TED2006
Michael Shermer: Why people believe weird things
Michael Shermer habla sobre creer cosas extrañas
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¿Por qué la gente ve a la Vírgen María en sandwiches de queso o escucha letras demoníacas en "Stairway to Heaven"? Usando video y musica, el escéptico Michael Shermer nos muestra cómo nos convencemos a nosotros mismos de creer -- pasando por alto los hechos.
Michael Shermer - Skeptic
Michael Shermer debunks myths, superstitions and urban legends -- and explains why we believe them. Along with publishing Skeptic Magazine, he's author of Why People Believe Weird Things and The Mind of the Market. Full bio
Michael Shermer debunks myths, superstitions and urban legends -- and explains why we believe them. Along with publishing Skeptic Magazine, he's author of Why People Believe Weird Things and The Mind of the Market. Full bio
Double-click the English transcript below to play the video.
00:24
I'm Michael Shermer,
director of the Skeptics Society,
director of the Skeptics Society,
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Hola, soy Michael Shermer, director de la Skeptics Society,
editora de la revista Skeptic.
00:27
publisher of "Skeptic" magazine.
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Investigamos alegatos de eventos paranormales, pseudociencias,
00:28
We investigate claims of the paranormal,
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grupos marginales, cultos y todo lo que está en el medio.
00:30
pseudo-science, fringe groups and cults,
and claims of all kinds between,
and claims of all kinds between,
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00:34
science and pseudo-science
and non-science and junk science,
and non-science and junk science,
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Ciencias, pseudociencias, ciencia basura,
ciencia voodoo, ciencia patológica, mala ciencia, no ciencias
00:37
voodoo science, pathological science,
bad science, non-science,
bad science, non-science,
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00:40
and plain old non-sense.
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y el conocido sin sentido.
00:42
And unless you've been on Mars recently,
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A menos que hayan estado en Marte últimamente
00:44
you know there's a lot of that out there.
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sabrán que hay mucho de eso ahí afuera.
00:46
Some people call us debunkers,
which is kind of a negative term.
which is kind of a negative term.
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Alguna gente nos llama los refutadores lo cual es un término algo negativo,
00:49
But let's face it, there's a lot of bunk.
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pero aceptémoslo, hay un montón de basura.
Somos como el escuadrón de calidad del departamento de policía refutando
00:51
We are like the bunko squads
of the police departments out there --
of the police departments out there --
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00:55
well, we're sort of like
the Ralph Naders of bad ideas,
the Ralph Naders of bad ideas,
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la basura, bueno, más o menos como los Ralph Naders de las malas ideas,
00:58
(Laughter)
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(Risas)
tratando de reemplazar malas ideas por buenas ideas.
01:00
trying to replace bad ideas
with good ideas.
with good ideas.
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01:02
I'll show you an example of a bad idea.
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Les muestro un ejemplo de una mala idea.
01:04
I brought this with me,
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Traje esto conmigo,
01:05
this was given to us
by NBC Dateline to test.
by NBC Dateline to test.
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nos lo dió Dateline, de NBC, para probarlo.
01:09
It's produced by the Quadro
Corporation of West Virginia.
Corporation of West Virginia.
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Lo produce la Quadro Corporation de Virginia Occidental,
se llama el Quadro 2000 Dowser Rod.
01:13
It's called the Quadro 2000 Dowser Rod.
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(Risas)
01:16
(Laughter)
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01:17
This was being sold to high-school
administrators for $900 apiece.
administrators for $900 apiece.
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Esto estaba siendo vendido a los directores de escuelas por 900 dólares cada uno.
01:22
It's a piece of plastic with a Radio
Shack antenna attached to it.
Shack antenna attached to it.
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Es un pedazo de plástico con una antena de Radioshack pegada.
Pueden hacer uno de estos para buscar todo tipo de cosas, pero este en especial
01:27
You could dowse for all sorts of things,
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01:29
but this particular one was built
to dowse for marijuana
to dowse for marijuana
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se diseñó para buscar marihuana en los casilleros de los estudiantes.
01:32
in students' lockers.
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01:33
(Laughter)
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(Risas)
Así funciona, van caminando por el pasillo y ven si la antena
01:36
So the way it works
is you go down the hallway,
is you go down the hallway,
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01:39
and you see if it tilts
toward a particular locker,
toward a particular locker,
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se mueve hacia un casillero en particular, y entonces abren el casillero.
