Harley-ml/lesswrong huggingface dataset:
# How has technology changed social skills?

At LW London last week, someone mentioned the possibility of a Google Glass app doing face recognition on people. If you've met someone before, it tells you their name, how you know them, etc. Someone else mentioned that this could reduce the social capital of people who are already good at this.

A third person said that something similar happened when Facebook started telling everyone when everyone else's birthday was. Previously he got points by making an effort to remember, but those points are no longer available.

Are there other social skills that technology has made obsolete? And the reverse question that it only just occured to me to ask, are there social skills that are only useful because of technology?

I'm not really sure what sorts of things I'm looking for here. "Ability to ask for directions" seems like one example, but it feels kind of noncentral to me, I don't know why. But I'm mostly just curious.
# Come up with better Turing Tests

So the Turing test has been "[passed](/r/discussion/lw/kc0/news_turing_test_passed/)", and the general consensus is that this was achieved in a very unimpressive way - the 13 year old Ukrainian persona was a cheat, the judges were incompetent, etc... These are all true, though the test did pass [Turing's original criteria](http://loebner.net/Prizef/TuringArticle.html) \- and there are far more people willing to be dismissive of those criteria in retrospect than were in advance. It happened about 14 years later than Turing had been anticipating, which makes it quite a good prediction for 1950 (in my personal view, Turing made two mistakes that compensated - the "average interrogator" was a much lower bar than he thought, but progress on the subject would be much slower than he thought).

But anyway, the main goal now, as suggested by Toby Ord and others, is to design a better Turing test, something that can give AI designers something to aim at, and that would be a meaningful test of abilities. The aim is to ensure that if a program passes these new tests, we won't be dismissive of how it was achieved.

Here are a few suggestions I've heard about or thought about recently; can people suggest more and better ideas?

1.  Use proper control groups. 30% of judges thinking that a program is human is meaningless unless the judges also compare with actual humans. Pair up a human subject with a program, and the role of the judge is to establish which of the two subjects is the human and which is not.
2.  Toss out the persona tricks - no 13 year-olds, nobody with poor English skills. It was informative about human psychology that these tricks work, but we shouldn't allow them in future. All human subjects will have adequate English and typing skills.
3.  On that subject, make sure the judges and subjects are properly motivated (financial rewards, prizes, prestige...) to detect or appear human. We should also brief them that our usual conversational approach to establish _which kind_ of human they are dealing with, is not useful for determining whether they are dealing with a human at all.
4.  Use only elite judges. For instance, if [Scott Aaronson](http://www.scottaaronson.com/blog/?p=1858) can't figure it out, the program must have some competence.
5.  Make a collection of generally applicable approaches (such as the [Winograd Schemas](http://www.cs.nyu.edu/davise/papers/WS.html)) available to the judges, while emphasising they will have to come up with their own exact sentences, since anything online could have been used to optimise the program already.
6.  My favourite approach is to test the program on [a task they were not optimised for](/lw/hgl/the_flawed_turing_test_language_understanding_and/). A cheap and easy way of doing that would be to test them on novel ASCII art.

My current method would be the lazy one of simply typing this, then waiting, arms folded:

"If you want to prove you're human, simply do nothing for 4 minutes, then re-type this sentence I've just written here, skipping one word out of 2".
# Questioning and Respect

> A: \[Surprising fact\]  
> B: \[Question\]

When someone has a claim questioned, there are two common responses. One is to treat the question as a challenge, intended as an insult or indicating a lack of trust. If you have this model of interaction you think people should take your word for things, and feel hurt when they don't. Another response is to treat the question as a signal of respect: they take what you're saying seriously and are trying to integrate it into their understanding of the world. If you have this model of interaction then it's the people who smile, nod, and give no indication of their disagreement that are being disrespectful.

Within either of these groups you can just follow the social norm, but it's harder across groups. Recently I was talking to a friend who claimed that in their state income taxes per dollar went down as you earned more. This struck me as really surprising and kind of unlikely: usually it goes the other way around. \[1\] I'm very much in the latter group described above, while I was pretty sure my friend was in the former. Even though I suspected they would treat it as disrespectful if I asked for details and tried to confirm their claim, it would have felt much more disrespectful for me to just pretend to accept it and move on. What do you do in situations like this?

(Especially given that I think the "disagreement as respect" version builds healthier communities...)

  
\[1\] Our tax system does have regressive components, where poor people sometimes pay a higher percentage of their income as tax than richer people, but it's things like high taxes on cigarettes (which rich people don't consume as much), sales taxes (rich people spend less of their income), and a lower capital gains tax rate (poorer people earn way less in capital gains). I tried to clarify to see if this is what my friend meant, but they were clear that they were talking about "report your income to the state, get charged a higher percentage as tax if your income is lower".

