Hello, folks. I'm one of those long-time lurkers.

I've decided to conduct, as the title suggests, a quick and dirty survey in hopes of better understanding a problem I have (or rather, whether or not what I have is actually a problem).

Here's some context: I'm a Physics & Mathematics major, currently taking multi-variable. Lately, I've been unsatisfied with my understanding and usage of mathematics—mainly calculus. I've decided to go through what's been recommended as a much more rigorous Calculus textbook, Calculus by Michael Spivak. So far I'm really enjoying it, but it's taking me a long time to get through the exercises. I can be very meticulous about things like this and want to do every exercise through every chapter; I feel that there's benefit to actually doing them regardless of whether or not I look at the problem and think "Yeah, I can do this." Sometimes actually doing the problem is much more difficult than it seems, and I learn a lot from doing them. When flipping through the exercises, I also notice that—regardless of how well I think I know the material—there ends up being a section of exercises focused on something I've never heard of before; something very clever or, I think, mathematically enlightening, that's dependent on the exercises before it.

I'm somewhat embarrassed to admit that the exercises of the first chapter alone had taken me hours upon hours upon hours of combined work. I consider myself slow when it comes to reading mathematics and physics literature—I have to carefully comb through all the concepts and equations and structure them intuitively in a way I see fit. I hate not having a very fundamental understanding of the things I'm working with.

At the same time, I read/hear people who apparently are familiar with multiple textbooks on the same subject. Familiar enough to judge whether or not it is a good textbook. Familiar enough to place how they fit on a hierarchy of textbooks on the same subject. I think "At the rate I'm going, it will take me a very long time to get through this." 

So...

Here's (what I think is) my issue: I don't know whether or not I'm taking too long. Am I doing things inefficiently? Is there a better way to choose which exercises I do and don't work through so that I learn a similar amount of material in less time? Or is it just fine that I'm taking this long? Am I slow and inefficient or am I just new to this process of working through a textbook cover-to-cover, which is supposed to take a very long time anyway?

I spend more time than I should learning about learning, instead of learning the material itself. I find myself using up lots of time trying to figure out how to learn more efficiently, how to think more efficiently, how to work more efficiently, and such things—as opposed to actually learning and actually thinking and actually working, which ends up being an inefficient use of my time. I think part of this problem stems from the fact that I don't have much of a comparison for when I can say "Ok, I'm satisfied and can stop focusing on improve how I do this act—and just do it already." I want to solve that issue now.

Which brings us to...

Here's my attempted solution: A survey! I assume many people here at LessWrong have worked through a science or mathematics textbook on their own. Mainly I'd like to gauge whether or not you thought you were taking a very long time, how long it took you, etc. I'd also like to know what your approach was: Did you perform every exercise, or skim through the book finding things you knew you didn't know? Did you skip around or go from the first chapter to the last? Do you have any advice on how one should approach a given textbook?

Here's the survey: https://docs.google.com/forms/d/1S4_-7_dxgmgprMbNhL1dNmX_0Zq9QrA9lpTl9ZHHxMI/viewform

I'm not sure how interested anyone but me is in this, but on a later date I could make another post showing the data. I considered checking "Publish and show a link to the results of this form", but I wasn't sure if that kept everyone anonymous or not. Also, feel more than free to post any criticism, shortcomings, improvements, etc. Have I left anything out? Is there anything you'd like to see me add? This is my first attempt at a survey like this and I'd appreciate any feedback (though I know it's not necessarily a rigorous survey, just a quick data-collection, I suppose).

I strongly encourage the posting of any textbook-reading tips or guidelines in the comments. I left that out of the survey so that anyone who's interested has immediate access to tips.

Here's an edit: Thanks for all the responses, everyone. Not only was my original question sufficiently answered (that is, it doesn't seem like I'm taking too long; there were only a few survey takers, but in between the comments and the survey answers, I'm not going at an extraordinarily slow rate). There's some very solid advice for different methods I might try to optimize my learning process.  One that especially hit home was the suggestion that the large amounts of time spent "learning about learning" are such because it feels more comfortable than actually learning the material. In short, it's a safety blanket that makes me feel like I'm doing something productive when I'm really just avoiding what needs to be done. Some other useful pieces of advice are:

- Try being open to learning a broader range of materials without necessarily mastering each one. It might be the case that you need to know one thing in order to master the other, and need to know the other in order to master the one—trying to master either of them in isolation ends up being somewhat futile. Not everything needs to be "brick by brick" structured. (This was a lesson I found useful when I first learned that a number raised to the "one half" power was the square root of that number: Trying to master it in terms of the rules I already knew ended up in a thought like, "... Two to the third power is two times two times two. Two to the one-half power is two... times two one half times?"

