The following information is used for educational purposes only.
Julian Treasure:
How to speak so that people want to listen
TEDGlobal 2013 · Filmed Feb 2013
Have you ever felt like you're talking, but nobody is listening? Here's Julian Treasure to help. In this useful talk, the sound expert demonstrates the how-to's of powerful speaking — from some handy vocal exercises to tips on how to speak with empathy. A talk that might help the world sound more beautiful.
Transcript:
The human voice: It's the instrument we all play. It's the most powerful sound in the world, probably. It's the only one that can start a war or say "I love you." And yet many people have the experience that when they speak, people don't listen to them. And why is that? How can we speak powerfully to make change in the world?
What I'd like to suggest, there are a number of habits that we need to move away from. I've assembled for your pleasure here seven deadly sins of speaking. I'm not pretending this is an exhaustive list, but these seven, I think, are pretty large habits that we can all fall into.
First, gossip, speaking ill of somebody who's not present. Not a nice habit, and we know perfectly well the person gossiping five minutes later will be gossiping about us.
Second, judging. We know people who are like this in conversation, and it's very hard to listen to somebody if you know that you're being judged and found wanting at the same time.
Third, negativity. You can fall into this. My mother, in the last years of her life, became very, very negative, and it's hard to listen. I remember one day, I said to her, "It's October 1 today," and she said, "I know, isn't it dreadful?" (Laughter) It's hard to listen when somebody's that negative.
And another form of negativity, complaining. Well, this is the national art of the U.K. It's our national sport. We complain about the weather, about sport, about politics, about everything, but actually complaining is viral misery. It's not spreading sunshine and lightness in the world.
Excuses. We've all met this guy. Maybe we've all been this guy. Some people have a blamethrower. They just pass it on to everybody else and don't take responsibility for their actions, and again, hard to listen to somebody who is being like that.
Penultimate, the sixth of the seven, embroidery, exaggeration. It demeans our language, actually, sometimes. For example, if I see something that really is awesome, what do I call it? (Laughter) And then of course this exaggeration becomes lying, out and out lying, and we don't want to listen to people we know are lying to us.
And finally, dogmatism, the confusion of facts with opinions. When those two things get conflated, you're listening into the wind. You know, somebody is bombarding you with their opinions as if they were true. It's difficult to listen to that.
So here they are, seven deadly sins of speaking. These are things I think we need to avoid. But is there a positive way to think about this? Yes, there is. I'd like to suggest that there are four really powerful cornerstones, foundations, that we can stand on if we want our speech to be powerful and to make change in the world. Fortunately, these things spell a word. The word is "hail," and it has a great definition as well. I'm not talking about the stuff that falls from the sky and hits you on the head. I'm talking about this definition, to greet or acclaim enthusiastically, which is how I think our words will be received if we stand on these four things.
So what do they stand for? See if you can guess. The H, honesty, of course, being true in what you say, being straight and clear. The A is authenticity, just being yourself. A friend of mine described it as standing in your own truth, which I think is a lovely way to put it. The I is integrity, being your word, actually doing what you say, and being somebody people can trust. And the L is love. I don't mean romantic love, but I do mean wishing people well, for two reasons. First of all, I think absolute honesty may not be what we want. I mean, my goodness, you look ugly this morning. Perhaps that's not necessary. Tempered with love, of course, honesty is a great thing. But also, if you're really wishing somebody well, it's very hard to judge them at the same time. I'm not even sure you can do those two things simultaneously. So hail.
Also, now that's what you say, and it's like the old song, it is what you say, it's also the way that you say it. You have an amazing toolbox. This instrument is incredible, and yet this is a toolbox that very few people have ever opened. I'd like to have a little rummage in there with you now and just pull a few tools out that you might like to take away and play with, which will increase the power of your speaking.
Register, for example. Now, falsetto register may not be very useful most of the time, but there's a register in between. I'm not going to get very technical about this for any of you who are voice coaches. You can locate your voice, however. So if I talk up here in my nose, you can hear the difference. If I go down here in my throat, which is where most of us speak from most of the time. But if you want weight, you need to go down here to the chest. You hear the difference? We vote for politicians with lower voices, it's true, because we associate depth with power and with authority. That's register.
Then we have timbre. It's the way your voice feels. Again, the research shows that we prefer voices which are rich, smooth, warm, like hot chocolate. Well if that's not you, that's not the end of the world, because you can train. Go and get a voice coach. And there are amazing things you can do with breathing, with posture, and with exercises to improve the timbre of your voice.
Then prosody. I love prosody. This is the sing-song, the meta-language that we use in order to impart meaning. It's root one for meaning in conversation. People who speak all on one note are really quite hard to listen to if they don't have any prosody at all. That's where the world monotonic comes from, or monotonous, monotone. Also we have repetitive prosody now coming in, where every sentence ends as if it were a question when it's actually not a question, it's a statement. (Laughter) And if you repeat that one over and over, it's actually restricting your ability to communicate through prosody, which I think is a shame, so let's try and break that habit.
Pace. I can get very, very excited by saying something really, really quickly, or I can slow right down to emphasize, and at the end of that, of course, is our old friend silence. There's nothing wrong with a bit of silence in a talk, is there? We don't have to fill it with ums and ahs. It can be very powerful.
Of course, pitch often goes along with pace to indicate arousal, but you can do it just with pitch. Where did you leave my keys? Where did you leave my keys? So slightly different meaning in those two deliveries.
And finally, volume. I can get really excited by using volume. Sorry about that if I startled anybody. Or, I can have you really pay attention by getting very quiet. Some people broadcast the whole time. Try not to do that. That's called sodcasting, imposing your sound on people around you carelessly and inconsiderately. Not nice.
Of course, where this all comes into play most of all is when you've got something really important to do. It might be standing on a stage like this and giving a talk to people. It might be proposing marriage, asking for a raise, a wedding speech. Whatever it is, if it's really important, you owe it to yourself to look at this toolbox and the engine that it's going to work on, and no engine works well without being warmed up. Warm up your voice.
Actually, let me show you how to do that. Would you all like to stand up for a moment? I'm going to show you the six vocal warmup exercises that I do before every talk I ever do. Anytime you're going to talk to anybody important, do these. First, arms up, deep breath in, and sigh out, ahhhhh, like that. One more time. Ahhhh, very good. Now we're going to warm up our lips, and we're going to go ba, ba, ba, ba, ba, ba, ba, ba. Very good. And now, brrrrrrrrrr, just like when you were a kid. Brrrr. Now your lips should be coming alive. We're going to do the tongue next with exaggerated la, la, la, la, la, la, la, la, la. Beautiful. You're getting really good at this. And then, roll an R. Rrrrrrr. That's like champagne for the tongue. Finally, and if I can only do one, the pros call this the siren. It's really good. It starts with "we" and goes to "aw." The "we" is high, the "aw" is low. So you go, weeeaawww, weeeaawww.
Fantastic. Give yourselves a round of applause. Take a seat, thank you. (Applause)
Next time you speak, do those in advance.
Now let me just put this in context to close. This is a serious point here. This is where we are now, right? We speak not very well into people who simply aren't listening in an environment that's all about noise and bad acoustics. I have talked about that on this stage in different phases. What would the world be like if we were speaking powerfully to people who were listening consciously in environments which were actually fit for purpose? Or to make that a bit larger, what would the world be like if we were creating sound consciously and consuming sound consciously and designing all our environments consciously for sound? That would be a world that does sound beautiful, and one where understanding would be the norm, and that is an idea worth spreading.
Thank you.
Thank you. (Applause)
In this blog, teachers, students and all those who have some kind of interest in the English language will find useful, interesting, curious and funny things to be used in their classes or for their personal enjoyment.
Saturday, July 12, 2014
TECH/GralInt-TED Talks-Lorrie Faith Cranor: What’s wrong with your pa$$w0rd?
The following information is used for educational purposes only.
Lorrie Faith Cranor:
What’s wrong with your pa$$w0rd?
TEDxMU · Filmed Mar 2014
Lorrie Faith Cranor studied thousands of real passwords to figure out the surprising, very common mistakes that users — and secured sites — make to compromise security. And how, you may ask, did she study thousands of real passwords without compromising the security of any users? That's a story in itself. It's secret data worth knowing, especially if your password is 123456 ...
Transcript:
I am a computer science and engineering professor here at Carnegie Mellon, and my research focuses on usable privacy and security, and so my friends like to give me examples of their frustrations with computing systems, especially frustrations related to unusable privacy and security.
So passwords are something that I hear a lot about. A lot of people are frustrated with passwords, and it's bad enough when you have to have one really good password that you can remember but nobody else is going to be able to guess. But what do you do when you have accounts on a hundred different systems and you're supposed to have a unique password for each of these systems? It's tough.
At Carnegie Mellon, they used to make it actually pretty easy for us to remember our passwords. The password requirement up through 2009 was just that you had to have a password with at least one character. Pretty easy. But then they changed things, and at the end of 2009, they announced that we were going to have a new policy, and this new policy required passwords that were at least eight characters long, with an uppercase letter, lowercase letter, a digit, a symbol, you couldn't use the same character more than three times, and it wasn't allowed to be in a dictionary.
Now, when they implemented this new policy, a lot of people, my colleagues and friends, came up to me and they said, "Wow, now that's really unusable. Why are they doing this to us, and why didn't you stop them?"
And I said, "Well, you know what? They didn't ask me."
But I got curious, and I decided to go talk to the people in charge of our computer systems and find out what led them to introduce this new policy, and they said that the university had joined a consortium of universities, and one of the requirements of membership was that we had to have stronger passwords that complied with some new requirements, and these requirements were that our passwords had to have a lot of entropy. Now entropy is a complicated term, but basically it measures the strength of passwords. But the thing is, there isn't actually a standard measure of entropy. Now, the National Institute of Standards and Technology has a set of guidelines which have some rules of thumb for measuring entropy, but they don't have anything too specific, and the reason they only have rules of thumb is it turns out they don't actually have any good data on passwords. In fact, their report states, "Unfortunately, we do not have much data on the passwords users choose under particular rules. NIST would like to obtain more data on the passwords users actually choose, but system administrators are understandably reluctant to reveal password data to others."
So this is a problem, but our research group looked at it as an opportunity. We said, "Well, there's a need for good password data. Maybe we can collect some good password data and actually advance the state of the art here.
So the first thing we did is, we got a bag of candy bars and we walked around campus and talked to students, faculty and staff, and asked them for information about their passwords. Now we didn't say, "Give us your password." No, we just asked them about their password. How long is it? Does it have a digit? Does it have a symbol? And were you annoyed at having to create a new one last week? So we got results from 470 students, faculty and staff, and indeed we confirmed that the new policy was very annoying, but we also found that people said they felt more secure with these new passwords. We found that most people knew they were not supposed to write their password down, and only 13 percent of them did, but disturbingly, 80 percent of people said they were reusing their password. Now, this is actually more dangerous than writing your password down, because it makes you much more susceptible to attackers. So if you have to, write your passwords down, but don't reuse them. We also found some interesting things about the symbols people use in passwords. So CMU allows 32 possible symbols, but as you can see, there's only a small number that most people are using, so we're not actually getting very much strength from the symbols in our passwords.
So this was a really interesting study, and now we had data from 470 people, but in the scheme of things, that's really not very much password data, and so we looked around to see where could we find additional password data? So it turns out there are a lot of people going around stealing passwords, and they often go and post these passwords on the Internet. So we were able to get access to some of these stolen password sets. This is still not really ideal for research, though, because it's not entirely clear where all of these passwords came from, or exactly what policies were in effect when people created these passwords. So we wanted to find some better source of data. So we decided that one thing we could do is we could do a study and have people actually create passwords for our study. So we used a service called Amazon Mechanical Turk, and this is a service where you can post a small job online that takes a minute, a few minutes, an hour, and pay people, a penny, ten cents, a few dollars, to do a task for you, and then you pay them through Amazon.com. So we paid people about 50 cents to create a password following our rules and answering a survey, and then we paid them again to come back two days later and log in using their password and answering another survey. So we did this, and we collected 5,000 passwords, and we gave people a bunch of different policies to create passwords with. So some people had a pretty easy policy, we call it Basic8, and here the only rule was that your password had to have at least eight characters. Then some people had a much harder policy, and this was very similar to the CMU policy, that it had to have eight characters including uppercase, lowercase, digit, symbol, and pass a dictionary check. And one of the other policies we tried, and there were a whole bunch more, but one of the ones we tried was called Basic16, and the only requirement here was that your password had to have at least 16 character.
All right, so now we had 5,000 passwords, and so we had much more detailed information. Again we see that there's only a small number of symbols that people are actually using in their passwords. We also wanted to get an idea of how strong the passwords were that people were creating, but as you may recall, there isn't a good measure of password strength. So what we decided to do was to see how long it would take to crack these passwords using the best cracking tools that the bad guys are using, or that we could find information about in the research literature.
