[APPLAUSE] >> Hi everyone, thanks for
being here. It's my honor to be here to, my name is Jia Li.
I'm the head of R&D at Google Cloud AI.
I've also been a researcher throughout my career.
These rules have allowed me to participate in every step
that AI needs to go from vision to reality.
I've been fortunate enough to participate in algorithm
research, building data sets, and shipping products in
reality AI is an incredibly, interesting and
exciting technology. And today, I'm here to share
some of the avenues that it could change life for
millions of people. To start with, I'd like to talk about
two very traditional industries, education and
healthcare. As we know, healthcare
is a very complex field with a lot of challenges.
AI could have the potential to change the outcome
from individual patients to the entire hospitals.
Healthcare typically start with the patient's lifestyle.
AI could help to provide accurate guidance to their
lifestyle, diet, etc. Based on their past disease history,
genetics and prescriptions, etc, etc.
It can also provide automated monitoring and
early assessment of critical conditions
associating subtle precursors of signals that could
correlate to emergent critical conditions that
a human would not be able to detect.
When a hospital visit is necessary, AI can play
additional roles to help provide deep insights during
and before and after the patient and the doctors when
one session. It could also help ease the workflow for
doctors by automatically transcribing the session and
filling out paperwork. AI
could even provide deeper assisting diagnosis so
that our doctors could be able to provide sophisticated
diagnosis. Once the diagnosis recommendation is made,
artificial intelligence can also help to provide further
treatment strategies including change of lifestyle,
prescription, surge, surgery, etc or all of them above.
When a long term stay is necessary whether a surgical
patient or a senior patient in senior care,
intelligent system can provide further help to reduce
the burden of nurses and doctors making rounds.
It can help to predict abnormal signals
such as falling and agitated movement, etc.
In addition, machine learning can also help the entire
hospital to learn much more efficiently. Patient
triage will take multiple patients medical record and
help to ensure care is carefully designed and
distributed. In some cases,
medical conversation agents will help the patients to
understand their symptoms without leaving their house in
the first place. Here, I'd like to talk more
about some of the new research that I have participated in,
specifically the thoracic disease identification and
the localization research. As some of you probably know,
diagnosis skill is a very delicate skill.
Some of the even very tiny mistake,
could cost very severe consequences. In fact,
10% of patient deaths is related to diagnosis errors.
And according to professor, Kurt 4% of all
radiological interpretations contain clinically
significant errors. This number is especially
significant if we consider that over 400 million such
medical interpretations are carried out each year in
the United States alone. So let's look at the chest
X-ray disease identification problem even further, chest
X-ray remains a significant radiology challenge.
Radiologists have to invest significant effort
to understand a go through every single
radiology image in order to make diagnosis recommendation.
If we can have some AI-assisted the tool for them
to get more insights about the real radiology image, for
example, we could to predict the abnormal area of some
potential disease in the radiological image.
That will help them to ease the process and
make the entire process much more efficient.
However, we're facing a chicken and egg problem here.
First, we know our radiologists are facing a lot
of challenges and effort in order to get through all
the medical images in order to give their interpretation.
We want to invent an AI assisted tool in order
to make lab entire process much more efficient. But
in order to do so, we need to get additional data and
ask our radiologists to label a lot of data, to train our
method to build models. This goes back to the exact
problem that we want to help the radiologists to ease.
So in order to solve this problem,
we tried to turn to the open source
NIH Chest X-Ray data set. This is a fairly large data
set with over 100,000 radiology images.
Each of the images is associated with about up to
14 disease labels, mined automatically from the report,
which is relatively easier to get. And as you can see here,
less than 1,000 of the images has found bounding
boxes associated to them. Which would, each of them
would require a board certified radiologist to label
the bounding box, and that will require a lot of efforts.
So typically, this kind of data set is not well suited
for traditional supervised learning, which require a lot
of detailed labeling data. So,
towards this problem we come up with a novel approach by
combining the holistic global information about the disease,
as well as the local detailed annotation. And
we are able to predict both the disease type based on
the global information, as well as the local
predicted area and highlight where the abnormal areas
disease types could be. And overall disease prediction and
suspicious region highlights, works much better than state
of the art machine learning approach.
