9 ms·
On a related note, sometimes someone may be wrong because he knows more about the subject (but not enough). A canonical example could be about the "leap year"
by rom1v 3y ago
On a related note, sometimes someone may be wrong because he knows more about the subject (but not enough).
A canonical example could be about the "leap year" rules.
For context, the Earth makes a full rotation around the Sun in about 365.2425 days, so we use leap years to compensate:
- add 1 day every 4 years (365 + 1/4 = 365.25)
- but not every 100 years (365.25 - 1/100 = 365.24)
- but add a day anyway every 400 years (365.24 + 1/400 = 365.2425)
Suppose most people only know the first part (1 additional day every 4 years). If we ask "is 2000 a leap year?", they would answer "Of course, 2000 is a multiple of 4". And they would get the correct result.
Now, suppose someone started to study the "subject" (here, there is nothing to study, this is a trivial example for illustration), and is aware of the second rule (but not the third one). He would say "Ahah, no, 2000 is not a leap year, because it is a multiple of 100". But he would get the wrong answer.
My impression is that this kind of mistakes happens often while learning a subject: by studying, we encounter exceptions or surprising facts, that we may apply too broadly (to the point we make absurd claims, but that appear absurd for the wrong reasons).
- EGreg 3y agoFar more broadly, think of logic as just a low-dimensional approximation of something complex. Humans deal with logic and are taught “i before e except after c” etc. But even with describing human languages that may be inadequate. What AI does is it essentially makes a model by which you can search a latent space quickly — both to clasify input and to generate output. You can throw the recorded motions of the planets and stars at it and it might find physical laws that have 80 variables while humans want to deal only with simplified versions like Kepler’s laws of motion. In the vacuum of space those laws may be enough but when it comes to the complexity of chemistry, biology, genetics, politica, etc. the AI might have way better model that we can never understand. Like for dietary recommendations. Would people follow them? And what if they are wrong in some other ways? Like how humans beat AlphaGo through its blind spot or how you can fool face recognition by wearing a hoodie etc.
- yllautcaj 3y ago"A little knowledge is a dangerous thing."
- robgibbons 3y agoFrom "A Little Learning," by Alexander Pope A little learning is a dangerous thing; Drink deep, or taste not the Pierian spring: There shallow draughts intoxicate the brain, And drinking largely sobers us again.
- deleted 3y ago[deleted]
- behnamoh 3y agoAlso applies to creativity. - Knowledge = little ==> little creativity to add something new - Knowledge = mid ==> great creativity - Knowledge = high ==> little creativity to add something new
- onemoresoop 3y agoCould you please expand on this?
- The_Colonel 3y agoState of the art is that X is impossible. An expert knows that, and therefore does not pursue it. A non-expert doesn't know X is impossible, pursues it and solves it. I don't have any specific example, though.
- onemoresoop 3y agoI think I get it. Applying lateral or novel thinking to a problem unfamiliar...
- anon____ 3y agoThere's a famous story that's a good example here, I think: George Dantzig solved two open problems in statistical theory, which he had mistaken for homework after arriving late to a lecture at university. https://en.wikipedia.org/wiki/George_Dantzig https://en.wikipedia.org/wiki/George_Dantzig > In 1939, a misunderstanding brought about surprising results. Near the beginning of a class, Professor Neyman wrote two problems on the blackboard. Dantzig arrived late and assumed that they were a homework assignment. According to Dantzig, they "seemed to be a little harder than usual", but a few days later he handed in completed solutions for both problems, still believing that they were an assignment that was overdue. Six weeks later, an excited Neyman eagerly told him that the "homework" problems he had solved were two of the most famous unsolved problems in statistics. He had prepared one of Dantzig's solutions for publication in a mathematical journal. This story began to spread and was used as a motivational lesson demonstrating the power of positive thinking. Over time, some facts were altered, but the basic story persisted in the form of an urban legend and as an introductory scene in the movie Good Will Hunting.
- sopooneo 3y agoThis follows a pattern I've noticed where an expert's approach may seem similar to a pure novice's. With only the intermediate practitioner seeming to follow any rules.