01:42
and then you open the locker.
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Es algo así,
01:44
So it looks something like this.
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les muestro.
01:45
I'll show you.
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01:48
(Laughter)
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(Risas)
01:49
Well, it has kind of a right-leaning bias.
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Bueno, tiene cierta tendencia de girar hacia la derecha
Esto es ciencia, así que haremos un experimento controlado,
01:53
Well, this is science,
so we'll do a controlled experiment.
so we'll do a controlled experiment.
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seguro que va para el otro lado.
01:56
It'll go this way for sure.
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(Risas)
01:57
(Laughter)
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02:01
Sir, do you want to empty
your pockets, please, sir?
your pockets, please, sir?
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Señor, ¿podría vaciar sus bolsillos, por favor?
(Risas)
02:04
(Laughter)
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Entonces la pregunta era, ¿puedo encontrar marihuana en los casilleros de los
02:06
So the question was, can it actually
find marijuana in students' lockers?
find marijuana in students' lockers?
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alumnos? Y la respuesta es, si abren suficientes casilleros, sí.
02:09
And the answer is,
if you open enough of them, yes.
if you open enough of them, yes.
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(Risas)
02:12
(Laughter)
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(Aplausos)
02:13
(Applause)
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02:14
But in science, we have to keep track
of the misses, not just the hits.
of the misses, not just the hits.
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Pero en ciencia debemos tener en cuenta los errores, no sólo los aciertos
Esa es probablemente la principal lección de mi corta charla aquí, es que
02:18
And that's probably the key lesson
to my short talk here:
to my short talk here:
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02:21
This is how psychics work, astrologers,
tarot card readers and so on.
tarot card readers and so on.
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así funcionan todos estos psíquicos, astrólogos, lectores de cartas de tarot y
demás: La gente recuerda los aciertos y olvida los errores.
02:25
People remember the hits
and forget the misses.
and forget the misses.
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En ciencia, debemos mantener toda la base de datos
02:27
In science, we keep the whole database,
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y ver si el número de aciertos de alguna forma es mayor
02:29
and look to see if the number
of hits somehow stands out
of hits somehow stands out
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al número total que usted esperaría por azar.
02:32
from the total number
you'd expect by chance.
you'd expect by chance.
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En este caso, hicimos pruebas.
02:34
In this case, we tested it.
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02:35
We had two opaque boxes:
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Teníamos dos cajas opacas,
02:37
one with government-approved THC
marijuana, and one with nothing.
marijuana, and one with nothing.
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una con THC marihuana aprobada por el gobierno, y la otra vacía.
Funcionó el 50% de las veces,
02:40
And it got it 50 percent of the time --
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02:42
(Laughter)
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justo lo que esperarían lanzando una moneda.
02:43
which is exactly what you'd expect
with a coin-flip model.
with a coin-flip model.
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02:46
So that's just a fun little example
here of the sorts of things we do.
here of the sorts of things we do.
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Ese es sólo un pequeño y divertido ejemplo del tipo de cosas que hacemos;
Skeptic es una publicación trimestral,
02:50
"Skeptic" is the quarterly publication.
Each one has a particular theme.
Each one has a particular theme.
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cada una tiene un tema particular, como esta, es sobre el futuro de la inteligencia.
02:54
This one is on the future of intelligence.
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¿La gente se está volviendo más inteligente o más tonta?
02:56
Are people getting smarter or dumber?
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Tengo una opinión propia al respecto, por el negocio en el que estoy.
02:58
I have an opinion of this myself
because of the business I'm in,
because of the business I'm in,
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Pero, de hecho, resulta que la gente se está volviendo más inteligente
03:01
but in fact, people, it turns out,
are getting smarter.
are getting smarter.
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Tres puntos de CI (IQ) por cada 10 años que pasan,
03:03
Three IQ points per 10 years, going up.
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03:06
Sort of an interesting thing.
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lo cual es una cosa interesante.
03:08
With science, don't think of skepticism
as a thing, or science as a thing.
as a thing, or science as a thing.
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Con ciencia, no piensen en el escepticismo como una cosa o incluso en la
ciencia como una cosa ¿Son la ciencia y la religión compatibles?
