How to read the genome and build a human being

The next 60 minutes,
I'm going to take you on a journey

that is probably the biggest dream
of humanity:

to understand the code of life.

So for me, everything started
many, many years ago

when I met the first 3D printer.

The concept was fascinating,

a 3D printer needed three elements:

a bit of information, some raw material,
some energy,

and it can produce any object
that was not there before.

I was doing physics, I was coming back
home

and I realized that I actually always knew
a 3D printer and everyone does.

It was my mom.

(Laughter)

My mom takes three elements:

a bit of information, which is between
my father and my mom in this case,

raw elements and energy
in the same medium, that is food.

And after several months, produces me,

and I was not existent before.

So a part from the shock of my mom
discovering that she's a 3D priner,

I immediately got mesmerized
by that piece, the first one, the information.

What amount of information takes
to build and asemble a human?

Is it much? Is it little?

How many thumb drives can you fill?

Well, I was studying physics
in the beginning

and I took this approximation
of a human

as a gigantic lego piece.

You can imagine that the building blocks
are little atoms

and there is a hydrogen here, a carbon here,
a nitrogen here.

So in the first approximation,

if I can list the number of atoms
that compose a human being,

I can build it.

Now you can round some numbers

and that happens to be
quite an astonishing number.

So the number of atoms,
the file that I will save in my thumb drive

to assemble a little baby

will actually fill an entire Titanic
of thumb drives.

Multiplied 2,000 times, this is
the miracle of life.

Every time you see from now on
a pregnant lady,

she's assembling the biggest
amount of information

that you will ever encounter.

Forget big data, forget anything
you heard of.

This is the biggest amount
of information that exists.

(Applause)

But nature, fortunately, is much smarter
than a young physicist

and in 4 billion years, managed
to pack this information

in a small crystal we call DNA.

We met it for the first time in 1950
when Rosalind Franklin,

an amazing scientist,
a woman,

took a picture of it.

But it took us more than 40 years
to finally poke inside a human cell,

take out this crystal,

unroll it, and read it for the first time.

While the code comes out to be
a fairly simple alphabet,

four letters: A,T,C and G.

And to build a human,
you need 3 billion of them.

3 billion, how many are 3 billion?

Doesn't really make
much sense as a number?

So I was thinking how
I could explain myself better

about how big and enormous
is this code.

But there is...I'm going
to have some help

and the best person who is
going to help me introduce the code

is actually the first man
to sequence it, Dr. Craig Venter.

So welcome on stage Dr. Craig Venter.

(Applause)

Not the man in his flesh,

but for the first time in history

this is the genome of a specific human
printed page -by-page, letter-by-letter,

262,000 pages of information,

450 kilograms shipped from
the United States to Canada

all the thanks to ?,

it was an amazing feat.

But this is the visual perception
of what is the code of life,

and now, for the first time,
I can do something funny.

I can actually poke inside it
and read.

So let me take some interesting book
like this one.

I have an annotation, it's a
fairly big book.

So just to let you see
what is the code of life.

Thousands and thousands
and thousands

and millions of letters.

And they apparently
make any sense.

Let's get to a specific part.

Let em read it to you:

(Laughter)

"AAGAATATA."

To you it sounds like mute letters,

but this sequence gives
the color of the eyes to Craig.

I'll show you another part
of the book.

It's actually a little more
complicated.

Chromosome 14, book 132:

(Laughter)

As you might expect.

(Laughter)

"ATTCTTGATT."

This human is lucky, because if
you miss just two letters in this position,

two letters out of 3 billion,

he will condemned to
a terrible disease:

cystic fibrosis.

We have no cure for it, we don't
know how to solve it,

and it's just two letter of difference
from what we are.

A wonderful book, a mighty book,

a mighty book that helped me understand
and show you something quite remarkable.

Every one of you, what makes me, me
and you, you

is just bout 5 million of these,
half a book.

For the rest, we all absolutely
identical.

500 pages if the miracle
of life that you are.

The rest, we all share it.

So think about that again
when you think you are different.

This is the amount that we share.

So now that I have your attention,

the next question is,
how do I read it?

How do I make sense of it?

Well, for how good you can be
at assembling Swedish furnitures,

this instruction manual is nothing
crack in your life.

(Laughter)

And so, in 2014, two famous TEDsters
Peter Diamandis and Craig Venture himself

decided to assemble a new company
and Human Longevity was born,

with one mission;

trying everything we can try and learning
everything we can learn from these books

with one target; making real
the dream of personalized medicine.

And understanding what things
should be done to have better health

and what are the secrets in these books.

An amazing team, 14 other scientists
and many, many more people,

a pleasure to work with,

the ceoncept is actually very simple.

We're going to use a technology
called machine learning.

On one side, we have genomes,
thousands of them.

On the other side, we collected
the biggest database of human beings:

phenotypes, 3D scan, MRI, everything
you can think of.

Inside there, on the two opposite sides,
there is the secret the translation.

And in the middle, we build a machine.

We build a machine
and we train a machine --

well, not exactly one machine,
many, many machines --

to try to understand and translate
the genome in a phenotype.

