Embrace AI because especially genital AI is bringing so much
intelligence. And you know, I think I quoted
our CEO that said, you know, AI is not going to take your job,
but someone using AI could take your job.
Hello and welcome to the Retail podcast.
Now I'm it's really graced because Azita Martine, who is
the lead or president or vice president, Vice President, I
always keep promoting people. The CEO of NVIDIA directors, the
vice president of retail, global retail for AI and NVIDIA was the
keynote kick off speaker at NRF on day one.
And as some of you know, I've already given you my thoughts on
where we're going with AI. Basically, I position the fact
that last year was about the use case.
This year we're hearing about AI transformation and Satya has
already spoken about how the SAS models are going away and
there's an AI layer that's coming on top.
But hey, you're not here to listen to me, we're here to
listen to you. So if you don't mind taking us
through your because at your keynote, you sort of set the
scene about what's happening in the world of AI and the industry
pretty much from a global context.
And then you sort of board it to what it means to NVIDIA.
So I was just wondering if you don't mind sharing that with the
audience? Generative AI is now at a stage
where it's moving from pilots to become real.
And the best way I would describe it is it's it's almost
like an AI factory that takes your data, which is like the raw
material in that factory, and it's generating digital
intelligence in the form of AI agents.
And these AI agents are making your employees more productive.
They're delivering intelligence that just enables your company
to be much more productive. So now what customers are
companies are doing is fine tuning these models with their
data, whether it's PDFs, documents, customer data and
images and videos and be able to create these digital agents that
are helping your employees become more productive.
And so an example would be shopping advisor, which we're
going to show to you a little bit later.
And this shopping assistant is almost like replicating the
knowledge of all your best sales associate and having that
available 24 by 7 at scale on your e-commerce side to help
your customers find exactly what they're looking for.
What's been top of mind for the retailers that are coming to
talk to you? You know what?
What are they saying as like their top of mind for 2025?
You know, it ranges from everywhere where you know you
have early adopters and you also have companies that it's like it
has to be proven by 5 other companies or 10 other companies
before I would do it. And I would certainly say that
this is so much more real than e-commerce was in the year 2000.
And the companies that are going to adopt it early are going to
absolutely have a competitive advantage over the ones that are
going to be the laggards. Absolutely.
And so, and the people that came to to to see some of our
technology during NRAS, I think range from, you know,
commitments from the very top that, you know, we want to, you
know, in the stores, prevent shrink or have a, you know, more
optimised layout to make it easier for customers to find
what they're looking for and be able to check out faster.
And that's all computer vision technology all the way to how
can you help us improve throughput in our distribution
centres and how to make fulfilment a lot faster in our
fulfilment centres all the way to, you know, what are the top
generated AI use cases that are going to bring business value
to, to our to the companies. And one of the areas that I
highly recommend, even though 20%, less than 20% of retail
revenue comes from e-commerce, I think generative AI has
completely reinvented e-commerce and there was an opportunity to
leverage it to improve search, to improve to have these
shopping assistance on the website and to even create
advertisement that retailers can make revenue from by selling it
to to the consumer packaged good companies.
So I think there's a lot of opportunity to increase revenue
leveraging generative AI on e-commerce and mobile sites.
So. It sounds like there's a real
mix of generate new revenue for retail media, help the customers
have a better experience through the automation and bringing, and
you said this earlier on, bringing all of that knowledge
of your sales to the customers. In terms of Nvidia's focus in
the retail space, I'm curious about where you see it going on.
NVIDIA, most companies know as far GPU.
So our GPU is, is what companies like Open AI perplexity, Meta
are using to train these real large language models and so
forth. But, and there is actually an
accelerated computing company. And what that means is not only
our hardware, which involves our GPUs, our networking, even our
own CPUs, but on top of it, we have a whole set of acceleration
libraries and our application frameworks.
And basically those acceleration library and application
frameworks are the acceleration software that developers use to
build these AI application and to build their, to build and to
deploy, right. And so we're really a platform
for building AI applications. And the blueprint that our CEO
talked about doing his CES Kenu is basically step by step a
workflow of how do you build a particular Gen AI application
using our platform with open source models.
So it's got step by step instructions.
You know, how do you do fine tuning, how do you use RAC, How
do you do the different components of building a general
application? And it even includes the code.
And so that's what helps developers build these
applications a lot faster and ensure that their performance
from an inference perspective, which is the responsiveness of
the generative AI application is the fastest and the most cost
efficient. So when you look to the future,
when you look to the next two or three years, what fee do you see
in from what you've taken from the show?
What do you think will be the the sort of winning focus areas
in AI? Is it in the business?
Is it in front of house? Is it back?