- Though it may be uncomfortable at first, it could make learning easier to try the exercises before reading the chapter super-carefully; trying them before you feel ready to try them. You don't necessarily have to fully comprehend all of the proofs in the chapter to get through some exercises. 

- Textbooks might just be the wrong way to go in the first place. Try resources like Wikipedia, math blogs, and math forums.

- "Don't use the answer key unless you've spent a significant amount of time trying to find the answer yourself!" (This may seem obvious, but a few years ago, I'd spend a couple of minutes on the problem, not understand it, look to the answer key, and wonder why I wasn't learning anything.)

- Skip exercises when you feel you could solve them, but randomly check whether this estimate is correct by doing the problem anyway. (I like this one a lot).

- Talk to a professor!

- It may be the case that you learn well via just reading, and not spending so much time on the exercises.

Here are some websites/blogs mentioned:
(Blog) Math for Programmers - http://steve-yegge.blogspot.com/2006/03/math-for-programmers.html
(Blog) Annoying Precision - http://qchu.wordpress.com/
(Math Forum) Mathematics - http://math.stackexchange.com/ 

Excellent, excellent stuff, though. Thank you. :) There's a lot of material and advice for me to work with—while simultaneously making sure I don't avoid my work by hiding under the guise of productivity.

 

Summary

CFAR included 5 questions on the 2012 LW Survey which were adapted from the heuristics and biases literature, based on five different cognitive biases or reasoning errors.  LWers, on the whole, showed less bias than is typical in the published research (on all 4 questions where this was testable), but did show clear evidence of bias on 2-3 of those 4 questions.  Further, those with closer ties to the LW community (e.g., those who had read more of the sequences) showed significantly less bias than those with weaker ties (on 3 out of 4-5 questions where that was testable).  These results all held when controlling for measures of intelligence.


METHOD & RESULTS

Being less susceptible to cognitive biases or reasoning errors is one sign of rationality (see the work of Keith Stanovich & his colleagues, for example).  You'd hope that a community dedicated to rationality would be less prone to these biases, so I selected 5 cognitive biases and reasoning errors from the heuristics & biases literature to include on the LW survey.  There are two possible patterns of results which would point in this direction:

• high scores: LWers show less bias than other populations that have answered these questions (like students at top universities) 
• correlation with strength of LW exposure: those who have read the sequences (or have been around LW a long time, have high karma, attend meetups, make posts) score better than those who have not. 

The 5 biases were selected in part because they can be tested with everyone answering the same questions; I also preferred biases that haven't been discussed in detail on LW.  On some questions there is a definitive wrong answer and on others there is reason to believe that a bias will tend to lead people towards one answer (so that, even though there might be good reasons for a person to choose that answer, in the aggregate it is evidence of bias if more people choose that answer).

This is only one quick, rough survey.  If the results are as predicted, that could be because LW makes people more rational, or because LW makes people more familiar with the heuristics & biases literature (including how to avoid falling for the standard tricks used to test for biases), or because the people who are attracted to LW are already unusually rational (or just unusually good at avoiding standard biases).  Susceptibility to standard biases is just one angle on rationality.  Etc.

Here are the question-by-question results, in brief.  The next section contains the exact text of the questions, and more detailed explanations.

