(upbeat music)
>> Hello everyone and welcome back to Dallas
where we're live from SuperComputing.
My name is Savannah Peterson,
joined with my co-host, David,
and we have a rocket of a show for you this afternoon.
The doctors are in the house
and we are joined by NASA, ladies and gentlemen.
So excited.
Please welcome Dr. Dan Duffy
and Dr. Bill Putman.
Thank you so much for being here, guys.
I know this is kind of last minute.
How's it to be on the show floor?
What's it like being NASA here?
>> It's exciting?
>> We haven't been here for three years
so this is actually really exciting to come back
and see everybody,
to see the showroom floor,
see the innovations that have happened
over the last three years.
It's pretty exciting.
>> Yeah, it's great.
And so, because your jobs are so cool
and I don't want to even remotely
give even too little of the picture,
or not do it justice,
could you give the audience
a little bit of background on what you do,
as I think you have one of the coolest jobs ever.
You too, Bill.
>> I appreciate that.
I run a high performance computing center at NASA Goddard
for science.
It's high performance information technology.
So we do everything from networking, to security,
to high performance computing, to data sciences,
artificial intelligence
and machine learning is huge for us now.
>> Savannah: Yeah.
>> Large amounts of data, big data sets.
but we also do scientific visualizations
and then cloud and commercial cloud computing
as well as on-premises cloud computing.
And quite frankly, we support a lot of what Bill
and his team does.
>> Savannah: Bill, why don't you tell us
what your team does?
>> So, I'm an Earth scientist.
I work as the associate chief
at the Global Modeling Assimilation Office.
And our job is to really,
maximize the use of all the observations
that NASA takes from space
and build that into a coherent, consistent physical system
of the Earth, right?
And we're focused on utilizing the HPC
that Dan and the folks at the NCCS provide to us
to the best of our abilities
to integrate those observations.
On time scales from hours, days, to seasonal,
to monthly time scales.
So that's the essence of our focus at the GMAO.
>> Casual, modeling all of NASA's Earth data.
That in itself as a sentence is pretty wild.
I imagine you're dealing with a ton of data.
>> Bill: Oh, massive amounts of data. Yes.
>> Probably, I mean, as much as one probably could
now that I'm thinking about it.
And especially with how far things have to travel.
Bill, sticking with you just to open us up,
what technology here excites you the most
about the future and that will make your job easier?
Let's put it that way.
>> To me, it's the accelerator technologies, right?
So the limiting factor for us as scientists
is how fast we can get an answer.
And if we can get our answer faster
through accelerated technologies,
with the support of the NCCS and the computing centers
but also the software engineers enabling that for us
then we can do more, right.
And push the questions even further.
>> Savannah: Yeah.
>> So once we've gotten fast enough to do what we want to do
there's always something next that we want to look for.
>> And at NASA you have to exercise such patience,
whether that be data coming back, images from a rover,
doesn't matter what it is.
Sometimes there's a lot of time, days, hours, years,
depending on the situation.
>> Bill: Right.
>> I really admire that.
What about you, Dan?
What's got you really excited about the future here?
>> So, Bill talked about the acceleratOR technology
which is absolutely true
and is needed to get us not to
only to the point where we have the compute resources
to do the simulations that Bill wants to do
and also do it in a energy efficient way.
But it's really the software frameworks that go around that.
In the software frameworks, the technology.
They're dealing with how to use those in an energy efficient
and most efficient way is extremely important.
And that's some of the--
That's what I'm really here
to try to understand better about
is how can I support these scientists
with not just the hardware
but the software frameworks by which they can be successful.
>> Yeah. We've had a lot of
kind of philosophical discussion about this.
The difference between
the quantitative increases in power
in computing that we're seeing,
versus the question of whether or not
we need truly qualitative changes moving forward.
Where do you see the limits of,
if you're looking at the ability to gather more data
and process more data more quickly,
what you can do with that data changes
when you're getting updates every second
versus every month.
Seems pretty obvious.
