faul_sname

• faul_sname 18 Jun 2022 9:03 UTC

  2 points

  in reply to: anonymousaisafety’s comment on: I No Longer Believe In­tel­li­gence to be “Mag­i­cal”

  I don’t think this algorithm could decode arbitrary data in a reasonable amount of time. I think it could decode some particularly structured types of data, and I think “fairly unprocessed sensor data from a very large number of nearly identical sensors” is one of those types of data.

  I actually don’t know if my proposed method would work with jpgs—the whole discrete cosine transform thing destroys data in a way that might not leave you with enough information to make much progress. In general if there’s lossy compression I expect that to make things much harder, and lossless compression I’d expect either makes it impossible (if you don’t figure out the encoding) or not meaningfully different than uncompressed (if you do).

  RAW I’d expect is better than a bitmap, assuming no encryption step, on the hypothesis that more data is better.

  Also I kind of doubt that the situation where some AI with no priors encounters a single frame of video but somehow has access to 1000 GPU years to analyze that frame would come up IRL. The point as I see it is more about whether it’s possible with a halfway reasonable amount of compute or whether That Alien Message was completely off-base.

  What links here?

  • anonymousaisafety's comment on I No Longer Believe In­tel­li­gence to be “Mag­i­cal” by DragonGod (18 Jun 2022 18:19 UTC; 1 point)

• faul_sname 18 Jun 2022 8:10 UTC

  2 points

  in reply to: anonymousaisafety’s comment on: I No Longer Believe In­tel­li­gence to be “Mag­i­cal”

  Here is an example of a large file that:

  I don’t see the file, but again if you want to run this the mentioned dates from the other thread work best.

  I am not exactly sure what you mean by the phrase “derive structure but not meaning”—a couple possibilities come to mind.

  Let’s say I have a file that is composed of 1,209,600 bytes of 0s. That file was generated by taking pressure readings, in kPa, once per second on the Hubble Space Telescope for a two week period. If I said “this file is a sequence of 2^11 x 3^3 x 5^2 x 7 zeros”, would that be a minimal example of “deriving the structure but not the meaning”? If so I expect that situation to be pretty common.

  If not, some further questions:

  1. Is it coherent to “understand the meaning but not the structure”?

  2. Would it be possible to go from “understands the structure but not the meaning” to “understands both” purely through receiving more data?

  3. If not , what about through interaction

  4. If not, what differences would you expect to observe between a world where I understood the structure but not the meaning of something and a world where I understood both?

• faul_sname 17 Jun 2022 20:59 UTC

  3 points

  in reply to: gallabytes’s comment on: I No Longer Believe In­tel­li­gence to be “Mag­i­cal”

  You will not do search over all 64 byte programs

  I was not imagining you would, no. I was imagining something more along the lines of “come up with a million different hypotheses for what a 2-d grid could encode”, which would be tedious for a human but would not really require extreme intelligence so much as extreme patience, and then for each of those million hypotheses try to build a model, and iteratively optimize for programs within that model for closeness to the output.

  I expect, though I cannot prove, that “a 2d projection of shapes in a 3d space” is a pretty significant chunk of the hypothesis space, and that all of the following hypotheses would make it into the top million

  1. The 2-d points represent a rectangular grid oriented on a plane within that 3-d space. The values at each point are determined by what the plane intersects.

  2. The 2-d points represent a grid oriented on a plane within that 3-d space. The values at each point are determined by drawing lines orthogonal to the plane and seeing what they intersect and where. The values represent distance.

  3. The 2-d points represent a grid oriented on a plane within that 3-d space. The values at each point are determined by drawing lines orthogonal to the plane and seeing what they intersect and where. The values represent something else about what the lines intersect. 4-6. Same as 1-3, but with a cylinder. 6-9. Same as 1-3, but with a sphere. 10-12. Same as 1-3, but with a torus. 13-21: Same as 4-12 but the rectangular grid is projected onto only part of the cylinder/​sphere/​torus instead of onto the whole thing.

  I am not a superintelligence though, nor do I have any special insight into what the universal prior looks like, so I don’t know if that’s actually a reasonable assumption or whether it’s an entity embedded in a space that detects other things within that space using signals privileging the hypothesis that “a space where it’s possible to detect other stuff in that space using signals” is a simple construct.

  as well as an actually quite detailed specification of the “scene” you input to that ray tracer (which is probably way more bits than the original png).