So to give you an idea of how bad guys go about cracking passwords, they will steal a password file that will have all of the passwords in kind of a scrambled form, called a hash, and so what they'll do is they'll make a guess as to what a password is, run it through a hashing function, and see whether it matches the passwords they have on their stolen password list. So a dumb attacker will try every password in order. They'll start with AAAAA and move on to AAAAB, and this is going to take a really long time before they get any passwords that people are really likely to actually have. A smart attacker, on the other hand, does something much more clever. They look at the passwords that are known to be popular from these stolen password sets, and they guess those first. So they're going to start by guessing "password," and then they'll guess "I love you," and "monkey," and "12345678," because these are the passwords that are most likely for people to have. In fact, some of you probably have these passwords. So what we found by running all of these 5,000 passwords we collected through these tests to see how strong they were, we found that the long passwords were actually pretty strong, and the complex passwords were pretty strong too. However, when we looked at the survey data, we saw that people were really frustrated by the very complex passwords, and the long passwords were a lot more usable, and in some cases, they were actually even stronger than the complex passwords. So this suggests that, instead of telling people that they need to put all these symbols and numbers and crazy things into their passwords, we might be better off just telling people to have long passwords. Now here's the problem, though: Some people had long passwords that actually weren't very strong. You can make long passwords that are still the sort of thing that an attacker could easily guess. So we need to do more than just say long passwords. There has to be some additional requirements, and some of our ongoing research is looking at what additional requirements we should add to make for stronger passwords that also are going to be easy for people to remember and type.
Another approach to getting people to have stronger passwords is to use a password meter. Here are some examples. You may have seen these on the Internet when you were creating passwords. We decided to do a study to find out whether these password meters actually work. Do they actually help people have stronger passwords, and if so, which ones are better? So we tested password meters that were different sizes, shapes, colors, different words next to them, and we even tested one that was a dancing bunny. As you type a better password, the bunny dances faster and faster. So this was pretty fun.
What we found was that password meters do work. (Laughter) Most of the password meters were actually effective, and the dancing bunny was very effective too, but the password meters that were the most effective were the ones that made you work harder before they gave you that thumbs up and said you were doing a good job, and in fact we found that most of the password meters on the Internet today are too soft. They tell you you're doing a good job too early, and if they would just wait a little bit before giving you that positive feedback, you probably would have better passwords.
Now another approach to better passwords, perhaps, is to use pass phrases instead of passwords. So this was an xkcd cartoon from a couple of years ago, and the cartoonist suggests that we should all use pass phrases, and if you look at the second row of this cartoon, you can see the cartoonist is suggesting that the pass phrase "correct horse battery staple" would be a very strong pass phrase and something really easy to remember. He says, in fact, you've already remembered it. And so we decided to do a research study to find out whether this was true or not. In fact, everybody who I talk to, who I mention I'm doing password research, they point out this cartoon. "Oh, have you seen it? That xkcd. Correct horse battery staple." So we did the research study to see what would actually happen.
So in our study, we used Mechanical Turk again, and we had the computer pick the random words in the pass phrase. Now the reason we did this is that humans are not very good at picking random words. If we asked a human to do it, they would pick things that were not very random. So we tried a few different conditions. In one condition, the computer picked from a dictionary of the very common words in the English language, and so you'd get pass phrases like "try there three come." And we looked at that, and we said, "Well, that doesn't really seem very memorable." So then we tried picking words that came from specific parts of speech, so how about noun-verb-adjective-noun. That comes up with something that's sort of sentence-like. So you can get a pass phrase like "plan builds sure power" or "end determines red drug." And these seemed a little bit more memorable, and maybe people would like those a little bit better. We wanted to compare them with passwords, and so we had the computer pick random passwords, and these were nice and short, but as you can see, they don't really look very memorable. And then we decided to try something called a pronounceable password. So here the computer picks random syllables and puts them together so you have something sort of pronounceable, like "tufritvi" and "vadasabi." That one kind of rolls off your tongue. So these were random passwords that were generated by our computer.
So what we found in this study was that, surprisingly, pass phrases were not actually all that good. People were not really better at remembering the pass phrases than these random passwords, and because the pass phrases are longer, they took longer to type and people made more errors while typing them in. So it's not really a clear win for pass phrases. Sorry, all of you xkcd fans. On the other hand, we did find that pronounceable passwords worked surprisingly well, and so we actually are doing some more research to see if we can make that approach work even better. So one of the problems with some of the studies that we've done is that because they're all done using Mechanical Turk, these are not people's real passwords. They're the passwords that they created or the computer created for them for our study. And we wanted to know whether people would actually behave the same way with their real passwords.
So we talked to the information security office at Carnegie Mellon and asked them if we could have everybody's real passwords. Not surprisingly, they were a little bit reluctant to share them with us, but we were actually able to work out a system with them where they put all of the real passwords for 25,000 CMU students, faculty and staff, into a locked computer in a locked room, not connected to the Internet, and they ran code on it that we wrote to analyze these passwords. They audited our code. They ran the code. And so we never actually saw anybody's password.
We got some interesting results, and those of you Tepper students in the back will be very interested in this. So we found that the passwords created by people affiliated with the school of computer science were actually 1.8 times stronger than those affiliated with the business school. We have lots of other really interesting demographic information as well. The other interesting thing that we found is that when we compared the Carnegie Mellon passwords to the Mechanical Turk-generated passwords, there was actually a lot of similarities, and so this helped validate our research method and show that actually, collecting passwords using these Mechanical Turk studies is actually a valid way to study passwords. So that was good news.
Okay, I want to close by talking about some insights I gained while on sabbatical last year in the Carnegie Mellon art school. One of the things that I did is I made a number of quilts, and I made this quilt here. It's called "Security Blanket." (Laughter) And this quilt has the 1,000 most frequent passwords stolen from the RockYou website. And the size of the passwords is proportional to how frequently they appeared in the stolen dataset. And what I did is I created this word cloud, and I went through all 1,000 words, and I categorized them into loose thematic categories. And it was, in some cases, it was kind of difficult to figure out what category they should be in, and then I color-coded them.
So here are some examples of the difficulty. So "justin." Is that the name of the user, their boyfriend, their son? Maybe they're a Justin Bieber fan. Or "princess." Is that a nickname? Are they Disney princess fans? Or maybe that's the name of their cat. "Iloveyou" appears many times in many different languages. There's a lot of love in these passwords. If you look carefully, you'll see there's also some profanity, but it was really interesting to me to see that there's a lot more love than hate in these passwords. And there are animals, a lot of animals, and "monkey" is the most common animal and the 14th most popular password overall. And this was really curious to me, and I wondered, "Why are monkeys so popular?" And so in our last password study, any time we detected somebody creating a password with the word "monkey" in it, we asked them why they had a monkey in their password. And what we found out -- we found 17 people so far, I think, who have the word "monkey" -- We found out about a third of them said they have a pet named "monkey" or a friend whose nickname is "monkey," and about a third of them said that they just like monkeys and monkeys are really cute. And that guy is really cute.
So it seems that at the end of the day, when we make passwords, we either make something that's really easy to type, a common pattern, or things that remind us of the word password or the account that we've created the password for, or whatever. Or we think about things that make us happy, and we create our password based on things that make us happy. And while this makes typing and remembering your password more fun, it also makes it a lot easier to guess your password. So I know a lot of these TED Talks are inspirational and they make you think about nice, happy things, but when you're creating your password, try to think about something else.
Thank you.
(Applause)
Lorrie Faith Cranor:
What’s wrong with your pa$$w0rd?
TEDxMU · Filmed Mar 2014
Lorrie Faith Cranor studied thousands of real passwords to figure out the surprising, very common mistakes that users — and secured sites — make to compromise security. And how, you may ask, did she study thousands of real passwords without compromising the security of any users? That's a story in itself. It's secret data worth knowing, especially if your password is 123456 ...
Transcript:
I am a computer science and engineering professor here at Carnegie Mellon, and my research focuses on usable privacy and security, and so my friends like to give me examples of their frustrations with computing systems, especially frustrations related to unusable privacy and security.
So passwords are something that I hear a lot about. A lot of people are frustrated with passwords, and it's bad enough when you have to have one really good password that you can remember but nobody else is going to be able to guess. But what do you do when you have accounts on a hundred different systems and you're supposed to have a unique password for each of these systems? It's tough.
At Carnegie Mellon, they used to make it actually pretty easy for us to remember our passwords. The password requirement up through 2009 was just that you had to have a password with at least one character. Pretty easy. But then they changed things, and at the end of 2009, they announced that we were going to have a new policy, and this new policy required passwords that were at least eight characters long, with an uppercase letter, lowercase letter, a digit, a symbol, you couldn't use the same character more than three times, and it wasn't allowed to be in a dictionary.
Now, when they implemented this new policy, a lot of people, my colleagues and friends, came up to me and they said, "Wow, now that's really unusable. Why are they doing this to us, and why didn't you stop them?"
And I said, "Well, you know what? They didn't ask me."
But I got curious, and I decided to go talk to the people in charge of our computer systems and find out what led them to introduce this new policy, and they said that the university had joined a consortium of universities, and one of the requirements of membership was that we had to have stronger passwords that complied with some new requirements, and these requirements were that our passwords had to have a lot of entropy. Now entropy is a complicated term, but basically it measures the strength of passwords. But the thing is, there isn't actually a standard measure of entropy. Now, the National Institute of Standards and Technology has a set of guidelines which have some rules of thumb for measuring entropy, but they don't have anything too specific, and the reason they only have rules of thumb is it turns out they don't actually have any good data on passwords. In fact, their report states, "Unfortunately, we do not have much data on the passwords users choose under particular rules. NIST would like to obtain more data on the passwords users actually choose, but system administrators are understandably reluctant to reveal password data to others."
So this is a problem, but our research group looked at it as an opportunity. We said, "Well, there's a need for good password data. Maybe we can collect some good password data and actually advance the state of the art here.
So the first thing we did is, we got a bag of candy bars and we walked around campus and talked to students, faculty and staff, and asked them for information about their passwords. Now we didn't say, "Give us your password." No, we just asked them about their password. How long is it? Does it have a digit? Does it have a symbol? And were you annoyed at having to create a new one last week? So we got results from 470 students, faculty and staff, and indeed we confirmed that the new policy was very annoying, but we also found that people said they felt more secure with these new passwords. We found that most people knew they were not supposed to write their password down, and only 13 percent of them did, but disturbingly, 80 percent of people said they were reusing their password. Now, this is actually more dangerous than writing your password down, because it makes you much more susceptible to attackers. So if you have to, write your passwords down, but don't reuse them. We also found some interesting things about the symbols people use in passwords. So CMU allows 32 possible symbols, but as you can see, there's only a small number that most people are using, so we're not actually getting very much strength from the symbols in our passwords.
So this was a really interesting study, and now we had data from 470 people, but in the scheme of things, that's really not very much password data, and so we looked around to see where could we find additional password data? So it turns out there are a lot of people going around stealing passwords, and they often go and post these passwords on the Internet. So we were able to get access to some of these stolen password sets. This is still not really ideal for research, though, because it's not entirely clear where all of these passwords came from, or exactly what policies were in effect when people created these passwords. So we wanted to find some better source of data. So we decided that one thing we could do is we could do a study and have people actually create passwords for our study. So we used a service called Amazon Mechanical Turk, and this is a service where you can post a small job online that takes a minute, a few minutes, an hour, and pay people, a penny, ten cents, a few dollars, to do a task for you, and then you pay them through Amazon.com. So we paid people about 50 cents to create a password following our rules and answering a survey, and then we paid them again to come back two days later and log in using their password and answering another survey. So we did this, and we collected 5,000 passwords, and we gave people a bunch of different policies to create passwords with. So some people had a pretty easy policy, we call it Basic8, and here the only rule was that your password had to have at least eight characters. Then some people had a much harder policy, and this was very similar to the CMU policy, that it had to have eight characters including uppercase, lowercase, digit, symbol, and pass a dictionary check. And one of the other policies we tried, and there were a whole bunch more, but one of the ones we tried was called Basic16, and the only requirement here was that your password had to have at least 16 character.
All right, so now we had 5,000 passwords, and so we had much more detailed information. Again we see that there's only a small number of symbols that people are actually using in their passwords. We also wanted to get an idea of how strong the passwords were that people were creating, but as you may recall, there isn't a good measure of password strength. So what we decided to do was to see how long it would take to crack these passwords using the best cracking tools that the bad guys are using, or that we could find information about in the research literature.