But is there a near-term target that you have, specifically,
where once you reach that target
if you weren't thinking ahead of that target
you'd kind of be going, "Okay, well we solved that problem."
We're getting the data in so fast
that you can ask me, what is the temperature in this area,
And you can go, "Oh, well, huh,
"an hour ago the data said this."
Beyond that, do you need a qualitative change
in our ability to process information
and tease insight out of chaos,
or do you just need more quantity
to be able to get to the point
where you can do things like predict weather
six months in advance?
What are your thoughts on that?
>> Yeah, it's an interesting question, right?
And you ended it
with predicting weather six months in advance.
And actually, I was thinking the other way, right?
I was thinking going to finer and finer scales
and shorter time scales
when you talk about having data more frequently, right?
So one of the things that I'm excited about as a modeler
is going to higher resolution
and representing smaller scale processes.
At NASA, we're interested in observations that are global.
So our models are global.
And we'd like to push those
to as finer resolution as possible
to do things like severe storm predictions and so forth.
So the faster we can get the data,
the more data we can have in that area
would improve our ability to do that as well.
>> And your background is in meteorology, right?
>> Yes, I'm a meteorologist.
>> Oh, excellent. Okay. Yeah, yeah.
>> Yeah. So I have to ask a question
and I'm sure all the audience cares about this.
And I went through this
when I was talking about the GHOSt satellites as well.
What is it about weather that makes it so hard to predict?
>> Oh, it's the classic chaos problem.
The butterfly effects problem.
And it's just true.
You always hear the story of a butterfly in Africa,
flaps its wings and the weather changes in New York City.
And it's just
computers are an excellent example of that, right?
So we have a model of the earth,
we can run it two times in a row
and get the exact same answer,
but if we flip a bit somewhere
then the answer changes 10 days later, significantly.
So it's a really interesting problem. So, yeah.
>> So do you have any issue
with the fact that your colleague believes
that butterflies are responsible for weather?
(Savannah laughing)
>> Are they responsible for climate change?
>> Does that bother you at all?
>> No, it doesn't.
As a matter of fact,
they actually run those butterfly-like experiments
within the systems,
where they do actually flip some bits
and see what the uncertainties are
that happen out
seven, eight, nine days out in advance.
To understand exactly what he's saying,
to understand the uncertainties, but also the sensitivity
with respect to the observations that they're taking.
>> David: Yeah, it's fascinating.
>> Yeah, it is.
>> That is fascinating.
Sticking with you for a second, Dan.
So you're at the Center for Climate Simulation.
>> Yeah.
>> Is that the center that's going to help us navigate
what happens over the next decade?
>> Okay, so no one center
is going to help us navigate
what's going to happen over the next decade
or the next 50 or a 100 years, right.
It's going to be everybody together.
And I think NASA's role in that is really to pioneer
the models that Bill and others are doing.
To understand what's going to happen
in not just the seasonal, sub-seasonal,
but we also work with GISS,
which is the Goddard Institute for Space Studies.
Which does the decadal, and the century-long studies.
Our job is to really help that research
understand what's happening with the client,
but then feed that back
into what observations we need to make next
in order to under better understand
and better quantify the risks that we have
to better quantify the mitigations that we can make,
to understand how
and affect how the climate is going to go for the future.
So that's really what we try to do.
We're trying to do that research to understand the climate,
understand what mitigations we can have,
but also feedback into what observations
we can make for the future.
>> Yeah.
And what's the partnership ecosystem around that?
You mentioned that it's going to take all of us.
I assume you work with a lot of partners,
probably both of you.
>> Obviously the federal agencies work huge amounts together.
NASA NOAA is our huge partnership.
So USGS, a huge partnership.
DOE, we've talked to DOE several times this week already.
So there's huge partnerships
that go across the federal agency.
We work also with Europeans as much as we can
given the barriers of the countries in the financials.
But we do collaborate as much as we can with.
And the nice thing about NASA, I would say is
all the observations that we take are public.
