man: There's been many innovations over the years
that drive science.
If you go back in history,
many scientific insights actually derived from
new tools that were able to measure new things.
AI is very, very good at finding new paths
that haven't been seen before.
It's almost an enhancement on our ability to sense the world.
man: The main benefit of machine learning
is the ability to learn from lots and lots of data.
man: We can show a computer a lot of examples,
and rather than tell a model how to evaluate that data
can learn actually how to interpret it
and figure out what to do next.
woman: Here at Google, we've been using
machine-learning technologies in all of our products,
things like Search, Translate, Google Photos,
and the Assistant.
man: People are realizing that learning from examples
is a very powerful tool.
man: So researchers in the health care community
were looking at some of the work that Google was doing
in artificial intelligence and actually reached out to us
and said, "Is there a way that you could apply
these same kind of technologies in health care?"
Doctors today have more artificial intelligence
working for them on their smartphone
for their personal use than they have working for them
in the clinical context.
woman: And as doctors, we've just gotten more information
kind of shoved at us in terms of records,
in terms of images.
woman: Think about all the different type of cells
that you have.
man: 15,000, 20,000 different diagnosis codes.
man: Just a cubic millimeter of tissue,
it's like taking, you know, a billion photos.
man: And if you print it out, it would probably be
about as tall as a ten-story building.
man: Really any kind of data that people can generate
on a massive scale is really an area
where machine learning can help.
[upbeat music]
man: Now there's an opportunity to use all our digital
technologies that have been developed at Google
to really try to help doctors.
man: Let's take some of these tools that are great
for analyzing videos and YouTube and apply them
to problems that matter to science.
man: One of the things we've been working on
in medical imaging is in the area of pathology.
Traditionally, pathologists take some tissue sample,
and they look around in a sea of cells
looking for the kind of needle in a haystack cancerous tissue.
man: We know that the earlier you detect the cancer
the greater your chance of curing it
and the greater your chance of curing it
without chemotherapy or radiation.
So the biggest challenges are speed
and accuracy of diagnosis.
So far we've trained models for breast cancer
and prostate cancer.
woman: These technologies can actually identify
suspicious areas to direct the doctor's attention.
man: So one of the things we wanted to do was get
this work into the hands of as many people as possible.
man: And so we developed something called
the augmented reality microscope,
where you can actually see machine learning assistance
overlaid in real time as you're looking
through the microscope.
man: These units can be attached onto
any existing microscope greatly reducing the cost
We're really excited to bring machine learning
to parts of the world with limited access.
[light vibraphone music]
man: One of the tools some biologists use is,
they will dye cells with different colors
to highlight certain important features
that make sense to them.
man: The problem is, you have to kill the cells
in order to color them.
man: So we said, "Well, if we can do this virtually
in a computer, we can preserve the cell
in its natural state."
man: One of the things that you can do on your pixel phone
right now is given a selfie, you can predict the depth,
and you can do interesting visual effects.
So we thought, "Hey, can we take this same technology
and apply it in a biological context"
and essentially use machine learning
to predict the stain.
We weren't sure whether this would work or not,
and it ended up working so well.
We're very hopeful that this technology can be used
to just have the computer generate the pretty pictures
that people know how to interpret.
[orchestral music]
man: The brain is probably the most complex
physical object in the known universe.
We know that there are these basic units called neurons,
and they're connected in many different ways.
What's shocking is how little is known about
what those patterns of connectivity look like
and what that means for how the brain works.
The problem is very hard,
because the connections are too large to analyze.
For example, a fly brain has 100,000 neurons,
whereas a human brain has 100 billion neurons.
So fortunately, at Google, you know,
there's been already a lot of work put into dealing
with data sets of that size.
Machine learning and the computer vision technology
that we've developed has been designed
to accurately trace the wiring of the brain in 3-D.
Prior to that technology,
it would have taken thousands of years
to basically finish mapping the fly brain.
Now you can do it within a year or two.
It's a warm-up to understanding larger and more complex brains,
hopefully human brains.
We're hoping that mapping a brain
could potentially help us understand
a lot of the neurodegenerative disorders--
for example, Schizophrenia or Parkinson's--
then we're gonna be able to design better therapies
that might improve those conditions.
[delicate piano music]
man: There are tremendous inequities
in the way that health care is distributed across the globe.
Any disease or outcome is predicted
as much by your zip code as it is by your biology.
man: So what can we do with AI to bring the expertise
to where no expertise exists?
woman: One of the complications of diabetes
is diabetic retinopathy.
It causes blindness,
and it's diagnosed by seeing little lesions in the eye.
But in India, there is a shortage of eye doctors.
And as a result, about half the patients suffer
some form of vision loss.
This disease is completely preventable.
This shouldn't really be happening.
So we were able to train a model to read these images
and match board-certified ophthalmologists.
We're now figuring out how to deploy this into the clinic.
In India, people who did not have access now have access.
[light piano music]
man: A lot of science are is driven by a hypothesis
that someone has.
But some of the biggest breakthroughs in science
come from surprises, things no one expected to have happened.
man: One of the really exciting things about deep learning
is where you can just give it the raw data,
and it finds the important features.
That's interacting with scientists
at a very different level than saying,
"Well, let me show you something
you've never seen before."
man: We had one research project,
we were looking at human retinas.
But what surprised us was machine learning started
seeing things that people didn't know was possible.
woman: Turns out that a deep-learning model
is actually able to identify things
that have nothing to do with your eyes,
like your cardiovascular health and your metabolic profile.
These are things that if you had asked experts,
"What do you think we'll find?"
They would have said nothing.
man: The important thing about this is,
it's a visual biomarker that we did not know existed before.
man: Those unexpected things may lead to a whole new idea,
a whole new approach, a whole new hypothesis
for how to attack the problem you're trying to address.
man: There are real patients suffering today,
and if we're not doing everything we can
with all the technology at our disposal
to help those patients, then what are we doing?
man: I think that 10 or 15 years from now,
the use of machine learning in health care
is just going to be how health care is one
woman: I think this is how we're gonna find
new discoveries.
I think this is how we're gonna find ways
to care of more people.
man: The more we can accelerate that basic work
and give all these scientists new tools,
we will as humans really benefit
from the new discoveries that one day end up
in our doctors' offices.