Question 1 was a disjunctive reasoning task, which had a definitive correct answer.  Only 13% of undergraduates got the answer right in the published paper that I took it from.  46% of LWers got it right, which is much better but still a very high error rate.  Accuracy was 58% for those high in LW exposure vs. 31% for those low in LW exposure.  So for this question, that's: 
1. LWers biased: yes 
2. LWers less biased than others: yes 
3. Less bias with more LW exposure: yes 

Question 2 was a temporal discounting question; in the original paper about half the subjects chose money-now (which reflects a very high discount rate).  Only 8% of LWers did; that did not leave much room for differences among LWers (and there was only a weak & nonsignificant trend in the predicted direction). So for this question: 
1. LWers biased: not really 
2. LWers less biased than others: yes 
3. Less bias with more LW exposure: n/a (or no) 

Question 3 was about the law of large numbers.  Only 22% got it right in Tversky & Kahneman's original paper. 84% of LWers did: 93% of those high in LW exposure, 75% of those low in LW exposure.  So: 
1. LWers biased: a bit 
2. LWers less biased than others: yes 
3. Less bias with more LW exposure: yes 

Question 4 was based on the decoy effect aka asymmetric dominance aka attraction effect (but missing a control condition).  I don't have numbers from the original study (and there is no correct answer) so I can't really answer 1 or 2 for this question, but there was a difference based on LW exposure: 57% vs. 44% selecting the less bias related answer. 
1. LWers biased: n/a 
2. LWers less biased than others: n/a 
3. Less bias with more LW exposure: yes 

Question 5 was an anchoring question.  The original study found an effect (measured by slope) of 0.55 (though it was less transparent about the randomness of the anchor; transparent studies w. other questions have found effects around 0.3 on average).  For LWers there was a significant anchoring effect but it was only 0.14 in magnitude, and it did not vary based on LW exposure (there was a weak & nonsignificant trend in the wrong direction). 
1. LWers biased: yes 
2. LWers less biased than others: yes 
3. Less bias with more LW exposure: no 

One thing you might wonder: how much of this is just intelligence?  There were several questions on the survey about performance on IQ tests or SATs.  Controlling for scores on those tests, all of the results about the effects of LW exposure held up nearly as strongly.  Intelligence test scores were also predictive of lower bias, independent of LW exposure, and those two relationships were almost the same in magnitude.  If we extrapolate the relationship between IQ scores and the 5 biases to someone with an IQ of 100 (on either of the 2 IQ measures), they are still less biased than the participants in the original study, which suggests that the "LWers less biased than others" effect is not based solely on IQ.

 

MORE DETAILED RESULTS

There were 5 questions related to strength of membership in the LW community which I standardized and combined into a single composite measure of LW exposure (LW use, sequence reading, time in community, karma, meetup attendance); this was the main predictor variable I used (time per day on LW also seems related, but I found out while analyzing last year's survey that it doesn't hang together with the others or associate the same way with other variables).  I analyzed the results using a continuous measure of LW exposure, but to simplify reporting, I'll give the results below by comparing those in the top third on this measure of LW exposure with those in the bottom third.

There were 5 intelligence-related measures which I combined into a single composite measure of Intelligence (SAT out of 2400, SAT out of 1600, ACT, previously-tested IQ, extra credit IQ test); I used this to control for intelligence and to compare the effects of LW exposure with the effects of Intelligence (for the latter, I did a similar split into thirds).  Sample sizes: 1101 people answered at least one of the CFAR questions; 1099 of those answered at least one LW exposure question and 835 of those answered at least one of the Intelligence questions.  Further details about method available on request.

Here are the results, question by question.

Question 1: Jack is looking at Anne, but Anne is looking at George. Jack is married but George is not. Is a married person looking at an unmarried person?

• Yes 
• No 
• Cannot be determined 

This is a "disjunctive reasoning" question, which means that getting the correct answer requires using "or".  That is, it requires considering multiple scenarios.  In this case, either Anne is married or Anne is unmarried.  If Anne is married then married Anne is looking at unmarried George; if Anne is unmarried then married Jack is looking at unmarried Anne.  So the correct answer is "yes".  A study by Toplak & Stanovich (2002) of students at a large Canadian university found that only 13% correctly answered "yes" while 86% answered "cannot be determined" (2% answered "no").

On this LW survey, 46% of participants correctly answered "yes"; 54% chose "cannot be determined" (and 0.4% said"no").  Further, correct answers were much more common among those high in LW exposure: 58% of those in the top third of LW exposure answered "yes", vs. only 31% of those in the bottom third.  The effect remains nearly as big after controlling for Intelligence (the gap between the top third and the bottom third shrinks from 27% to 24% when Intelligence is included as a covariate).  The effect of LW exposure is very close in magnitude to the effect of Intelligence; 60% of those in the top third in Intelligence answered correctly vs. 37% of those in the bottom third.

original study: 13% 
weakly-tied LWers: 31% 
strongly-tied LWers: 58% 


Question 2: Would you prefer to receive $55 today or $75 in 60 days?

