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>> Howdy you all, and welcome back to Supercomputing 2022.
We're theCUBE and we are live from Dallas, Texas.
I'm joined by my cohost, David Nicholson.
David, hello. >> Hello.
>> We are going to be talking about a data and enterprise AI
at scale during this segment, and we have the pleasure
of being joined by both Dell and NVIDIA.
Anthony and Bob, welcome to the show.
How you both doing?
>> Doing good. >> Doing great.
Great show so far. >> Love that enthusiasm,
especially in the afternoon on day two,
I think we all, what's in that cup?
Is there something exciting in there
that maybe we should be sharing with you?
>> Anthony: Let's just say it's just distilled
>> Yeah. >> Water.
>> Yeah, yeah, I love that.
So I want to make sure that,
'cause we haven't talked about this at all
during the show yet on theCUBE,
I want to make sure that everyone's on the same page
when we're talking about data,
unstructured versus structured data.
It's in your title, Anthony, tell me what's the difference?
>> Well, look, the world has been based
in analytics around rows and columns, spreadsheets,
data warehouses,
and we've made predictions around the forecast of sales
maintenance issues,
but when we take computers and we give them eyes, ears,
and fingers, cameras, microphones,
and temperature and vibration sensors,
we now translate that into more human experience.
But that kind of data, the sensor data, the video camera
is unstructured or semi-structured,
that's what that means. >> We live in a world
of unstructured data, structure is something
we add to later after the fact.
But the world that we see,
and the world that we experience is unstructured data.
And one of the promises of AI is to be able
to take advantage of everything that's going on
around us and augment that, improve that,
solve problems based on that.
And so if we're going to do that job effectively
we can't just depend on structured data
to get the problem done,
we have to be able to incorporate everything
that we can see hear, taste, smell, touch,
and use that >> All the chaos.
>> as part of the problem solving.
>> Anthony: We want the chaos, bring it.
>> Chaos has been a little bit of a theme of our show.
>> It has been, yeah.
And chaos is in the eye of the beholder.
You think about the reason for structuring data,
to a degree we had limited processing horsepower
back when everything was being structured,
as a way to allow us to be able to reason over it
and gain insights,
so it made sense to put things into rows and tables.
How does, I'm curious, diving right into
where NVIDIA fits into this puzzle,
how does NVIDIA accelerate or enhance our ability
to glean insight from, or reason over unstructured data
in particular?
>> Yeah, great question.
It's really all about, I would say it's all about AI
and NVIDIA is a leader in the AI space,
we've been investing and focusing on AI
since at least 2012 if not before,
accelerated computing that we do at NVIDIA
is an important part of it.
Really we believe that AI is going to revolutionize
nearly every aspect of computing,
really nearly every aspect of problem solving,
even nearly every aspect of programming.
And one of the reasons is for what we're talking about now
which is >> Just a little impact.
>> Being able to incorporate unstructured data
into problem solving is really critical
to being able to solve the next generation of problems.
AI unlocks tools and methodologies
that we can realistically do that with.
It's not realistic to write procedural code
that's going to look at a picture and solve all the problems
that we need to solve, if we're talking
about a complex problem like autonomous driving.
But with AI and its ability to naturally absorb
unstructured data and make intelligent reason decisions
based on it, it's really a breakthrough,
and that's what NVIDIA's been focusing on
for at least a decade or more.
>> And how does NVIDIA fit into Dell's strategy?
>> Well, I mean, look, we've been partners
for many, many years delivering beautiful experiences
on workstations and laptops.
But as we see the transition away
from taking something that was designed
to make something pretty on screen,
to being useful in solving problems in life sciences,
manufacturing, and other places,
we work together to provide integrated solutions.
So take for example, the DGX A100 platform
brilliant design, revolutionary bus technologies,
but the rocket ship can't go to Mars without the fuel.
And so you need a tank that can scale in performance
at the same rate as you throw GPUs at it.
And so that's where the relationship really comes alive,
we enable people to curate the data, organize it,
and then feed those algorithms that get the answers
that Bob's been talking about.
>> So as a gamer I must say,
you're little shot at making things pretty on a screen,
come on, that was a low blow, that was a low blow.
>> Sassy.
What I >> Now what's in your cup?
That's what I want to know, Dave.
>> I apparently have the most boring cup
of anyone on here today, I don't know what happened.
We're going to have to talk to the production team,
I'm looking at all of you,
we're going to have to make that better.
One of the themes that's been on this show,
and I love that you all embrace the chaos,
we're seeing a lot of trend in the experimentation phase,
or stage rather, and we're in an academic zone
of it with AI, companies are excited to adopt,
but most companies haven't really rolled out their strategy.
What is necessary for us to move
from this science experiment, science fiction in our heads,
to practical application at scale?
>> Well let me take this, Bob.
So I've noticed there's a pattern
of three levels of maturity.