So to give you an idea of how bad guys go about cracking passwords, they will steal a password file that will have all of the passwords in kind of a scrambled form, called a hash, and so what they'll do is they'll make a guess as to what a password is, run it through a hashing function, and see whether it matches the passwords they have on their stolen password list. So a dumb attacker will try every password in order. They'll start with AAAAA and move on to AAAAB, and this is going to take a really long time before they get any passwords that people are really likely to actually have. A smart attacker, on the other hand, does something much more clever. They look at the passwords that are known to be popular from these stolen password sets, and they guess those first. So they're going to start by guessing "password," and then they'll guess "I love you," and "monkey," and "12345678," because these are the passwords that are most likely for people to have. In fact, some of you probably have these passwords. So what we found by running all of these 5,000 passwords we collected through these tests to see how strong they were, we found that the long passwords were actually pretty strong, and the complex passwords were pretty strong too. However, when we looked at the survey data, we saw that people were really frustrated by the very complex passwords, and the long passwords were a lot more usable, and in some cases, they were actually even stronger than the complex passwords. So this suggests that, instead of telling people that they need to put all these symbols and numbers and crazy things into their passwords, we might be better off just telling people to have long passwords. Now here's the problem, though: Some people had long passwords that actually weren't very strong. You can make long passwords that are still the sort of thing that an attacker could easily guess. So we need to do more than just say long passwords. There has to be some additional requirements, and some of our ongoing research is looking at what additional requirements we should add to make for stronger passwords that also are going to be easy for people to remember and type.
Another approach to getting people to have stronger passwords is to use a password meter. Here are some examples. You may have seen these on the Internet when you were creating passwords. We decided to do a study to find out whether these password meters actually work. Do they actually help people have stronger passwords, and if so, which ones are better? So we tested password meters that were different sizes, shapes, colors, different words next to them, and we even tested one that was a dancing bunny. As you type a better password, the bunny dances faster and faster. So this was pretty fun.
What we found was that password meters do work. (Laughter) Most of the password meters were actually effective, and the dancing bunny was very effective too, but the password meters that were the most effective were the ones that made you work harder before they gave you that thumbs up and said you were doing a good job, and in fact we found that most of the password meters on the Internet today are too soft. They tell you you're doing a good job too early, and if they would just wait a little bit before giving you that positive feedback, you probably would have better passwords.
Now another approach to better passwords, perhaps, is to use pass phrases instead of passwords. So this was an xkcd cartoon from a couple of years ago, and the cartoonist suggests that we should all use pass phrases, and if you look at the second row of this cartoon, you can see the cartoonist is suggesting that the pass phrase "correct horse battery staple" would be a very strong pass phrase and something really easy to remember. He says, in fact, you've already remembered it. And so we decided to do a research study to find out whether this was true or not. In fact, everybody who I talk to, who I mention I'm doing password research, they point out this cartoon. "Oh, have you seen it? That xkcd. Correct horse battery staple." So we did the research study to see what would actually happen.
So in our study, we used Mechanical Turk again, and we had the computer pick the random words in the pass phrase. Now the reason we did this is that humans are not very good at picking random words. If we asked a human to do it, they would pick things that were not very random. So we tried a few different conditions. In one condition, the computer picked from a dictionary of the very common words in the English language, and so you'd get pass phrases like "try there three come." And we looked at that, and we said, "Well, that doesn't really seem very memorable." So then we tried picking words that came from specific parts of speech, so how about noun-verb-adjective-noun. That comes up with something that's sort of sentence-like. So you can get a pass phrase like "plan builds sure power" or "end determines red drug." And these seemed a little bit more memorable, and maybe people would like those a little bit better. We wanted to compare them with passwords, and so we had the computer pick random passwords, and these were nice and short, but as you can see, they don't really look very memorable. And then we decided to try something called a pronounceable password. So here the computer picks random syllables and puts them together so you have something sort of pronounceable, like "tufritvi" and "vadasabi." That one kind of rolls off your tongue. So these were random passwords that were generated by our computer.
So what we found in this study was that, surprisingly, pass phrases were not actually all that good. People were not really better at remembering the pass phrases than these random passwords, and because the pass phrases are longer, they took longer to type and people made more errors while typing them in. So it's not really a clear win for pass phrases. Sorry, all of you xkcd fans. On the other hand, we did find that pronounceable passwords worked surprisingly well, and so we actually are doing some more research to see if we can make that approach work even better. So one of the problems with some of the studies that we've done is that because they're all done using Mechanical Turk, these are not people's real passwords. They're the passwords that they created or the computer created for them for our study. And we wanted to know whether people would actually behave the same way with their real passwords.
So we talked to the information security office at Carnegie Mellon and asked them if we could have everybody's real passwords. Not surprisingly, they were a little bit reluctant to share them with us, but we were actually able to work out a system with them where they put all of the real passwords for 25,000 CMU students, faculty and staff, into a locked computer in a locked room, not connected to the Internet, and they ran code on it that we wrote to analyze these passwords. They audited our code. They ran the code. And so we never actually saw anybody's password.
We got some interesting results, and those of you Tepper students in the back will be very interested in this. So we found that the passwords created by people affiliated with the school of computer science were actually 1.8 times stronger than those affiliated with the business school. We have lots of other really interesting demographic information as well. The other interesting thing that we found is that when we compared the Carnegie Mellon passwords to the Mechanical Turk-generated passwords, there was actually a lot of similarities, and so this helped validate our research method and show that actually, collecting passwords using these Mechanical Turk studies is actually a valid way to study passwords. So that was good news.
Okay, I want to close by talking about some insights I gained while on sabbatical last year in the Carnegie Mellon art school. One of the things that I did is I made a number of quilts, and I made this quilt here. It's called "Security Blanket." (Laughter) And this quilt has the 1,000 most frequent passwords stolen from the RockYou website. And the size of the passwords is proportional to how frequently they appeared in the stolen dataset. And what I did is I created this word cloud, and I went through all 1,000 words, and I categorized them into loose thematic categories. And it was, in some cases, it was kind of difficult to figure out what category they should be in, and then I color-coded them.
So here are some examples of the difficulty. So "justin." Is that the name of the user, their boyfriend, their son? Maybe they're a Justin Bieber fan. Or "princess." Is that a nickname? Are they Disney princess fans? Or maybe that's the name of their cat. "Iloveyou" appears many times in many different languages. There's a lot of love in these passwords. If you look carefully, you'll see there's also some profanity, but it was really interesting to me to see that there's a lot more love than hate in these passwords. And there are animals, a lot of animals, and "monkey" is the most common animal and the 14th most popular password overall. And this was really curious to me, and I wondered, "Why are monkeys so popular?" And so in our last password study, any time we detected somebody creating a password with the word "monkey" in it, we asked them why they had a monkey in their password. And what we found out -- we found 17 people so far, I think, who have the word "monkey" -- We found out about a third of them said they have a pet named "monkey" or a friend whose nickname is "monkey," and about a third of them said that they just like monkeys and monkeys are really cute. And that guy is really cute.
So it seems that at the end of the day, when we make passwords, we either make something that's really easy to type, a common pattern, or things that remind us of the word password or the account that we've created the password for, or whatever. Or we think about things that make us happy, and we create our password based on things that make us happy. And while this makes typing and remembering your password more fun, it also makes it a lot easier to guess your password. So I know a lot of these TED Talks are inspirational and they make you think about nice, happy things, but when you're creating your password, try to think about something else.
Thank you.
(Applause)
GralInt-TED Talks-Shaka Senghor: Why your worst deeds don’t define you
The following information is used for educational purposes only.
Shaka Senghor:
Why your worst deeds don’t define you
TED2014· Filmed Mar 2014
In 1991, Shaka Senghor shot and killed a man. He was, he says, "a drug dealer with a quick temper and a semi-automatic pistol." Jailed for second degree murder, that could very well have been the end of the story. But it wasn't. Instead, it was the beginning of a years-long journey to redemption, one with humbling and sobering lessons for us all.
Transcript:
Twenty-three years ago, at the age of 19, I shot and killed a man. I was a young drug dealer with a quick temper and a semi-automatic pistol.
But that wasn't the end of my story. In fact, it was beginning, and the 23 years since is a story of acknowledgment, apology and atonement. But it didn't happen in the way that you might imagine or think. These things occurred in my life in a way that was surprising, especially to me.
See, like many of you, growing up, I was an honor roll student, a scholarship student, with dreams of becoming a doctor. But things went dramatically wrong when my parents separated and eventually divorced.
The actual events are pretty straightforward. At the age of 17, I got shot three times standing on the corner of my block in Detroit. My friend rushed me to the hospital. Doctors pulled the bullets out, patched me up, and sent me back to the same neighborhood where I got shot. Throughout this ordeal, no one hugged me, no one counseled me, no one told me I would be okay. No one told me that I would live in fear, that I would become paranoid, or that I would react hyper-violently to being shot. No one told me that one day, I would become the person behind the trigger. Fourteen months later, at 2 a.m., I fired the shots that caused a man's death.
When I entered prison, I was bitter, I was angry, I was hurt. I didn't want to take responsibility. I blamed everybody from my parents to the system. I rationalized my decision to shoot because in the hood where I come from, it's better to be the shooter than the person getting shot. As I sat in my cold cell, I felt helpless, unloved and abandoned. I felt like nobody cared, and I reacted with hostility to my confinement. And I found myself getting deeper and deeper into trouble. I ran black market stores, I loan sharked, and I sold drugs that were illegally smuggled into the prison. I had in fact become what the warden of the Michigan Reformatory called "the worst of the worst." And because of my activity, I landed in solitary confinement for seven and a half years out of my incarceration.
Now as I see it, solitary confinement is one of the most inhumane and barbaric places you can find yourself, but find myself I did. One day, I was pacing my cell, when an officer came and delivered mail. I looked at a couple of letters before I looked at the letter that had my son's squiggly handwriting on it. And anytime I would get a letter from my son, it was like a ray of light in the darkest place you can imagine. And on this particular day, I opened this letter, and in capital letters, he wrote, "My mama told me why you was in prison: murder." He said, "Dad, don't kill. Jesus watches what you do. Pray to Him."
Now, I wasn't religious at that time, nor am I religious now, but it was something so profound about my son's words. They made me examine things about my life that I hadn't considered. It was the first time in my life that I had actually thought about the fact that my son would see me as a murderer. I sat back on my bunk and I reflected on something I had read in [Plato], where Socrates stated in "Apology" that the unexamined life isn't worth living.
At that point is when the transformation began. But it didn't come easy. One of the things I realized, which was part of the transformation, was that there were four key things. The first thing was, I had great mentors. Now, I know some of you all are probably thinking, how did you find a great mentor in prison? But in my case, some of my mentors who are serving life sentences were some of the best people to ever come into my life, because they forced me to look at my life honestly, and they forced me to challenge myself about my decision making.
The second thing was literature. Prior to going to prison, I didn't know that there were so many brilliant black poets, authors and philosophers, and then I had the great fortune of encountering Malcolm X's autobiography, and it shattered every stereotype I had about myself.
The third thing was family. For 19 years, my father stood by my side with an unshakable faith, because he believed that I had what it took to turn my life around. I also met an amazing woman who is now the mother of my two-year-old son Sekou, and she taught me how to love myself in a healthy way.
The final thing was writing. When I got that letter from my son, I began to write a journal about things I had experienced in my childhood and in prison, and what it did is it opened up my mind to the idea of atonement. Earlier in my incarceration, I had received a letter from one of the relatives of my victim, and in that letter, she told me she forgave me, because she realized I was a young child who had been abused and had been through some hardships and just made a series of poor decisions. It was the first time in my life that I ever felt open to forgiving myself.
One of the things that happened after that experience is that I thought about the other men who were incarcerated alongside of me, and how much I wanted to share this with them. And so I started talking to them about some of their experiences, and I was devastated to realize that most of them came from the same abusive environments, And most of them wanted help and they wanted to turn it around, but unfortunately the system that currently holds 2.5 million people in prison is designed to warehouse as opposed to rehabilitate or transform. So I made it up in my mind that if I was ever released from prison that I would do everything in my power to help change that.
In 2010, I walked out of prison for the first time after two decades. Now imagine, if you will, Fred Flintstone walking into an episode of "The Jetsons." That was pretty much what my life was like. For the first time, I was exposed to the Internet, social media, cars that talk like KITT from "Knight Rider." But the thing that fascinated me the most was phone technology. See, when I went to prison, our car phones were this big and required two people to carry them. So imagine what it was like when I first grabbed my little Blackberry and I started learning how to text. But the thing is, the people around me, they didn't realize that I had no idea what all these abbreviated texts meant, like LOL, OMG, LMAO, until one day I was having a conversation with one of my friends via text, and I asked him to do something, and he responded back, "K." And I was like, "What is K?" And he was like, "K is okay." So in my head, I was like, "Well what the hell is wrong with K?" And so I text him a question mark. And he said, "K = okay." And so I tap back, "FU." (Laughter) And then he texts back, and he asks me why was I cussing him out. And I said, "LOL FU," as in, I finally understand. (Laughter)
And so fast forward three years, I'm doing relatively good. I have a fellowship at MIT Media Lab, I work for an amazing company called BMe, I teach at the University of Michigan, but it's been a struggle because I realize that there are more men and women coming home who are not going to be afforded those opportunities. I've been blessed to work with some amazing men and women, helping others reenter society, and one of them is my friend named Calvin Evans. He served 24 years for a crime he didn't commit. He's 45 years old. He's currently enrolled in college. And one of the things that we talked about is the three things that I found important in my personal transformation, the first being acknowledgment. I had to acknowledge that I had hurt others. I also had to acknowledge that I had been hurt. The second thing was apologizing. I had to apologize to the people I had hurt. Even though I had no expectations of them accepting it, it was important to do because it was the right thing. But I also had to apologize to myself. The third thing was atoning. For me, atoning meant going back into my community and working with at-risk youth who were on the same path, but also becoming at one with myself.
