Thank you, Rafael and Eric for that inspiring discussion.
I'm honored to introduce our next speaker, Dr. Sophie
Vandebroek, who's the Chief Operating
Officer of IBM Research.
Sophie is well known to many of us here at MIT.
She has been serving on the Dean's Advisory Council
for the School of Engineering for the last decade
and was a member of the board of the MIT Sandbox Innovation Fund
for the first few years.
She recently played an instrumental role
in the creation of the MIT-IBM Watson AI Lab,
a new collaboration for research on the frontiers
of artificial intelligence.
The MIT-IBM Watson AI Lab has inspired exciting research
partnerships and provided MIT with new resources, colleagues,
and tremendous access to real-world data
and computational power.
We have received tremendous interest
in participating from PIs across campus.
We took their enthusiasm as a directive.
Today, we're launching the MIT Intelligence Quest
and owe a lot of its momentum to our cornerstone collaborator,
IBM.
I'm thrilled that Sophie has joined us today
to provide an update on the lab and its research direction.
Please join me in welcoming Sophie.
[APPLAUSE]
Let me thank [INAUDIBLE] because the first week he became dean,
he and I at lunch discussed this opportunity
to build a super strong AI research community here
on the East Coast.
Less than six weeks later, that picture
was taken in the middle, where the actual agreement was
signed.
So it was a lot of trust, and a great opportunity,
and I'm delighted that we have at least a decade, hopefully
more, a $240 million joint investment, 100 researchers
and PIs between MIT and IBM working
on super important projects that will impact billions
of people around the globe.
So the lab has a portfolio across four pillars,
and I thought the best way of showing you what's going on
is to give you a peek in the initial projects that
are being funded.
So about 300 PIs submitted projects.
We selected 37 or so projects across these four areas.
The first area-- and we discussed this a lot today--
is all about the core AI algorithms,
pushing the boundaries of explainability
or working with limited data, as well as applying
AI capabilities to industries.
I mean, so just a couple blocks from here
is where the lap is headquartered.
It's co-located with IBM research lab on Binney Street.
We also have our health care, Watson for Health,
headquarters in the same building,
as well as Watson Cybersecurity, as well as Watson cloud
business.
So all these businesses jointly are funding this lab,
and we wanted to make sure we had also projects
that were application-focused to these different-- at least
to security as well as health care.
So let me give you a couple projects.
This is actually one that already is bearing fruit.
We just open-sourced or launched this moments-in-time database
where we have about--
it's not a database.
Its over a million videos that have been annotated.
It's very similar to ImageNet, those of you that know
ImageNet with static images.
These are short actions, three-second actions,
that have been annotated, and every action
has more than 2000 videos annotated.
And why is that important?
For example, if you see in a picture somebody in a door,
you don't quite know if they're going maybe inside or outside,
what way they might be going if a door is open.
Right?
And so in the three seconds, you can really look at actions,
and then these actions put together
you can then apply them in everything from autonomous cars
to security situations to health care situations.
And what the purpose is to really have pre-trained AI
models such that with little additional data
you can fine tune these models for the particular problem
or workflow that you are trying to make more secure,
or make more safe, or make better, or personalize.
So another issue with limited data,
or area where there is a lot of limited data,
is in health care.
For example, in hospitals they have medical records
of a couple thousand patients.
But again, that is not enough to be able to really train
these deep neural networks to understand
certain diseases, for example.
And so in each of the slides, there
are the projects I'm showing.
I show both to MIT as well as IBM PI.
All these projects are joint projects.
So in this particular project, we're going to be focusing
on leveraging the rational physician, for example,
right down to the site, whether or not a certain disease exists
and highlighting that, not only to more quickly of what
the particular disease is-- and in this case,
we can give an example related to cancer--
but also to provide some explainability,
to be able to have the algorithm.
Say, OK, this is why I believe it
is skin cancer because of the color,
or the border, or the asymmetric shape--
so to give explainability because right now
a lot of deep learning algorithms
are truly black boxes.
And this builds trust and obviously
is super critical for physicians to be
able to explain the why to their patients,
are for insurance companies to be able to explain the why
if something is rejected or if certain--
and not just blame it on the algorithm
or say the algorithm told me so.
So in addition, if a neural net makes a mistake--
for example, in this case there is a little girl brushing
her teeth, but the AI says she's washing dishes, so it's wrong.
Again, it's a black box.
