-: Hello, everyone, and welcome back.
I'm Ramesh Raskar associate professor at MIT Media Lab.
And today we're gonna talk about contract racing
reopening businesses with safe paths.
But the session will really be focused on
how can you create privacy preserving AI,
machine learning techniques that do not expect us
to release raw data.
Now safe paths is about building
a privacy preserving platform,
so that we have an app that can do exposure notification,
personalized guidance, symptom tracking,
as well as the dashboard for public health and businesses,
for conversations for predicting hotspots
coordinate at home population,
and using a variety of proximity technology API
such as GPS, Bluetooth, Wifi and others,
and achieving all this coordination,
all this orchestration in a privacy preserving way.
And so Safe Path has three pillars the technology,
which is we published the app, but also we are here
to help everybody else create
their software solutions as well.
And the software solution has three parts,
the app, the server and the dashboards.
And two of the one of the largest open source repos
for COVID-19 solutions, that listing algorithms,
interoperable backbone privacy guidelines in our Think Tank,
convening stakeholders, landscape analysis,
and also the Pathtrick foundation that partners with us
is very busy with implementation,
which is public health engagements, training,
monitoring and so on.
Synthet safe paths has three different outputs.
One is for citizens, which is the app
and information for governments,
we are helping many nations and states
launch their own solutions
with our open source platform
and through our implementation partners,
dashboards, and we're really here to amplify and simplify
the manual contact tracing operation.
And provide different governments and nations
an interoperable backbone so that an individual
who goes from one state to another or crosses border
or is in the roaming phase, they can still get the benefits.
As well as for large employers for back to work solutions.
And so we're delighted that the Alliance has,
of course, MIT faculty,
but the work is also shaped by deep conversations
with WHO, and HHS and Mayo Clinic,
as well as our partner Foundation, Boss Check foundation.
And a lot of the solutions are built on top
of the Google Apple proximity API's where an infected person
who has transmitted the tokens,
or the bluetooth channel can upload those tokens
to a representation of the tokens to the server,
and all the healthy People can download them
from the server to see what they heard
over the bluetooth channel is the same
as the heard over the server.
There's some second order details.
But that's the basic idea and so we're using
the Google Apple API's as well.
And in terms of what an individual experiences,
whether they are exposed or suspected to have COVID-19
is of course exposure notification that can be used by GPS
or using the apple Google protocol.
We support both as well as contact tracing,
which is an ability for the manual contact racer
to call this person and ask for their for the condition,
triage solutions, symptom checking, symptom analysis,
tele health, testing, quarantine verification
resumed daily activities and closing the loop
with public health in case they have infected anybody else
along that path.
The goal however, is to achieve all of this
in a privacy preserving way, every stage
not just exposure notification or contact tracing,
but every stage of the individual's experience
should be privacy preserving,
which means that without asking for name or phone number
or email address, or a national ID number,
absolutely anything at all that's personally identifiable,
we should be able to provide the solutions.
Of course, once you start using the health insurance
they would have to identify themselves but everything else
we should be able to provide in a privacy preserving way.
And by privacy really mean consent,
regulation, trade secrets, even national security issue,
and how do we provide incentive
for individuals to participate?
So by privacy we really mean all these five concepts
and then the flow for businesses
could also look very different.
Because you have to check symptoms at the point of entry,
track interactions amongst employees and workers,
give proximity warning schedule communication,
staggering of events, and if somebody is diagnosed positive,
what's the work flow as well?
So Safe Path is working with many partners,
and piloting many of these ideas.
And beyond a workplace there are many other stakeholders,
there're workers, there're labs, there's insurance players,
the schools who want to figure out how to scan entrances
to attend school, and so on.
And we really want to provide all the solutions
in a privacy preserving way.
So, if you're interested in algorithm
and software and technology
we invite you to join one of the largest repos on GitHub
for code as well as for algorithms.