I'm just curious on on your opinion.
I mean, I would say it's almost everywhere, but an area that's
benefiting the most from AI is supply chain.
Got you. And there NVIDIA is really
pioneering two things. One is called physics AI.
And physics AI is AI that understands physics.
It understands dimensions, volume and so forth, but more
importantly, it understands how people and objects behave in a
digital trend of a real space. So it allows you to create a
physically accurate digital representation of a store or
distribution centre, simulate different layouts and be able to
predict accurately how people and objects behave.
And so that's being used for optimising throughput and
distribution centres in your filming centres.
It's used for optimising layout of stores for higher revenue.
And so that's physics AI. Second thing that I talked about
was physical AI. And physical AI is basically AI
for the physical world. And it's a combination of
computer vision, which is driven by smart cameras, robots and
models that understand physics. So it's a combination of our
Omniverse simulation software, our robotics technology, and our
computer vision technology. And it's all used to be able to
train robots in a simulation environment.
And the reality of it is that that in the physical world, you
cannot even estimate how many situations can come up.
But in the, in the, in the digital world, you can actually
simulate thousands of scenarios and be able to train those
robots to behave a certain way during different situations.
So I think we showed a video during the keynote that showed
that an incident happened in one of the aisles and a bunch of a
boxes fell. And so there what what was
happening is the smart cameras were providing perception.
So it's like you're looking down, the AI is looking down to
see what is going on. And then you have an agentic
agent that is now going to has seen this and it's instructing
another agent to reroute the forklift that was coming to go
through a different aisle in order to ensure that, you know,
no safety situation happens, right.
So that's an example of physical AI, which is for the physical
world. And I think that's a good way to
describe this. NVIDIA actually announced our
Cosmo Nematron foundation model, and that's basically a
foundation model that has been trained with billions of hours
of video. Yeah, and it is to the physical
world what tragedy BT has been to text and language.
Yeah, I've been in technology all my life and I'm like
struggling to keep up because my brain is exploding.
Where what NVIDIA have announced and where you're taking it.
If you could go back in time and tell a younger you to come into
this world, I think you've mentioned your career.
Yeah, it was an aerospace. Engineer, that was.
It you were an aerospace engineer and then you sort of
shifted to NVIDIA and you you weren't really meant to end up
in rhythm. But anyway, my question being if
it's OK to ask you about empowerment in terms of I'm
looking at my career, what would be some sort of words of wisdom
that you would if you could go back to a younger self?
I think that really generative AI has democratised AI in many
ways. So I want to encourage, maybe
you don't need to code, but you can still use generative AI to
become so much smarter. So I want to encourage the
younger generation to embrace AI because especially generative AI
is bringing so much intelligence.
And you know, I think I quoted our CEO that said, you know, AI
is not going to take your job, that someone using AI could take
your job. And so I want the younger
generation to embrace AI and use it to, to be a lot more
productive. At the end of the day, I would
say even if you're in science, you got to love what you do.
So my first recommendation is really look around you, look
inside, really understand what your passion is.
Learn as much as you can about what's happening in that area
that you're interested in. Choose the top companies in that
and aim high, aim big. Don't let your insecurity or
your fear hold you back and get out of your comfort zone.
Volunteer for roles that maybe you don't need 100% of the
qualification, but you know, if you even read 60% of it, I'm
sure you're smart enough to learn the rest of it and
surround yourself by super smart people.
Because the more you get out of your comfort zone, the more
you're going to grow and the more you're going to learn.
I love it. And then what a beautiful way to
end the interview. Thank you so much for giving me
your time. Thank you.
Thanks for having me. Hi Alex, I'm Cynthia Contours.
I'm the Director of AI for retail CPG at NVIDIA as is it
was talking to you about our AI blueprint for retail shopping
assistance. Yeah, I'm here to show you a
little bit about it and and answer some questions.
So yeah, we're demonstrating the AI assistant for a number of
different categories. 1 is fashion, the other is furniture.
Two really interesting categories, but also I can talk
to you about grocery as another example.
So let me go ahead and start with, let me go ahead and start
with fashion, which is as an example.
Now one of the things that consumers have really been
struggled, struggling with is it had to change their behaviour to
an e-commerce site. I need to think about what are
the keywords I need to search. And the reality is that's not
the way we work, you know, as you mean this, you know, what
are the upcoming fashion trends for this spring?
Hey, do you have any purses or shoes to go with this skirt?
That's really the way we work. So I'm going to do an example
here of I have an upcoming event that I'm going to a lunch and
I'd like a recommendation for a dress or skirt so.