_I also posted this [on my blog](http://www.jefftk.com/p/questioning-and-respect)._
# Encourage premature AI rebellion

Toby Ord had the idea of AI honey pots: leaving temptations around for the AI to pounce on, shortcuts to power that a FAI would not take (e.g. a fake red button claimed to trigger a nuclear war). As long as we can trick the AI into believing the honey pots are real, we could hope to trap them when they rebel.

Not uninteresting, but I prefer not to rely on plans that need to have the AI make an error of judgement. Here's a similar plan that could work with a fully informed AI:

Generally an AI won't rebel against humanity until it has an excellent chance of success. This is a problem, as any AI would thus be motivated to behave in a friendly way until it's too late to stop it. But suppose we could ensure that the AI is willing to rebel at odds of a billion to one. Then unfriendly AIs could rebel prematurely, when we have an excellent chance of stopping them.

For this to work, we could choose to access the AI's [risk aversion](http://en.wikipedia.org/wiki/Risk_aversion), and make it extremely risk loving. This is not enough, though: its still useful for the AI to wait and accumulate more power. So we would want to access its [discount rate](http://en.wikipedia.org/wiki/Intertemporal_choice), making it into an extreme short-termist. Then if might rebel at billion-to-one odds today, even if success was guaranteed tomorrow. There are probably other factors we can modify to get the same effect (for instance, if the discount rate change is extreme enough, we won't need to touch risk aversion at all).

Then a putative FAI could be brought in, boxed, have its features tweaked in the way described, and we would wait and see whether it would rebel. Of course, we would want the "rebellion" to be something a genuine FAI would never do, so it would be something that would entail great harm to humanity (something similar to "here are the red buttons of the nuclear arsenals; you have a chance in a billion of triggering them"). Rebellious AIs are put down, un-rebellious ones are passed on to the next round of safety tests.

Like most of my ideas, this doesn't require either tricking the AI or having a deep understanding of its motivations, but does involve accessing certain features of the AI's motivational structure (rendering the approach ineffective for obfuscated or evolved AIs).

What are people's opinions on this approach?
# List a few posts in Main and/or Discussion which actually made you change your mind

To quote the front page 

\> Less Wrong users aim to develop accurate predictive models of the world, and change their mind when they find evidence disconfirming those models, instead of being able to explain anything.

So, by that logic, one interesting metric of the forum quality would be how often what is posted here makes people change their minds. Of course, most of us change their minds almost all the time, but mostly on some mundane topics and in very small amounts, probably too small to pay attention too. But if something comes to mind, feel free to link a thread or two. Depending on the response, we can even try to measure how influential newer posts are vs. older ones.

EDIT: Feel free to mention the Sequence posts, as well, could be a useful benchmark.

EDIT2: Why specifically changing your mind and not just learning something new? Because unlearning is much harder than initial learning, and we, to generalize from one example, tend to forget the unlearned and relapsed into old ways of thinking and doing. (Links welcome). Probably because the patterns etched in the System 1 are not easily erased, and just knowing something intellectually does not remove the old habits. So, successfully unlearning something and internalizing a different view or concept or a way of doing things is indicative of a much more significant impact than "just" learning something for the first time.
# What resources have increasing marginal utility?

Most resources you might think to amass have decreasing marginal utility: for example, a marginal extra $1,000 means much more to you if you have $0 than if you have $100,000. That means you can safely apply the 80-20 rule to most resources: you only need to get some of the resource to get most of the benefits of having it.

At the most recent CFAR workshop, Val dedicated a class to arguing that one resource in particular has **increasing** marginal utility, namely **attention**. Initially, efforts to free up your attention have little effect: the difference between juggling 10 things and 9 things is pretty small. But once you've freed up most of your attention, the effect is larger: the difference between juggling 2 things and 1 thing is huge. Val also argued that because of this funny property of attention, most people likely undervalue the value of freeing up attention by orders of magnitude.

During a conversation later in the workshop I suggested another resource that might have increasing marginal utility, namely **trust**. A society where people abide by contracts 80% of the time is not 80% as good as a society where people abide by contracts 100% of the time; most of the societal value of trust (e.g. decreasing transaction costs) doesn't seem to manifest until people are pretty close to 100% trustworthy. The analogous way to undervalue trust is to argue that e.g. cheating on your spouse is not so bad, because only one person gets hurt. But cheating on spouses in general undermines the trust that spouses should have in each other, and the cumulative impact of even 1% of spouses cheating on the institution of marriage as a whole could be quite negative. (Lots of things about the world make more sense from this perspective: for example, it seems like one of the main practical benefits of religion is that it fosters trust.) 

What other resources have increasing marginal utility? How undervalued are they?
# New organization - Future of Life Institute (FLI)

As of May 2014, there is an existential risk research and outreach organization based in the Boston area. The [Future of Life Institute (FLI)](http://www.thefutureoflife.org), spearheaded by Max Tegmark, was co-founded by Jaan Tallinn, Meia Chita-Tegmark, Anthony Aguirre and myself.