So we have many algorithms that we have already published
and early white papers, just early drafts.
We're putting them out in real time,
like how to use Wifi in a privacy preserving way,
or privacy guidelines, or Bluetooth, or GPS.
How do you use different machine learning techniques?
what's the landscape of contact tracing solutions and so on.
So please take a look at that.
So let's spend the rest of this webinar on talking about
how can you use machine learning in a COVID-19 situation.
And a lot of this would be because on the phone,
you might want to train neural networks
to deal with things like questionnaire or symptom mapping,
or using sensors such as motion or video or audio.
And some of it even could be for diagnostics.
How can you check pneumonia by using audio?
Or how can you take pictures to do quarantine verification
or doing tele health and so on.
So, we really want to provide the ability to use the phone
as effectively a tricorder in the pocket
and still preserve privacy.
So on the horizontal axis, we have different ways
to preserve privacy and on the vertical axis
is the utility of the data.
And So the simplest form of privacy is anonymization.
But as you know, anonymization is not the same as privacy,
even aggregation and de-identification
does not eliminate the risk of re identification
The next level of privacy is using obfuscation.
Using some kind of a noise
using techniques such as differential privacy.
The problem with that it affects machine learning,
because maybe you can build inference models
but cannot do full a full fledged training,
or learning of machine learning.
Of course, the best form of privacy is encryption
using techniques such as home optical encryption,
and that allows us to do statistical operations,
but again, not full fledged machine learning.
So two techniques have emerged in the last few years.
One is called federated learning from Google,
as well as a technical split learning
from our group here at MIT.
And our goal is to provide sufficient privacy
while creating abilities
to create full fledged machine learning operations.
And the key idea behind these techniques is
to not share raw data, but share the wisdom from the data.
Share the learning share the knowledge from the data
without sharing raw data.
So let me explain how this works.
Imagine for COVID-19 we want to create a master algorithm
to detect pneumonia in COVID-19 patients
from their chest X rays.
And let's say there are 100 hospitals,
each of them with 1000 patients.
Now, it will be amazing if you can get all those
hundred thousand X rays in a central server
and train a machine learning algorithm to very quickly
detect this new type of pneumonia for COVID-19
from those experts, that would have been amazing.
The challenge is that because of regulations
because of data silos because of trade secrets
this hundred hospitals cannot coordinate fast enough
to just provide them to a central server, right.
So this is a loss in time and effectiveness.
So one idea would be we can do
collective intelligence, right?
In simple words, I could just take hundred medical interns
and deploy them in those hundred hospitals.
And each of the intern will learn something specific
of the demographic at that hospitals.
Maybe the first hospital has more diabetics.
Maybe the second hospital has people in 70s and 80s.
Maybe the third hospital has people from a certain
ethnic background, and so on.
So each of these medical interns will learn
how to detect pneumonia in the chest X rays
of that particular population, but they won't understand
how to do it for the other 99 hospitals, which is fine.
At the end of the month, I can invite
all the hundred medical interns back to my office
and say I have a new chest X ray, which one of you
can help me figure out if this has pneumonia or not.
And the concept is that the collective intelligence
of this hundred medical interns would allow us to do that.
Now, the beauty of this is this medical interns
that partially trained on hospital one
and hospital two hospital three,
but they never brought me back
any raw chest X rays or treatment plants
of those patients.
So in that sense, the privacy was preserved.
They've walked out of the hospital just with the knowledge
and wisdom without bringing in any raw data.
Can use the same concept for machine learning.
And that idea is called federated learning where
we can deploy machine learning models
in each of the hospitals.
And they will just train locally that we partially
trained machine learning models, kind of local AI.
And then we can just average all those local AI's
to create one global AI.
What about split learning, which is another way
of creating privacy preserving machine learning.
In this case, it's more like the elephant and the blind man.
And without them realizing that they're looking
at an elephant, each of them can give a feature,
one person could say,
Hey, I'm touching something
that feels like a snake that's six fit long.