We're just at the beginning of this direction, and
we're not alone. There are many partners and
customers who are leveraging Google Cloud.
For example Zebra Medical are using Google Cloud to
analyze new scans, and deliver insights to hospitals,
to inform clinical decisions at scale. But
there are still much more remains to be explored and
innovated in this space. Hopefully in the future our
specialists can spend less time on repetitive and
error prone tasks by working together with AI assisted
tools Another area that AI could help is education.
As we know, education is another very traditional
field that is facing a lot of challenges.
It needs to balance the need of students and teachers,
with the complexity of schools and resources. AI
could unlock a lot of unique potential solutions here.
To start with, AI could help to ensure our students
have a very safe environment to study. And
prevent them from dangerous actions such as falling,
fighting, or any other dangerous activities. So
that our educators can focus on teaching, and artificial
intelligence systems can help taking care of, of the rest.
So, more potential would exist in the education experience
itself. Artificial intelligence algorithms
could help to customize courses that is personalized
to each of the students, based on their past experience,
strength, weakness, and personal preferences, etc.
It can also turn abstract examples to be very vivid in
real world applications and examples.
And it could help our teachers to scale up the effort
by doing automated homework and exams assessment.
So, this kind of experience can repeat
through the course, over the course of semester a year, and
even the entire education experience. So
that we can provide highly personalized experience to
each of the individual students. And best of all,
such technologies can be both applied to STEM, as
well as the Arts. For example we can easily extend some of
the technology to a students dance and violin performance.
So, I have talked about how AI could help potentially change
healthcare and education in the future. What about
the countless other businesses beyond healthcare and
education? The real power of AI can be felt once
its power can be leveraged by every possible business.
But that's a very challenging problem. As we know that
Machine Learning development is a very complex and
resource-consuming process.
It will require investment and
expertise in every single step of the Machine Learning
developments. Collect the data, design model,
turn model into, tune model parameters,
evaluate, deploy it and finally update and
iterate the entire process. It will be
challenging for most of the businesses because of,
of the over 21 million developers,
only 1 million of them have data science background. And
even fewer, like thousands, have deep learning background.
How do we solve this problem? We made,
we made some attempts towards the solution of this
by introducing the AutoML technology. All we need to
bring to AutoML is data that we want to label and predict.
And AutoML will handle everything from there.
It gives the opportunity for any business or
organization who wants to create customized models with
very limited Machine Learning expertise. Earlier this year,
we've introduced the AutoML vision product. Basically,
the idea is the customers can upload and
bring their labeled images. And AutoML technology will
generate a customized visual recognition model,
based on the data that they want to predict.
Here is an example, lets see how we do, we could do
weather prediction, weather image classification.
Here, there a ten, over ten different kinds of clouds.
Each of them indicates a different weather pattern.
If we use the generically trained visual models,
here is what we are going to get. Will be easy to
predict there is sky and cloud but, we won't be able to know
what kind of weather Or what kind of cloud there is.
Now if we try to upload all these domain-specific training
images to AutoML Vision, here is what we can get.
AutoML Vision can learn what specific cloud or
weather it means and give the prediction here, for
example cirrus here. And
AutoML is a product that based on
multiple advanced technologies including learning to learn,
neural architecture search, transfer learning,
hyperparameter tuning and more. Now let's take
a look at how, about how our customers are using AutoML.
Zoological Society of London is a very good example.
It is a non-profit organization that uses camera
trap to track the wildlife population over the world.
But that would generate millions of unlabeled images
for them, to manually label each of the image as one of
the wild animal type. So
Zoological Society of London has been closely collaborating
with our team to shape the AutoML product. And
now they are able to automatically label
different wild animal types by using AutoML.
And we're very excited. The potential of AI could bring
to the po, the way we protect wild animal.
Another example is Disney. Disney is an early
adopter of motion learning and the cloud platforms.