Through my experience of being locked up, one of the things I discovered is this: the majority of men and women who are incarcerated are redeemable, and the fact is, 90 percent of the men and women who are incarcerated will at some point return to the community, and we have a role in determining what kind of men and women return to our community.
My wish today is that we will embrace a more empathetic approach toward how we deal with mass incarceration, that we will do away with the lock-them-up-and-throw-away-the-key mentality, because it's proven it doesn't work.
My journey is a unique journey, but it doesn't have to be that way. Anybody can have a transformation if we create the space for that to happen. So what I'm asking today is that you envision a world where men and women aren't held hostage to their pasts, where misdeeds and mistakes don't define you for the rest of your life. I think collectively, we can create that reality, and I hope you do too.
Thank you.
(Applause)
Shaka Senghor:
Why your worst deeds don’t define you
TED2014· Filmed Mar 2014
In 1991, Shaka Senghor shot and killed a man. He was, he says, "a drug dealer with a quick temper and a semi-automatic pistol." Jailed for second degree murder, that could very well have been the end of the story. But it wasn't. Instead, it was the beginning of a years-long journey to redemption, one with humbling and sobering lessons for us all.
Transcript:
Twenty-three years ago, at the age of 19, I shot and killed a man. I was a young drug dealer with a quick temper and a semi-automatic pistol.
But that wasn't the end of my story. In fact, it was beginning, and the 23 years since is a story of acknowledgment, apology and atonement. But it didn't happen in the way that you might imagine or think. These things occurred in my life in a way that was surprising, especially to me.
See, like many of you, growing up, I was an honor roll student, a scholarship student, with dreams of becoming a doctor. But things went dramatically wrong when my parents separated and eventually divorced.
The actual events are pretty straightforward. At the age of 17, I got shot three times standing on the corner of my block in Detroit. My friend rushed me to the hospital. Doctors pulled the bullets out, patched me up, and sent me back to the same neighborhood where I got shot. Throughout this ordeal, no one hugged me, no one counseled me, no one told me I would be okay. No one told me that I would live in fear, that I would become paranoid, or that I would react hyper-violently to being shot. No one told me that one day, I would become the person behind the trigger. Fourteen months later, at 2 a.m., I fired the shots that caused a man's death.
When I entered prison, I was bitter, I was angry, I was hurt. I didn't want to take responsibility. I blamed everybody from my parents to the system. I rationalized my decision to shoot because in the hood where I come from, it's better to be the shooter than the person getting shot. As I sat in my cold cell, I felt helpless, unloved and abandoned. I felt like nobody cared, and I reacted with hostility to my confinement. And I found myself getting deeper and deeper into trouble. I ran black market stores, I loan sharked, and I sold drugs that were illegally smuggled into the prison. I had in fact become what the warden of the Michigan Reformatory called "the worst of the worst." And because of my activity, I landed in solitary confinement for seven and a half years out of my incarceration.
Now as I see it, solitary confinement is one of the most inhumane and barbaric places you can find yourself, but find myself I did. One day, I was pacing my cell, when an officer came and delivered mail. I looked at a couple of letters before I looked at the letter that had my son's squiggly handwriting on it. And anytime I would get a letter from my son, it was like a ray of light in the darkest place you can imagine. And on this particular day, I opened this letter, and in capital letters, he wrote, "My mama told me why you was in prison: murder." He said, "Dad, don't kill. Jesus watches what you do. Pray to Him."
Now, I wasn't religious at that time, nor am I religious now, but it was something so profound about my son's words. They made me examine things about my life that I hadn't considered. It was the first time in my life that I had actually thought about the fact that my son would see me as a murderer. I sat back on my bunk and I reflected on something I had read in [Plato], where Socrates stated in "Apology" that the unexamined life isn't worth living.
At that point is when the transformation began. But it didn't come easy. One of the things I realized, which was part of the transformation, was that there were four key things. The first thing was, I had great mentors. Now, I know some of you all are probably thinking, how did you find a great mentor in prison? But in my case, some of my mentors who are serving life sentences were some of the best people to ever come into my life, because they forced me to look at my life honestly, and they forced me to challenge myself about my decision making.
The second thing was literature. Prior to going to prison, I didn't know that there were so many brilliant black poets, authors and philosophers, and then I had the great fortune of encountering Malcolm X's autobiography, and it shattered every stereotype I had about myself.
The third thing was family. For 19 years, my father stood by my side with an unshakable faith, because he believed that I had what it took to turn my life around. I also met an amazing woman who is now the mother of my two-year-old son Sekou, and she taught me how to love myself in a healthy way.
The final thing was writing. When I got that letter from my son, I began to write a journal about things I had experienced in my childhood and in prison, and what it did is it opened up my mind to the idea of atonement. Earlier in my incarceration, I had received a letter from one of the relatives of my victim, and in that letter, she told me she forgave me, because she realized I was a young child who had been abused and had been through some hardships and just made a series of poor decisions. It was the first time in my life that I ever felt open to forgiving myself.
One of the things that happened after that experience is that I thought about the other men who were incarcerated alongside of me, and how much I wanted to share this with them. And so I started talking to them about some of their experiences, and I was devastated to realize that most of them came from the same abusive environments, And most of them wanted help and they wanted to turn it around, but unfortunately the system that currently holds 2.5 million people in prison is designed to warehouse as opposed to rehabilitate or transform. So I made it up in my mind that if I was ever released from prison that I would do everything in my power to help change that.
In 2010, I walked out of prison for the first time after two decades. Now imagine, if you will, Fred Flintstone walking into an episode of "The Jetsons." That was pretty much what my life was like. For the first time, I was exposed to the Internet, social media, cars that talk like KITT from "Knight Rider." But the thing that fascinated me the most was phone technology. See, when I went to prison, our car phones were this big and required two people to carry them. So imagine what it was like when I first grabbed my little Blackberry and I started learning how to text. But the thing is, the people around me, they didn't realize that I had no idea what all these abbreviated texts meant, like LOL, OMG, LMAO, until one day I was having a conversation with one of my friends via text, and I asked him to do something, and he responded back, "K." And I was like, "What is K?" And he was like, "K is okay." So in my head, I was like, "Well what the hell is wrong with K?" And so I text him a question mark. And he said, "K = okay." And so I tap back, "FU." (Laughter) And then he texts back, and he asks me why was I cussing him out. And I said, "LOL FU," as in, I finally understand. (Laughter)
And so fast forward three years, I'm doing relatively good. I have a fellowship at MIT Media Lab, I work for an amazing company called BMe, I teach at the University of Michigan, but it's been a struggle because I realize that there are more men and women coming home who are not going to be afforded those opportunities. I've been blessed to work with some amazing men and women, helping others reenter society, and one of them is my friend named Calvin Evans. He served 24 years for a crime he didn't commit. He's 45 years old. He's currently enrolled in college. And one of the things that we talked about is the three things that I found important in my personal transformation, the first being acknowledgment. I had to acknowledge that I had hurt others. I also had to acknowledge that I had been hurt. The second thing was apologizing. I had to apologize to the people I had hurt. Even though I had no expectations of them accepting it, it was important to do because it was the right thing. But I also had to apologize to myself. The third thing was atoning. For me, atoning meant going back into my community and working with at-risk youth who were on the same path, but also becoming at one with myself.
Through my experience of being locked up, one of the things I discovered is this: the majority of men and women who are incarcerated are redeemable, and the fact is, 90 percent of the men and women who are incarcerated will at some point return to the community, and we have a role in determining what kind of men and women return to our community.
My wish today is that we will embrace a more empathetic approach toward how we deal with mass incarceration, that we will do away with the lock-them-up-and-throw-away-the-key mentality, because it's proven it doesn't work.
My journey is a unique journey, but it doesn't have to be that way. Anybody can have a transformation if we create the space for that to happen. So what I'm asking today is that you envision a world where men and women aren't held hostage to their pasts, where misdeeds and mistakes don't define you for the rest of your life. I think collectively, we can create that reality, and I hope you do too.
Thank you.
(Applause)
SCIEN/GralInt-TED Talks-Naomi Oreskes: Why we should trust scientists
The following information is used for educational purposes only.
Naomi Oreskes:
Why we should trust scientists
TEDSalon NY2014 · Filmed May 2014
Many of the world's biggest problems require asking questions of scientists — but why should we believe what they say? Historian of science Naomi Oreskes thinks deeply about our relationship to belief and draws out three problems with common attitudes toward scientific inquiry — and gives her own reasoning for why we ought to trust science.
Transcript:
Every day we face issues like climate change or the safety of vaccines where we have to answer questions whose answers rely heavily on scientific information. Scientists tell us that the world is warming. Scientists tell us that vaccines are safe. But how do we know if they are right? Why should be believe the science? The fact is, many of us actually don't believe the science. Public opinion polls consistently show that significant proportions of the American people don't believe the climate is warming due to human activities, don't think that there is evolution by natural selection, and aren't persuaded by the safety of vaccines.
So why should we believe the science? Well, scientists don't like talking about science as a matter of belief. In fact, they would contrast science with faith, and they would say belief is the domain of faith. And faith is a separate thing apart and distinct from science. Indeed they would say religion is based on faith or maybe the calculus of Pascal's wager. Blaise Pascal was a 17th-century mathematician who tried to bring scientific reasoning to the question of whether or not he should believe in God, and his wager went like this: Well, if God doesn't exist but I decide to believe in him nothing much is really lost. Maybe a few hours on Sunday. (Laughter) But if he does exist and I don't believe in him, then I'm in deep trouble. And so Pascal said, we'd better believe in God. Or as one of my college professors said, "He clutched for the handrail of faith." He made that leap of faith leaving science and rationalism behind.
Now the fact is though, for most of us, most scientific claims are a leap of faith. We can't really judge scientific claims for ourselves in most cases. And indeed this is actually true for most scientists as well outside of their own specialties. So if you think about it, a geologist can't tell you whether a vaccine is safe. Most chemists are not experts in evolutionary theory. A physicist cannot tell you, despite the claims of some of them, whether or not tobacco causes cancer. So, if even scientists themselves have to make a leap of faith outside their own fields, then why do they accept the claims of other scientists? Why do they believe each other's claims? And should we believe those claims?
So what I'd like to argue is yes, we should, but not for the reason that most of us think. Most of us were taught in school that the reason we should believe in science is because of the scientific method. We were taught that scientists follow a method and that this method guarantees the truth of their claims. The method that most of us were taught in school, we can call it the textbook method, is the hypothetical deductive method. According to the standard model, the textbook model, scientists develop hypotheses, they deduce the consequences of those hypotheses, and then they go out into the world and they say, "Okay, well are those consequences true?" Can we observe them taking place in the natural world? And if they are true, then the scientists say, "Great, we know the hypothesis is correct."
So there are many famous examples in the history of science of scientists doing exactly this. One of the most famous examples comes from the work of Albert Einstein. When Einstein developed the theory of general relativity, one of the consequences of his theory was that space-time wasn't just an empty void but that it actually had a fabric. And that that fabric was bent in the presence of massive objects like the sun. So if this theory were true then it meant that light as it passed the sun should actually be bent around it. That was a pretty startling prediction and it took a few years before scientists were able to test it but they did test it in 1919, and lo and behold it turned out to be true. Starlight actually does bend as it travels around the sun. This was a huge confirmation of the theory. It was considered proof of the truth of this radical new idea, and it was written up in many newspapers around the globe.
Now, sometimes this theory or this model is referred to as the deductive-nomological model, mainly because academics like to make things complicated. But also because in the ideal case, it's about laws. So nomological means having to do with laws. And in the ideal case, the hypothesis isn't just an idea: ideally, it is a law of nature. Why does it matter that it is a law of nature? Because if it is a law, it can't be broken. If it's a law then it will always be true in all times and all places no matter what the circumstances are. And all of you know of at least one example of a famous law: Einstein's famous equation, E=MC2, which tells us what the relationship is between energy and mass. And that relationship is true no matter what.