  If the size-optimal scene such that “ray-trace this scene and then apply corrections from this table” is larger than a naively compressed version of the png, my whole line of thought does indeed fall apart. I don’t expect that to be the case, because ray tracers are small and pngs are large, but this is a testable hypothesis (and not just “in principle it could be tested with a sufficiently powerful AI” but rather “an actual human could test it in a reasonable amount of time and effort using known techniques”). I don’t at this moment have time to test it, but maybe it’s worth testing at some point?

• faul_sname 17 Jun 2022 19:59 UTC

  5 points

  in reply to: Conor Sullivan’s comment on: I No Longer Believe In­tel­li­gence to be “Mag­i­cal”

  Extremely confident you could construct a file such that I could not even start giving you the provenance of that information. Trivially, you could do something like “aes256 encrypt a 2^20 bytes of zeros with the passphrase ec738958c3e7c2eeb3b4”.

  My contention is not so much “intelligence is extremely powerful” as it is “an image taken by a modern camera contains a surprisingly large amount of easy-to-decode information about the world, in the information-theoretic sense of the word information”.

  If you were to give me a binary file with no extension and no metadata that is

  1. Above 1,000,000 bytes in size

  2. Able to be compressed to under 50% of its uncompressed size with some simple tool like gzip (to ensure that there is actually some discoverable structure)

  3. Not able to be compressed under 10% of its uncompressed size by any well-known existing tools (to ensure that there is actually a meaningful amount of information in the file)

  4. Not generated by some tricky gotcha process (e.g. a file that is 250,000 bytes from /dev/random followed by 750,000 bytes from /dev/zero)

  then I’d expect that

  1. It would be possible for me, given some time to examine the data, create a decompressor and a payload such that running the decompressor on the payload yields the original file, and the decompressor program + the payload have a total size of less than the original gzipped file

  2. The decompressor would legibly contain a substantial amount of information about the structure of the data.

  I would not expect that I would be able to tell you the entire provenance of the information, even if it were possible in principle to deduce from the file, since I am not a superintelligence or even particularly smart by human standards.

  I notice that this is actually something that could be empirically tested—if you wanted we could actually run this experiment at some point (I anticipate being pretty busy for the next few weeks, but on the weekend of July ¹⁶⁄₁₇ I will be tied to my computer for the duration of the weekend (on-call for work), so if you have significantly different expectations about what would happen, and want to actually run this experiment, that would be a good time to do it for me.

  ---

  P.S. A lot of my expectations here come from the Hutter Prize, a substantial cash prize for generating good compressions of the first gigabyte of Wikipedia. The hypothesis behind the prize is that, in order to compress data, it is helpful to understand that data. The current highest-performing algorithm does not have source code available, but one of the best runner-ups that actually gives source code works using a process that looks like

  The transform done by paq8hp1 through paq8hp5 is based on WRT by Przemyslaw Skibinski, which first appeared in PAsQDa and paqar, and later in paq8g and xml-wrt. The steps are as follows:

  • The input is parsed into seqences of all uppercase letters or all lowercase letters, or one uppercase letter followed by lowercase letters, e.g. “THE”, “the”, or “The”.

  • All uppercase words are prefixed by a special symbol (0E hex in paq8hp3, paq8hp4, paq8hp5). If a lowercase letter follows with no intervening characters (e.g. “THEre”, then a special symbol (0C hex) marks the end. (e.g. 0E “the” 0C “re”).

  • Capitalized words are prefixed with 7F hex (paq8hp3) or 40 hex (paq8hp4, paq8hp5) (e.g. “The” → 40 “the”).

  • All letters are converted to lower case.

  • Words are looked up in the dictionary. The first 80 words in the dictionary are coded with 1 byte: 80, 81, … CF (hex).

  • The next 2560 words (paq8hp1-4) or 3840 words (paq8hp5) are coded with 2 bytes: D080, D081, … EFCF (paq8hp1-4), or D080, … FFCF (paq8hp5).

  • The last 40960 words are coded with 3 bytes: F0D080, F0D081, … FFEFCF.