03:12
Are science and religion compatible?
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03:13
It's like, are science
and plumbing compatible?
and plumbing compatible?
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Es como preguntar si la ciencia y la plomería son compatibles.
Son, simplemente, cosas diferentes.
03:16
They're just two different things.
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03:17
Science is not a thing. It's a verb.
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La ciencia no es una cosa, es un verbo.
03:19
It's a way of thinking about things.
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Es una forma de pensar acerca de las cosas,
03:21
It's a way of looking for natural
explanations for all phenomena.
explanations for all phenomena.
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Es una forma de buscar una explicación natural a todos los fenómenos.
Me refiero a, ¿qué es más probable?
03:24
I mean, what's more likely:
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03:25
that extraterrestrial intelligences
or multi-dimensional beings
or multi-dimensional beings
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¿Que inteligencias extraterrestres o seres multidimensionales hayan viajado
03:29
travel across vast distances
of interstellar space
of interstellar space
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la vastedad del espacio interestelar para dejar una marca en las cosechas
03:31
to leave a crop circle in Farmer Bob's
field in Puckerbrush, Kansas
field in Puckerbrush, Kansas
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en el la granja de Bob en Puckerbrush, Kansas para promover Skeptic.com, nuestro sitio web?
03:34
to promote skeptic.com, our web page?
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¿o es más probable que un lector de Skeptic haya hecho esto con Photoshop?
03:36
Or is it more likely that a reader
of "Skeptic" did this with Photoshop?
of "Skeptic" did this with Photoshop?
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En cualquier caso, tenemos que preguntar
03:41
And in all cases we have to ask --
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(Risas)
03:42
(Laughter)
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03:44
What's the more likely explanation?
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cuál es la explicación más probable,
03:46
Before we say something
is out of this world,
is out of this world,
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y antes de decir que algo es de fuera de este mundo,
tenemos que asegurarnos de que no está en este mundo
03:48
we should first make sure
that it's not in this world.
that it's not in this world.
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¿Qué es más probable?
03:51
What's more likely:
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¿Que Arnold haya tenido un poco de ayuda extraterrestre en las elecciones para
03:52
that Arnold had extraterrestrial help
in his run for the governorship,
in his run for the governorship,
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gobernador, o que el World Weekly News invente cosas?
03:56
or that the "World Weekly News"
makes stuff up?
makes stuff up?
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03:58
(Laughter)
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(Risas)
Y parte de eso, el mismo tema, se expresa muy bien
04:00
The same theme is expressed nicely
here in this Sidney Harris cartoon.
here in this Sidney Harris cartoon.
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en esta caricatura de Sidney Harris.
04:04
For those of you in the back,
it says here: "Then a miracle occurs.
it says here: "Then a miracle occurs.
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Para aquellos en el fondo, dice: "Entonces sucede un milagro".
04:08
I think you need to be more
explicit here in step two."
explicit here in step two."
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"Creo que deberías ser más explícito en el paso dos".
04:10
This single slide completely dismantles
the intelligent design arguments.
the intelligent design arguments.
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Esta diapositiva, sola, desmantela completamente los argumentos sobre el
diseño inteligente. No es nada más que eso.
04:15
There's nothing more to it than that.
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(Aplausos)
04:17
(Applause)
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Pueden decir "Entonces un milagro sucede",
04:18
You can say a miracle occurs,
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04:19
it's just that it doesn't explain
anything or offer anything.
anything or offer anything.
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sólo que eso no explica nada,
no ofrece nada, no hay nada para probar.
04:22
There's nothing to test.
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Es el fin de la conversación para los creacionistas de diseño inteligente.
04:23
It's the end of the conversation
for intelligent design creationists.
for intelligent design creationists.
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Mientras que, es cierto, la ciencia a veces pone nombres tentativos
04:27
And it's true, scientists sometimes throw
terms out as linguistic place fillers --
terms out as linguistic place fillers --
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para llenar el espacio lingüistico, energía oscura o materia oscura, o algo así.
04:32
dark energy or dark matter,
something like that --
something like that --
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Hasta que descubramos qué es lo llamaremos así.
04:34
until we figure out what it is,
we'll call it this.
we'll call it this.
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Es el comienzo de la cadena causal para la ciencia.
04:37
It's the beginning of the causal
chain for science.
chain for science.
