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For the next 16 minutes,
I'm going to take you on a journey
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that is probably
the biggest dream of humanity:
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to understand the code of life.
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So for me, everything started
many, many years ago
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when I met the first 3D printer.
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The concept was fascinating.
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A 3D printer needs three elements:
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a bit of information, some
raw material, some energy,
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and it can produce any object
that was not there before.
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I was doing physics,
I was coming back home
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and I realized that I actually
always knew a 3D printer.
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And everyone does.
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It was my mom.
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(Laughter)
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My mom takes three elements:
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a bit of information, which is between
my father and my mom in this case,
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raw elements and energy
in the same media, that is food.
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And after several months, produces me,
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and I was not existent before.
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So apart from the shock of my mom
discovering that she was a 3D printer,
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I immediately got mesmerized
by that piece,
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the first one, the information.
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What amount of information does it take
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to build and assemble a human?
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Is it much? Is it little?
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How many thumb drives can you fill?
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Well, I was studying physics
at the beginning
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and I took this approximation of a human
as a gigantic Lego piece.
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So, imagine that the building
blocks are little atoms
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and there is a hydrogen here,
a carbon here, a nitrogen here.
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So in the first approximation,
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if I can list the number of atoms
that compose a human being,
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I can build it.
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Now, you can round some numbers
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and that happens to be
quite an astonishing number.
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So the number of atoms,
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the file that I will save in my thumb
drive to assemble a little baby,
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will actually fill an entire Titanic
of thumb drives --
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multiplied 2,000 times.
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This is the miracle of life.
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Every time you see from now on
a pregnant lady,
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she's assembling the biggest
amount of information
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that you will ever encounter.
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Forget big data, forget
anything you heard of.
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This is the biggest amount
of information that exists.
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(Applause)
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But nature, fortunately, is much smarter
than a young physicist,
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and in four billion years, managed
to pack this information
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in a small crystal we call DNA.
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We met it for the first time in 1950
when Rosalind Franklin,
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an amazing scientist, a woman,
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took a picture of it.
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But it took us more than 40 years
to finally poke inside a human cell,
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take out this crystal,
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unroll it, and read it for the first time.
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The code comes out to be
a fairly simple alphabet,
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four letters: A, T, C and G.
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And to build a human,
you need three billion of them.
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Three billion.
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How many are three billion?
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It doesn't really make
any sense this number, right?
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So I was thinking how
could I explain myself better
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about how big and enormous this code is?
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But there is -- I mean,
I'm going to have some help,
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and the best person to help me
introduce the code
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is actually the first man
to sequence it, Dr. Craig Venter.
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So welcome onstage, Dr. Craig Venter.
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(Applause)
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Not the man in the flesh,
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but for the first time in history,
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this is the genome of a specific human,
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printed page-by-page, letter-by-letter:
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262,000 pages of information,
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450 kilograms, shipped
from the United States to Canada
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thanks to -- Bruno brought it onstage,
Lulu.com, a start-up, did everything.
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It was an amazing feat.
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But this is the visual perception
of what is the code of life.
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And now, for the first time,
I can do something fun.
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I can actually poke inside it and read.
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So let me take an interesting
book ... like this one.
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I have an annotation;
it's a fairly big book.
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So just to let you see
what is the code of life.
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Thousands and thousands and thousands
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and millions of letters.
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And they apparently make sense.
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Let's get to a specific part.
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Let me read it to you:
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(Laughter)
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"AAG, AAT, ATA."
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To you it sounds like mute letters,
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but this sequence gives
the color of the eyes to Craig.
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I'll show you another part of the book.
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This is actually a little
more complicated.
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Chromosome 14, Book 132:
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(Laughter)
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As you might expect.
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(Laughter)
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"ATT, CTT, GATT."
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This human is lucky,
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because if you miss just
two letters in this position --
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two letters of our three billion --
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he will be condemned
to a terrible disease:
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cystic fibrosis.
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We have no cure for it,
we don't know how to solve it,
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and it's just two letters
of difference from what we are.
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A wonderful book, a mighty book,
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a mighty book that helped me understand
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and show you something quite remarkable.
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Every one of you -- what makes
me, me and you, you --
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is just about five million of these,
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half a book.
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For the rest,
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we are all absolutely identical.
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Five hundred pages
is the miracle of life that you are.
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The rest, we all share it.
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So think about that again
when we think that we are different.
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This is the amount that we share.
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So now that I have your attention,
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the next question is:
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How do I read it?
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How do I make sense out of it?
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Well, for however good you can be
at assembling Swedish furniture,
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this instruction manual
is nothing you can crack in your life.
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(Laughter)
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And so, in 2014, two famous TEDsters,
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Peter Diamandis and Craig Venter himself,
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decided to assemble a new company.
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Human Longevity was born,
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with one mission:
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trying everything we can try
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and learning everything
we can learn from these books,
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with one target --
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making real the dream
of personalized medicine,
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understanding what things
should be done to have better health
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and what are the secrets in these books.
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An amazing team, 40 data scientists
and many, many more people,
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a pleasure to work with.
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The concept is actually very simple.
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We're going to use a technology
called machine learning.
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On one side, we have genomes --
thousands of them.
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On the other side, we collected
the biggest database of human beings:
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phenotypes, 3D scan, NMR --
everything you can think of.
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Inside there, on these two opposite sides,
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there is the secret of translation.
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And in the middle, we build a machine.
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We build a machine
and we train a machine --
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well, not exactly one machine,
many, many machines --
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to try to understand and translate
the genome in a phenotype.