  • If a word does not match, then the longest matching prefix with length at least 6 is coded and the rest of the word is spelled.

  • If there is no matching prefix, then the longest matching suffix with length at least 6 is coded after spelling the preceding letters.

  • If no matching word, prefix, or suffix is found, the word is spelled. Capitalization coding occurs regardless.

  • Any input bytes with special meaning are escaped by prefixing with 06: 06, 0C, 0E, 40 or 7F, 80-FF.

  WRT has additional capabilities depending on input, such as skipping encoding if little or no text is detected. The dictionary format is one word per line (linefeed only) with a 13 line header.

  If there was a future entry into the Hutter Prize which substantially cut down the compressed size, and that future entry contained fewer rather than more rules about how the English language worked, I would be very surprised and consider that to go a long way towards invalidating my model of what it means to “understand” something.

• faul_sname 16 Jun 2022 3:26 UTC

  4 points

  in reply to: Yitz’s comment on: I No Longer Believe In­tel­li­gence to be “Mag­i­cal”

  “Pure Occam’s Razor” is roughly how I would describe it too. I suspect the difference in our mental models is one of “how far can Occam’s Razor take you?”. My suspicion is that “how well you can predict the next bit in a stream of data” and “how well you understand a stream of data” are, in most¹ cases, the same thing.

  In terms of concrete predictions, I’d expect that if we

  1. Had someone² generate a description of a physical scene in a universe that runs on different laws than ours, recorded by sensors that use a different modality than we use

  2. Had someone code up a simulation of what sensor data with a small amount of noise would look like in that scenario and dump it out to a file

  3. Created a substantial prize structured mostly³ like the Hutter Prize for compressed file + decompressor

  we would see that the winning programs would look more like “generate a model and use that model and a similar rendering process to what was used to original file, plus an error correction table” and less like a general-purpose compressor⁴.

  ¹ But I probably wouldn’t go so far as to say “all”—if you handed me a stream of bits corresponding to concat(sha1(concat(secret, "1")), sha1(concat(secret, "2")), sha1(concat(secret, "3")), ...sha1(concat(secret, "999999"))), and I knew everything about the process of how you generated that stream of bits except what the value of secret was, I would say that I have a nonzero amount of understanding of that stream of bits despite having zero ability to predict the next bit in the sequence given all previous bits.
  ² Maybe Greg Egan, he seems to be pretty good at the “what would it be like to live in a universe with different underlying physics” thing
  ³ Ideally minus the bit about the compression and decompression algos having to work with such limited resources
  ⁴ Though I will note that, counter to my expectation here, as far as I know modern lossless photo compression does not look like “build a simulation of some objects in space, fine tune, and apply a correction layer” despite this being what my model of the world predicting humanity would create in a world where computation is cheap and bandwidth is expensive.

  Edit: formatting

• faul_sname 15 Jun 2022 23:35 UTC

  11 points

  in reply to: gallabytes’s comment on: I No Longer Believe In­tel­li­gence to be “Mag­i­cal”

  This seems maybe possible for png (though it could be hard—the pixel data will likely be stored as a single contiguous array, not a bunch of individual rows, and it will be run-length encoded, which you might be able to figure out but very well might not—and if it’s jpg compressed this is even further from the truth).

  I mean, once you’ve got your single continuous array it’s pretty easy to notice “hey this pattern almost repeats every 1080 triplets”. Getting from the raw data stream to your single continuous array might be very simple (if your video format is an uncompressed “read off the RGB values left-to-right, top-to-bottom, frame-by-frame”), fairly simple (“sequence of pngs”), fairly complex but probably still within the first million guesses a mathy human with a computer and an unbounded attention span might make (“sequence of jpgs”), or possibly harder than all the other parts combined if it’s H.264 or something like that, or probably impossibly no matter how good the superintelligence is if you hand it an sequence of bitmaps encrypted with a 1024 bit key or something like that.

  It’s a single frame, you’re not supposed to have seen physics before. How did ray tracers enter into this?

  You don’t need physics to build a ray tracer. You need geometry and linear algebra, which is why I specified that it was a mathematically inclined programmer. Have a look at this raytracer implementation in 35 lines of Javascript, and note how there’s lots of stuff about defining what the dot product of a vector is and how to compute the intersection between a ray and a sphere, and no stuff about atoms or molecules or quantum mechanics.