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para el creacionismo de diseño inteligente es el fin de la cadena.
04:39
For intelligent design creationists,
it's the end of the chain.
it's the end of the chain.
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Entonces, podemos preguntarnos esto, ¿qué es más probable?
04:43
So again, we can ask this:
what's more likely?
what's more likely?
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¿Ovnis, platillos voladores, errores cognitivos perceptuales o incluso engaños?
04:46
Are UFOs alien spaceships, or perceptual
cognitive mistakes, or even fakes?
cognitive mistakes, or even fakes?
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04:50
This is a UFO shot from my house
in Altadena, California,
in Altadena, California,
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Esta es la foto de un ovni desde mi casa en Altadena, California,
04:53
looking down over Pasadena.
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mirando hacia Pasadena.
04:55
And if it looks a lot like a Buick
hubcap, it's because it is.
hubcap, it's because it is.
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Y si se ve muy parecido a un plato desechable, es porque eso es.
04:59
You don't even need Photoshop
or high-tech equipment,
or high-tech equipment,
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Ni siquiera necesitan Photoshop ni equipos de alta tecnología,
no necesitan computadoras.
05:02
you don't need computers.
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05:03
This was shot with a throwaway
Kodak Instamatic camera.
Kodak Instamatic camera.
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Esto fue tomado con una cámara Kodak desechable.
05:06
You just have somebody off on the side
with a hubcap ready to go.
with a hubcap ready to go.
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Alguien está al costado con el plato listo para tirarlo,
la cámara está lista, ya está.
05:09
Camera's ready -- that's it.
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(Risas)
05:11
(Laughter)
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Aunque es posible que la mayoría de estas cosas sean engaños,
05:13
So, although it's possible
that most of these things are fake
that most of these things are fake
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05:16
or illusions or so on,
and that some of them are real,
and that some of them are real,
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o ilusiones o algo así, y algunas sean reales,
es más probable que todas sean falsas como las marcas en las cosechas.
05:20
it's more likely that all of them
are fake, like the crop circles.
are fake, like the crop circles.
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En un plano más serio, en todas las ciencias buscamos balance
05:23
On a more serious note, in all of science
we're looking for a balance
we're looking for a balance
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entre datos y teoría.
05:27
between data and theory.
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05:28
In the case of Galileo,
he had two problems
he had two problems
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En el caso de Galileo, él tuvo dos problemas
05:32
when he turned his telescope to Saturn.
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al mirar con el telescopio hacia Saturno.
Primero, no había una teoría de anillos planetarios y,
05:35
First of all, there was no
theory of planetary rings.
theory of planetary rings.
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segundo, sus datos eran vagos y difusos.
05:38
Second of all, his data
was grainy and fuzzy,
was grainy and fuzzy,
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No podía entender muy bien qué era lo que estaba mirando,
05:40
and he couldn't quite make out
what he was looking at.
what he was looking at.
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entonces escribió lo que vió:
05:43
So he wrote that he had seen --
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05:44
"I have observed that the furthest
planet has three bodies."
planet has three bodies."
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"He observado que el planeta más lejano tiene tres cuerpos".
Esto es lo que terminó concluyendo.
05:48
And this is what he ended up
concluding that he saw.
concluding that he saw.
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Entonces, sin una teoría de anillos planetarios y sólo datos vagos,
05:51
So without a theory of planetary
rings and with only grainy data,
rings and with only grainy data,
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05:54
you can't have a good theory.
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no pueden tener una buena teoría.
05:56
It wasn't solved until 1655.
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Esto no se resolvió sino hasta 1655.
05:58
This is Christiaan Huygens's book
that catalogs all the mistakes
that catalogs all the mistakes
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Esto es del libro de Christiaan Huygens, en donde catalogó todos los errores
06:01
people made trying to figure out
what was going on with Saturn.
what was going on with Saturn.
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que las personas hicieron tratando de entender qué pasaba cuando miraban a
Saturno. No fue sino hasta que Huygens tuvo dos cosas:
06:04
It wasn't till Huygens had two things:
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Una buena teoría de anillos planetarios y de cómo operaba el sistema solar,
06:06
He had a good theory of planetary rings
and how the solar system operated,
and how the solar system operated,
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06:10
and he had better telescopic,
more fine-grain data
more fine-grain data
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y mejores telescopios, mejores datos.
