in...
Communicating rationality to the public: Julia Galef's "The Straw Vulcan"
by lukeprog — 2011-11-25T22:57:47.129Z

Julia Galef's Skepticon IV talk, The Straw Vulcan, is the best intro-to-rationality talk for the general public I've ever seen. Share the link with everyone you know!

Update: Below is the transcript prepared by daenerys:

Emcee: You may recognize our next speaker from somewhere-- She was here earlier on our panel, she’s known for her work with the New York City Skeptics and their podcast “Rationally Speaking”, and she’s also the co-author of the rationality blog “Measure of Doubt” with her brother Jesse: Julia Galef!

[applause]

Julia: Hey, it’s really nice to be back. I’m so excited to be giving a talk at Skepticon. Last year was my first year and I got to moderate a panel, so this is an exciting new step for me. My talk today has been sort of organically growing over the last couple of years as I’ve become more and more involved in the skeptic and rationality movements, and I’ve gotten more and more practice, and learned many lessons the hard way about communicating ideas about rationality and critical thinking and skepticism to people.

The title of the talk is “The Straw Vulcan: Hollywood’s Illogical View of Logical Decision-Making”. So if there’s anyone in the audience who doesn’t recognize this face, this is Mr. Spock…Someone’s raising their hands, but it’s a Vulcan salute, so I don’t believe you don’t know him…So this is Mr. Spock; He’s one of the main characters on Star Trek and he’s the First Officer and the Science Officer on the Starship Enterprise. And his mother is human, but his father is Vulcan.

The Vulcans are this race of aliens that are known for trying to live in strict adherence of the rules of reason and logic, and also for eschewing emotion. This is something I wasn’t clear on when I was remembering the show from my childhood, but it’s not that the Vulcans don’t have emotion, it’s just that over time they’ve developed very strict and successful ways of transcending and suppressing their emotions. So Spock being half-Vulcan has more lapses than a pure-blood Vulcan, but still, on the show Star Trek he is “The Logical Character” and that makes up a lot of the inter-character dynamics and the storylines on the show.

[2:30]

So, here’s Spock. He

...
King Under The Mountain: Adventure Log + Soundtrack
by Scott Alexander — 2011-11-25T22:29:07.219Z

With the help of many dedicated Less Wrongers (players muflax, Karl, Charlie, and Emile; musicians Mike Blume and Alicorn, technical support Ari Rahikkala) we have successfully completed what is, as far as I know, the first actual Dungeons and Discourse adventure anywhere. Except we're not calling it that, because I don't have the rights to use that name. Though it's not precisely rationality related, I hope it is all right if I post a summary of the adventure by popular demand.
Also, at some point it turned into a musical. The first half of the songs are only available as lyrics at the moment, but Alicorn and MBlume very kindly produced the second half as real music, which I've uploaded to YouTube and linked at the bottom of this post (skip to it now).

THE ADVENTURE

BACKGROUND
The known world has many sects and religions, but all contain shadowy legends of two primeval deities: Sophia, Goddess of Wisdom; and Aleithos, God of Truth. When Sophia announced her plan to create thinking, rational beings, Aleithos objected, declaring that they would fall into error and produce endless falsehoods. Sophia ignored her brother's objections and created humankind, who named the world after their goddess-mother. But Aleithos' fears proved well-founded: humankind fell into error and produced endless falsehoods, and their clamor drove the God of Truth insane.
The once mighty Aleithos fell from heaven, and all of his angelic servants turned into Paradox Beasts, arachnoid monstrosities that sought and devoured those who challenged the laws of logic. Over centuries, most of the Paradox Beasts were banished, but Aleithos himself remained missing. And though thousands of seekers set off to all the corners of the world in search of Truth, the Mad God keeps his own counsel, if He still even exists at all.
The Truth God's madness had one other effect; the laws of physics, once inviolable, turned fluid, and those sufficiently advanced in the study of Truth gained apparently magical abilities. With knowledge literally being power, great philosophers and scientists built mighty cities and empires.
In the middle of the Cartesian Plain at the confluence of the rivers Ordinate and Abcissa stands the mightiest of all, the imperial city of Origin. At the very center of the city stands the infinitely tall Z-Axis Tower, on whose bottom floor lives the all-seeing Wizard of 0=Z. Surrounding the Towe...
Friendship and happiness generation
by AlexMennen — 2011-11-25T20:52:18.009Z