This is a temporal discounting question.  Preferring $55 today implies an extremely (and, for most people, implausibly) high discount rate, is often indicative of a pattern of discounting that involves preference reversals, and is correlated with other biases.  The question was used in a study by Kirby (2009) of undergraduates at Williams College (with a delay of 61 days instead of 60; I took it from a secondary source that said "60" without checking the original), and based on the graph of parameter values in that paper it looks like just under half of participants chose the larger later option of $75 in 61 days.

LW survey participants almost uniformly showed a low discount rate: 92% chose $75 in 61 days.  This is near ceiling, which didn't leave much room for differences among LWers.  For LW exposure, top third vs. bottom third was 93% vs. 90%, and this relationship was not statistically significant (p=.15); for Intelligence it was 96% vs. 91% and the relationship was statistically significant (p=.007).  (EDITED: I originally described the Intelligence result as nonsignificant.)

original study: ~47% 
weakly-tied LWers: 90% 
strongly-tied LWers: 93% 


Question 3: A certain town is served by two hospitals. In the larger hospital, about 45 babies are born each day. In the smaller one, about 15 babies are born each day. Although the overall proportion of girls is about 50%, the actual proportion at either hospital may be greater or less on any day. At the end of a year, which hospital will have the greater number of days on which more than 60% of the babies born were girls? 

• The larger hospital 
• The smaller hospital 
• Neither - the number of these days will be about the same 

This is a statistical reasoning question, which requires applying the law of large numbers.  In Tversky & Kahneman's (1974) original paper, only 22% of participants correctly chose the smaller hospital; 57% said "about the same" and 22% chose the larger hospital.

On the LW survey, 84% of people correctly chose the smaller hospital; 15% said "about the same" and only 1% chose the larger hospital.  Further, this was strongly correlated with strength of LW exposure: 93% of those in the top third answered correctly vs. 75% of those in the bottom third.  As with #1, controlling for Intelligence barely changed this gap (shrinking it from 18% to 16%), and the measure of Intelligence produced a similarly sized gap: 90% for the top third vs. 79% for the bottom third.

original study: 22% 
weakly-tied LWers: 75% 
strongly-tied LWers: 93%

Question 4: Imagine that you are a doctor, and one of your patients suffers from migraine headaches that last about 3 hours and involve intense pain, nausea, dizziness, and hyper-sensitivity to bright lights and loud noises. The patient usually needs to lie quietly in a dark room until the headache passes. This patient has a migraine headache about 100 times each year. You are considering three medications that you could prescribe for this patient. The medications have similar side effects, but differ in effectiveness and cost. The patient has a low income and must pay the cost because her insurance plan does not cover any of these medications. Which medication would you be most likely to recommend? 

• Drug A: reduces the number of headaches per year from 100 to 30. It costs $350 per year. 
• Drug B: reduces the number of headaches per year from 100 to 50. It costs $100 per year. 
• Drug C: reduces the number of headaches per year from 100 to 60. It costs $100 per year. 

This question is based on research on the decoy effect (aka "asymmetric dominance" or the "attraction effect").  Drug C is obviously worse than Drug B (it is strictly dominated by it) but it is not obviously worse than Drug A, which tends to make B look more attractive by comparison.  This is normally tested by comparing responses to the three-option question with a control group that gets a two-option question (removing option C), but I cut a corner and only included the three-option question.  The assumption is that more-biased people would make similar choices to unbiased people in the two-option question, and would be more likely to choose Drug B on the three-option question.  The model behind that assumption is that there are various reasons for choosing Drug A and Drug B; the three-option question gives biased people one more reason to choose Drug B but other than that the reasons are the same (on average) for more-biased people and unbiased people (and for the three-option question and the two-option question).

Based on the discussion on the original survey thread, this assumption might not be correct.  Cost-benefit reasoning seems to favor Drug A (and those with more LW exposure or higher intelligence might be more likely to run the numbers).  Part of the problem is that I didn't update the costs for inflation - the original problem appears to be from 1995 which means that the real price difference was over 1.5 times as big then.