06:13
in which he could figure out that
as the Earth is going around faster --
as the Earth is going around faster --
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Así pudo entender que lo que ocurría es que la Tierra iba más rápido,
de acuerdo a las leyes de Kepler, que Saturno y después lo alcanzamos
06:17
according to Kepler's Laws --
than Saturn, then we catch up with it.
than Saturn, then we catch up with it.
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y vemos los anillos en diferentes ángulos, ahí.
06:20
And we see the angles of the rings
at different angles, there.
at different angles, there.
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06:23
And that, in fact, turns out to be true.
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Que es lo que de hecho resultó ser cierto.
El problema con tener una teoría
06:26
The problem with having a theory is that
it may be loaded with cognitive biases.
it may be loaded with cognitive biases.
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es que su teoría puede estar cargada de sesgos cognitivos.
Entonces uno de los problemas explicando por qué las personas creen cosas
06:31
So one of the problems of explaining
why people believe weird things
why people believe weird things
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extrañas es que tenemos cosas a un nivel más simple
06:34
is that we have things, on a simple level,
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y después ire a otras más serias
06:36
and then I'll go to more serious ones.
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como, tenemos una tendencia a ver caras.
06:38
Like, we have a tendency to see faces.
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Esta es la cara en Marte que era,
06:40
This is the face on Mars.
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en 1976, donde había todo un movimiento para que la NASA
06:42
In 1976, where there was a whole movement
to get NASA to photograph that area
to get NASA to photograph that area
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tomara fotografías del área, porque la gente pensaba
06:46
because people thought this was monumental
architecture made by Martians.
architecture made by Martians.
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que era una monumental arquitectura hecha por marcianos.
Bueno, resultó que, acá hay un acercamiento del 2001,
06:50
Here's the close-up of it from 2001.
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06:53
If you squint, you can still see the face.
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si entrecierran los ojos todavía pueden ver la cara.
Cuando entrecierran los ojos lo que están haciendo es
06:56
And when you're squinting,
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06:57
you're turning that from fine-grain
to coarse-grain,
to coarse-grain,
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cambiando de alta calidad a baja calidad,
reduciendo así la calidad de los datos.
07:00
so you're reducing
the quality of your data.
the quality of your data.
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Y si no les dijera qué buscar, todavía podrían ver la cara,
07:02
And if I didn't tell you what to look for,
you'd still see the face,
you'd still see the face,
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porque estamos programados por la evolución para reconocer rostros.
07:05
because we're programmed
by evolution to see faces.
by evolution to see faces.
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Los rostros son importantes para nosotros a nivel social.
07:08
Faces are important for us socially.
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Y claro, caras felices,
07:10
And of course, happy faces,
faces of all kinds are easy to see.
faces of all kinds are easy to see.
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caras de todo tipo son fáciles de ver.
(Risas)
07:14
You see the happy face on Mars, there.
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Pueden ver la cara feliz en Marte ahí.
07:16
(Laughter)
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Si los astrónomos fuesen ranas quizás verían a la rana René,
07:17
If astronomers were frogs,
perhaps they'd see Kermit the Frog.
perhaps they'd see Kermit the Frog.
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la ven ahí,
07:20
Do you see him there? Little froggy legs.
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con patitas de rana...
07:22
Or if geologists were elephants?
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O si los geólogos fuesen elefantes...
07:25
Religious iconography.
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Iconografía religiosa,
07:28
(Laughter)
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(Risas)
descubierta por un pastelero en Tennesee en 1996,
07:31
Discovered by a Tennessee baker in 1996.
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07:33
He charged five bucks a head
to come see the nun bun
to come see the nun bun
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cobraba 5 dólares por cabeza para verla
hasta que recibió una orden del abogado de la madre Teresa.
07:36
till he got a cease-and-desist
from Mother Teresa's lawyer.
from Mother Teresa's lawyer.
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Esta es la Virgen de Guadalupe y la Virgen de Watsonville, acá bajando la calle
07:40
Here's Our Lady of Guadalupe and Our
Lady of Watsonville, just down the street,
Lady of Watsonville, just down the street,
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07:43
or is it up the street from here?
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o subiendo la calle desde acá.