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What are those letters
and what do they do?
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It's an approach that can
be used for everything,
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but using it in genomics
is particularly complicated.
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Little by little we grew and we wanted
to build different challenges.
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We started from the beginning,
from common traits.
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Common traits are comfortable
because they are common,
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everyone has them.
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So we started to ask our questions:
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Can we predict height?
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Can we read the books
and predict your height?
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Well, we actually can,
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with five centimeters of precision.
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BMI is fairly connected to your lifestyle,
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but we still can, we get in the ballpark,
eight kilograms of precision.
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Can we predict eye color?
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Yeah, we can.
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Eighty percent accuracy.
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Can we predict skin color?
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Yeah we can, 80 percent accuracy.
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Can we predict age?
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We can, because apparently,
the code changes during your life.
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It gets shorter, you lose pieces,
it gets insertions.
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We read the signals and we make a model.
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Now, an interesting challenge:
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Can we predict a human face?
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It's a little complicated,
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because a human face is scattered
among millions of these letters.
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And a human face is not
a very well-defined object.
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So, we had to build an entire tier of it
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to learn and teach
a machine what a face is,
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and embed and compress it.
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And if you're [familiar]
with machine learning,
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you understand what the challenge is here.
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Now, after 15 years -- 15 years after
we read the first sequence --
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this October, we started
to see some signals.
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And it was a very emotional moment.
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What you see here is a subject
coming in our lab.
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This is a face for us.
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So we take the real face of a subject,
we reduce the complexity,
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because not everything is in your face --
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lots of features and defects
and asymmetries come from your life.
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We symmetrize the face
and we run our algorithm.
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The results that I show you right now,
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this is the prediction we have
from the blood.
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(Applause)
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Wait a second.
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In these seconds, your eyes are watching,
left and right, left and right,
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and your brain wants
those pictures to be identical.
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So I ask you to do
another exercise, to be honest.
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Please search for the differences,
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which are many.
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The biggest amount of signal
comes from gender,
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then there is age, BMI,
the ethnicity component of a human.
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And scaling up over that signal
is much more complicated.
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But what you see here,
even in the differences,
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lets you understand
that we are in the right ballpark,
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that we are getting closer.
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And it's already giving you some emotions.
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This is another subject
that comes in place,
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and this is a prediction.
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A little smaller face, we didn't get
the complete cranial structure,
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but still, it's in the ballpark.
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This is a subject that comes in our lab,
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and this is the prediction.
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So these people have never been seen
in the training of the machine.
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These are the so-called "held-out" set.
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But these are people that you will
probably never believe.
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We're publishing everything
in a scientific publication,
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you can read it.
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But since we are onstage,
Chris challenged me.
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I probably exposed myself
and tried to predict
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someone that you might recognize.
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So, in this vial of blood --
and believe me, you have no idea
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what we had to do to have
this blood now, here --
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in this vial of blood is the amount
of biological information
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that we need to do a full genome sequence.
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We just need this amount.
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We ran this sequence
and I'm going to do it with you.
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And we start to layer up
all the understanding we have.
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In the vial of blood,
we predicted he's a male.
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And the subject is a male.
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We predict that he's a meter and 76.
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The subject is a meter and 77.
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So, we predicted that he's 76;
the subject is 82.
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We predict his age, 38.
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The subject is 35.
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We predict his eye color.
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Too dark.
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We predict his skin color.
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We are almost there.
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That's his face.
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Now, the reveal moment:
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the subject is this person.
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(Laughter)
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And I did it intentionally.
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I am a very particular
and peculiar ethnicity.
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Southern European Italians,
they never fit in models.
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And it's particular -- that ethnicity
is a complex corner case for our model.
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But there is another point.
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So, one of the things that we use
a lot to recognize people
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will never be written in the genome.
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It's our free will, it's how I look.
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Not my haircut in this case,
but my beard cut.
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So I'm going to show you, I'm going to,
in this case, transfer it --
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and this is nothing more
than Photoshop, no modeling --
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the beard on the subject.
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And immediately, we get
much, much better infilling.
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So, why do we do this?
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We certainly don't do it
for predicting height
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or taking a beautiful picture
out of your blood.
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We do it because the same technology
and the same approach,
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the machine learning of this code,
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is helping us to understand how we work,
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how your body works,
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how your body ages,
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how disease generates in your body,
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how your cancer grows and develops,
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how drugs work
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and if they work on your body.
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This is a huge challenge.
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This is a challenge that we share
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with thousands of other
researchers around the world.
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It's called personalized medicine.
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It's the ability to move
from a statistical approach
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where you're a dot in the ocean,
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to a personalized approach,
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where we read all these books
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and we get an understanding
of exactly how you are.
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But it is a particularly
complicated challenge,
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because of all these books, as of today,
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we just know probably two percent:
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four books of more than 175.
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And this is not the topic of my talk,
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because we will learn more.
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There are the best minds
in the world on this topic.
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The prediction will get better,
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the model will get more precise.
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And the more we learn,
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the more we will
be confronted with decisions
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that we never had to face before
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about life,
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about death,
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about parenting.
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So, we are touching the very
inner detail on how life works.
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And it's a revolution
that cannot be confined
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in the domain of science or technology.
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This must be a global conversation.
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We must start to think of the future
we're building as a humanity.
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We need to interact with creatives,
with artists, with philosophers,
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with politicians.
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Everyone is involved,
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because it's the future of our species.
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Without fear, but with the understanding
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that the decisions
that we make in the next year
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will change the course of history forever.
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Thank you.
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(Applause)