  Where is the hypothesis that it’s a projection coming from, other than assuming your conclusion?

  Trying a bunch of different hypotheses and seeing which one fits best.

  “This 2-d grid was generated by taking a point and a plane in a higher-dimensional space, drawing a grid on that plane, and then generating a line from that point to each grid intersection on that plane, and then [doing something with] those rays and a small number of simple geometric shapes in that space” seems like a pretty obvious hypothesis to me. If it doesn’t seem like one of the first million hypotheses a mathematically inclined alien that lived in a universe that operated on different rules would come up with, I don’t know what to tell you. And “reflect off at the same angle as the angle of incidence” is I’d guess among the hundred simplest [doing something with] you can do with a ray and a shape.

  I imagine “a sphere that reflects mostly red at (x=0,y=0,z=+1,r=1/​16), a plane that emits on the green channel at (y=-1), another plane that emits mainly on the blue channel at (y=+1), and a sphere that strongly emits approximately the same on each channel at (x=-1/​2,y=1,z=-1/​2)” would describe a scene with a red apple falling onto a green lawn under a sunny blue sky surprisingly well for the length of program needed to encode it, probably much better than “a blue rectangle, a green rectangle, and a red circle on a 2-d plane and also the red circle has this particular complicated shading pattern” per bit.

  Before I go any further with this explanation, can you enumerate where, if anywhere, you disagree with a human-level but extremely alien intelligence with a computer, unlimited patience, and lots of time being able to eventually do the following

  1. When receiving a definitely nonrandom signal, hypothesize that signal was probably generated by some process which it is possible to model

  2. Hypothesize that a one-dimensional array of 1,572,864 bytes that nearly repeats every 3 bytes, and which nearly repeats every 1,024 triplets, might represent a 1024 x 512 x 3 grid of something

  3. Observe that the triplets, have correlated but not perfectly correlated values which are more tightly correlated for nearby (on the other two axes) triplets than faraway ones. Hypothesize that they might represent measures of similar things on three different channels

  4. Some simple short program might be able to generate a grid which looks somewhat like that grid

  5. A ray traced scene or similar, being rather simple, is one of the programs the alien will eventually try

  6. Once the alien has the idea of a ray-traced scene, they can tinker with adding /​ removing /​ moving /​ changing the other properties of shapes in the scene to make it more clearly fit the grid it received

  7. The alien will be able to get quite close to the original scene by doing this for long enough.

  8. There will come a point where tinkering with the properties of the objects in the scene does not actually allow the alien to improve the model more.

  9. That point will come much later for the “ray tracing a scene in 3d space” model than the “paint a picture in 2d space” model, implying that the “ray tracing in 3d space” is a better model

  10. At the time this happens, the difference between the scene the alien generates and the actual photo will still show some sort of regular structure, implying that “a ray-traced 3d scene” does not fully describe the process that generated the original grid.

  Alternatively, is your hypothesis that said alien might be able to do each of these steps individually, but would eventually get stuck due to the combinatorial explosion in the number of different combinations of things to try?

• faul_sname 10 Jun 2022 18:02 UTC

  16 points

  on: I No Longer Believe In­tel­li­gence to be “Mag­i­cal”

  During my 2017 binge of LW, I recall Yudkowsky suggesting that a superintelligence could infer the laws of physics from a single frame of video showing a falling apple (Newton apparently came up with his idea of gravity from observing a falling apple).

  I now think that’s somewhere between deeply magical and utter nonsense.

  Interesting! My intuition says roughly the opposite—“be able to infer the laws of physics from a single frame of video showing an apple” seems to be barely superhuman to me. I would expect the first few steps to be achievable by a smart-but-not-exceptionally-smart and mathematically inclined programmer with a decently powerful computer, given enough time, and more specifically would look like.

  1. Determine the format of the video (easy for a human if not compressed, probably still possible for a computer-assisted human if compressed with something around the complexity level of gzip)

  2. See that the format describes something like a grid of cells, where each cell has three [something] values.

  3. Come up with the hypothesis that the grid represents a 2d projection of a 3d space (this does not feel like a large jump to me given that ray tracers exist and are not complicated, but I can go into more detail on this step if you’d like).