07:45
Tree bark is particularly good
because it's nice and grainy, branchy,
because it's nice and grainy, branchy,
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Las ramas de árboles con particularmente buenas porque son irregulares,
07:48
black-and-white splotchy and you can
get the pattern-seeking --
get the pattern-seeking --
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en blanco y negro, borrosas y pueden buscar patrones;
los humanos somos animales buscadores de patrones.
07:51
humans are pattern-seeking animals.
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07:53
Here's the Virgin Mary on the side
of a glass window in Sao Paulo.
of a glass window in Sao Paulo.
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Aquí está la Virgen María en una ventana de vidrio en Sao Paulo.
Aquí la Virgen María hizo su aparicion en un sándwich de queso
07:57
Here's when the Virgin Mary made
her appearance on a cheese sandwich --
her appearance on a cheese sandwich --
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que incluso pude sostener en un casino en las Vegas...
08:00
which I got to actually
hold in a Las Vegas casino --
hold in a Las Vegas casino --
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Claro, esto es Estados Unidos...
08:03
of course, this being America.
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(Risas)
08:04
(Laughter)
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08:06
This casino paid $28,500
on eBay for the cheese sandwich.
on eBay for the cheese sandwich.
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Este casino pagó 28.500 dólares en eBay por el sándwich de queso
08:10
(Laughter)
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(Risas)
08:12
But who does it really look like?
The Virgin Mary?
The Virgin Mary?
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¿Pero realmente a quién se parece? ¿a la Virgen María?
(Risas)
08:15
(Laughter)
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Tiene esos labios de los años 40...
08:17
It has that sort of
puckered lips, 1940s-era look.
puckered lips, 1940s-era look.
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La Virgen María en Clearwater, Florida.
08:21
Virgin Mary in Clearwater, Florida.
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08:22
I actually went to see this one.
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Esta pude verla...
08:24
There was a lot of people there.
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Hay un montón de personas, los creyentes que vienen,
08:26
The faithful come in their wheelchairs
and crutches, and so on.
and crutches, and so on.
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sillas de ruedas y muletas y así.
Fuimos a investigar,
08:31
We went down and investigated.
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para darles idea del tamaño, ese es Dawkins, yo y el asombroso Randi
08:33
Just to give you a size, that's Dawkins,
me and The Amazing Randi,
me and The Amazing Randi,
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junto a esta imagen de dos, dos pisos y medio.
08:36
next to this two,
two and a half story-sized image.
two and a half story-sized image.
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Muchas velas, miles de velas que la gente deja de tributo.
08:38
All these candles, thousands of candles
people had lit in tribute to this.
people had lit in tribute to this.
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Entonces caminamos a la parte de atrás para ver qué estaba pasando aquí,
08:42
So we walked around the backside,
to see what was going on.
to see what was going on.
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resulta que donde sea hay un rociador y una palmera
08:45
It turns out wherever there's
a sprinkler head and a palm tree,
a sprinkler head and a palm tree,
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se obtiene este efecto.
08:48
you get the effect.
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08:49
Here's the Virgin Mary on the backside,
which they started to wipe off.
which they started to wipe off.
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Aquí está la virgen María en la parte de atrás, la cual empezaron a limpiar...
Parece que sólo pueden tener un milagro por edificio.
08:53
I guess you can only have
one miracle per building.
one miracle per building.
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08:55
(Laughter)
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(Risas)
Entonces, ¿realmente es el milagro de María o es el milagro de Marge?
08:59
So is it really a miracle of Mary,
or is it a miracle of Marge?
or is it a miracle of Marge?
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(Risas)
09:02
(Laughter)
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1001
Voy a terminar con otro ejemplo de esto
09:03
And now I'm going to finish up
with another example of this,
with another example of this,
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con audio, ilusiones auditivas.
09:07
with auditory illusions.
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Está esta película "Voces del Más Allá"
09:10
There's this film, "White Noise,"
with Michael Keaton,
with Michael Keaton,
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con Michael Keaton sobre los muertos hablándonos...
09:12
about the dead talking back to us.
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Por cierto, este negocio de hablar con los muertos, no es tan complicado,
09:15
By the way, the whole business of talking
to the dead is not that big a deal.
to the dead is not that big a deal.
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cualquiera puede hacerlo; pero resulta
09:19
Anybody can do it, turns out.