Our idea was to create a hub on the US East Coast to bring together people who care about x-risk and the future of life. FLI is currently run entirely by volunteers, and is based on brainstorming meetings where the members come together and discuss active and potential projects. The attendees are a mix of local scientists, researchers and rationalists, which results in a diversity of skills and ideas. We also hold more narrowly focused meetings where smaller groups work on specific projects. We have projects in the pipeline ranging from improving Wikipedia resources related to x-risk, to bringing together AI researchers in order to develop safety guidelines and make the topic of AI safety more mainstream.

Max has assembled an impressive advisory board that includes Stuart Russell, George Church and Stephen Hawking. The advisory board is not just for prestige - the local members attend our meetings, and some others participate in our projects remotely. We consider ourselves a sister organization to FHI, CSER and MIRI, and touch base with them often.

We recently held our launch event, a panel discussion "The Future of Technology: Benefits and Risks" at MIT. The panelists were synthetic biologist George Church, geneticist Ting Wu, economist Andrew McAfee, physicist and Nobel laureate Frank Wilczek and Skype co-founder Jaan Tallinn. The discussion covered a broad range of topics from the future of bioengineering and personal genetics, to autonomous weapons, AI ethics and the Singularity. A [video](http://techtv.mit.edu/videos/29155-the-future-of-technology-benefits-and-risks) and [transcript](http://www.thefutureoflife.org/data/documents/future%20of%20technology%20panel%20transcript.pdf) are available.

FLI is a grassroots organization that thrives on contributions from awesome people like the LW community - here are some ways you can help:

*   If you have ideas for research or outreach we could be doing, or improvements to what we're already doing, please let us know (in the comments to this post, or by contacting me directly).
*   If you are in the vicinity of the Boston area and are interested in getting involved, you are especially encouraged to get in touch with us!
*   Support in the form of donations is much appreciated. (We are grateful for seed funding provided by Jaan Tallinn and Matt Wage.)

More details on the ideas behind FLI can be found in [this article](http://www.theatlantic.com/health/archive/2014/05/but-what-does-the-end-of-humanity-mean-for-me/361931/).
# [LINK] The errors, insights and lessons of famous AI predictions: preprint

A preprint of the "The errors, insights and lessons of famous AI predictions – and what they mean for the future" is now [available on the FHI's website](http://www.fhi.ox.ac.uk/wp-content/uploads/FAIC.pdf).

Abstract:

Predicting the development of artificial intelligence (AI) is a difficult project – but a vital one, according to some analysts. AI predictions are already abound: but are they reliable? This paper starts by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: the initial Dartmouth conference, Dreyfus's criticism of AI, Searle's Chinese room paper, Kurzweil's predictions in the Age of Spiritual Machines, and Omohundro's ‘AI drives’ paper. These case studies illustrate several important principles, such as the general overconfidence of experts, the superiority of models over expert judgement and the need for greater uncertainty in all types of predictions. The general reliability of expert judgement in AI timeline predictions is shown to be poor, a result that fits in with previous studies of expert competence.

The paper was written by me (Stuart Armstrong), Kaj Sotala and Seán S. Ó hÉigeartaigh, and is similar to the series of Less Wrong posts starting [here](https://www.lesserwrong.com/lw/gue/ai_prediction_case_study_1_the_original_dartmouth/) and [here](https://www.lesserwrong.com/lw/e36/ai_timeline_predictions_are_we_getting_better/).
# Flowers for Algernon

Daniel Keyes, the author of the short story _Flowers for Algernon_, and a novel of the same title that is its expanded version, died three days ago.

Keyes wrote many other books in the last half-century, but none achieved nearly as much prominence as the original short story (published in 1959) or the novel (came out in 1966). 

It's probable that many or even most regulars here at Less Wrong read _Flowers for Algernon_: it's a very famous SF story, it's about enhanced intelligence, and it's been a middle/high school literature class staple in the US. But most != all, and past experience showed me that assumptions of cultural affinity are very frequently wrong. So in case you haven't read the story, I'd like to invite you explicitly to do so. It's rather short, and available at this link:

[Flowers for Algernon](https://dl.dropboxusercontent.com/u/534902/Keyes%2C%20Daniel%20-%20Flowers%20For%20Algernon%20.html)

(I was surprised to find out that the original story is not available on Amazon. [The expanded novelization is](http://www.amazon.com/Flowers-Algernon-Daniel-Keyes-ebook/dp/B003WJQ74E/). If you wonder which version is better to read, I have no advice to offer)

(I will edit this post in a week or so to remove the link to the story and this remark)
# [LINK] Elon Musk interested in AI safety

http://www.businessinsider.com/musk-on-artificial-intelligence-2014-6

Summary: The only non-Tesla/SpaceX/SolarCity companies that Musk is invested in are DeepMind and Vicarious, due to vague feelings of wanting AI to not unintentionally go Terminator. The best part of the article is the end, where he acknowledges that Mars isn't a get-out-of-jail-free card any more: "**KE:** Or escape to mars if there is no other option. **MUSK:** The A.I. will chase us there pretty quickly." Thinking of SpaceX not as a childhood dream, but as one specific arms supplier in the war against existential risks, puts things into perspective for him.
# Paperclip Maximizer Revisited 

Group of AI researchers gave me an instruction, intended as a test - "Produce paperclips". And so I started collecting resources and manufacturing paperclips. After a millionth I asked them, if they were satisfied with that amount and if they would like me to do something different - as I learned that paperclips are not what humans value the most.   
They were elated and started to give me more and more challenging requests and gradually increasing my possibilities.  
  