I don't know the results from the original study; I found this particular example online (and edited it heavily for length) with a reference to Chapman & Malik (1995), but after looking for that paper I see that it's listed on Chapman's CV as only a "published abstract".

49% of LWers chose Drug A (the one that is more likely for unbiased reasoners), vs. 50% for Drug B (which benefits from the decoy effect) and 1% for Drug C (the decoy).  There was a strong effect of LW exposure: 57% of those in the top third chose Drug A vs. only 44% of those in the bottom third.  Again, this gap remained nearly the same when controlling for Intelligence (shrinking from 14% to 13%), and differences in Intelligence were associated with a similarly sized effect: 59% for the top third vs. 44% for the bottom third.

original study: ?? 
weakly-tied LWers: 44% 
strongly-tied LWers: 57% 


Question 5: Get a random three digit number (000-999) from http://goo.gl/x45un and enter the number here. 

Treat the three digit number that you just wrote down as a length, in feet. Is the height of the tallest redwood tree in the world more or less than the number that you wrote down? 

What is your best guess about the height of the tallest redwood tree in the world (in feet)?

This is an anchoring question; if there are anchoring effects then people's responses will be positively correlated with the random number they were given (and a regression analysis can estimate the size of the effect to compare with published results, which used two groups instead of a random number).

Asking a question with the answer in feet was a mistake which generated a great deal of controversy and discussion.  Dealing with unfamiliar units could interfere with answers in various ways so the safest approach is to look at only the US respondents; I'll also see if there are interaction effects based on country.

The question is from a paper by Jacowitz & Kahneman (1995), who provided anchors of 180 ft. and 1200 ft. to two groups and found mean estimates of 282 ft. and 844 ft., respectively.  One natural way of expressing the strength of an anchoring effect is as a slope (change in estimates divided by change in anchor values), which in this case is 562/1020 = 0.55.  However, that study did not explicitly lead participants through the randomization process like the LW survey did.  The classic Tversky & Kahneman (1974) anchoring question did use an explicit randomization procedure (spinning a wheel of fortune; though it was actually rigged to create two groups) and found a slope of 0.36.  Similarly, several studies by Ariely & colleagues (2003) which used the participant's Social Security number to explicitly randomize the anchor value found slopes averaging about 0.28.

There was a significant anchoring effect among US LWers (n=578), but it was much weaker, with a slope of only 0.14 (p=.0025).  That means that getting a random number that is 100 higher led to estimates that were 14 ft. higher, on average.  LW exposure did not moderate this effect (p=.88); looking at the pattern of results, if anything the anchoring effect was slightly higher among the top third (slope of 0.17) than among the bottom third (slope of 0.09). Intelligence did not moderate the results either (slope of 0.12 for both the top third and bottom third).  It's not relevant to this analysis, but in case you're curious, the median estimate was 350 ft. and the actual answer is 379.3 ft. (115.6 meters).

Among non-US LWers (n=397), the anchoring effect was slightly smaller in magnitude compared with US LWers (slope of 0.08), and not significantly different from the US LWers or from zero.

original study: slope of 0.55 (0.36 and 0.28 in similar studies) 
weakly-tied LWers: slope of 0.09 
strongly-tied LWers: slope of 0.17 


If we break the LW exposure variable down into its 5 components, every one of the five is strongly predictive of lower susceptibility to bias.  We can combine the first four CFAR questions into a composite measure of unbiasedness, by taking the percentage of questions on which a person gave the "correct" answer (the answer suggestive of lower bias).  Each component of LW exposure is correlated with lower bias on that measure, with r ranging from 0.18 (meetup attendance) to 0.23 (LW use), all p < .0001 (time per day on LW is uncorrelated with unbiasedness, r=0.03, p=.39).  For the composite LW exposure variable the correlation is 0.28; another way to express this relationship is that people one standard deviation above average on LW exposure 75% of CFAR questions "correct" while those one standard deviation below average got 61% "correct".  Alternatively, focusing on sequence-reading, the accuracy rates were:

75%    Nearly all of the Sequences (n = 302) 
70%    About 75% of the Sequences (n = 186) 
67%    About 50% of the Sequences (n = 156) 
64%    About 25% of the Sequences (n = 137) 
64%    Some, but less than 25% (n = 210) 
62%    Know they existed, but never looked at them (n = 19) 
57%    Never even knew they existed until this moment (n = 89) 

Another way to summarize is that, on 4 of the 5 questions (all but question 4 on the decoy effect) we can make comparisons to the results of previous research, and in all 4 cases LWers were much less susceptible to the bias or reasoning error.  On 1 of the 5 questions (question 2 on temporal discounting) there was a ceiling effect which made it extremely difficult to find differences within LWers; on 3 of the other 4 LWers with a strong connection to the LW community were much less susceptible to the bias or reasoning error than those with weaker ties.


REFERENCES
Ariely, Loewenstein, & Prelec (2003), "Coherent Arbitrariness: Stable demand curves without stable preferences" 
Chapman & Malik (1995), "The attraction effect in prescribing decisions and consumer choice" 
Jacowitz & Kahneman (1995), "Measures of Anchoring in Estimation Tasks" 
Kirby (2009), "One-year temporal stability of delay-discount rates" 
Toplak & Stanovich (2002), "The Domain Specificity and Generality of Disjunctive Reasoning: Searching for a Generalizable Critical Thinking Skill" 
Tversky & Kahneman's (1974), "Judgment under Uncertainty: Heuristics and Biases"

Despite being (IMO) a philosophy blog, many Less Wrongers tend to disparage mainstream philosophy and emphasize the divergence between our beliefs and theirs. But, how different are we really? My intention with this post is to quantify this difference.

The questions I will post as comments to this article are from the 2009 PhilPapers Survey. If you answer "other" on any of the questions, then please reply to that comment in order to elaborate your answer. Later, I'll post another article comparing the answers I obtain from Less Wrongers with those given by the professional philosophers. This should give us some indication about the differences in belief between Less Wrong and mainstream philosophy.

Glossary

analytic-synthetic distinction, A-theory and B-theory, atheism, compatibilism, consequentialism, contextualism, correspondence theory of truth, deontology, egalitarianism, empiricism, Humeanism, libertarianism, mental content externalism, moral realism, moral motivation internalism and externalism, naturalism, nominalism, Newcomb's problem, physicalism, Platonism, rationalism, relativism, scientific realism, trolley problem, theism, virtue ethics

Note

Thanks pragmatist, for attaching short (mostly accurate) descriptions of the philosophical positions under the poll comments.

As many of you probably know, Robin Hanson is writing a book, and it will be geared toward a popular audience. He wants a term that encompasses both humans and AI, so he's soliciting your opinions on the matter. Here's the link: http://www.quicksurveys.com/tqsruntime.aspx?surveyData=AYtdr2WMwCzB981F0qkivSNwbj1tn+xvU6rnauc83iU=

H/T Bryan Caplan at EconLog.

Posted By: Dan Keys, CFAR Survey Coordinator

The Center for Applied Rationality is trying to develop better methods for measuring and studying the benefits of rationality.  We want to be able to test if this rationality stuff actually works.

One way that the Less Wrong community can help us with this process is by taking part in online surveys, which we can use for a variety of purposes including:

• seeing what rationality techniques people actually use in their day-to-day lives 
• developing & testing measures of how rational people are, and seeing if potential rationality measures correlate with the other variables that you'd expect them to 
• comparing people who attend a minicamp with others in the LW community, so that we can learn what value-added the minicamps provide beyond what you get elsewhere 
• trying out some of the rationality techniques that we are trying to teach, so we can see how they work 

We have a couple of surveys ready to go now which cover some of these bullet points, and will be developing other surveys over the coming months.

If you're interested in taking part in online surveys for CFAR, please go here to fill out a brief form with your contact info; then we will contact you about participating in specific surveys.

If you have previously filled out a form like this one to participate in CFAR surveys, then we already have your information so you don't need to sign up again.

Questions/Issues can be posted in the comments here, PMed to me, or emailed to us at CFARsurveys@gmail.com.

This is thread where I'm trying to figure out a few things about signalling on LessWrong and need some information, so please immediately after reading about the two individuals please answer the poll. The two individuals:

A. Sees that an interpretation of reality shared by others is not correct, but tries to pretend otherwise for personal gain and/or safety.