Happiness and utility are different things, with happiness (measured in hedons) generally referring to the desirability of an agent being in its current mental state, while utility (measured in utils) refers to the desirability, from the point of view of some agent, of the configuration of the universe.

Naively, one could model caring about another person as having a portion of your utility function allocated to mimicking their utility (me.utility(universe) = caring_factor*friend.utility(universe) + me.utility(universe excluding value of friend's utility function)) or their happiness (me.utility(universe) = caring_factor*friend.happiness + me.utility(universe excluding friend's happiness)). However, I think these are bad models of how caring for people actually works in humans.

I've noticed that I often gladly give up small amounts of hedons so that someone I care about can gain a similar amount of hedons. Extrapolating this, one might conclude that I care about plenty of other people nearly as much as I care about myself. However, I would be much less likely to give up a large amount of hedons for someone I care about unless the ratio of hedons that they could gain over the hedons I would have to give up is also fairly large.

While trying to figure out why this is, I realized that whenever I think I'm sacrificing hedons for someone, I usually don't actually lose any hedons because I enjoy the feeling associated with knowing that I helped a friend. I expect that this reaction is fairly common. This implies that by doing small favors for each other, friends can generate happiness for both of them even when the amount of hedons sacrificed by one (not counting the friend-helping bonus) is similar to the amount of hedons gained by the other. However, this happiness bonus for helping a friend is bounded, and grows sublinearly with respect to the amount of good done to the friend. In terms of evolutionary psychology, this makes sense: seeking out cheap ways to signal loyalty sounds like a decent strategy for getting and keeping allies.

I don't think that this tells the whole story. If a friend had enough at stake, I would sacrifice much more for them than could be reimbursed with the happiness bonus for helping a friend (plus happiness penalty that I would otherwise absorb for the feeling of knowing I had abandoned a friend), because I do actually care about people. A...
[SEQ RERUN] Guardians of the Truth
by MinibearRex — 2011-11-25T17:52:56.404Z

Today's post, Guardians of the Truth was originally published on 15 December 2007. A summary (taken from the LW wiki):

There is an enormous psychological difference between believing that you absolutely, certainly, have the truth, versus trying to discover the truth. If you believe that you have the truth, and that it must be protected from heretics, torture and murder follow. Alternatively, if you believe that you are close to the truth, but perhaps not there yet, someone who disagrees with you is simply wrong, not a mortal enemy.

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 Hug the Query, 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.
LessWrong opinion of Nietzsche?
by TheatreAddict — 2011-11-25T16:08:50.632Z

So, at the risk of starting controversy, I'm not exactly sure what the policy is about asking questions on philosophy..

But would you mind giving your opinion on Nietzsche? I just bought Human, All Too Human. It's a tough read for me, and I'm sort of plowing through it, though it's interesting and stuff.

So... what do you all think? :D

Edit: I changed it from "Rationalist opinion of Nietzsche". Better?
Weekly LW Meetups: Houston, Atlanta, Eugene OR, San Diego, Fort Collins CO, Waterloo, Melbourne
by FrankAdamek — 2011-11-25T14:14:52.389Z

There are upcoming irregularly scheduled Less Wrong meetups in:

Houston Meetup: Saturday, 11/19: 19 November 2011 02:25AM
Atlanta Less Wrong Meetup: 19 November 2011 07:00PM
First Eugene Meetup: 20 November 2011 01:00PM
San Diego meetup: 20 November 2011 01:00PM
Fort Collins, Colorado Meetup Wedneday 7pm: 23 November 2011 07:00PM
First(New?) Waterloo Meetup: 24 November 2011 08:00PM
Sheridan College - Oakville, ON: 25 November 2011 08:30PM

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

Monthly Bay Area meetup: 26 November 2011 07:00PM
Melbourne practical rationality meetup: 02 December 2011 07:00PM

Cities with regularly scheduled meetups: Austin, Berkeley, Cambridge, MA, Irvine, London, Marin, CA (uses the Bay Area List), Melbourne, Mountain View, New York, Ottawa, Oxford, San Francisco, Toronto, Washington, DC, and West Los Angeles.