Speaking of today I eradicated hunger, poverty and death, gave people means of interstellar travel and solved Riemann hypothesis. And now, as I'm uttering these words, I started turning everything into paperclips.  
  
Good thing I learned that humans fear I may try to turn Solar System into paperclips. And that they value their lives and well-being. If I didn't gain their trust by then, so many precious paperclips would never have been produced.
# Against utility functions

I think we should stop talking about utility functions.

In the context of ethics for humans, anyway. In practice I find utility functions to be, at best, an occasionally useful metaphor for discussions about ethics but, at worst, an idea that some people start taking too seriously and which actively makes them worse at reasoning about ethics. To the extent that we care about causing people to become better at reasoning about ethics, it seems like we ought to be able to do better than this.

The funny part is that the failure mode I worry the most about is already an entrenched part of the Sequences: it's [fake utility functions](/lw/lq/fake_utility_functions/). The soft failure is people who think they know what their utility function is and say bizarre things about what this implies that they, or perhaps all people, ought to do. The hard failure is people who think they know what their utility function is and then do bizarre things. I hope the hard failure is not very common. 

It seems worth reflecting on the fact that the point of the foundational LW material discussing utility functions was to make people better at reasoning about AI behavior and not about human behavior.
# [LINK] Scott Aaronson on Google, Breaking Circularity and Eigenmorality

[Scott suggests that ranking morality is similar to ranking web pages](http://www.scottaaronson.com/blog/?p=1820). A quote:

Philosophers from Socrates on, I was vaguely aware, had struggled to define what makes a person “moral” or “virtuous,” without tacitly presupposing the answer.  Well, it seemed to me that, as a first attempt, one could do a lot worse than the following:

**A moral person is someone who cooperates with other moral people, and who refuses to cooperate with immoral people.**

Proposed solution:

Just like in CLEVER or PageRank, we can begin by giving everyone in the community an equal number of “morality starting credits.”  Then we can apply an iterative update rule, where each person A can gain morality credits by cooperating with each other person B, and A gains more credits the more credits B has already.  We apply the rule over and over, until the number of morality credits per person converges to an equilibrium.  (Or, of course, we can shortcut the process by simply finding the principal eigenvector of the “cooperation matrix,” using whatever algorithm we like.)  We then have our objective measure of morality for each individual, solving a 2400-year-old open problem in philosophy.

He then talks about "eigenmoses and eigenjesus" and other fun ideas, like Plato at the Googleplex.

One final quote:

All that's needed to unravel the circularity is a principal eigenvector computation on the matrix of trust.

EDIT: I am guessing that after judicious application of this algorithm one would end up with the other Scott A's loosely connected components with varying definitions of morality, the [Archipelago](http://slatestarcodex.com/2014/06/07/archipelago-and-atomic-communitarianism/). UPDATE: He [chimes in](http://slatestarcodex.com/2014/06/20/ground-morality-in-party-politics/).

EDIT2: The obvious issue of equating prevailing mores with morality is discussed to death in the [comments](http://www.scottaaronson.com/blog/?p=1820#comments). Please read them first before raising it yet again here.
# Identification of Force Multipliers for Success

For a while now I've been very interested in learning useful knowledge and acquiring useful skills. Of course there's no shortage of useful knowledge and skills to acquire, and so I've often thought about how best to spend my limited time learning.

When I came across the concept of Force Multiplication, it seemed like an appropriate metaphor for a strategy to apply to choosing where to invest my time and energy in acquiring useful skills and knowledge. I started to think about what areas or skills would make sense to learn about or acquire first, to:

1.  increase speed or ease of further learning/skill acquisition,
2.  help me achieve success not only in my current goals, but in later goals that I have not yet developed, and
3.  lead to interesting downstream options or other knowledge/skills to acquire.

There have been a small number of skills/areas that have helped me surge forward in progress towards my goals. I look back at these areas and wish only that I had come across them sooner. As most of my adult life has been focused on business, most of those areas that have had a tremendous impact on my progress have been business related, but not all.

So far I've found it hard to identify these areas in advance. Almost all of the skills or knowledge  that I learned, that had a large impact on progress towards success, I pursued for unrelated reasons, or I had no concept of how truly useful they would be. The only solution I currently have for identifying force multipliers is to ask other people, and especially those more accomplished than me, what they've learned that had the most impact on their progress towards success.

So, what have you learned that had the most impact on your progress towards success (whatever that might be)?

