- What is FDL?
It is a wild ride.
- The Frontier Development Lab
is an AI program that brings researchers together
to tackle some of the biggest challenges humanity faces.
- When we think
about space exploration,
we often focus on the technology
required to travel to distant moons and distant planets—
but various doses of cosmic radiation
can increase the incidence
of lifetime development of cancer.
Can we build a machine-learning framework
that can identify the biological factors, genetics,
that make one astronaut more susceptible
to space radiation-induced cancer?
- Honestly, a lot of us thought,
at the beginning, that may not be possible.
- If you can identify the causes,
you can identify the 'holy grail,'
which is preventative mechanisms.
- We were thinking about that in terms of how we translate
that into space, but of course,
there's a huge impact of cancer on everyone on Earth.
- Intel's Federated Learning has the potential
to be used for cancer,
but also for a lot of other diseases that are out there.
- I am Dr. John Kalantari.
I'm assistant professor at the Mayo Clinic.
I try to develop AI models and AI systems
that tackle the greatest challenges
in medicine and healthcare.
The primary challenge in healthcare and medicine
is understanding the causes of a disease.
There are decades and decades of data
on our patients that we record in our patient records.
How do we mine that trove of data
to understand what genes are causal for cancer progression?
- Machine learning has radically changed
how we do lots of tasks.
However, it has some big limitations
when it comes to healthcare and the biosciences.
- The challenges across biosciences are always the same.
You have very small amounts of data for very complex things.
- And to overcome that,
we need to amass data from across different populations
so that we can fill in the gaps—
and this is possible
if you look beyond the walls of a single institution.
- The challenge is accessing healthcare data.
- Data, especially in healthcare settings,
can't be sent outside because of restrictions such as HIPAA.
- If the data is hard to get to,
is there a way to bring the AI model to that data?
The group came together
and realized the most efficient solution
is implementing Intel's 'Federated Learning Framework.'
- What federated learning does
is it flips the conventional way
of doing deep learning training,
which is to bring all of your data to a central place,
and instead, the data doesn't need to be sent.
It's just the model,
which happens to be quite a bit smaller.
The models get sent back and averaged
or combined in some way
to actually preserve a lot of the information
without needing to violate
any of the regulatory restrictions around it.
With federated learning,
it's possible to actually get access to data
that normally wouldn't be available.
- Now that the team can receive the data,
the next problem: getting a bigger data set.
- There's only so many astronauts
that have been to space
to actually study these causal effects
of cosmic radiation on their DNA.
- 106 to be exact.
- How do you make a model
based on a very small sample size?
- To solve this challenge,
the team came up with a novel approach.
- Hey, Paul.
- Hi, John.
So we have human astronaut data.
We have a collection of mouse data as well.
So the idea there was can we combine human data
with mouse-model data
in order to train these algorithms
using the open FL framework?
- If I understand you correctly,
you guys are basically using rodent data
as a homolog for human-radiation data.
- Yeah, precisely.
- The team built up a big enough data set
to effectively train a viable AI model
that can predict which genes cause cancer in astronauts.
- Sounds like magic.
- Yes. In some ways, it is.
- This is an approach that can be applied
to everyone on Earth.
- I think we're really on the verge of AI
making some really transformative steps forward.
- With Intel's Federated Learning,
AI can mine that trove of data
that we've been collecting in healthcare
for the past century,
and begin to build a more
comprehensive model of disease.
Pursuing challenges in healthcare,
life sciences, space biology, using AI—
is possible and should be focused on tackling "blue sky."
It's upon us to find those problems that seem impossible
because those have the greatest potential
to make an impact in ways that we can't imagine.