  4. From the presence of chromatic aberration, determine that the three channels represent intensities of the same thing at different levels (from here on out I’ll say “intensity of light at different frequencies” because talking of “things” is not that useful, but at this stage I’d expect their model to be something along the lines of “this grid was generated by something like a ray tracer with these particular shapes at these points, a light source over here”)

  5. Determine the shape of the lens by looking at edges and how they are distorted.

  6. If the apple is out in sunlight, I expect that between the three RGB channels and the rainbows generated by chromatic aberration would be sufficient to determine that the intensity-by-frequency of light approximately matches the blackbody radiation curves (though again, not so much with that name as just “these equations seem to be a good fit”)..

  From there, I think the next bits of evidence would have to do with the particulars of the sensors used, e.g.

  • If they’re sensitive enough that the observer can pick up emission lines of hydrogen from the sun and absorption lines from the atmosphere, I expect those would be informative and suggest some sort of simple mathematical model

  • If there are any shutter artifacts, that’s probably enough to build a model of “things are moving over time”.

  • Any post-processing of the image probably leaves signs

  Whether or not that’s enough to reconstruct all physical laws seems to me to depend mostly on whether it’s possible to go from understanding optic + enough QM to explain emission spectra to a complete understanding of the physical laws underlying our universe, or whether you end up with something that looks more like newtonian mechanics.

  It’s not that it’s magic, it’s that a single image taken of the physical world with modern physical sensors contains a lot of information about the physical world. I expect that a superintellegent AI would be able to pick up a lot more information from a picture of a physical apple falling than it would from a video generated by raytracing a scene in which an apple falls.

• faul_sname 9 Jun 2022 23:54 UTC

  7 points

  0 ∶ 0

  on: AGI Safety FAQ /​ all-dumb-ques­tions-al­lowed thread

  A significant fraction of the stuff I’ve read about AI safety has referred to AGIs “inspecting each others’ source code/​utility function”. However, when I look at the most impressive (to me) results in ML research lately, everything seems to be based on doing a bunch of fairly simple operations on very large matrices.

  I am confused, because I don’t understand how it would be a sensible operation to view the “source code” in question when it’s a few billion floating point numbers and a hundred lines of code that describe what sequence of simple addition/​multiplication/​comparison operations transform those inputs and those billions of floating point numbers into outputs. But a bunch of people much smarter and mathematically inclined than me do seem to think that it’s important, and the idea of recursive self-improvement with stable values seems to imply that it must be possible, which leads me to wonder if

  1. There’s some known transformation from “bag of tensors” to “readable and verifiable source code” I’m not aware of. You can do something like “from tensorflow import describe_model” or use some comparably well-known tools, similar to how there are decompilers for taking an executable and making it fairly readable. The models are too large and poorly labelled for a human to actually verify it does what they want, but a sufficiently smart machine would not have the problem.

  2. The expectation is that neural nets are not the architecture that a superhuman AGI would run on.

  3. The expectation is that, in the worlds we’re not completely doomed no matter what we do, neural nets are not the architecture that a superhuman AGI would run on.

  4. The “source code” in question is understood to be the combination of the training data, the means of calculating the loss function, and the architecture (so the “source code” of a human would be the combined life experiences of that human plus the rules for how those life experiences influenced the development of their brain, rather than the specific pattern of neurons in their brain).

  5. Something else entirely.

  I suspect the answer is “5: something else entirely”, but I have no idea what the particulars of that “something else entirely” might look like and it feels like it’s probably critical to understanding the discussion.

  So I guess my question is “which, if any, of the above describes what is meant by inspecting source code in the context of AI safety”.

• faul_sname 21 Jan 2022 20:31 UTC

  2 points

  on: Long covid: prob­a­bly worth avoid­ing—some considerations

  According to a massive controlled study published in Nature (more readable summary here). It also looks like they are saying that this is for non-hospitalized covid patients, though the paper is confusing to me.