Now, it turns out, though, that there are several problems with this model. The main problem is that it's wrong. It's just not true. (Laughter) And I'm going to talk about three reasons why it's wrong. So the first reason is a logical reason. It's the problem of the fallacy of affirming the consequent. So that's another fancy, academic way of saying that false theories can make true predictions. So just because the prediction comes true doesn't actually logically prove that the theory is correct. And I have a good example of that too, again from the history of science. This is a picture of the Ptolemaic universe with the Earth at the center of the universe and the sun and the planets going around it. The Ptolemaic model was believed by many very smart people for many centuries. Well, why? Well the answer is because it made lots of predictions that came true. The Ptolemaic system enabled astronomers to make accurate predictions of the motions of the planet, in fact more accurate predictions at first than the Copernican theory which we now would say is true. So that's one problem with the textbook model. A second problem is a practical problem, and it's the problem of auxiliary hypotheses. Auxiliary hypotheses are assumptions that scientists are making that they may or may not even be aware that they're making. So an important example of this comes from the Copernican model, which ultimately replaced the Ptolemaic system. So when Nicolaus Copernicus said, actually the Earth is not the center of the universe, the sun is the center of the solar system, the Earth moves around the sun. Scientists said, well okay, Nicolaus, if that's true we ought to be able to detect the motion of the Earth around the sun. And so this slide here illustrates a concept known as stellar parallax. And astronomers said, if the Earth is moving and we look at a prominent star, let's say, Sirius -- well I know I'm in Manhattan so you guys can't see the stars, but imagine you're out in the country, imagine you chose that rural life — and we look at a star in December, we see that star against the backdrop of distant stars. If we now make the same observation six months later when the Earth has moved to this position in June, we look at that same star and we see it against a different backdrop. That difference, that angular difference, is the stellar parallax. So this is a prediction that the Copernican model makes. Astronomers looked for the stellar parallax and they found nothing, nothing at all. And many people argued that this proved that the Copernican model was false.
So what happened? Well, in hindsight we can say that astronomers were making two auxiliary hypotheses, both of which we would now say were incorrect. The first was an assumption about the size of the Earth's orbit. Astronomers were assuming that the Earth's orbit was large relative to the distance to the stars. Today we would draw the picture more like this, this comes from NASA, and you see the Earth's orbit is actually quite small. In fact, it's actually much smaller even than shown here. The stellar parallax therefore, is very small and actually very hard to detect.
And that leads to the second reason why the prediction didn't work, because scientists were also assuming that the telescopes they had were sensitive enough to detect the parallax. And that turned out not to be true. It wasn't until the 19th century that scientists were able to detect the stellar parallax.
So, there's a third problem as well. The third problem is simply a factual problem, that a lot of science doesn't fit the textbook model. A lot of science isn't deductive at all, it's actually inductive. And by that we mean that scientists don't necessarily start with theories and hypotheses, often they just start with observations of stuff going on in the world. And the most famous example of that is one of the most famous scientists who ever lived, Charles Darwin. When Darwin went out as a young man on the voyage of the Beagle, he didn't have a hypothesis, he didn't have a theory. He just knew that he wanted to have a career as a scientist and he started to collect data. Mainly he knew that he hated medicine because the sight of blood made him sick so he had to have an alternative career path. So he started collecting data. And he collected many things, including his famous finches. When he collected these finches, he threw them in a bag and he had no idea what they meant. Many years later back in London, Darwin looked at his data again and began to develop an explanation, and that explanation was the theory of natural selection.
Besides inductive science, scientists also often participate in modeling. One of the things scientists want to do in life is to explain the causes of things. And how do we do that? Well, one way you can do it is to build a model that tests an idea.
So this is a picture of Henry Cadell, who was a Scottish geologist in the 19th century. You can tell he's Scottish because he's wearing a deerstalker cap and Wellington boots. (Laughter) And Cadell wanted to answer the question, how are mountains formed? And one of the things he had observed is that if you look at mountains like the Appalachians, you often find that the rocks in them are folded, and they're folded in a particular way, which suggested to him that they were actually being compressed from the side. And this idea would later play a major role in discussions of continental drift. So he built this model, this crazy contraption with levers and wood, and here's his wheelbarrow, buckets, a big sledgehammer. I don't know why he's got the Wellington boots. Maybe it's going to rain. And he created this physical model in order to demonstrate that you could, in fact, create patterns in rocks, or at least, in this case, in mud, that looked a lot like mountains if you compressed them from the side. So it was an argument about the cause of mountains.
Nowadays, most scientists prefer to work inside, so they don't build physical models so much as to make computer simulations. But a computer simulation is a kind of a model. It's a model that's made with mathematics, and like the physical models of the 19th century, it's very important for thinking about causes. So one of the big questions to do with climate change, we have tremendous amounts of evidence that the Earth is warming up. This slide here, the black line shows the measurements that scientists have taken for the last 150 years showing that the Earth's temperature has steadily increased, and you can see in particular that in the last 50 years there's been this dramatic increase of nearly one degree centigrade, or almost two degrees Fahrenheit.
So what, though, is driving that change? How can we know what's causing the observed warming? Well, scientists can model it using a computer simulation. So this diagram illustrates a computer simulation that has looked at all the different factors that we know can influence the Earth's climate, so sulfate particles from air pollution, volcanic dust from volcanic eruptions, changes in solar radiation, and, of course, greenhouse gases. And they asked the question, what set of variables put into a model will reproduce what we actually see in real life? So here is the real life in black. Here's the model in this light gray, and the answer is a model that includes, it's the answer E on that SAT, all of the above. The only way you can reproduce the observed temperature measurements is with all of these things put together, including greenhouse gases, and in particular you can see that the increase in greenhouse gases tracks this very dramatic increase in temperature over the last 50 years. And so this is why climate scientists say it's not just that we know that climate change is happening, we know that greenhouse gases are a major part of the reason why.
So now because there all these different things that scientists do, the philosopher Paul Feyerabend famously said, "The only principle in science that doesn't inhibit progress is: anything goes." Now this quotation has often been taken out of context, because Feyerabend was not actually saying that in science anything goes. What he was saying was, actually the full quotation is, "If you press me to say what is the method of science, I would have to say: anything goes." What he was trying to say is that scientists do a lot of different things. Scientists are creative.
But then this pushes the question back: If scientists don't use a single method, then how do they decide what's right and what's wrong? And who judges? And the answer is, scientists judge, and they judge by judging evidence. Scientists collect evidence in many different ways, but however they collect it, they have to subject it to scrutiny. And this led the sociologist Robert Merton to focus on this question of how scientists scrutinize data and evidence, and he said they do it in a way he called "organized skepticism." And by that he meant it's organized because they do it collectively, they do it as a group, and skepticism, because they do it from a position of distrust. That is to say, the burden of proof is on the person with a novel claim. And in this sense, science is intrinsically conservative. It's quite hard to persuade the scientific community to say, "Yes, we know something, this is true." So despite the popularity of the concept of paradigm shifts, what we find is that actually, really major changes in scientific thinking are relatively rare in the history of science.
So finally that brings us to one more idea: If scientists judge evidence collectively, this has led historians to focus on the question of consensus, and to say that at the end of the day, what science is, what scientific knowledge is, is the consensus of the scientific experts who through this process of organized scrutiny, collective scrutiny, have judged the evidence and come to a conclusion about it, either yea or nay.
So we can think of scientific knowledge as a consensus of experts. We can also think of science as being a kind of a jury, except it's a very special kind of jury. It's not a jury of your peers, it's a jury of geeks. It's a jury of men and women with Ph.D.s, and unlike a conventional jury, which has only two choices, guilty or not guilty, the scientific jury actually has a number of choices. Scientists can say yes, something's true. Scientists can say no, it's false. Or, they can say, well it might be true but we need to work more and collect more evidence. Or, they can say it might be true, but we don't know how to answer the question and we're going to put it aside and maybe we'll come back to it later. That's what scientists call "intractable."
But this leads us to one final problem: If science is what scientists say it is, then isn't that just an appeal to authority? And weren't we all taught in school that the appeal to authority is a logical fallacy? Well, here's the paradox of modern science, the paradox of the conclusion I think historians and philosophers and sociologists have come to, that actually science is the appeal to authority, but it's not the authority of the individual, no matter how smart that individual is, like Plato or Socrates or Einstein. It's the authority of the collective community. You can think of it is a kind of wisdom of the crowd, but a very special kind of crowd. Science does appeal to authority, but it's not based on any individual, no matter how smart that individual may be. It's based on the collective wisdom, the collective knowledge, the collective work, of all of the scientists who have worked on a particular problem. Scientists have a kind of culture of collective distrust, this "show me" culture, illustrated by this nice woman here showing her colleagues her evidence. Of course, these people don't really look like scientists, because they're much too happy. (Laughter)
Okay, so that brings me to my final point. Most of us get up in the morning. Most of us trust our cars. Well, see, now I'm thinking, I'm in Manhattan, this is a bad analogy, but most Americans who don't live in Manhattan get up in the morning and get in their cars and turn on that ignition, and their cars work, and they work incredibly well. The modern automobile hardly ever breaks down.
So why is that? Why do cars work so well? It's not because of the genius of Henry Ford or Karl Benz or even Elon Musk. It's because the modern automobile is the product of more than 100 years of work by hundreds and thousands and tens of thousands of people. The modern automobile is the product of the collected work and wisdom and experience of every man and woman who has ever worked on a car, and the reliability of the technology is the result of that accumulated effort. We benefit not just from the genius of Benz and Ford and Musk but from the collective intelligence and hard work of all of the people who have worked on the modern car. And the same is true of science, only science is even older. Our basis for trust in science is actually the same as our basis in trust in technology, and the same as our basis for trust in anything, namely, experience.
But it shouldn't be blind trust any more than we would have blind trust in anything. Our trust in science, like science itself, should be based on evidence, and that means that scientists have to become better communicators. They have to explain to us not just what they know but how they know it, and it means that we have to become better listeners.
Thank you very much.
(Applause)
Naomi Oreskes:
Why we should trust scientists
TEDSalon NY2014 · Filmed May 2014
Many of the world's biggest problems require asking questions of scientists — but why should we believe what they say? Historian of science Naomi Oreskes thinks deeply about our relationship to belief and draws out three problems with common attitudes toward scientific inquiry — and gives her own reasoning for why we ought to trust science.
Transcript:
Every day we face issues like climate change or the safety of vaccines where we have to answer questions whose answers rely heavily on scientific information. Scientists tell us that the world is warming. Scientists tell us that vaccines are safe. But how do we know if they are right? Why should be believe the science? The fact is, many of us actually don't believe the science. Public opinion polls consistently show that significant proportions of the American people don't believe the climate is warming due to human activities, don't think that there is evolution by natural selection, and aren't persuaded by the safety of vaccines.
So why should we believe the science? Well, scientists don't like talking about science as a matter of belief. In fact, they would contrast science with faith, and they would say belief is the domain of faith. And faith is a separate thing apart and distinct from science. Indeed they would say religion is based on faith or maybe the calculus of Pascal's wager. Blaise Pascal was a 17th-century mathematician who tried to bring scientific reasoning to the question of whether or not he should believe in God, and his wager went like this: Well, if God doesn't exist but I decide to believe in him nothing much is really lost. Maybe a few hours on Sunday. (Laughter) But if he does exist and I don't believe in him, then I'm in deep trouble. And so Pascal said, we'd better believe in God. Or as one of my college professors said, "He clutched for the handrail of faith." He made that leap of faith leaving science and rationalism behind.
Now the fact is though, for most of us, most scientific claims are a leap of faith. We can't really judge scientific claims for ourselves in most cases. And indeed this is actually true for most scientists as well outside of their own specialties. So if you think about it, a geologist can't tell you whether a vaccine is safe. Most chemists are not experts in evolutionary theory. A physicist cannot tell you, despite the claims of some of them, whether or not tobacco causes cancer. So, if even scientists themselves have to make a leap of faith outside their own fields, then why do they accept the claims of other scientists? Why do they believe each other's claims? And should we believe those claims?
So what I'd like to argue is yes, we should, but not for the reason that most of us think. Most of us were taught in school that the reason we should believe in science is because of the scientific method. We were taught that scientists follow a method and that this method guarantees the truth of their claims. The method that most of us were taught in school, we can call it the textbook method, is the hypothetical deductive method. According to the standard model, the textbook model, scientists develop hypotheses, they deduce the consequences of those hypotheses, and then they go out into the world and they say, "Okay, well are those consequences true?" Can we observe them taking place in the natural world? And if they are true, then the scientists say, "Great, we know the hypothesis is correct."