That changed the way they interact with the customers,
and they extend their ability of visual recognition
to recognize product images using AutoML.
Now they are able to automatically detect
characters and brand animal, elements, such as logo and
color schemes. And by leveraging this ability they
are now able to provide more relevant search results and
product recommendations.
Another example is tactile graphics. For
those who are not familiar with tactile graphics.
It is a special type of images designed for blind to
understand the content. It is very challenging to
design such graphics because it needs to be drawn without
perspective. Needs to be very simple and clear so
that the blind readers can understand the content
without being distracted by other unnecessary details.
Because of the challenge of designing it,
it's very, different countries all over the world.
They are collecting these tactile graphics into
repositories for reuse purpose.
However, these repositories
they're not connected. So
a group of researchers try to use AutoML to differentiate,
what is a good tactile graphics? What is not? And
then, they can search online and find good tactile graphics
candidates. Now, content publishers for
the blind are able to find good tactile candidates for
their readers to understand.
So AutoML is part of the effort towards the trend
of democratizing AI. The real meaning of it
is not just about how powerful a technology is. It is about,
also about how accessible it is. AutoML Vision
is just one of the features that we've democratized and
we've seen so powerful examples from Disney,
from Zoological of London, from the tactile graphics
search engine, it's impact. We've seen that,
in Disney, that we are able to enhance the retail experience
of one of the world's largest retailers. And we've
been able to empower wildlife conservation in a scale that
we've never been possible to do in the past. And
we've also helped to improve interaction with the blind.
AutoML Vision is just the beginning. We are going to
also extend these to more features such as speech,
natural language processing, and translation, and more,
to bring more of these features to other fields.
AutoML Vision, as a single feature,
can already do so much.
We're very excited to see what the next wave can unlock.
Technologies like AutoML point to an exciting future,
in which AI is available to everyone in a format
that is easy to use regardless of what kind of
problems you want to solve. But
solving a concrete problem isn't enough yet.
It's important, it's equally important to understand
what kind of problems we need to solve and
to understand what people need. In business,
in academia, healthcare, entertainment, and
countless other fields that are driving our society today.
AI is an incredibly exciting direction. And
the most exciting about it is its potential to
make life better for all of us.
I hope that every one of us can contribute to this effort
to make AI even more impactful. Thank you.
>> [APPLAUSE]
>> Great, thank you very much,
Jil. So we have plenty of time for questions,
so please you know the drill, raise your hand if you have
a question. And the mic will come through.
Here, yep. >> Thank you.
Hi, I had a question regarding,
so as the models are abstracted, and even combined,
and this becomes more accessible, what tools do you
have for introspection on why and how a prediction was made?
So for example say a retailer wants to identify a potential
shop lifter. >> Very good question.
So, basically in order to understand what
kind of like technology we can
offer to different users we are also trying to understand
what kind of problems they want to solve, right? So
in the case of a retailer, wants to identify shoplifter,
they will help us to define what is a shoplifter and
we can help them to come up with just the technology that
to help that. >> Hi, my name's Samantha and
I work at USAA as a software engineer. My question to
you is, I mean, it's really obvious, everyone in this room
that, you know, the need for machine learning and
artificial intelligence in our community is prominent. But
what are you doing, or how do you build a product that
recognize the complexity around these type of
techniques? And, you know, you're going for
education and scalability with these projects or products so
that everyone can utilize this technique but
what are you doing to mitigate the risk of the misuse of
these techniques? And the misuse of these products,
right? Because I mean, I we've heard it today from Latonya
and, I think from Daniella like there is a huge risk
in using these techniques and the need for basic
statistics etc., is obviously prominent. So, what are you
doing to kind of mitigate that when you're building products
for widespread use? >> That's a very good
question. I think as technologists and
researchers this is very important question for
us to explore how, and
make sure how AI technology can be used only for
good purpose. In fact, at Google we have a internal team
who are especially focusing on this kind of problem,
how to understand bias. How to understand, and
how to make sure there is no misuse of technology.