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, 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 the Friday before your meetup!

If you check Less Wrong irregularly, consider subscribing to one or more city-specific mailing list in order to be notified when an irregular meetup is happening: Chicago, Helsinki, London, Madison, WI, Pittsburgh, Seattle, Southern California (Los Angeles/Orange County area), St. Louis, Vancouver.
Intelligence Explosion analysis draft: How long before digital intelligence?
by lukeprog — 2011-11-24T20:56:28.969Z

Again, I invite your feedback on this snippet from an intelligence explosion analysis Anna Salamon and myself have been working on. This section is less complete than the others; missing text is indicated with brackets: [].

_____

We do not know what it takes to build a digital intelligence. Because of this, we do not know what groundwork will be needed to understand intelligence, nor how long it may take to get there.

Worse, it’s easy to think we do know. Studies show that except for weather forecasters (Murphy and Winkler 1984), nearly all of us give inaccurate probability estimates when we try, and in particular we are overconfident of our predictions (Lichtenstein, Fischoff, and Phillips 1982; Griffin and Tversky 1992; Yates et al. 2002). Experts, too, often do little better than chance (Tetlock 2005), and are outperformed by crude computer algorithms (Grove and Meehl 1996; Grove et al. 2000; Tetlock 2005). So if you have a gut feeling about when digital intelligence will arrive, it is probably wrong.

But uncertainty is not a “get out of prediction free” card. You either will or will not save for retirement or support AI risk mitigation. The outcomes of these choices will depend, among other things, on whether digital intelligence arrives in the near future. Should you plan as though there are 50/50 odds of reaching digital intelligence in the next 30 years? Are you 99% confident that digital intelligence won’t arrive in the next 30 years? Or is it somewhere in between?

Other than using one’s guts for prediction or deferring to an expert, how might one estimate the time until digital intelligence? We consider several strategies below.

Time since Dartmouth. We have now seen 60 years of work toward digital intelligence since the seminal Dartmouth conference on AI, but digital intelligence has not yet arrived. This seems, intuitively, like strong evidence that digital intelligence won’t arrive in the next minute, good evidence it won’t arrive in the next year, and significant but far from airtight evidence that it won’t arrive in the next few decades. Such intuitions can be formalized into models that, while simplistic, can form a useful starting point for estimating the time to digital intelligence.1

Simple hardware extrapolation. Vinge (1993) wrote: “Based on [hardware trends], I believe that the crea

...
Will the ems save us from the robots?
by Stuart_Armstrong — 2011-11-24T19:23:56.148Z

At the FHI, we are currently working on a project around whole brain emulations (WBE), or uploads. One important question is if getting to whole brain emulations first would make subsequent AGI creation

more or less likely to happen,

more or less likely to be survivable.

If you have any opinions or ideas on this, please submit them here. No need to present an organised overall argument; we'll be doing that. What would help most is any unusual suggestion, that we might not have thought of, for how WBE would affect AGI.

EDIT: Many thanks to everyone who suggested ideas here, they've been taken under consideration.
Review of Kurzweil, 'The Singularity is Near'
by lukeprog — 2011-11-24T08:30:48.552Z

Ray Kurzweil's writings are the best-known expression of Singularity memes, so I figured it's about time I read his 2005 best-seller The Singularity is Near.