So there are many famous examples in the history of science of scientists doing exactly this. One of the most famous examples comes from the work of Albert Einstein. When Einstein developed the theory of general relativity, one of the consequences of his theory was that space-time wasn't just an empty void but that it actually had a fabric. And that that fabric was bent in the presence of massive objects like the sun. So if this theory were true then it meant that light as it passed the sun should actually be bent around it. That was a pretty startling prediction and it took a few years before scientists were able to test it but they did test it in 1919, and lo and behold it turned out to be true. Starlight actually does bend as it travels around the sun. This was a huge confirmation of the theory. It was considered proof of the truth of this radical new idea, and it was written up in many newspapers around the globe.
Now, sometimes this theory or this model is referred to as the deductive-nomological model, mainly because academics like to make things complicated. But also because in the ideal case, it's about laws. So nomological means having to do with laws. And in the ideal case, the hypothesis isn't just an idea: ideally, it is a law of nature. Why does it matter that it is a law of nature? Because if it is a law, it can't be broken. If it's a law then it will always be true in all times and all places no matter what the circumstances are. And all of you know of at least one example of a famous law: Einstein's famous equation, E=MC2, which tells us what the relationship is between energy and mass. And that relationship is true no matter what.
Now, it turns out, though, that there are several problems with this model. The main problem is that it's wrong. It's just not true. (Laughter) And I'm going to talk about three reasons why it's wrong. So the first reason is a logical reason. It's the problem of the fallacy of affirming the consequent. So that's another fancy, academic way of saying that false theories can make true predictions. So just because the prediction comes true doesn't actually logically prove that the theory is correct. And I have a good example of that too, again from the history of science. This is a picture of the Ptolemaic universe with the Earth at the center of the universe and the sun and the planets going around it. The Ptolemaic model was believed by many very smart people for many centuries. Well, why? Well the answer is because it made lots of predictions that came true. The Ptolemaic system enabled astronomers to make accurate predictions of the motions of the planet, in fact more accurate predictions at first than the Copernican theory which we now would say is true. So that's one problem with the textbook model. A second problem is a practical problem, and it's the problem of auxiliary hypotheses. Auxiliary hypotheses are assumptions that scientists are making that they may or may not even be aware that they're making. So an important example of this comes from the Copernican model, which ultimately replaced the Ptolemaic system. So when Nicolaus Copernicus said, actually the Earth is not the center of the universe, the sun is the center of the solar system, the Earth moves around the sun. Scientists said, well okay, Nicolaus, if that's true we ought to be able to detect the motion of the Earth around the sun. And so this slide here illustrates a concept known as stellar parallax. And astronomers said, if the Earth is moving and we look at a prominent star, let's say, Sirius -- well I know I'm in Manhattan so you guys can't see the stars, but imagine you're out in the country, imagine you chose that rural life — and we look at a star in December, we see that star against the backdrop of distant stars. If we now make the same observation six months later when the Earth has moved to this position in June, we look at that same star and we see it against a different backdrop. That difference, that angular difference, is the stellar parallax. So this is a prediction that the Copernican model makes. Astronomers looked for the stellar parallax and they found nothing, nothing at all. And many people argued that this proved that the Copernican model was false.
So what happened? Well, in hindsight we can say that astronomers were making two auxiliary hypotheses, both of which we would now say were incorrect. The first was an assumption about the size of the Earth's orbit. Astronomers were assuming that the Earth's orbit was large relative to the distance to the stars. Today we would draw the picture more like this, this comes from NASA, and you see the Earth's orbit is actually quite small. In fact, it's actually much smaller even than shown here. The stellar parallax therefore, is very small and actually very hard to detect.
And that leads to the second reason why the prediction didn't work, because scientists were also assuming that the telescopes they had were sensitive enough to detect the parallax. And that turned out not to be true. It wasn't until the 19th century that scientists were able to detect the stellar parallax.
So, there's a third problem as well. The third problem is simply a factual problem, that a lot of science doesn't fit the textbook model. A lot of science isn't deductive at all, it's actually inductive. And by that we mean that scientists don't necessarily start with theories and hypotheses, often they just start with observations of stuff going on in the world. And the most famous example of that is one of the most famous scientists who ever lived, Charles Darwin. When Darwin went out as a young man on the voyage of the Beagle, he didn't have a hypothesis, he didn't have a theory. He just knew that he wanted to have a career as a scientist and he started to collect data. Mainly he knew that he hated medicine because the sight of blood made him sick so he had to have an alternative career path. So he started collecting data. And he collected many things, including his famous finches. When he collected these finches, he threw them in a bag and he had no idea what they meant. Many years later back in London, Darwin looked at his data again and began to develop an explanation, and that explanation was the theory of natural selection.
Besides inductive science, scientists also often participate in modeling. One of the things scientists want to do in life is to explain the causes of things. And how do we do that? Well, one way you can do it is to build a model that tests an idea.
So this is a picture of Henry Cadell, who was a Scottish geologist in the 19th century. You can tell he's Scottish because he's wearing a deerstalker cap and Wellington boots. (Laughter) And Cadell wanted to answer the question, how are mountains formed? And one of the things he had observed is that if you look at mountains like the Appalachians, you often find that the rocks in them are folded, and they're folded in a particular way, which suggested to him that they were actually being compressed from the side. And this idea would later play a major role in discussions of continental drift. So he built this model, this crazy contraption with levers and wood, and here's his wheelbarrow, buckets, a big sledgehammer. I don't know why he's got the Wellington boots. Maybe it's going to rain. And he created this physical model in order to demonstrate that you could, in fact, create patterns in rocks, or at least, in this case, in mud, that looked a lot like mountains if you compressed them from the side. So it was an argument about the cause of mountains.
Nowadays, most scientists prefer to work inside, so they don't build physical models so much as to make computer simulations. But a computer simulation is a kind of a model. It's a model that's made with mathematics, and like the physical models of the 19th century, it's very important for thinking about causes. So one of the big questions to do with climate change, we have tremendous amounts of evidence that the Earth is warming up. This slide here, the black line shows the measurements that scientists have taken for the last 150 years showing that the Earth's temperature has steadily increased, and you can see in particular that in the last 50 years there's been this dramatic increase of nearly one degree centigrade, or almost two degrees Fahrenheit.
So what, though, is driving that change? How can we know what's causing the observed warming? Well, scientists can model it using a computer simulation. So this diagram illustrates a computer simulation that has looked at all the different factors that we know can influence the Earth's climate, so sulfate particles from air pollution, volcanic dust from volcanic eruptions, changes in solar radiation, and, of course, greenhouse gases. And they asked the question, what set of variables put into a model will reproduce what we actually see in real life? So here is the real life in black. Here's the model in this light gray, and the answer is a model that includes, it's the answer E on that SAT, all of the above. The only way you can reproduce the observed temperature measurements is with all of these things put together, including greenhouse gases, and in particular you can see that the increase in greenhouse gases tracks this very dramatic increase in temperature over the last 50 years. And so this is why climate scientists say it's not just that we know that climate change is happening, we know that greenhouse gases are a major part of the reason why.
So now because there all these different things that scientists do, the philosopher Paul Feyerabend famously said, "The only principle in science that doesn't inhibit progress is: anything goes." Now this quotation has often been taken out of context, because Feyerabend was not actually saying that in science anything goes. What he was saying was, actually the full quotation is, "If you press me to say what is the method of science, I would have to say: anything goes." What he was trying to say is that scientists do a lot of different things. Scientists are creative.
But then this pushes the question back: If scientists don't use a single method, then how do they decide what's right and what's wrong? And who judges? And the answer is, scientists judge, and they judge by judging evidence. Scientists collect evidence in many different ways, but however they collect it, they have to subject it to scrutiny. And this led the sociologist Robert Merton to focus on this question of how scientists scrutinize data and evidence, and he said they do it in a way he called "organized skepticism." And by that he meant it's organized because they do it collectively, they do it as a group, and skepticism, because they do it from a position of distrust. That is to say, the burden of proof is on the person with a novel claim. And in this sense, science is intrinsically conservative. It's quite hard to persuade the scientific community to say, "Yes, we know something, this is true." So despite the popularity of the concept of paradigm shifts, what we find is that actually, really major changes in scientific thinking are relatively rare in the history of science.
So finally that brings us to one more idea: If scientists judge evidence collectively, this has led historians to focus on the question of consensus, and to say that at the end of the day, what science is, what scientific knowledge is, is the consensus of the scientific experts who through this process of organized scrutiny, collective scrutiny, have judged the evidence and come to a conclusion about it, either yea or nay.
So we can think of scientific knowledge as a consensus of experts. We can also think of science as being a kind of a jury, except it's a very special kind of jury. It's not a jury of your peers, it's a jury of geeks. It's a jury of men and women with Ph.D.s, and unlike a conventional jury, which has only two choices, guilty or not guilty, the scientific jury actually has a number of choices. Scientists can say yes, something's true. Scientists can say no, it's false. Or, they can say, well it might be true but we need to work more and collect more evidence. Or, they can say it might be true, but we don't know how to answer the question and we're going to put it aside and maybe we'll come back to it later. That's what scientists call "intractable."
But this leads us to one final problem: If science is what scientists say it is, then isn't that just an appeal to authority? And weren't we all taught in school that the appeal to authority is a logical fallacy? Well, here's the paradox of modern science, the paradox of the conclusion I think historians and philosophers and sociologists have come to, that actually science is the appeal to authority, but it's not the authority of the individual, no matter how smart that individual is, like Plato or Socrates or Einstein. It's the authority of the collective community. You can think of it is a kind of wisdom of the crowd, but a very special kind of crowd. Science does appeal to authority, but it's not based on any individual, no matter how smart that individual may be. It's based on the collective wisdom, the collective knowledge, the collective work, of all of the scientists who have worked on a particular problem. Scientists have a kind of culture of collective distrust, this "show me" culture, illustrated by this nice woman here showing her colleagues her evidence. Of course, these people don't really look like scientists, because they're much too happy. (Laughter)
Okay, so that brings me to my final point. Most of us get up in the morning. Most of us trust our cars. Well, see, now I'm thinking, I'm in Manhattan, this is a bad analogy, but most Americans who don't live in Manhattan get up in the morning and get in their cars and turn on that ignition, and their cars work, and they work incredibly well. The modern automobile hardly ever breaks down.
So why is that? Why do cars work so well? It's not because of the genius of Henry Ford or Karl Benz or even Elon Musk. It's because the modern automobile is the product of more than 100 years of work by hundreds and thousands and tens of thousands of people. The modern automobile is the product of the collected work and wisdom and experience of every man and woman who has ever worked on a car, and the reliability of the technology is the result of that accumulated effort. We benefit not just from the genius of Benz and Ford and Musk but from the collective intelligence and hard work of all of the people who have worked on the modern car. And the same is true of science, only science is even older. Our basis for trust in science is actually the same as our basis in trust in technology, and the same as our basis for trust in anything, namely, experience.
But it shouldn't be blind trust any more than we would have blind trust in anything. Our trust in science, like science itself, should be based on evidence, and that means that scientists have to become better communicators. They have to explain to us not just what they know but how they know it, and it means that we have to become better listeners.
Thank you very much.
(Applause)
MUS/TECH/GralInt-TED Talks-Ge Wang: The DIY orchestra of the future
The following information is used for educational purposes only.
Ge Wang:
The DIY orchestra of the future
TEDxStanfo · Filmed May 2014
Ge Wang makes computer music, but it isn’t all about coded bleeps and blips. With the Stanford Laptop Orchestra, he creates new instruments out of unexpected materials—like an Ikea bowl—that allow musicians to play music that’s both beautiful and expressive.
Transcript:
I want to talk to you about one thing and just one thing only, and this has to do with when people ask me, what do you do? To which I usually respond, I do computer music.
Now, a number of people just stop talking to me right then and there, and the rest who are left usually have this blank look in their eye, as if to say, what does that mean? And I feel like I'm actually depriving them of information by telling them this, at which point I usually panic and spit out the first thing that comes to my mind, which is, I have no idea what I'm doing. Which is true. That's usually followed by a second thought, which is, whatever it is that I'm doing, I love it. And today, I want to, well, share with you something I love, and also why.
And I think we'll begin with just this question: What is computer music? And I'm going to try to do my best to provide a definition, maybe by telling you a story that goes through some of the stuff I've been working on.
And the first thing, I think, in our story is going to be something called ChucK. Now, ChucK is a programming language for music, and it's open-source, it's freely available, and I like to think that it crashes equally well on all modern operating systems. And instead of telling you more about it, I'm just going to give you a demo. By the way, I'm just going to nerd out for just a few minutes here, so I would say, don't freak out. In fact, I would invite all of you to join me in just geeking out. If you've never written a line of code before in your life, do not worry. I'll bet you'll be able to come along on this.