- ajuc 3y agoThis is so common there's a meme template :) https://pbs.twimg.com/media/FffvEX5UAAAysZC?format=jpg&name=medium https://pbs.twimg.com/media/FffvEX5UAAAysZC?format=jpg&name=...
- cfiggers 3y agoFirst you learn the conventional wisdom. Then, you learn that the conventional wisdom is wrong. Finally, you learn that you didn't actually learn the conventional wisdom the first time around, typically by rediscovering it yourself.
- gonehome 3y agoThis can come up in other interesting ways. There are human behaviors that appear to be entirely selected for (and then kept around via culture) [0]. In this case a population may do something like have pregnant women avoid eating sharks which are otherwise a normal part of their diet. They don't know why they don't eat the sharks, they just don't. It turns out the sharks contain something that causes birth defects. Commonly people think someone must have realized this and then that original knowledge was forgotten, but it's quite likely it was never known and the behavior was entirely selected (pregnant women that didn't eat the sharks more successfully reproduced). When you press the women to answer why they don't know, if forced to answer they make something up (it'll give my baby shark skin). You could imagine someone thinking that's stupid and then eating the sharks and getting a baby with birth defects. I think this explains a lot about why superstitious belief is so common in humans and animals. Reason is obviously selectively advantageous, but a little reason incorrectly or over confidently applied can also be harmful, most people are bad at individual reasoning (endless conspiracy theories) and are often better off with consensus. For every contrarian that unlocks massive value by being contrarian and correct there are ten cranks that just hold false beliefs that potentially harm themselves and others. [0]: https://slatestarcodex.com/2019/06/04/book-review-the-secret-of-our-success/ https://slatestarcodex.com/2019/06/04/book-review-the-secret...
- ilyt 3y agoIn IT we call those people "power users", the absolute bane of tech support.
- gorlilla 3y agoIn power-user-land we call IT glorified gatekeepers. Neither would be entirely accurate and not every of either is always one of either and either can be both at the same or different times..
- ilyt 3y agoOh there is plenty of bad IT. But I'd wager at least 2/3 is just overworked and understaffed so the "less important" stuff get sidelined and IT dept just becomes a hole no request comes back from unless your boss yells enough...
- banannaise 3y agoThe true expert realizes what question is being asked, and grabs a calendar. Two people, equally knowledgeable and given the same problem, will not necessarily come up with the same solution. Leap years are not a mathematical fact; they are an engineering solution to a mathematical challenge. An expert looking at orbits is unlikely to decide that we must specifically add an extra day to February; you only know that we use this solution by deriving it from calendars.
- ndsipa_pomu 3y agoA simpler example may be that a person with one clock knows the time, but a person with two clocks is never quite sure.
- mrob 3y agoIt's a popular phrase, but it doesn't make sense if you think about it. The person with two clocks can average the time of both of them and get a result they can be more confident in than the person with only one.
- ndsipa_pomu 3y agoThat only works if both clocks are wrong in opposite directions and so confidence wouldn't be appropriate. Taking the average might work if you've got a whole bunch of clocks and they show a distribution such as a bell curve - that's what's required to become more confident than the single clock owner.
- mrob 3y agoI'm assuming the error of every clock has the same probability distribution. I think this is a reasonable assumption, because the phrase only mentions the number of clocks. In this case, the more clocks you average the lower your expected error.
- ndsipa_pomu 3y agoBut with just two clocks, the distribution isn't evident. They could both be running fast and thus the average would be worse than the clock showing the earlier time. You'd have no way of knowing without consulting more clocks. Also, with clocks you should be measuring the rate at which they measure time. Typically clocks can run fast or slow which means that the time shown is a function of how long they've been running since they were last correct.
- mrob 3y ago
- karaterobot 3y agoThe bimodal variation of this is the famous (?) quote about the U.S. civil war, which goes something like: in elementary school, you learn the Civil War was about slavery. In high school, you learn it was about economics. In college, you learn it was actually about slavery.
- hsod 3y agoI think this is pretty much what the term "midwit" refers to