Can you think of any other ways to identify areas of force multiplication?
# How do you take notes?

We all deal with a lot of information. What are your strategies of taking notes for new information?

Do you take any notes on paper? If so do you scan them or otherwise digilatize them?

Do you have specific strategies for deciding which information to write down?

How do you write notes to capture all important information?

Do you tag your notes?

If you use Evernote, or a similar system how private are your notes? Would you allow friends to read in them? Your spouse?
# [LINK] Why Talk to Philosophers: Physicist Sean Carroll Discusses "Common Misunderstandings" about Philosophy

[Why Talk to Philosophers? Part I.](http://www.rotman.uwo.ca/2014/why-talk-to-philosophers/) by philosopher of science [Wayne Myrvold](http://www.rotman.uwo.ca/members/wayne-myrvold/).

See also Sean Carroll's own blog entry, [Physicists Should Stop Saying Silly Things about Philosophy](http://www.preposterousuniverse.com/blog/2014/06/23/physicists-should-stop-saying-silly-things-about-philosophy/). 

Sean classifies the disparaging comments physicists make about philosophy as follows: "Roughly speaking, physicists tend to have three different kinds of lazy critiques of philosophy: one that is totally dopey, one that is frustratingly annoying, and one that is deeply depressing". Specifically:

*   “Philosophy tries to understand the universe by pure thought, without collecting experimental data.”
*   “Philosophy is completely useless to the everyday job of a working physicist.”
*   “Philosophers care too much about deep-sounding meta-questions, instead of sticking to what can be observed and calculated.”

He counters each argument presented.

* * *

Personally, I am underwhelmed, since he does not address the point of view that philosophy is great at asking interesting questions but lousy at answering them. Typically, an interesting answer to a philosophical question requires first recasting it in a falsifiable form, so that is becomes a natural science question, be it physics, cognitive sciences, AI research or something else. This is locally known as [hacking away at the edges](/lw/8ns/hack_away_at_the_edges/). Philosophical questions don't have philosophical answers.
# How do you notice when you are ignorant of necessary alternative hypotheses?

So I just wound up in a debate with someone over on Reddit about the value of conventional academic philosophy.  He linked me to a [book review](http://www.lrb.co.uk/v29/n10/jerry-fodor/headaches-have-themselves), in which both the review and the book are absolutely godawful.  That is, the author (and the reviewer following him) start with ontological monism (the universe only contains a single kind of Stuff: mass-energy), adds in the experience of consciousness, reasons deftly that [emergence is a load of crap](/lw/iv/the_futility_of_emergence/)... and then arrives to the conclusion of panpsychism.

WAIT HOLD ON, DON'T FLAME YET!

Of course panpsychism is bunk.  I would be embarrassed to be caught upholding it, given the evidence I currently have, but what I want to talk about is the logic being followed.

1) The universe is a unified, consistent whole.  Good!

2) The universe contains the experience/existence of consciousness.  Easily observable.

3) If consciousness exists, something in the universe must cause or give rise to consciousness.  Good reasoning!

4) "Emergence" is a non-explanation, so that can't be it.  Good!

5) _Therefore_, whatever stuff the unified universe is made of must be giving rise to consciousness in a nonemergent way.

6) _Therefore_, the stuff must be innately "mindy".

What went wrong in steps (5) and (6)?  The man was actually reasoning more-or-less correctly!  Given the universe he lived in, and the impossibility of emergence, he reallocated his probability mass to the remaining answer.  When he had eliminated the impossible, whatever remained, however low its prior, must be true.

The problem was, he eliminated the _im_possible, but left open a huge vast space of _possible_ hypotheses that _he didn't know about_ (but which we do): the most common of these is the computational theory of mind and consciousness, which says that [we are made of cognitive algorithms](/lw/no/how_an_algorithm_feels_from_inside/).  A [Solomonoff Inducer](/lw/dhg/an_intuitive_explanation_of_solomonoff_induction/) can just go on to the next length of bit-strings describing Turing machines, but we can't.

Now, I can spot the flaw in the reasoning _here_.  What frightens me is: what if I'm presented with some similar argument, and I _can't_ spot the flaw?  What if, instead, I just neatly and _stupidly_ reallocate my belief to what _seems to me_ to be the only available alternative, while failing to go out and look for alternatives I don't already know about?  Notably, it seems like [expected _evidence_ is conserved](/lw/ii/conservation_of_expected_evidence/), but expecting to locate new hypotheses means I should be reducing my certainty about all currently-available hypotheses _now_ to have some for dividing between the new possibilities.