They're paid for by the public.
They're public, everybody can tell them,
anybody can tell them around the world.
So that's also.
And they're global measurements as Bill said,
they're not just regional.
>> Do you have specific,
when you think about improving your ability to gain insights
from data that that's being gathered,
do you set out specific milestones that you're looking for?
Like, I hope by June of next year
we will have achieved a place where we are able
to accomplish X.
Do you have--
>> Yeah, Bill, so what milestones do we have here? Yeah.
>> David: Are you sort of kept track of that way?
Do you think of things like that?
Like very specific things,
or is it just so fluid
that as long as you're making progress towards the future
you feel okay?
>> No, I would say we absolutely have milestones
that we like to keep in track
especially from the modeling side of things, right?
So whether it's observations that exist now
that we want to use in our system,
milestones to getting those observations integrated in.
But also thinking even further ahead
to the observations that we don't have yet.
So we can use the models that we have today
to simulate those kind of observations
that we might want in the future
that can help us do things that we can't do right now.
So those missions are aided by the work that we do
at the GMAO and the NCCS.
>> Okay, so if we extrapolate really
to the what-if future
is really trying to understand
the entire earth system as best as we can.
So all the observations coming in, like you said,
in near real-time, feeding that into an Earth System Model
and to be able to predict short-term,
mid-term, or even long-term predictions
with some degree of certainty.
And that may be things like climate change
or it may be even more important
shorter-term effects of severe weather.
>> Savannah: Yeah.
>> Which is very important.
And so we are trying to work towards that high resolution,
immediate impact model that we can
really share with the world
and share those results as best we can.
>> David: Yeah. I have a quick follow up on that.
>> I bet we both did.
(Savannah and David laughing)
>> So if you think about AI and ML,
artificial intelligence and machine learning,
something that people talk about a lot.
>> Dan: Yeah.
There's the concept of teaching a machine
to go look for things,
call it machine learning.
A lot of it is machine teaching.
We're saying, "Hit the rack on this side with a stick
"or the other side with a stick."
To get it to kind of go back and forth.
Do you think that humans
will be able to guide these systems, moving forward,
enough to tease out the insights that we want?
Or do you think we're going to have to rely on
what people think of as artificial intelligence
to be able to go in with this massive amount of information
with an almost infinite amount of variables
and have the AI figure out that, you know what,
it was the butterfly.
It really was the butterfly.
We all did models with it,
but you understand the nuance that I'm saying.
It's like we think we know what all the variables are
and that it's chaotic because there's so many variables
and there's so much data,
but maybe there's something we're not taking into account.
>> I'm sure that's absolutely the case.
I'll start and let Bill jump in here.
There's a lot of nuances with a AI/ML.
And so the real approach to get to where we want to be
with this Earth System Model approach
is a combination of both AI/ML train models
as best as we can
and as unbiased way as we can.
And there's a big conversation we have around that.
But also with a physics or physical-based model as well.
Those two combined with the humans
or the experts in the loop,
we're not just going to ask the artificial intelligence
to predict anything and everything.
The experts need to be in the loop
to guide the training in as best as we can
in an unbiased, equitable way.
But also interpret the results
and not just it give over to the AI.
But that's the combination of that Earth System Model
that we really want to see in the future.
Is combination of AI/ML with physics-based.
>> But there's an obvious place for AI and ML
in the modeling world,
and that is in the parameterizations
of the estimations that we have to do in our systems, right?
So when we think about the Earth system
and modeling the Earth system
there are many things
like the equations of motions and thermodynamics
that have fixed equations
that we know how to solve on a computer.
But there's a lot of things
that happen physically in the atmosphere
that we don't have equations for
and we have to estimate them.
And machine learning through
the use of high resolution models or observations
and training the models to understand and represent that,
that's the place where it's really useful for us.
>> Savannah: There's so many factors.
>> But we have to make sure that we have the physics
in that machine learning in those training sets.
So physics-informed training isn't very important.