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que lograr que los muertos respondan, esa es la parte difícil.
09:20
It's getting the dead to talk
back that's the really hard part.
back that's the really hard part.
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(Risas)
09:23
(Laughter)
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En este caso, supuestamente estos mensajes están escondidos en fenómenos
09:24
In this case, supposedly, these messages
are hidden in electronic phenomena.
are hidden in electronic phenomena.
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09:28
There's a ReverseSpeech.com web page
where I downloaded this stuff.
where I downloaded this stuff.
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electrónicos. Está esta página, reversespeech.com, de donde bajé esto.
Aquí está la versión normal, este es uno de los más famosos,
09:31
This is the most famous one
of all of these.
of all of these.
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esta es la versión normal de una canción muy famosa...
09:35
Here's the forward version
of the very famous song.
of the very famous song.
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09:37
(Music with lyrics)
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09:39
If there's a bustle in your hedgerow
don't be alarmed now.
don't be alarmed now.
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09:44
It's just a spring clean
for the May Queen.
for the May Queen.
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09:50
Yes, there are two paths you
can go by, but in the long run,
can go by, but in the long run,
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09:56
There's still time to change
the road you're on
the road you're on
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¿uno podría escuchar esto todo el día, cierto?
10:03
Couldn't you just listen to that all day?
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(Risas)
Aquí está al revés.
10:06
All right, here it is backwards,
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10:07
and see if you can hear the hidden
messages that are supposedly in there.
messages that are supposedly in there.
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A ver si pueden entender el mensaje que está supuestamente ahí..
10:12
(Music with unintelligible lyrics)
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10:26
(Lyrics) Satan!
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10:27
(Unintelligible lyrics continue)
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¿Qué entendieron?
10:41
What did you get?
Audience: Satan!
Audience: Satan!
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(Audiencia: Satán)
10:43
Satan. OK, at least we got "Satan".
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Shermer: ¿Satán? ok, por lo menos escuchamos Satán.
10:45
Now, I'll prime the auditory
part of your brain
part of your brain
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Ahora voy a preparar a la parte auditiva de su cerebro
10:47
to tell you what you're supposed
to hear, and then hear it again.
to hear, and then hear it again.
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para decirle qué se supone que escuche y lo escucharemos de nuevo.
10:50
(Music with lyrics)
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11:17
(Music ends)
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(Risas)
11:18
(Laughter)
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(Aplausos)
11:19
(Applause)
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No pueden dejar de escucharlo cuando les digo que está ahí.
11:26
You can't miss it
when I tell you what's there.
when I tell you what's there.
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11:28
(Laughter)
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(Risas)
Déjenme terminar con una historia positiva...
11:32
I'm going to just end
with a positive, nice little story.
with a positive, nice little story.
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Skeptics es una organización educacional sin fines de lucro,
11:36
The Skeptics is a nonprofit
educational organization.
educational organization.
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siempre buscamos cositas buenas que la gente hace.
11:39
We're always looking for little
good things that people do.
good things that people do.
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En Inglaterra hay una cantante de pop,
11:41
And in England, there's a pop singer.
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2009
una cantante muy popular hoy en Inglaterra, Katie Melua.
11:43
One of the top popular singers
in England today, Katie Melua.
in England today, Katie Melua.
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3745
Ella escribió una hermosa canción que
11:47
And she wrote a beautiful song.
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estuvo en el top 5 en el 2005 llamada "Nine Millon Bicycles in Beijing" (nueve
11:49
It was in the top five in 2005, called,
"Nine Million Bicycles in Beijing."
"Nine Million Bicycles in Beijing."
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11:54
It's a love story -- she's sort
of the Norah Jones of the UK --
of the Norah Jones of the UK --
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millones de bicicletas en Beijing). Es una historia de amor, ella es como la Norah
Jones de Reino Unido. Es sobre cuánto ama a su novio,
11:57
about how she much loves her guy,
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11:59
and compared to nine million
bicycles, and so forth.
bicycles, and so forth.
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y lo compara con nueve millones de bicicletas y así...
Y tiene este pasaje
12:02
And she has this one passage here.
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12:04
(Music)
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Bueno, eso es bonito.
12:05
(Lyrics) We are 12 billion
light-years from the edge
light-years from the edge
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Por lo menos estuvo cerca.