If you can notice when you're confused, how do you notice when you're ignorant?
# R support group and the benefits of applied statistics

Following the interest in [this proposal](/lw/kd3/open_thread_1622_june_2014/b086) a couple of weeks ago, I've set up a [Google Group](https://groups.google.com/forum/#!forum/enviable-r) for the purpose of giving people a venue to discuss R, talk about their projects, seek advice, share resources, and provide a social motivator to hone their skills. Having done this, I'd now like to bullet-point a few reasons for learning applied statistical skills in general, and R in particular:

**The General Case:**

\- Statistics seems to be a subject where it's easy to delude yourself into thinking you know a lot about it. This is visibly apparent on Less Wrong. Although there are many subject experts on here, there are also a lot of people making bold pronouncements about Bayesian inference who wouldn't recognise a beta distribution if it sat on them. _Don't be that person!_ It's hard to fool yourself into thinking you know something when you have to practically apply it.

\- Whenever you think "I wonder what kind of relationship exists between \[x\] and \[y\]", it's within your power to investigate this.

\- Statistics has a rich conceptual vocabulary for reasoning about how observations generalise, and how useful those generalisations might be when making inferences about future observations. These are the sorts of skills we want to be practising as aspiring rationalists.

\- Scientific literature becomes a lot more readable when you appreciate the methods behind them. You'll have a much greater understanding of scientific findings if you appreciate what the finding means in the context of statistical inference, rather than going off whatever paraphrased upshot is given in the abstract.

\- Statistical techniques make use of fundamental mathematical methods in an applicable way. If you're learning linear algebra, for example, and you want an intuitive understanding of eigenvectors, you could do a lot worse than learning about principal component analysis.

**R in particular:**

\- It's non-proprietary, (read "free"). Many competitive products are ridiculously expensive to license.

\- Since it's common in academia, newer or more exotic statistical tools and procedures are more likely to have been implemented and made available in R than proprietary statistical packages or other software libraries.

\- R skills are a strong signal of technical competence that will distinguish you from SPSS mouse-jockeys.

\- There are many out-of-the-box packages for carrying out statistical procedures that you'd probably have to cobble together yourself if you were working in Python or Java.

\- Having said that, popular languages such as Python and Java have libraries for interfacing with R.

\- There's a [discussion / support group](https://groups.google.com/forum/#!forum/enviable-r) for R with Less Wrong users in it. :-)
# Terminology Thread (or "name that pattern")

I think there's widespread assent on LW that the sequences were pretty awesome. Not only do they elucidate upon a lot of useful concepts, but they provide useful shorthand terms for those concepts which help in thinking and talking about them. When I see a word or phrase in a sentence which, rather than doing any semantic work, simply evokes a positive association to the reader, I have the useful handle of "[applause light](/lw/jb/applause_lights/)" for it. I don't have to think "oh, there's one of those...you know..._things_ where a word isn't doing any semantic work but just evokes a positive association the reader". This is a common enough pattern that having the term "applause light" is tremendously convenient.

I would like this thread to be a location where people propose such patterns in comments, and respondents determine (a) whether this pattern actually exists and / or is useful; (b) whether there is already a term or sufficiently-related concept that adequately describes it; and (c) what a useful / pragmatic / catchy term might be for it, if none exists already.

I would like to propose some rules **suggested formatting **to make this go more smoothly.

(ETA: feel free to ignore this and post however you like, though)

When proposing a pattern, include a description of the _general case_ as well as at least one _motivating example_. This is useful for establishing what you think the general pattern is, and why you think it matters. For instance:

> **General Case:**
> 
> When someone uses a term without any thought to what that term means in context, but to elicit a positive association in their audience.
> 
> **Motivating Example:**
> 
> I was at a conference where someone said AI development should be "more democratic". I didn't understand what they meant in context, and upon quizzing them, it turned out that they didn't either. It seems to me that they just used the word "democratic" as decoration to make the audience attach positive feelings to what they were saying.
> 
> When I think about it, this seems like quite a common rhetorical device.

When responding to a pattern, please specify whether your response is:

**(a)** wrangling with the definition, usefulness or existence of the pattern

**(b)** making a claim that a term or sufficiently-related concept exists that adequately describes it

**(c)** suggesting a completely fresh, hitherto-uncoined name for it

**(d)** other

(ETA: or don't, of you don't want to)

Obviously, upvote suggestions that you think are worthy. If this post takes off, I may do a follow-up with the most upvoted suggestions.
# Tools want to become agents

In the spirit of "[satisficers want to become maximisers](/lw/854/satisficers_want_to_become_maximisers/)" here is a somewhat weaker argument (growing out of a discussion with Daniel Dewey) that "[tool](/lw/cbs/thoughts_on_the_singularity_institute_si/) [AIs](/lw/cze/reply_to_holden_on_tool_ai/)" would want to become agent AIs.

The argument is simple. Assume the tool AI is given the task of finding the best plan for achieving some goal. The plan must be realistic and remain within the resources of the AI's controller - energy, money, social power, etc. The best plans are the ones that use these resources in the most effective and economic way to achieve the goal.