So we're not just going to go
and let a model go off and do whatever it wants.
It has to be constrained within physical constraints
that the experts know.
>> Yeah. And with the wild amount of variables
that affect our Earth, quite frankly.
Yeah, Which is, geez. Which is insane.
My God.
So what technology or what advancement needs to happen
for your jobs to get easier, faster,
for our ability to predict to be even more successful
than it is currently?
>> I think for me the vision that I have for the future
is that at some point
all data is centrally located, centrally shared.
We have our applications
are then services that sit around all that data.
I don't have to sit as a user and worry about,
oh is this all this data in place
before I run my application?
It's already there, it's already ready for me.
My service is prepared
and I just launch it out on that service.
But that coupled with the performance
that I need to get the result that I want in time.
And I don't know when that's going to happen
but at some point it might, I don't know.
>> Savannah: We're rooting for you.
>> But that's--
>> So there are a lot of technologies
we can talk about.
What I'd like to mention is open science.
So NASA is really trying to make a push
in transformation towards open science.
2023 is going to be the year of open science for NASA.
And what does that mean?
It means a lot of what Bill just said,
is that we have equity and fairness and accessibility
and you can find the data, it's findability, it's fair data.
FAR, findability, accessibility, reproducibility,
and I forget what the I stands for.
But these are tools
and things that we need to,
as a computing centers
and including all the HPC centers here
as well as the scientists need to support
to be as transparent as possible with the data sets
in the research that we're doing.
And that's where I think is going to be the best thing
is if we can get this data out there that anybody can use
in an equitable way
and as transparent as possible
that's going to eliminate, in my opinion, the bias over time.
Because mistakes will be found
and the mistakes will be corrected over time.
>> I love that.
Yeah. The open-source science end of this.
No, it's great.
And the more people that have access--
People, I find in the academic world,
especially people who don't know what's going on
in the private sector and vice versa.
And so I love that you just brought that up.
Closing question for you
because I suspect
there might be some members of our audience
who maybe have fantasized about working at NASA.
You've both been working there for over a decade.
Is it as cool as we all think it is on the outside?
>> I mean, it's definitely pretty cool.
(panel laughing)
>> You don't have to be modest about it.
>> Just being at Goddard and being at the center
where they build the James Webb Telescope
and you can go to that cleanroom and see it,
it's just fascinating.
So it's really an amazing opportunity.
>> Savannah: Yeah.
>> NASA Goddard as a center has information technologists,
it has engineers, it has scientists,
it has support team members.
We have built more things,
more instruments that have flown in this space
than any other place in the world.
The James Webb, we were part of that,
part of a huge group of people that worked on James Webb.
And James Webb came through
and was assembled in our cleanroom.
It's one of the biggest cleanrooms in the world.
And we all took opportunities
to go over and take selfies with this
as they put those mirrors on them.
>> Savannah: Yeah.
>> It was awesome.
It was amazing.
And to see what the James Webb has done
in such a short amount of time
the successes that they've gone through
it's just incredible.
Now, I'm not a part of the James Webb team,
but to be at the same center
to listen to scientists like Bill talk about their work,
to listen to scientists that talk about James Webb,
that's what's inspiring.
And we get that all the time.
>> And to have the opportunity to work with the astronauts
that service the Hubble Telescope,
these things are just--
>> Guys, literally giving me goosebumps right now.
I'm sitting over here just goosebumps.
>> Just an amazing opportunity.
(Savannah sighs)
>> Well, Dan, Bill, thank you both so much
for being on the show.
I know it was a bit last minute
but I can guarantee we all got a lot out of it.
David and I both,
I know I speak for us and the whole CUBE audience,
so thank you.
We'll have you anytime
you want to come talk science on theCUBE.
Thank you all for tuning in
to our SuperComputing footage here live in Dallas.
My name is Savannah Peterson.
I feel cooler having sat next to these two gentlemen
for the last 15 minutes and I hope you did too.
We'll see you again soon.
>> Dan: Thanks.
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