First thing I'm going to do is to make a sine wave oscillator, and we're going to called the sine wave generator "Ge." And then we're going to connect "Ge" to the DAC. Now this is kind of the abstraction for the sound output on my computer. Okay? So I've connected myself into the speaker. Next, I'm going to say my frequency is 440 hertz, and I'm going to let time advance by two seconds through this operation. All right, so if I were to play this -- (Tone) — you would hear a sine wave at 440 hertz for two seconds. Okay, great. Now I'm going to copy and paste this, and then just change some of these numbers, 220.5, 440 I shall leave it as that, and .5 and 880. By doubling the frequency, we're actually going up in successive octaves, and then we have this sequence -- (Tones) — of tones. Okay, great, now I can imagine creating all kinds of really horrible single sine wave pieces of music with this, but I'm going to do something that computers are really good at, which is repetition. I'm going to put this all in a while loop, and you actually don't need to indent, but this is purely for aesthetic reasons. It's good practice. And when we do this — (Tones) — that's going to go on for a while. In fact, it's probably not going to stop until this computer disintegrates. And I can't really empirically prove that to you, but I hope you'll believe me when I say that. Next, I'm going to replace this 220 by math.random2f. I'm going to generate a random number between 30 and 1,000 and send that to the frequency of me. And I'm going to do this every half a second. (Tones) Let's do this every 200 milliseconds. (Tones) One hundred. (Tones) All right. At this point, we've reached something that I would like to think of as the canonical computer music. This is, to me, the sound that mainframes are supposed to be making when they're thinking really hard. It's this sound, it's like, the square root of five million.
So is this computer music? Yeah, I guess by definition, it's kind of computer music. It's probably not the kind of music you would listen to cruising down the highway, but it's a foundation of computer-generated music, and using ChucK, we've actually been building instruments in the Stanford Laptop Orchestra, based right here at Stanford Center for Computer Research in Music and Acoustics.
Now the Laptop Orchestra is an ensemble of laptops, humans and special hemispherical speaker arrays. Now the reason we have these is so that for the instruments that we create out of the laptop, we want the sound to come out of somewhere near the instrument and the performer, kind of much like a traditional, acoustic instrument. Like, if I were to play a violin here, the sound would naturally not come out of the P.A. system, but from the artifact itself. So these speakers are meant to emulate that. In fact, I'm going to show you how we actually built them. The first step is to go to IKEA and buy a salad bowl. This is an 11-inch Blanda Matt. That's the actual name, and I actually use one of these to make salad at home as well, I kid you not. And the first step is you turn it upside down, and then you drill holes in them, six holes per hemi, and then make a base plate, put car speaker drivers in them along with amplifiers in the enclosure, and you put that all together and you have these hemispherical speaker arrays. Add people, add laptops, you have a laptop orchestra.
And what might a laptop orchestra sound like? Well, let me give you a demonstration of about 200 instruments we've created so far for the Laptop Orchestra. And what I'm going to do is actually come over to this thing. This thing I have in front of me actually used to be a commodity gaming controller called a Gametrak. This thing actually has a glove you can put on your hands. It's tethered to the base, and this will track the position of your hands in real time. It was originally designed as a golfing controller to detect the motion of your swing. That turned out to be a rather large commercial non-success, at which point they slashed prices to 10 dollars, at which point computer music researchers said, "This is awesome! We can prototype instruments out of this."
So let me show you one instrument we've created, one of many, and this instrument is called "Twilight," and it's meant to go with this metaphor of pulling a sound out of the ground. So let me see if this will work.
(Music)
And put it back. And then if you go to the left, right, it sounds like an elephant in pain.
This is a slightly metallic sound. Turn it just a bit.
(Music) It's like a hovering car.
Okay.
This third one is a ratchet-like interaction, so let me turn it up.
(Music)
So it's a slightly different interaction.
The fourth one is a drone.
(Music) And finally, let's see, this is a totally different interaction, and I think you have to imagine that there's this giant invisible drum sitting right here on stage, and I'm going to bang it. (Drum) (Laughter) So there we go, so that's one of many instruments in the Laptop Orchestra.
(Applause)
Thank you.
And when you put that together, you get something that sounds like this.
(Music)
Okay, and so, I think from the experience of building a lot of instruments for the Laptop Orchestra, and I think from the curiosity of wondering, what if we took these hopefully expressive instruments and we brought it to a lot of people, plus then a healthy bout of insanity — put those three things together — led to me actually co-founding a startup company in 2008 called Smul
Now Smule's mission is to create expressive, mobile music things, and one of the first musical instruments we created is called Ocarina. And I'm going to just demo this for you real quick. So Ocarina — (Music) — is based on this ancient flute-like instrument called the ocarina, and this one is the four-hole English pendant configuration, and you're literally blowing into the microphone to make the sound. And there's actually a little ChucK script running in here that's detecting the strength of your blowing and also synthesizing the sound. (Music) And vibrato is mapped to the accelerometer, so you can get — (Music) All right. So let me play a little ditty for you, a little Bach. And here, you'll hear a little accompaniment with the melody. The accompaniment actually follows the melody, not the other way around.
(Music)
And this was designed to let you take your time and figure out where your expressive space is, and you can just hang out here for a while, for a really dramatic effect, if you want, and whenever you're ready —
(Music)
And on these longer notes, I'm going to use more vibrato towards the end of the notes to give it a little bit more of an expressive quality.
(Music)
Huh, that's a nice chord to end this excerpt on.
(Applause)
Thank you.
So I think a good question to ask about Ocarina is, is this a toy or it an instrument? Maybe it's both, but for me, I think the more important question is, is it expressive? And at the same time, I think creating these types of instruments asks a question about the role of technology, and its place for how we make music. Apparently, for example, not that long ago, like only a hundred years ago — that's not that long in the course of human history — families back then used to make music together as a common form of entertainment. I don't think that's really happening that much anymore. You know, this is before radio, before recording. In the last hundred years, with all this technology, we now have more access to music as listeners and consumers, but somehow, I think we're making less music than ever before. I'm not sure why that would be. Maybe it's because it's too easy just to hit play. And while listening to music is wonderful, there's a special joy to making music that's all its own. And I think that's one part of the goal of why I do what I do is kind of to take us back to the past a little bit. Right?
Now, if that's one goal, the other goal is to look to the future and think about what kind of new musical things can we make that we don't perhaps yet have names for that's enabled by technology, but ultimately might change the way that humans make music. And I'll just give you one example here, and this is Ocarina's other feature. This is a globe, and here you're actually listening to other users of Ocarina blow into their iPhones to play something. This is "G.I.R." from Texas, "R.I.K." I don't know why it's these three-letter names today, Los Angeles. They're all playing pretty, somewhat minimal music here.
(Music)
And the idea with this is that, well, technology should not be foregrounded here, and — (Laughter) — we've actually opened this up. The first thought is that, hey, you know there's somebody somewhere out there playing some music, and this is a small but I think important human connection to make that perhaps the technology affords.
As a final example, and perhaps my favorite example, is that in the wake of the 2011 earthquake and tsunami disaster in Japan, a woman reached out in one of our singing apps to try to get people to join in to sing with her on a version of "Lean on Me." Now, in these apps, there's this thing that allows any user to add their voice to an existing performance by any other user or group of users, so in some sense, she's created this kind of global ad hoc corral of strangers, and within weeks, thousands of people joined in on this, and you can kind of see people coming from all around the world and all these lines converging on the origin where the first rendition of the song was sung, and that's in Tokyo. And this is what it sounds like when there's 1,000 people. This is 1,000 voices.
(Recording) ♪ Sometimes in our lives ♪
♪ We all have pain, we all have sorrow ♪
♪ But if we are wise ♪
♪ We know that there's always tomorrow ♪
♪ Lean on me ♪
♪ When you're not strong ♪
♪ And I'll be your friend ♪
♪ I'll help you carry on ♪
♪ For it won't be long ♪
♪ Till I'm gonna need ♪
♪ Somebody to lean on ♪
♪ Just lean on — ♪
Is this computer music?
(Applause)
Was that computer music? Yeah, I guess so; it's something that you really couldn't have done without computers. But at the same time, it's also just human, and I think what I've essentially answered so far is maybe why I do the stuff that I do, and let's just finally return to the first question: What is computer music? And I think that the catch here is that, at least to me, computer music isn't really about computers. It is about people. It's about how we can use technology to change the way we think and do and make music, and maybe even add to how we can connect with each other through music.
And with that, I want to say, this is computer music, and thank you for listening.
(Applause)
Ge Wang:
The DIY orchestra of the future
TEDxStanfo · Filmed May 2014
Ge Wang makes computer music, but it isn’t all about coded bleeps and blips. With the Stanford Laptop Orchestra, he creates new instruments out of unexpected materials—like an Ikea bowl—that allow musicians to play music that’s both beautiful and expressive.
Transcript:
I want to talk to you about one thing and just one thing only, and this has to do with when people ask me, what do you do? To which I usually respond, I do computer music.
Now, a number of people just stop talking to me right then and there, and the rest who are left usually have this blank look in their eye, as if to say, what does that mean? And I feel like I'm actually depriving them of information by telling them this, at which point I usually panic and spit out the first thing that comes to my mind, which is, I have no idea what I'm doing. Which is true. That's usually followed by a second thought, which is, whatever it is that I'm doing, I love it. And today, I want to, well, share with you something I love, and also why.
And I think we'll begin with just this question: What is computer music? And I'm going to try to do my best to provide a definition, maybe by telling you a story that goes through some of the stuff I've been working on.
And the first thing, I think, in our story is going to be something called ChucK. Now, ChucK is a programming language for music, and it's open-source, it's freely available, and I like to think that it crashes equally well on all modern operating systems. And instead of telling you more about it, I'm just going to give you a demo. By the way, I'm just going to nerd out for just a few minutes here, so I would say, don't freak out. In fact, I would invite all of you to join me in just geeking out. If you've never written a line of code before in your life, do not worry. I'll bet you'll be able to come along on this.
First thing I'm going to do is to make a sine wave oscillator, and we're going to called the sine wave generator "Ge." And then we're going to connect "Ge" to the DAC. Now this is kind of the abstraction for the sound output on my computer. Okay? So I've connected myself into the speaker. Next, I'm going to say my frequency is 440 hertz, and I'm going to let time advance by two seconds through this operation. All right, so if I were to play this -- (Tone) — you would hear a sine wave at 440 hertz for two seconds. Okay, great. Now I'm going to copy and paste this, and then just change some of these numbers, 220.5, 440 I shall leave it as that, and .5 and 880. By doubling the frequency, we're actually going up in successive octaves, and then we have this sequence -- (Tones) — of tones. Okay, great, now I can imagine creating all kinds of really horrible single sine wave pieces of music with this, but I'm going to do something that computers are really good at, which is repetition. I'm going to put this all in a while loop, and you actually don't need to indent, but this is purely for aesthetic reasons. It's good practice. And when we do this — (Tones) — that's going to go on for a while. In fact, it's probably not going to stop until this computer disintegrates. And I can't really empirically prove that to you, but I hope you'll believe me when I say that. Next, I'm going to replace this 220 by math.random2f. I'm going to generate a random number between 30 and 1,000 and send that to the frequency of me. And I'm going to do this every half a second. (Tones) Let's do this every 200 milliseconds. (Tones) One hundred. (Tones) All right. At this point, we've reached something that I would like to think of as the canonical computer music. This is, to me, the sound that mainframes are supposed to be making when they're thinking really hard. It's this sound, it's like, the square root of five million.
So is this computer music? Yeah, I guess by definition, it's kind of computer music. It's probably not the kind of music you would listen to cruising down the highway, but it's a foundation of computer-generated music, and using ChucK, we've actually been building instruments in the Stanford Laptop Orchestra, based right here at Stanford Center for Computer Research in Music and Acoustics.
Now the Laptop Orchestra is an ensemble of laptops, humans and special hemispherical speaker arrays. Now the reason we have these is so that for the instruments that we create out of the laptop, we want the sound to come out of somewhere near the instrument and the performer, kind of much like a traditional, acoustic instrument. Like, if I were to play a violin here, the sound would naturally not come out of the P.A. system, but from the artifact itself. So these speakers are meant to emulate that. In fact, I'm going to show you how we actually built them. The first step is to go to IKEA and buy a salad bowl. This is an 11-inch Blanda Matt. That's the actual name, and I actually use one of these to make salad at home as well, I kid you not. And the first step is you turn it upside down, and then you drill holes in them, six holes per hemi, and then make a base plate, put car speaker drivers in them along with amplifiers in the enclosure, and you put that all together and you have these hemispherical speaker arrays. Add people, add laptops, you have a laptop orchestra.
And what might a laptop orchestra sound like? Well, let me give you a demonstration of about 200 instruments we've created so far for the Laptop Orchestra. And what I'm going to do is actually come over to this thing. This thing I have in front of me actually used to be a commodity gaming controller called a Gametrak. This thing actually has a glove you can put on your hands. It's tethered to the base, and this will track the position of your hands in real time. It was originally designed as a golfing controller to detect the motion of your swing. That turned out to be a rather large commercial non-success, at which point they slashed prices to 10 dollars, at which point computer music researchers said, "This is awesome! We can prototype instruments out of this."