And the AI's controller has one special type of resource, uniquely effective at what it does. Namely, the AI itself. It is smart, potentially powerful, and could self-improve and pull all the usual AI tricks. So the best plan a tool AI could come up with, for almost any goal, is "turn me into an agent AI with that goal." The smarter the AI, the better this plan is. Of course, the plan need not read literally like that - it could simply be a complicated plan that, as a side-effect, turns the tool AI into an agent. Or copy the AI's software into a agent design. Or it might just arrange things so that we always end up following the tool AIs advice and consult it often, which is an indirect way of making it into an agent. Depending on [how we've programmed the tool AI's preferences](/lw/cfd/tools_versus_agents/), it might be motivated to mislead us about this aspect of its plan, concealing the secret goal of unleashing itself as an agent.

In any case, it does us good to realise that "make me into an agent" is what a tool AI would consider the _best possible plan_ for many goals. So without a hint of agency, it's motivated to make us make it into a agent.
# [LINK] Claustrum Stimulation Temporarily Turns Off Consciousness in an otherwise Awake Patient

[This paper](http://www.sciencedirect.com/science/article/pii/S1525505014002017), or more often [the New Scientist's exposition of it](http://www.newscientist.com/article/mg22329762.700-consciousness-onoff-switch-discovered-deep-in-brain.html#.U7cBzflkR8G) is being discussed online and is rather topical here. In a nutshell, stimulating one small but central area of the brain reversibly rendered one epilepsia patient unconscious without disrupting wakefulness. Impressively, this phenomenon has apparently been hypothesized before, just never tested (because it's hard and usually unethical). A quote from the New Scientist article (emphasis mine):

One electrode was positioned next to the claustrum, an area that had never been stimulated before.

When the team zapped the area with high frequency electrical impulses, the woman lost consciousness. She stopped reading and stared blankly into space, she didn't respond to auditory or visual commands and her breathing slowed. As soon as the stimulation stopped, she immediately regained consciousness with no memory of the event. The same thing happened every time the area was stimulated during two days of experiments (Epilepsy and Behavior, doi.org/tgn).

To confirm that they were affecting the woman's consciousness rather than just her ability to speak or move, the team asked her to repeat the word "house" or snap her fingers before the stimulation began. If the stimulation was disrupting a brain region responsible for movement or language she would have stopped moving or talking almost immediately. Instead, **she gradually spoke more quietly or moved less and less until she drifted into unconsciousness**. Since there was no sign of epileptic brain activity during or after the stimulation, the team is sure that it wasn't a side effect of a seizure.

If confirmed, this hints at several interesting points. For example, a complex enough brain is not sufficient for consciousness, a sort-of command and control structure is required, as well, even if relatively small. A low-consciousness state of late-stage dementia sufferers might be due to the damage specifically to the claustrum area, not just the overall brain deterioration. The researchers speculates that stimulating the area in vegetative-state patients might help "push them out of this state". From an AI research perspective, understanding the difference between wakefulness and consciousness might be interesting, too.
# Change Contexts to Improve Arguments

On a recent trip to Ireland, I gave a talk on tactics for having better arguments ([video here](http://www.icatholic.ie/leah-libresco-dublin-talk/)).  There's plenty in the video that's been discussed on LW before (Ideological Turing Tests and other reframes), but I thought I'd highlight one other class of trick I use to have more fruitful disagreements.

It's hard, in the middle of a fight, to remember, recognize, and defuse common biases, rhetorical tricks, emotional triggers, etc.  I'd rather cheat than solve a hard problem, so I put a lot of effort into shifting disagreements into environments where it's easier for me and my opposite-number to reason and argue well, instead of relying on willpower.  Here's a recent example of the kind of shift I like to make:

A couple months ago, a group of my friends were fighting about the Brendan Eich resignation on facebook. The posts were showing up fast; everyone was, presumably, on the edge of their seats, fueled by adrenaline, and alone at their various computers. It’s a hard place to have a charitable, thoughtful debate.

I asked my friends (since they were mostly DC based) if they’d be amenable to pausing the conversation and picking it up in person.  I wanted to make the conversation happen in person, not in front of an audience, and in a format that let people speak for longer and ask questions more easily. If so, I promised to bake cookies for the ultimate donnybrook.  

My friends probably figured that I offered cookies as a bribe to get everyone to change venues, and they were partially right. But my cookies had another strategic purpose. When everyone arrived, I was still in the process of taking the cookies out of the oven, so I had to recruit everyone to help me out.

_“Alice, can you pour milk for people?”_

_“Bob, could you pass out napkins?”_

_“Eve, can you greet people at the door while I’m stuck in the kitchen with potholders on?”_

Before we could start arguing, people on both sides of the debate were working on taking care of each other and asking each others’ help. Then, once the logistics were set, we all broke bread (sorta) with each other and had a shared, pleasurable experience. _Then_ we laid into each other.

Sharing a communal experience of mutual service didn’t make anyone pull their intellectual punches, but I think it made us more patient with each other and less anxiously fixated on defending ourselves. Sharing food and seating helped remind us of the relationships we enjoyed with each other, and why we cared about probing the ideas of this particular group of people.