  I am also a bit confused by what this study is trying to show. In Figure 3 it shows that a lot of things occur more frequently in patients who tested positive for COVID than in their matched controls, more in those hospitalized than that, and yet more in those admitted to the ICU. Per their methods section, this was measured by comparing the number of patients first reporting that condition within the 6 month period following a COVID-19 diagnosis vs the 6 months following (not sure) for everyone else in the VA system.

  Looking at their data, they observe that people with a positive COVID-19 test are 1.17x, 2.34x, and 3.44x more likely for non-hospitalized, hospitalized non-ICU, and ICU-admitted patients respectively, to report obesity in the 6 month period than the COVID-19 negative controls. They cite this as an excess burden of obesity of 3.37, 26.15, 46.9 people per thousand among those three groups, which gives a baseline risk of about 1.95% for the control group reporting obesity within their 6 month period, if I’m doing my math right.

  So taken at face value, the average person in the VA system has about a 2% per 6 months chance of becoming obese, while the average person admitted to the ICU has about a 7% chance of becoming obese in the next 6 months. I can come up with two possible explanations for that:

  1. People who are admitted to the ICU gain about 3.37x more weight, on average, than people who do not get COVID-19, and as such are about 3.37x more likely to become obese.

  2. Getting COVID-19 has very little effect on your weight, but within the group of people who are not obese, the people who are closer to the threshold of obesity are more likely to be admitted to the ICU for COVID-19.

  I don’t have hard evidence one way or the other, but I did some searching for evidence that COVID-19 causes weight gain, and while I found a large number of articles talking about how people are gaining weight under lockdown, I didn’t find any discussing weight gain from COVID-19 directly. Additionally, my prior is that controlling for confounding factors is really really hard, and so even for a study published in Nature I would not be surprised if the authors failed to perfectly control for those confounding factors.

  I’d be interested to see what you get if you

  1. Look at weight changes in a group of people who tested positive vs negative for COVID-19 in a specific narrow period, to see if the COVID-positive people experienced more weight gain over that period, controlling for weight at the start of that period (and if so, whether that weight gain was correlated with disease severity).

  2. Reproduce this study, except look the relative risk of experiencing one of these negative sequelae in the 6 month period before a positive COVID-19 diagnosis (if this method successfully controls for confounding factors, doing this should show a hazard ratio very close to 1 for all conditions, assuming that getting COVID can’t cause you to experience negative health outcomes in the past).

• faul_sname 7 Jun 2021 18:25 UTC

  5 points

  in reply to: ChristianKl’s comment on: Core Path­ways of Aging

  My impression is that DNA repair mechanisms get dramatically less effective with age, and that piRNA and siRNA (and other such transposon repression mechanisms) are effective but not 100% effective even in germ cells. Since germ cells in males continue to divide through the entire lifespan, my naive expectation would be that the children of very old men to age faster than the children of younger men (not just “have worse health outcomes in general” but specifically “express the specific marks of senescence earlier”).

  Is that a valid prediction of the “transposons make more transposons and eventually the exponential increase in the number of transposons kills the cell” hypothesis?

• faul_sname 5 Apr 2021 22:45 UTC

  4 points

  on: Learn­ing Rus­sian Roulette

  I’m not convinced this is a problem with the reasoner rather than a problem with the scenario.

  Let’s say we start with an infinite population of people, who all have as a purpose in life to play Russian Roulette until they die. Let’s further say that one in a trillion of these people has a defective gun that will not shoot, no matter how many times they play.

  If you select from the people who have survived 1000 rounds, your population will be made almost entirely out of people with defective guns (1 /​ 1e12 with defective guns vs 1/​6e80 with working guns who have just gotten lucky).

  Alternatively, we could say that none of the guns at all are defective. Even if we make that assumption, if we count the number of observer moments of “about to pull the trigger”, we see that the median observer-moment is someone who has played 3 rounds, the 99.9th percentile observer-moment has played 26 rounds, and by the time you’re up to 100 rounds, approximately 99.999999% of observer-moments are from people who have pulled the trigger fewer times than you have survived. If we play a game of Follow the Improbability, we find that the improbability is the fact that we’re looking at a person who has won 1000 rounds of Russian Roulette in a row, so if we figure out why we’re looking at that particular person I think that solves the problem.