So let me show you one instrument we've created, one of many, and this instrument is called "Twilight," and it's meant to go with this metaphor of pulling a sound out of the ground. So let me see if this will work.
(Music)
And put it back. And then if you go to the left, right, it sounds like an elephant in pain.
This is a slightly metallic sound. Turn it just a bit.
(Music) It's like a hovering car.
Okay.
This third one is a ratchet-like interaction, so let me turn it up.
(Music)
So it's a slightly different interaction.
The fourth one is a drone.
(Music) And finally, let's see, this is a totally different interaction, and I think you have to imagine that there's this giant invisible drum sitting right here on stage, and I'm going to bang it. (Drum) (Laughter) So there we go, so that's one of many instruments in the Laptop Orchestra.
(Applause)
Thank you.
And when you put that together, you get something that sounds like this.
(Music)
Okay, and so, I think from the experience of building a lot of instruments for the Laptop Orchestra, and I think from the curiosity of wondering, what if we took these hopefully expressive instruments and we brought it to a lot of people, plus then a healthy bout of insanity — put those three things together — led to me actually co-founding a startup company in 2008 called Smul
Now Smule's mission is to create expressive, mobile music things, and one of the first musical instruments we created is called Ocarina. And I'm going to just demo this for you real quick. So Ocarina — (Music) — is based on this ancient flute-like instrument called the ocarina, and this one is the four-hole English pendant configuration, and you're literally blowing into the microphone to make the sound. And there's actually a little ChucK script running in here that's detecting the strength of your blowing and also synthesizing the sound. (Music) And vibrato is mapped to the accelerometer, so you can get — (Music) All right. So let me play a little ditty for you, a little Bach. And here, you'll hear a little accompaniment with the melody. The accompaniment actually follows the melody, not the other way around.
(Music)
And this was designed to let you take your time and figure out where your expressive space is, and you can just hang out here for a while, for a really dramatic effect, if you want, and whenever you're ready —
(Music)
And on these longer notes, I'm going to use more vibrato towards the end of the notes to give it a little bit more of an expressive quality.
(Music)
Huh, that's a nice chord to end this excerpt on.
(Applause)
Thank you.
So I think a good question to ask about Ocarina is, is this a toy or it an instrument? Maybe it's both, but for me, I think the more important question is, is it expressive? And at the same time, I think creating these types of instruments asks a question about the role of technology, and its place for how we make music. Apparently, for example, not that long ago, like only a hundred years ago — that's not that long in the course of human history — families back then used to make music together as a common form of entertainment. I don't think that's really happening that much anymore. You know, this is before radio, before recording. In the last hundred years, with all this technology, we now have more access to music as listeners and consumers, but somehow, I think we're making less music than ever before. I'm not sure why that would be. Maybe it's because it's too easy just to hit play. And while listening to music is wonderful, there's a special joy to making music that's all its own. And I think that's one part of the goal of why I do what I do is kind of to take us back to the past a little bit. Right?
Now, if that's one goal, the other goal is to look to the future and think about what kind of new musical things can we make that we don't perhaps yet have names for that's enabled by technology, but ultimately might change the way that humans make music. And I'll just give you one example here, and this is Ocarina's other feature. This is a globe, and here you're actually listening to other users of Ocarina blow into their iPhones to play something. This is "G.I.R." from Texas, "R.I.K." I don't know why it's these three-letter names today, Los Angeles. They're all playing pretty, somewhat minimal music here.
(Music)
And the idea with this is that, well, technology should not be foregrounded here, and — (Laughter) — we've actually opened this up. The first thought is that, hey, you know there's somebody somewhere out there playing some music, and this is a small but I think important human connection to make that perhaps the technology affords.
As a final example, and perhaps my favorite example, is that in the wake of the 2011 earthquake and tsunami disaster in Japan, a woman reached out in one of our singing apps to try to get people to join in to sing with her on a version of "Lean on Me." Now, in these apps, there's this thing that allows any user to add their voice to an existing performance by any other user or group of users, so in some sense, she's created this kind of global ad hoc corral of strangers, and within weeks, thousands of people joined in on this, and you can kind of see people coming from all around the world and all these lines converging on the origin where the first rendition of the song was sung, and that's in Tokyo. And this is what it sounds like when there's 1,000 people. This is 1,000 voices.
(Recording) ♪ Sometimes in our lives ♪
♪ We all have pain, we all have sorrow ♪
♪ But if we are wise ♪
♪ We know that there's always tomorrow ♪
♪ Lean on me ♪
♪ When you're not strong ♪
♪ And I'll be your friend ♪
♪ I'll help you carry on ♪
♪ For it won't be long ♪
♪ Till I'm gonna need ♪
♪ Somebody to lean on ♪
♪ Just lean on — ♪
Is this computer music?
(Applause)
Was that computer music? Yeah, I guess so; it's something that you really couldn't have done without computers. But at the same time, it's also just human, and I think what I've essentially answered so far is maybe why I do the stuff that I do, and let's just finally return to the first question: What is computer music? And I think that the catch here is that, at least to me, computer music isn't really about computers. It is about people. It's about how we can use technology to change the way we think and do and make music, and maybe even add to how we can connect with each other through music.
And with that, I want to say, this is computer music, and thank you for listening.
(Applause)
ARCH/SUST/GralInt-Construyeron una casa con basura reciclable en Inglaterra
The following information is used for educational purposes only.
Construyeron una casa con basura reciclable en Inglaterra
Algunos de los elementos que se usaron para la construcción fueron 20.000 cepillos de dientes, 4000 DVDs, 2000 disquettes y dos toneladas de jeans
Foto: Gentileza BBM architects
La premisa para construir esta casa fue: "no existe nada que sea basura, sólo materiales en el lugar equivocado". Así, con esta idea en la cabeza, el estudio de arquitectura inglés BBM edificó en la Universidad de Brighton la primera casa construida casi en su totalidad con materiales de desecho reciclados en el Reino Unido. La Waste House o "Casa basura", como fue llamada por sus creadores, fue construida en un 98% por alumnos de la universidad y se usaron desde cepillos de dientes hasta disquetes.
La casa se encuentra en el campus de la Universidad de Brighton, donde el codirector del estudio BBM Designs, Duncan Baker-Brown, da clases sobre el desarrollo de proyectos de diseño sostenible.
Baker- Brown propone con esta obra un cambio radical en la forma de usar los materiales y una llamada de atención para que las empresas de construcción apuesten a reutilizar los materiales sobrantes o ya usados. "Tiene que haber una forma de almacenar y reutilizar los excedentes en lugar de tirarlos en un vertedero", explicó el arquitecto.
La obra
Los alumnos trabajaron con asesoramiento de los arquitectos. Foto: Gentileza BBM architects
La construcción, que está realizada en un 85% con basura, tiene una planta de 7,6 metros de ancho y una altura de 5,3, y fue ejecutada por 253 estudiantes, aprendices y voluntarios. Los cimentos fueron realizados por aprendices de una empresa de construcción.
El material fue aportado por empresas y particulares de manera gratuita, y el trabajo llevó tres meses de diseño en las aulas de la universidad, y se ejecutó a lo largo de un curso académico. Además, otra de las características principales de la casa es que todos los materiales son bajos en carbono, por lo que también se intentara determinar el comportamiento de la construcción y su rendimiento energético durante los próximos años.
La construcción se dedicará a la difusión del reciclaje aplicado a la construcción y puede ser visitada libremente. El objetivo es que sea una prueba de que es posible construir una casa con elementos descartados, y que sea sólida y bien construida. También se ofrece como lugar para reuniones sociales y comunitarias, y para debates, o como centro artístico.
Los materiales
Los niños también ayudaron a construir la casa. Foto: Gentileza BBM architects
Algunos de los elementos más curiosos que usaron para construirla fueron los 19.800 cepillos de dientes, usados como aislamiento de la pared y donados por la empresa de limpieza del aeropuerto londinense Gatwick, dos toneladas de jeans, regalados por empresas de confección, 4000 cajas de DVDs, cintas de video VHS y 2000 disquettes.
Otros de los materiales reutilizados fueron lotes de madera sobrante de un aserradero, 600 pancartas de vinilo, que sirvieron para hacer la membrana de control de vapor para envolver la casa, 2000 pedazos de alfombras usadas, que sirvieron como tejas para las paredes exteriores, pintura de segunda mano, 1,2 metros cúbicos de poliestireno de embalaje viejo y 2000 pernos usados.
Por último, se utilizaron rollos de empapelado, folios y contrachapado, neumáticos, una cocina, y todo lo que les ofrecieron desde empresas, negocios particulares y comercios.
Retazos de alfombra sirvieron para recubrir las paredes externas. Foto: Gentileza BBM architects
Entre otras cosas, se usaron jeans en la cosntruccción. Foto: Gentileza BBM architects
Un equipo de alumnos construyó una casa reciclable. Foto: Gentileza BBM architects
Fuente:www.lanacion.com.ar
Construyeron una casa con basura reciclable en Inglaterra
Algunos de los elementos que se usaron para la construcción fueron 20.000 cepillos de dientes, 4000 DVDs, 2000 disquettes y dos toneladas de jeans
Foto: Gentileza BBM architects
La premisa para construir esta casa fue: "no existe nada que sea basura, sólo materiales en el lugar equivocado". Así, con esta idea en la cabeza, el estudio de arquitectura inglés BBM edificó en la Universidad de Brighton la primera casa construida casi en su totalidad con materiales de desecho reciclados en el Reino Unido. La Waste House o "Casa basura", como fue llamada por sus creadores, fue construida en un 98% por alumnos de la universidad y se usaron desde cepillos de dientes hasta disquetes.
La casa se encuentra en el campus de la Universidad de Brighton, donde el codirector del estudio BBM Designs, Duncan Baker-Brown, da clases sobre el desarrollo de proyectos de diseño sostenible.
Baker- Brown propone con esta obra un cambio radical en la forma de usar los materiales y una llamada de atención para que las empresas de construcción apuesten a reutilizar los materiales sobrantes o ya usados. "Tiene que haber una forma de almacenar y reutilizar los excedentes en lugar de tirarlos en un vertedero", explicó el arquitecto.
La obra
Los alumnos trabajaron con asesoramiento de los arquitectos. Foto: Gentileza BBM architects
La construcción, que está realizada en un 85% con basura, tiene una planta de 7,6 metros de ancho y una altura de 5,3, y fue ejecutada por 253 estudiantes, aprendices y voluntarios. Los cimentos fueron realizados por aprendices de una empresa de construcción.
El material fue aportado por empresas y particulares de manera gratuita, y el trabajo llevó tres meses de diseño en las aulas de la universidad, y se ejecutó a lo largo de un curso académico. Además, otra de las características principales de la casa es que todos los materiales son bajos en carbono, por lo que también se intentara determinar el comportamiento de la construcción y su rendimiento energético durante los próximos años.
La construcción se dedicará a la difusión del reciclaje aplicado a la construcción y puede ser visitada libremente. El objetivo es que sea una prueba de que es posible construir una casa con elementos descartados, y que sea sólida y bien construida. También se ofrece como lugar para reuniones sociales y comunitarias, y para debates, o como centro artístico.
Los materiales
Los niños también ayudaron a construir la casa. Foto: Gentileza BBM architects
Algunos de los elementos más curiosos que usaron para construirla fueron los 19.800 cepillos de dientes, usados como aislamiento de la pared y donados por la empresa de limpieza del aeropuerto londinense Gatwick, dos toneladas de jeans, regalados por empresas de confección, 4000 cajas de DVDs, cintas de video VHS y 2000 disquettes.
Otros de los materiales reutilizados fueron lotes de madera sobrante de un aserradero, 600 pancartas de vinilo, que sirvieron para hacer la membrana de control de vapor para envolver la casa, 2000 pedazos de alfombras usadas, que sirvieron como tejas para las paredes exteriores, pintura de segunda mano, 1,2 metros cúbicos de poliestireno de embalaje viejo y 2000 pernos usados.
Por último, se utilizaron rollos de empapelado, folios y contrachapado, neumáticos, una cocina, y todo lo que les ofrecieron desde empresas, negocios particulares y comercios.
Retazos de alfombra sirvieron para recubrir las paredes externas. Foto: Gentileza BBM architects
Entre otras cosas, se usaron jeans en la cosntruccción. Foto: Gentileza BBM architects
Un equipo de alumnos construyó una casa reciclable. Foto: Gentileza BBM architects
Fuente:www.lanacion.com.ar
Wednesday, July 9, 2014
9 DE JULIO-DIA DE LA INDEPENDENCIA
The following information is used for educational purposes only.
9 DE JULIO-DIA DE LA INDEPENDENCIA
Fuente: Google Images
9 DE JULIO-DIA DE LA INDEPENDENCIA
Fuente: Google Images
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