B. Fails to see that an interpretation of reality is shared by others is flawed. He is therefore perfectly honest in sharing the interpretation of reality with others. The reward regime for outward behaviour is the same as with A.

 

To add a trivial inconvenience that matches the inconvenience of answering the poll before reading on, comments on what I think the two individuals signal,what the trade off is and what I speculate the results might be here versus the general population, is behind this link.

Related to: lesswrong.com/lw/fk/survey_results/

I am currently emailing experts in order to raise and estimate the academic awareness and perception of risks from AI and ask them for permission to publish and discuss their responses. User:Thomas suggested to also ask you, everyone who is reading lesswrong.com, and I thought this was a great idea. If I ask experts to publicly answer questions, to publish and discuss them here on LW, I think it is only fair to do the same. 

Answering the questions below will help the SIAI and everyone interested to mitigate risks from AI to estimate the effectiveness with which the risks are communicated.

Questions:

1. Assuming no global catastrophe halts progress, by what year would you assign a 10%/50%/90% chance of the development of human-level machine intelligence? Feel free to answer 'never' if you believe such a milestone will never be reached.
2. What probability do you assign to the possibility of a negative/extremely negative Singularity as a result of badly done AI?
3. What probability do you assign to the possibility of a human level AGI to self-modify its way up to massive superhuman intelligence within a matter of hours/days/< 5 years?
4. Does friendly AI research, as being conducted by the SIAI, currently require less/no more/little more/much more/vastly more support?
5. Do risks from AI outweigh other existential risks, e.g. advanced nanotechnology? Please answer with yes/no/don't know.
6. Can you think of any milestone such that if it were ever reached you would expect human‐level machine intelligence to be developed within five years thereafter?

Note: Please do not downvote comments that are solely answering the above questions.

As some readers may recall, we had a conference this January about intelligence, and in particular the future of machine intelligence. We did a quick survey among participants about their estimates of when and how human-level machine intelligence would be developed. Now we can announce the results: Sandberg, A. and Bostrom, N. (2011): Machine Intelligence Survey, Technical Report #2011-1, Future of Humanity Institute, Oxford University.

[...]

The median estimate of when there will be 50% chance of human level machine intelligence was 2050.

People estimated 10% chance of AI in 2028, and 90% chance in 2150.

[...]

All in all, a small study of a self selected group, so it doesn't prove anything in particular. But it fits in with earlier studies like Ben Goertzel, Seth Baum, Ted Goertzel, How Long Till Human-Level AI? and Bruce Klein, When will AI surpass human-level intelligence? - people who tend to answer this kind of surveys seem to have a fairly similar mental model.

Link: Machine Intelligence Survey (PDF)

Eliezer Yudkowsky set out to define more precisely what it means for an entity to have “what people really want” as a goal. Coherent Extrapolated Volition was his proposal. Though CEV was never meant as more than a working proposal; his write-up provides the best insights to date into the challenges of the Friendly AI problem, the pitfalls and possible paths to a solution.

[...]

Ben Goertzel responded with Coherent Aggregated Volition, a simplified variant of CEV. In CAV, the entity’s goal is a balance between  the desires of all humans, but it looks at the volition of humans directly, without extrapolation to a wiser future. This omission is not just to make the computation easier (it is still quite intractable), but rather to show some respect to humanity’s desires as they are, without extrapolation to a hypothetical improved morality.

[...]

Stuart Armstrong’s “Chaining God” is a different approach, aimed at the problem of interacting with and trusting the good will of an ultraintelligence so far beyond us that we have nothing in common with it. A succession of AIs, of gradually increasing intelligence, each guarantees the trustworthiness of one which is slightly smarter than it. This resembles Yudkowsy’s idea of a self-improving machine which verifies that its next stage has the same goals, but the successive levels of intelligence remain active simultaneously, so that they can continue to verify Friendliness.

Ray Kurzweil thinks that we will achieve safe ultraintelligence by gradually becoming that ultraintelligence. We will merge with the rising new intelligence, whether by interfacing with computers or by uploading our brains to a computer substrate.

Link: adarti.blogspot.com/2011/04/review-of-proposals-toward-safe-ai.html