The first level is just what you described,
it's about having an experience, proof of value,
getting stakeholders on board,
and then just picking out what technology,
what algorithm do I need?
What's my data source?
That's all fun, but it is chaos.
Over time, people start actually making decisions
based on it.
This moves us into production.
And what's important there is normality,
predictability, commonality across,
but hidden and embedded in that is a center of excellence.
The community of data scientists
and business intelligence professionals
sharing a common platform.
In the last stage, we get hungry to replicate those results
to other use cases throwing even more information at it
to get better accuracy and precision,
but to do this in a budget you can afford.
And so how do you figure out all the knobs and dials to turn
in order to take billions of parameters and process that?
That's where >> Casual.
>> What's that? >> casual decision matrix there
with billions of parameters, yeah.
>> No I mean. >> But you're right.
>> That's exactly what we're on this continuum,
and this is where I think the partnership does really well
is to marry high performant enterprise grade scalability
that provides the consistency, the audit trail,
all of the things you need to make sure
you don't get in trouble, plus all of the horsepower
to get to the results.
Bob, what would you add there?
>> I think the thing that we've been talking about here
is complexity.
And there's complexity in the AI problem solving space,
there's complexity everywhere you look.
And we talked about the idea that NVIDIA can help
with some of that complexity from the architecture,
and the software development side of it.
And Dell helps with that in a whole range of ways
not the least of which is the infrastructure,
and the server design, and everything that goes
into unlocking the performance of the technology
that we have available to us today.
So even the center of excellence is an example
of how do I take this incredibly complex problem
and simplify it down,
so that the real world can absorb and use this?
And that's really what Dell and NVIDIA
are partnering together to do.
And that's really what the center of excellence is,
it's an idea to help us say,
"Let's take this extremely complex problem
and extract some good value out of it."
>> So what is NVIDIA's superpower in this realm?
I mean, look, we're in the era of, yeah, yeah, yeah.
We're in a season of microprocessor manufacturers
one-upping one another with their latest announcements.
There's been an ebb and a flow in our industry
between doing everything via the CPU
versus offloading processes.
NVIDIA comes up and says, "Hey, hold on a second,
GPU which again was focused on you graphics processing
originally doing something very, very specific."
How does that translate today?
What's the NVIDIA, again, what's the superpower?
Because people will say,
"Well hey, I've got a CPU, why do I need you?"
>> I think our superpower is accelerated computing
and that's really a hardware and software thing.
I think your question is slanted towards the hardware side
>> Yes it is. >> Which is very typical,
and we do make great processors,
but the processor, the graphics processor
that you talked about from 10 or 20 years ago
was designed to solve a very complex task,
and it was exquisitely designed to solve that task
with the resources that we had available at that time.
Now, fast forward 10 or 15 years,
we're talking about a new class of problems called AI
and it requires both exquisite processor design,
as well as very complex and exquisite software design
sitting on top of it as well.
And the systems, and infrastructure knowledge,
high performance storage, and everything
that we're talking about in the solution today.
So NVIDIA's superpower is really about
that accelerated computing stack.
At the bottom you've got hardware,
above that you've got systems,
above that you have middleware and libraries,
and above that you have what we call application SDKs,
that enable the simplification
of this really complex problem
to this domain, or that domain, or that domain,
while still allowing you to take advantage
of that processing horsepower that we put
in that exquisitely designed thing called a GPU.
>> Decreasing complexity and increasing speed
two very key themes of the show,
shocking no one, we all want to do more faster.
Speaking of that, and I'm curious
because you both serve a lot of different unique customers,
verticals, and use cases,
is there a specific project
that you're allowed to talk about,
or I mean you want to give us the scoop
that's totally cool too,
we're here for the scoop on theCUBE,
but is there a specific project
or use case that has you personally excited?
Anthony, we'll start with you.
>> Look, I'm, I've always been a big fan
of natural language processing.
I don't know why, but to derive intent
based on the word choices is very interesting to me.
I think what compliments that
is natural language generation,
so now we're having AI programs actually discover
and describe what's inside of a package.
It wouldn't surprise me that over time
we move from doing the typical summary
on the economics of the day, or what happened in football,
and we start moving that towards more
of the creative advertising and marketing arts,
where you are no longer needed because the AI's
going to spit out the result.
I don't think we're going to get there,
but I really love this idea of human language
and computational linguistics.
>> What a marriage, I agree, I think it's fascinating.
What about you Bob, what's got you pumped?
>> The thing that really excites me
is the problem solving, the tip of the spear
in problem solving.
The stuff that you've never seen before,
the stuff that in a geeky way takes your breath away.
And I'm going to jump or pivot off of what Anthony said,
large language models are really one of those areas
that are just, I think they're amazing,
and they're just surprising everyone with what they can do.
Here on the show floor, I was looking at a demonstration
from a large language model startup, basically,
and they were showing that you could ask a question
about some obscure news piece
that was reported only in a German newspaper,
it was about a little shipwreck that happened in a hardware,
and I could type in a query to this system,
and it would immediately know where to find
that information as if it read the article,
summarized it for you,
and it even could answer questions
that you could only answer by looking at pictures
in that article.