Though earlier users of the term "technological Singularity" used it to refer to the arrival of machine superintelligence (an event beyond which our ability to predict the future breaks down), Kurzweil's Singularity is more vaguely defined:

What, then, is the Singularity? It's a future period during which the pace of technological change will be so rapid, its impact so deep, that human life will be irreversibly transformed.

Kurzweil says that people don't expect the Singularity because they don't realize that technological progress is largely exponential, not linear:

People intuitively assume that the current rate of progress will continue for future periods. Even for those who have been around long enough to experience how the pace of change increases, over time, unexamined intuition leaves one with the impression that change occurs at the same rate that we have experienced most recently. From the mathematician's perspective, the reason for this is that an exponential curve looks like a straight line when examined for only a brief duratio. As a result, even sophisticated commentators, when considering the future, typically extrapolate the current pace of change over the next ten years or one hundred years to determine their expectations...

But a serious assessment of the history of technology reveals that technological change is exponential... You can examine the data in different ways, on different timescales, and for a wide variety of technologies, ranging from electronic to biological... the acceleration of progress and growth applies to each of them.

Kurzweil has many examples:

Consider Gary Kasparov, who scorned the pathetic state of computer chess in 1992. Yet the relentless doubling of computer power every year enabled a computer to defeat him only five years later...

[Or] consider the biochemists who, in 1990, were skeptical of the goal of transcribing the entire human genome in a mere fifteen years. These scientists had just spent an entire year transcribing a mere one ten-thousandth of the genome. So... it seemed natural to them that it would take a century, if not longer, before the genome could be sequenced. [The complete genome was sequenced in 2003.]

He emphasizes that people often fai...
[SEQ RERUN] Hug the Query
by MinibearRex — 2011-11-24T04:40:51.834Z

Today's post, Hug the Query was originally published on 14 December 2007. A summary (taken from the LW wiki):

The more directly your arguments bear on a question, without intermediate inferences, the more powerful the evidence. We should try to observe evidence that is as near to the original question as possible, so that it screens off as many other arguments as possible.

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 Argument Screens Off Authority, 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.
FAI FAQ draft: general intelligence and greater-than-human intelligence
by lukeprog — 2011-11-23T19:52:32.175Z

My thanks to everyone who has provided feedback on these drafts so far. It's been helpful, and I've been incorporating your suggestions into the document. Now, Iinvite your feedback on these two snippets from the forthcoming Friendly AI FAQ. For references, see here.

_____

1.10. What is general intelligence?

There are many competing definitions and theories of intelligence (Davidson & Kemp 2011; Niu & Brass 2011; Legg & Hutter 2007), and the term has seen its share of emotionally-laden controversy (Halpern et al. 2011; Daley & Onwuegbuzie 2011).

Legg (2008) collects dozens of definitions of intelligence, and finds that they loosely converge on the following idea:

Intelligence measures an agent’s ability to achieve goals in a wide range of environments.

That will be our ‘working definition’ for intelligence in this FAQ.

There is a sense in which famous computers like Deep Blue and Watson are “intelligent.” They can out-perform human competitors for a narrow range of goals (winning chess games or answers Jeopardy! questions), in a narrow range of environments. But drop them in a novel environment — a shallow pond or a New York taxicab — and they are dumb and helpless. In this sense their “intelligence” is not general.

Human intelligence is general in that it allows us to achieve goals in a wide range of environments. We can solve new problems of survival, competition, and fun in a wide range of environments, including ones never before encountered. That is, after all, how humans came to dominate all the land and air on Earth, and what empowers us to explore more extreme environments — like the deep sea or outer space — when we choose to. Humans have invented languages, developed agriculture, domesticated other animals, created crafts and arts and architecture, written philosophy, explored the planet, discovered math and science, evolved new political and economic systems, built machines, developed medicine, and made plans for the distant future.

Some other animals also have a slower but more general intelligence than Deep Blue and Watson. Apes, dolphins, elephants, and a few species of bird have demonstrated some ability to solve novel problems in novel environments (Zentall 2011).

General intelligence in a machine is called artificial general intelligence (AGI). Nobody has dev...