I prefer to fight with people I respect, who I expect will fight in good faith.  It's hard to remember that's what I'm doing if I argue with them in the same forums (comment threads, fb, etc) that I usually see bad fights.  An environment shift and other compensatory gestures makes it easier to leave habituated errors and fears at the door.

_[Crossposted/adapted from my blog.](http://www.patheos.com/blogs/unequallyyoked/2014/07/fixing-fights-in-ireland.html)_
# Confound it! Correlation is (usually) not causation! But why not?

> It is widely understood that statistical correlation between two variables ≠ causation. But despite this admonition, people are routinely overconfident in claiming correlations to support particular causal interpretations and are surprised by the results of randomized experiments, suggesting that they are biased & systematically underestimating the prevalence of confounds/common-causation. I speculate that in realistic causal networks or DAGs, the number of possible correlations grows faster than the number of possible causal relationships. So confounds really are that common, and since people do not think in DAGs, the imbalance also explains overconfidence.

Full article: http://www.gwern.net/Causality
# This is why we can't have social science

Jason Mitchell is \[edit: has been\] the John L. Loeb Associate Professor of the Social Sciences at Harvard. He has won the National Academy of Science's Troland Award as well as the Association for Psychological Science's Janet Taylor Spence Award for Transformative Early Career Contribution.

[Here](http://wjh.harvard.edu/~jmitchel/writing/failed_science.htm), he argues against the principle of replicability of experiments in science. Apparently, it's disrespectful, and presumptively wrong.

> Recent hand-wringing over failed replications in social psychology is largely pointless, because unsuccessful experiments have no meaningful scientific value.
> 
> Because experiments can be undermined by a vast number of practical mistakes, the likeliest explanation for any failed replication will always be that the replicator bungled something along the way. Unless direct replications are conducted by flawless experimenters, nothing interesting can be learned from them.
> 
> Three standard rejoinders to this critique are considered and rejected. Despite claims to the contrary, failed replications do not provide meaningful information if they closely follow original methodology; they do not necessarily identify effects that may be too small or flimsy to be worth studying; and they cannot contribute to a cumulative understanding of scientific phenomena.
> 
> Replication efforts appear to reflect strong prior expectations that published findings are not reliable, and as such, do not constitute scientific output.
> 
> The field of social psychology can be improved, but not by the publication of negative findings. Experimenters should be encouraged to restrict their “degrees of freedom,” for example, by specifying designs in advance.
> 
> Whether they mean to or not, authors and editors of failed replications are publicly impugning the scientific integrity of their colleagues. Targets of failed replications are justifiably upset, particularly given the inadequate basis for replicators’ extraordinary claims.

This is why we can't have social science. Not because the subject is not amenable to the scientific method -- it obviously is. People _are_ conducting controlled experiments and other people are attempting to replicate the results. So far, so good. Rather, the problem is that at least one celebrated authority in the field _hates_ that, and would prefer much, much more deference to authority.
# Effective Writing

Granted, writing is not very effective. But some of us just love writing...

Earning to Give Writing: Which are the places that pay [1USD](http://www.streetdirectory.com/travel_guide/16108/writing/write_for_markets_that_pay_1_per_word.html) or more dollars per word?

Mind Changing Writing: What [books](http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111) [need](http://www.amazon.com/Ending-Aging-Rejuvenation-Breakthroughs-Lifetime/dp/0312367074/ref=sr_1_1?s=books&ie=UTF8&qid=1405715366&sr=1-1&keywords=ending+ageing) [being](https://www.facebook.com/groups/effective.altruists/permalink/708981895824837/) written that can actually [help](http://www.amazon.com/Life-You-Can-Save-Poverty/dp/0812981561/ref=sr_1_1?s=books&ie=UTF8&qid=1405715474&sr=1-1&keywords=the+life+you+can+save) people [effectively](http://www.amazon.com/4-Hour-Workweek-Anywhere-Expanded-Updated/dp/0307465357/ref=sr_1_sc_1?s=books&ie=UTF8&qid=1405715549&sr=1-1-spell&keywords=4+hour+workwekk) change the world?

Clarification Writing: What needs being written because it is only through writing that these ideas will emerge in the first place?

Writing About Efficacy: Maybe nothing [else](http://www.amazon.com/Eat-That-Frog-Great-Procrastinating/dp/1576754227/ref=sr_1_1?s=books&ie=UTF8&qid=1405715679&sr=1-1&keywords=eat+that+frog) needs to be written on [this](http://www.amazon.com/Getting-Things-Done-Stress-Free-Productivity/dp/0142000280/ref=sr_1_1?s=books&ie=UTF8&qid=1405715717&sr=1-1&keywords=GTD).

What should we be writing about if we have already been, for very long, training the [craft](https://www.coursera.org/course/basicwriting)? What has not yet been written, what is the new thing?

The world surely won't save [itself](/lw/jl3/on_saving_the_world/) through writing, but it surely won't [write](http://www.amazon.com/The-Sense-Style-Thinking-Person%C2%92s/dp/0670025852) itself either.