Just amazing stuff that's going on, just phenomenal stuff.
>> That's a huge accessibility. >> That's right.
And I geek out when I see stuff like that,
and that's where I feel like all this work
that Dell and NVIDIA and many others
are putting into this space is really starting to show
potential in ways that we wouldn't have dreamed of
really five years ago, just really amazing.
>> And we see this in media and entertainment.
So in broadcasting, you have a sudden event,
someone leaves this planet where they discover
something new, where they get a divorce,
and they're a major quarterback.
You want to go back somewhere in all of your archives
to find that footage.
That's a very laborist project,
but if you can use AI technology to categorize that,
and provide the metadata tag so it's searchable,
then we're off to better productions,
more interesting content,
and a much richer viewer experience.
>> And a much more dynamic picture of what's really going on,
factoring all of that in, I love that.
I mean, David and I are both nerds,
and I know we've had take our breath away moments,
so I appreciate that you just brought that up.
Don't worry, you're in good company
in terms of the Geek Squad over here.
>> I think actually maybe this entire show for.
>> Yes, exactly. >> I mean, we were talking
about how steampunk some of the liquid cooling stuff is,
and this is the only place on earth really,
or the only show where you would come and see it
at this level in scale, and it's just, yeah,
it's very exciting.
How important for the future of innovation in HPC
are partnerships like the one that NVIDIA and Dell have?
>> You want to start? >> Sure, I would,
I mean, I'm going to be bold, and brash, and arrogant,
and say they're essential. >> Yeah.
>> You do not want to try and roll this on your own,
even if we just zoomed in to one little beat,
little piece of the technology,
the software stack that do modern accelerated deep learning
is incredibly complicated,
there can be easily 20 or 30 components
that all have to be the right version,
with the right buttons pushed,
built the right way, assembled the right way,
and we've got lots of technologies to help with that,
but you do not want to be trying
to pull that off on your own.
That's just one little piece of the complexity
that we talked about,
and we really need, as technology providers in this space,
we really need to do as much as we do
to try to unlock the potential,
we have to do a lot to make it usable, and capable as well.
>> I got a question for Anthony.
>> All right. >> So in your role,
and I'm projecting here, but I think your superpower
personally is likely in the realm of being able
to connect the dots between technology and the value that,
that technology holds in a variety of contexts.
>> That's right. >> Whether it's business,
or whatever, okay.
Now it's critical to have people like you
to connect those dots today,
in the era of pervasive AI,
how important will it be to have AI
have to explain its answer?
In other other words, should I trust the information
the AI is giving me, if I am a decision maker,
should I just trust it on face value,
or am I going to want a demand of the AI
kind of what you deliver today, which is,
"No, no, no, no, no, no, you need to explain this to me,
how did you arrive at that conclusion?"
>> Right. >> How important will that be
for people to move forward and trust the results?
We can all say, "Oh hey, just trust us.
Hey, it's AI, it's great, it's got NVIDIA Acceleration
and it's Dell, you can trust us."
But come on, so many variables in the background.
>> It's an interesting one.
And explainability is a big function of AI,
people want to know how the black box works
>> Right. >> Because I don't know,
if you have an AI engine that's looking
for potential maladies in an X-ray, but it misses it,
do you sue the hospital, the doctor,
or the software company? >> Right.
>> And so that accountability element is huge.
I think as we progress,
and we trust it to be part of our everyday decision making
it's just simply as a recommendation engine,
it isn't actually doing all of the decisions,
it's supporting us.
We still have after decades of advanced technology,
algorithms that have been proven,
we can't predict what the market price
of any object is going to be tomorrow.
And you know why?
You know why, human beings, we are so unpredictable,.
How we feel in the moment is radically different,
and whereas we can extrapolate for a population,
to an individual choice, we can't do that.
So humans and computers will not be separated,
it's a joint partnership.
But I want to get back to your point
and I think this is very fundamental
to the philosophy of both companies.
>> Yeah. >> It's about a community,
it's always about the people sharing ideas,
getting the best, and anytime you have
a center of excellence, an algorithm
that works for sales forecasting,
may actually be really interesting for churn analysis
to make sure the employees or students don't leave
the institution.
So it's that community of interest
that I think is unparalleled at other conferences,
this is the place where a lot of that happens.
>> I totally agree with that, we felt that on the show.
I think that's a beautiful note to close on,
Anthony, Bob, thank you so much for being here.
I'm sure everyone feels more educated
and perhaps more at peace with the chaos.
David, thanks for sitting next to me,
asking the best questions of any host on theCUBE.
And thank you all for being a part of our community.
Speaking of community,
here on theCUBE we're alive from Dallas, Texas.
It's supercomputing all week,
my name is Savannah Peterson and I'm grateful you're here.
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