• faul_sname 28 Mar 2018 6:18 UTC

  7 points

  in reply to: Qiaochu_Yuan’s comment on: A Mo­tor­cy­cle (and Cal­ibra­tion?) Accident

  For example, let’s say that I think I have about 20 years left to live (because of AI timelines). Then a micromort is 10 expected minutes of life lost. According to Wikipedia, skydiving costs 9 micromorts per jump so I lose 90 expected minutes of life every jump. Also according to Wikipedia, motorcycling costs 1 micromort every 6 miles so I lose about 2 expected minutes of life per mile I spend motorcycling.

  An interesting conclusion here is that if you save more than 2 minutes per mile (going 15 mph instead of 10mph, for example) of time sitting in traffic by riding a motorcycle instead of driving a car, the number of not-sitting-in-traffic minutes of your life actually increases by riding your bike.

  Though note that the above calculation assumed that your risk of dying in a motorcycle crash while weaving through stopped traffic is not higher than ¹⁄₆ micromorts per mile. Also note that if your expected life span is 60 years, you now need to save 6 minutes per mile to “break even”, which is unlikely—if traffic is moving that slowly (remember that this is based on the average speed across the entire trip) you’re probably better off walking.

• faul_sname 2 Feb 2017 18:19 UTC

  2 points

  in reply to: JenniferRM’s comment on: How of­ten do you check this fo­rum?

  ping

• faul_sname 20 Oct 2016 20:36 UTC

  0 points

  on: A prob­lem in an­throp­ics with im­pli­ca­tions for the sound­ness of the simu­la­tion ar­gu­ment.

  How many seconds have you been in the room?

  Let’s say the time between t1 and t2 is 1 trillion seconds. Let us further assume that all people go through the rooms in the same amount of time (thus people spend 1 second each in room A, and 1 million seconds each in room B).

  100 trillion of the 100.1 trillion observer moments between 0 and 1 seconds in a room occur in room A. All of the observer moments past 1 second occur in room B (this is somewhat flawed in that it is possible that the observers don’t all spend the same amount of time in a given room, but even in the case where 100 million people stay in room A for 1 million seconds each, and the rest spend zero time, an observer who’s been in a room for 1 million seconds is still overwhelmingly likely to be in room B. So basically the longer you’ve been in the room, the more probably you should consider it that you’re in room B).

  If an observer doesn’t know how long they’ve been in a given room, I’m not sure how meaningful it is to call them “an” observer.

• faul_sname 27 Mar 2016 7:06 UTC

  46 points

  in reply to: Huluk’s comment on: Less­wrong 2016 Survey

  I have taken the survey.

• faul_sname 19 Jan 2016 10:04 UTC

  0 points

  in reply to: bill’s comment on: Shut Up And Guess

  What does 0.01% on the wrong answer get you?

• faul_sname 26 Aug 2015 8:24 UTC

  −1 points

  in reply to: Dorikka’s comment on: Yud­kowsky’s brain is the pin­na­cle of evolution

  It was a memetic hazard.

  (not really)

• faul_sname 25 Aug 2015 6:38 UTC

  7 points

  in reply to: RichardKennaway’s comment on: Open Thread—Aug 24 - Aug 30

  I’m not sure, not having read the paper, but I would expect that “Lifetime manic features at age 22-23 years” means “number of manic features experienced in the time prior to 22-23 years of age” (i.e. we measured IQ of a bunch of 8-year-olds 15 years ago, and those people are now in the range of 22-23 years of age, and we ask how many manic episodes they’ve had in that time).

• faul_sname 13 Aug 2015 8:19 UTC

  0 points

  in reply to: Thomas’s comment on: Crazy Ideas Thread, Aug. 2015

  Why tunnels, not canals? Particularly in the case of Denver, you’ve got a huge elevation gain, so you’d need the locks anyway, and digging tunnels is expensive (and buying farmland to put your canal through is relatively cheap).

• faul_sname 13 Jul 2015 10:03 UTC

  0 points

  in reply to: drethelin’s comment on: I need a pro­to­col for dan­ger­ous or dis­con­cert­ing ideas.

  How difficult would this be, out of curiosity, keeping in mind that you don’t need 100% accuracy? I can think of a couple approaches, though probably nothing that would be supported by any revenue model I can think of off the top of my head.