12:11
That's a guess,
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En Estados Unidos sería estamos a 6000 años luz del borde
12:13
No one can ever say it's true,
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(Risas)
12:17
But I know that I will always be with you.
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Pero mi amigo Simon Singh, físico de partículas que ahora se dedica
12:22
Michael Shermer: Well, that's nice.
At least she got it close.
At least she got it close.
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a la divulgación científica, escribió el libro "el Big Bang",
usa cada oportunidad que tiene para promover la buena ciencia.
12:25
In America it'd be,
"We're 6,000 light years from the edge."
"We're 6,000 light years from the edge."
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Entonces escribió un artículo acerca de la canción de Katie en The Guardian
12:28
(Laughter)
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diciendo, mm, bueno, sabemos exactamente a cuánto estamos del borde
12:29
But my friend, Simon Singh, the particle
physicist now turned science educator,
physicist now turned science educator,
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Son 13.7 mil millones de años-luz, no es una suposición,
12:33
who wrote the book
"The Big Bang," and so on,
"The Big Bang," and so on,
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lo sabemos dentro de margenes de error bien definidos.
12:35
uses every chance he gets
to promote good science.
to promote good science.
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Entonces podemos decirlo, aunque sin certeza absoluta, es bastante cercano
12:37
And so he wrote an op-ed piece
in "The Guardian" about Katie's song,
in "The Guardian" about Katie's song,
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a ser verdad. Y para crédito suyo, Katie llamó un poco después de la aparición
12:41
in which he said, well, we know exactly
how far from the edge.
how far from the edge.
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del artículo. Dijo: "Estoy muy avergonzada,
12:46
You know, it's 13.7 billion light years,
and it's not a guess.
and it's not a guess.
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era miembro del club de astronomía, debí de haberlo sabido".
12:50
We know within precise
error bars how close it is.
error bars how close it is.
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Grabó de nuevo la canción,
12:54
So we can say, although not absolutely
true, it's pretty close to being true.
true, it's pretty close to being true.
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entonces voy a terminar con la nueva versión.
12:58
And, to his credit, Katie called him up
after this op-ed piece came out, and said,
after this op-ed piece came out, and said,
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(Aplausos)
13:02
"I'm so embarrassed.
I was in the astronomy club.
I was in the astronomy club.
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¿Acaso no es esto genial?
13:05
I should've known better."
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(Aplausos)
13:06
And she re-cut the song.
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13:07
So I will end with the new version.
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13:09
(Music with lyrics)
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13:11
We are 13.7 billion light years
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13:13
from the edge of the observable universe.
262
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13:17
That's a good estimate
with well-defined error bars.
with well-defined error bars.
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13:20
And with the available information,
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13:24
I predict that I will always be with you.
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13:28
(Laughter)
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13:29
How cool is that?
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13:30
(Applause)
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ABOUT THE SPEAKER
Michael Shermer - SkepticMichael Shermer debunks myths, superstitions and urban legends -- and explains why we believe them. Along with publishing Skeptic Magazine, he's author of Why People Believe Weird Things and The Mind of the Market.
Why you should listen
As founder and publisher of Skeptic Magazine, Michael Shermer has exposed fallacies behind intelligent design, 9/11 conspiracies, the low-carb craze, alien sightings and other popular beliefs and paranoias. But it's not about debunking for debunking's sake. Shermer defends the notion that we can understand our world better only by matching good theory with good science.
Shermer's work offers cognitive context for our often misguided beliefs: In the absence of sound science, incomplete information can powerfully combine with the power of suggestion (helping us hear Satanic lyrics when "Stairway to Heaven" plays backwards, for example). In fact, a common thread that runs through beliefs of all sorts, he says, is our tendency to convince ourselves: We overvalue the shreds of evidence that support our preferred outcome, and ignore the facts we aren't looking for.
He writes a monthly column for Scientific American, and is an adjunct at Claremont Graduate University and Chapman University. His latest book is The Believing Brain: From Ghosts and Gods to Politics and Conspiracies—How We Construct Beliefs and Reinforce Them as Truths. He is also the author of The Mind of the Market, on evolutionary economics, Why Darwin Matters: Evolution and the Case Against Intelligent Design, and The Science of Good and Evil. And his next book is titled The Moral Arc of Science.
Michael Shermer | Speaker | TED.com