Today we don't know what's going on,
but there is a research program ongoing
that will be able to look up what are the biggest
neurons in the network that defined this wrong output,
and then be able to go in and understand why.
And so the similarity is in we could envision--
like for software, the back-end software,
that is actually a debugger for neural nets, which, again,
is super critical when you talk about situations where these AI
algorithms are going to be used to make important decisions.
OK, so now moving on.
Today we heard an enormous amount
about what AI can do, the applications, the algorithms.
We have not talked a lot about another topic, which
is really the physics of AI.
I mean, equally important is that we continue
to design the hardware that will enable
AI to be ubiquitously present all around us, all the way
to the [INAUDIBLE] for inferencing
at super low cost, super high efficiency,
of course, significant processing power for training,
very complex AI algorithms.
So on the left here, you see an image.
This is today's hardware.
Today's hardware, it went from CPUs in the early 2010s.
Around 2012, two things happened.
Number one, the neural net, deep learning,
started to win some of these competitions.
This is a competition that showed classification
of images, as well as everybody started switching to GPUs.
And that enabled-- this combination together
enabled, ultimately, in this competition the system to be
better than humans.
There is also a lot of custom silicon emerging,
silicon ASICs to do inferencing or learning
of specific neural nets.
Our point of view at IBM is that, clearly, the silicon
space is going to run out of steam,
as we all know due to Moore's law, et cetera,
and that we have to move back, you
can say, to analog computing so that many of these synapses,
or the neuron on the network--
the multiplication, the math that's happening,
you can actually move it into the memory.
And you can then see that by doing
so our projections are that you can increase again
by a factor of 1,000.
What does that mean?
That means that you could do real-time imaging, diagnosis
on patients, for example, looking at the images
while doing the operation instead of waiting
for the image processing to be done afterwards.
So things will just go 1,000 times faster.
And so one of the projects here is
what this team that is really good at looking at energy
efficient algorithms--
the team has done it in the silicon space.
We're now going to look at applying that
into the analog space with these special in-memory devices.
We're looking both at the materials, the devices,
and the circuit or system level.
But then beyond analog computing,
there is quantum computing.
And right now, IBM has the best quantum system in the world.
We have 50 qubits running in the research lab.
In fact, we have open-sourced our quantum hardware.
70,000 researchers, and others around the globe,
have done experiments on the quantum hardware,
2 million experiments total, technical publications
published.
And so we are now looking at applications of quantum to AI.
Right?
I mean, what is going to be the difference if you
apply quantum computing to AI?
A couple of projects in this area--
actually, too many to mention and put pictures on.
And so in addition to doing the actual projects,
we have also funded an edX course--
all of you can go take it--
on the basics of quantum computing because obviously
this is, for most of us, a very new computing approach
and so many of us have to come up to speed.
And so there is course.
It's just launched, an already almost 4,000 people
have signed up from many different countries
around the globe.
So finally, the last topic that we
wanted to have a pillar of investment around gaining
share prosperity with AI.
These are the topics.
It covers the topics that were discussed at the panel earlier
today.
How do we create moral AI, ethical AI?
What is the impact of AI on jobs?
All of our jobs.
And so there was a lot of discussion at the panel,
so in the interest of time I want
to show a specific project that we thought
that is a fundamental ticket to the game in working on AI,
is to make sure that we look at it from impact on society
and what does it mean to the future of the world.
So let me end with the principles
that our CEO has for AI or for IBM,
for our company, in this AI era.
And so we look at three principles.
You can see them here.
The purpose of the work we do is to augment humans.
Right?
This is an AI system and the human working together.
Then, also very important, is transparency,
that everything we do has to be trusted and has to be ethical,
and that's why making sure that there is privacy, and security,
and explainability is super, super critical.
And again, a lot of scientists have
to help us make sure that we continuously put
that principle in front of us.
And the final principle is skills.
You can see it in how we have launching this quantum
course together with edX, but also IBM
was one of the founding members of the P-Tech schools, which
is like high school, early college.
About 12,000 students have been trained
in how to use and live with these AI systems,
how to interact with them, and then how to be the new collar
workers of the future.
And these principles, I'm so delighted because they're
very much aligned with MIT's principals.
And so together with MIT, IBM is also delighted
that MIT has built on this foundation for iQ Intelligence
Quest, and so looking forward to work together
with MIT and many of the other partners
that will be part of it.
Thank you.