Or, another woman on the team could say,
Hey, I'm looking at something that feels like a wall
with this particular type of texture and so on.
So all of them can send a texture.
Sorry all of them can send a feature vector
of what they can describe locally.
of what's going on with this elephant.
So without ever sending the information about the elephant,
and imagine the elephant needs privacy,
they can describe what the elephant looks like
without ever taking a picture of the elephant.
And all the information again can come back
to a central server.
So in case of chest X rays and COVID-19 pneumonia diagnosis,
each of the interns can take notes on each of the X rays.
Hey there's some cloudiness on the top left,
there's a symmetry here and so on.
And they can all pick this features
and send it to the central authority.
And without sending an actual photo of the chest X ray,
we can create this master AI from all the features.
So this idea of transferring wisdom,
rather than transferring raw data, it's critical
for federated machine learning, as well as split learning.
Another interesting thing here is that in split learning,
You start sending the full chest X ray,
which may be set 10 megapixel.
Now we can send just a feature vector which could be just
a few thousand bytes, and we can get
this ridiculous thousand x revenue 10,000 tax reduction
in data so we get not only great privacy,
but we can overcome all challenges because of trade secrets
or regulation and so on and send this information
to a central authority.
So we can very quickly look for this fast moving challenges
such as creating algorithm for COVID-19 patient.
But this idea is not just for X rays,
it can be used for questionnaires,
it can be used for diagnosis, it can be used
for audio snippets and so on.
So we're very excited about deploying split learning AI
as part of safe parts AI.
So to be precise, in split learning,
you can take a deep neural network and split it
and the early layers of the network execute at the hospital
or on the phone, and the later layers execute at the server.
And the early layers shown here
convert the raw data using the smasher into a smashed
wisdom and the smash wisdom would convert
that 12 megapixel X ray into thousands of bytes.
And a smash wisdom represented by thousand four bytes
is what's sent to the server.
And of course, the cleverness here is that the smash wisdom
is good enough for the server to do inference
or full fledged machine learning.
But the smash wisdom does not reveal anything personally
identifiable about the raw data in this case, X rays.
So, as we start thinking about
how do we use COVID-19 solutions
for privacy preserving exposure, denotificationt
contact tracing, as well as all kinds of different AI
and quarantine verification,
we're deploying all the solutions in an open source manner.
And if you are a public health or a business,
deciding which solutions to use, first of all,
we have some great papers shown here on the bottom right,
which is landscape analysis and also a paper called
Abscond rogue, the unintended consequences.
And the manufacturers, we encourage you to consider
such as it should be privacy first.
Because if you need adoption in the community,
and you want individuals not to fool the system
and go around it, we want an adoption.
And that has to be privacy first,
it should be open source to avoid vendor lock.
It should be interoperable with a backbone
so that as individuals roam and cross boundaries,
the solutions are still still meaningful,
support multiple languages support non smartphone users,
and also create an ecosystem for public health.
It also we have dashboards and modularity.
So to conclude, we know that to deal with COVID-19
we need to do a top down choreography
by government and businesses.
Unfortunately, such choreography with digital solution
will quickly lead to a surveillance state.
Which means that if we want to achieve a coordination
amongst individuals and communities,
the need to self organize privacy preserving tools.
So the combination of privacy conserving tools
that we discussed today and the choreographies
by the government they're really allow us to deal
with public health, in the short term,
allow us to restart the economy in the medium term,
and create resume society as time goes on.
So we encourage you to look at our website,
safepath.mit.edu.
We have apps already in the preview
and beta versions with pilots going on.
And also join our GitHub that has world's largest repo
of COVID-19 software, as well as
algorithms and technologies.
And right now we have two different solutions.
One based on GPS only and one based on
Google Apple Bluetooth only, which is called Project Aurora.
So please check that out thank you very much.