What are those letters and what
do they do?

It's an approach that can be used
for everything,

but using it in genomics is particularly
complicated.

Little by little we grew and we wanted
to build different challenges.

We start from the beginning
from common traits.

Common traits are comfortable
because they are common,

everyone has them.

So we started to ask our question,
can we predict height?

Can we read the books and predict
your height?

We actually can, with
5 centimeters of precision.

BMI is fairly connected
to your lifestyle,

but we still can, we get in the ballpark,
kilograms of precision.

Can we predict the eye color?

Yeah, we can.

80 percent accuracy.

Can we predict the skin color?

Yeah we can, 80 percent accuracy.

Can we predict age?

We can, because apparently the code
changes during your life.

It gets shorter, you loose pieces,
it gets insertions.

We read the signals
and we make a model.

Now, an interesting challenge.

Can we predict a human face?

It's a little complicated because
a human face

is scattered among millions
of these letters

and a human face is not
a very well-defined object.

So, we had to build an entire tier of it

to learn and teach a machine
what is a face

and embed and compress it,

and if you're comfortable with
machine learning,

you understand what is the challenge.

Now, after 15 years, 15 years after
we read the first sequence,

this October, we started to see
some ?

and it was a very emotional moment.

What you see here is a subject
coming in our lab.

This is a face for us.

So we take the real face of a subject,
we reduce the complecity

because not everything is in your face,

lots of features and defects
and asymmetries are coming from your life.

We symetrize the face and we run
our algorithm.

The results I show you right now,
this is the prediction we have

from the blot.

(Applause)

Wait a second.

In these seconds, your eyes are watching,
left and right, left and right,

and your brain wants those pictures
to be identical.

So I ask you to do another exercise
to be honest.

please search for the differences,
which are many.

The biggest amount of signal
comes from gender,

then there is age, BMI,
the ethnicity component of a human.

And scaling up after that signal
is much more complicated.

What you see here, even in the
differences,

let's you understand that we are
in the right ballpark.

That we are getting closer
and it's already giving you some emotions.

This is another subject that
comes in our lab

and this is a prediction.

A little smaller face, we didn't get
the complete cranial structure,

but still, it's in the ballpark.

This is a subject that comes in our lab,
and this is the prediction.

So these people have never been seen
in the training of the machine,

these are the "held out set" ?

But these are, as well, people that you'll
probably never believe.

We are publishing everything
in a scientific publication,

you can read it.

Since we are onstage,
Chris challeneged me.

I probably exposed myself
and tried to predict

someone that you might recognize.

In this vile of blood, and believe me,

you had no idea what we had to do
to have blood now, here.

In this vile of blood, there is mountain
of biological information

that we need to do
a full genome sequence on.

We just need this amount.

We ran this sequence and I'm
going to do it with you.

And we start to layer up
all the understanding we have.

In the vile of blood, we predicted
he's a male, the subject is a male.

We predict that he's a meter and 76,

the subject is a meter and 77.

We predict that he's 76,
the subject is 82.

We predict his age, 38.

The subject is 35.

Predict his eye color?

Too dark.

Predict his skin color?

We are almost there.

That's his face.

Now, the reveal moment
that the subject is this person.

And I did that intentionally.

I am a very particular
and peculiar ethnicity.

Southern European Italians, they never
fit in models.

And it's particular that ethnicity
is a complex corner case for our model.

But there is another point.

So, one of the things that we
use a lot to recognize poeple

will never be written in the genome,
it's our free will.

Not my haircut in this case,
but my beard cut.

So I'm going to show you, I"m going to,
in this case, transfer it --

and this is nothing more than photoshop,
no modeling -- the beard on the subject.

And immediately, we get
much, much better and filling.

So, why do we do this?

We certainly don't do it
for predicting the height

or taking a beautiful picture
out of your blood.

We do it because the same technology
and the same approach,

the machine learning of this code
is helping us to understand how we work,

how your body works,

how your body ages,

how disease generates in your body,

how your cancer develops,

how drugs work and if they work
on your body.

This is a huge challenge,

this is a challenge that we share with
thousands of other researchers

around the world.

It's called personalized medicine.

It's the ability to move from
a statistical appraoch

where you're a dot in the ocean,

to a personalized approach,

where we read all these books

and we get an understanding of exactly
how you are.

But it is a particularly
complicated challenge

because of all these books,
as of today,

we probably know just 2 percent:

4 books out of 175.

And this is not the topic of my talk

because we will learn more.

There are the best minds in the world
on this topic.

The prediction will better,

the model will get more precise.

And the more we will learn,

the more we will be confronted
with decisions

that we never had to face before

about life, about death,
about parenting.

So, this conversation....we are touching
the very inner detail of how life works.

And ti's a revolution
that cannot be confined

in the domain of science or technology.

This must be a global conversation.

We must start to think of the future
we're building as a humanity.

We need to interact creatives,
with artists, with philosophers,

with politicians,

everyone is involved because it's
the future of our species.

Without fear, but with the understanding
that the decisions

that we will take in the next year
will change the course of history forever.

Thank you.

(Applause)