I have to say we are all of us at the early stage. This is
some serious topic that we should all contribute and
explore down the road. >> Hi,
great by the way, thank you. So AI in education and
the arts for our children actually scares me,
especially when you're talking about courses, tests,
and even learning music tailored, customized for
each child's preferences and maybe even their biology.
As humans we get to challenge each other to think outside
the box, to dream, to learn what we thought we couldn't
learn, to become wiser. What is Google's vision and
promise around AI in education?
>> [COUGH] Thank you.
Wow, [LAUGH] that's a very big question. So,
here I'm listing out some of the potential AI,
AI research that we could, we could make education
software are more powerful to assist our teachers.
The goal is to hope that with more intelligent system and
intelligent algorithms our teachers can focus
on creative and less repetitive work. And
hope to maximize everybody's interest,
every students' interest and
capability during this education
experience. >> [INAUDIBLE]
>> Just a second,
we have a mic there.
Yeah, it's just a- >> Hi,
I had a question about some data you showed earlier on
this slide where we looking at just X-ray images and
you need label data. I have a naive question, but
I always wondered if you can just look across time to
eventually when a patient did show symptoms of some disease
you were trying to diagnose. And then go back and say, yes,
this patient did have this disease and
use that as the label. Do you know if that's possible or
if that's too ambitious? >> That's
definitely possible and it's a very good question.
We have been working closely with radiologists to
understand what's their real need? Because in the field
people are focusing on giving a radiology image and
trying to come up with a disease label. And after we
talk to many specialists they are telling us,
this is not what we want, because we have so
much other information. That we can get,
either from the patient, the disease history, or
from other signals, from different reports etc. And
it's more helpful to give us the indicator or
some proposal an abnormal area. That's eventually how we
come up with the idea to give assisted recommendation and
trying to give some of the recommendation about
abnormal area in our research. Hopefully by leveraging
the useful information come up from the AI, assisted tool,
the specialists com, combine with many other information,
information source they have to come
up with the best solution or decision in the radiology
analysis. >> So
I'm a student in business analytics currently, and
since you're an expert in artificial intelligence and
machine learning, I was wondering what your
experience has been in artificial intelligence
potentially creating a feedback loop. So
in the example of a potential shop lifter, for example,
if we're identifying what a shop lifter is,
that can create a feedback loop about shop lifters
and in future state that could change, so are these model's
dynamic. What are some of the challenges that
you've experienced with feedback loops in,
on the different types of artificial intelligence,
intelligence studies that you've done?
>> Exactly,
I think it's totally possible to keep
create the feedback loop and feedback loop would make it,
make any AI system to be more powerful and
effective. Some of the simple example as you,
some of you probably know the recommendation system. So,
based on how many clicks you've links that you've
clicked. Proposed by
the previous AI system.
We can learn a better AI algorithm based on that. And
that's one simple example in the more mature
direction. But there are many other fields
that we are still experiment,
experimenting and are trying to learn
how much we can improve. >> Hi,
>> One last question,
yes. >> Thank you,
the lucky one. >> [LAUGH]
>> So, when you,
when you talked about AI assisted diagnostic in
the health care industry there are other players in this
industry, particularly IBM Watson, who has had a lot of
coverage in that space. As a leader in the AI space,
in the machine learning and LP space. Can you, you know,
tell us the different approach that Google has taken versus
the other vendors? What's the niche area that you play
compared to the rest of the players in the this industry?
>> A very good question.
I have less access to other [LAUGH] company's solution,
but at Google. We really focus on collaborating closely with
our customers, for example, hospitals and specialists,
trying to understand what's the real need. And
try to bridge the gap between the technology and
the real solution. And you mentioned that there are many
players in this field. I want to say in health care,
that's the field we want as many players as possible.
We want everybody to contribute
to this space to help all of our life to be better.
>> That is a very nice and
political answer. >> [LAUGH]
>> Thanks very much again,
Lee, for talking to us today, congrats.
>> [APPLAUSE]
>> Yes
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