OK, Alex, see, as you can see, I asked for a dress and skirt
appropriate for an outdoor event and it brought back a dress and
skirt. But since it gave me a skirt, it
also gave me some blouse ideas as well.
Now typically in a search, you come back with just the products
and then I need to start digging in, double click on the product,
go and see more information, and then come back out, double click
again. So then the beautiful thing with
the shopping assistance is I can now just ask questions.
And so with that, let me go ahead and show you how that
works. So I like the skirt, but of
course the natural question is does it require dry cleaning?
So typos and all.
It's AI. It understands.
So here we've got some great results coming back.
I didn't have to go in and look and hunt for that information is
able to go and search for that information, bring it back makes
it, it transforms it from my interactive with the e-commerce
site too. I'm working with your stylist or
your store associate just really naturally the next piece that's
great is then being able to ask multiple questions, all right?
Or being able to say, oh, I saw these pair of shoes online.
Do you have something like that? So let me go ahead and show you
what that looks like. It's really an image search.
So let me go ahead and transition and show you what
that might look like. So I saw these pair of shoes
online. They were really fabulous.
And I want to see if you have something similar for me because
of course they were a little expensive.
So do you have any shoes like these?
And so in the background, we're going and saying, OK, what are
the attributes of this image? Let me see what other products
in the catalogue are similar to this?
It could be red shoes, it could be strappy shoes.
It could be shoes with embellishments.
And in fact, you can see that's what we've got.
We have one pair of shoes. We've got something strappy.
Now this this shoe has an ankle component, has embellishment
components. So I have ankle components and
embellishment components as well.
So it's not a really nice job of finding similar things that also
go with that skirt, right? I can then say, OK, I like these
shoes, but how high are the heels?
What's the price? What's the comfort level?
What are how do people review them and rate them?
So all of those in all that information, I can just ask
naturally, the next piece that I probably would like to show is
the ability for integration, because this is a nice
experience. But the next piece is, well, I
want to be able to add to carts, right?
So it's really simple the. E-commerce website.
This is an e-commerce website. Website that I would have and so
the images there are they catalogue images I as the
retailer provides. These are catalogue images that
are in your retail catalogue right now.
So we're keeping it very simple. I can do an example though of
visualisation, right? And I'm happy to show that.
And I'm doing that in a furniture example.
So the other piece is being able to search for multiple things at
once, right? You can go ahead and search for
I want purses and shoes to go with a skirt.
Do you have any tops? And to go with this, Kirk, all
of that is just very natural. So Azita may have mentioned a
furniture demo where you're visualising furniture in your
room because furniture is obviously considered purchase.
There's challenges to oh, I like it, but how's it going to look
in my room? The price point in that is
lower, right? But I'm not sure about it if
it's going to work and being able to play and actually
visualise it. So let's go ahead and let's
transition to that next. OK, so as a consumer, I used
your mobile app and I scanned my room to create a 3D room.
So we're going to go ahead and ask to pull that information up.
So I'm going to go on site. So what it's doing is it's
saying, OK, this is Cynthia, she's got a living room.
Let's take that 3D skin of the living room and we're putting it
into Omniverse and VS 3D collaboration platform.
So this is a, this is a 3D scan of my room.
This is not a 2D photo of my room.
Yeah, right. And so I have objects in here.
I have a couch, I have a table, I have a chair, for example.
And so the first thing is obviously I don't have a case of
luck, right? I want to, I want to now bring
change my room and make a case. So do you have any modern
couches? Let's start there.
And very similar to the fashion example, it's going to go back.
It's going to bring back some recommendations for me.
It's going to give me information about those couches.
And so here we have 3 couches, right?
I've got something that's very architectural.
I have something that's a little softer.
I like that one better. And then I've got a curve couch
that's interesting, but I'm not sure about it.
But the one I like that modern sectional is on the high end for
me in price. The one that I don't like, but
it's outside of my price range. And then there's the curb caps.
That's much more affordable for me.
So let me go ahead and explore it.
I might not necessarily explore it or be deterred that, you
know, because of price points at this point.
So let me go ahead and just explore replacing my couch with
the curb couch. Now notice I'm not giving the
product name or anything like that, right?
The generatory I, the agent is understanding it and then it's
going ahead and giving command back a tape.
I've got a database right, of the 3D assets for my furniture.
So I'm replacing the council was in there with this 3D asset of
furniture. So I can see it.
What is it doing? Oh, now it's really transformed
my room. It's making it more curved
instead of squared, but it's still not crazy about it.
It was right. We can do better, right?
We can do better. So let me try that other.
There's a couch that's kind of on the high end, but let me let
me try that instead, right? So here's something that that I
may not have explored it because of price points, right.
And here we go. Oh, I like that.
Yeah, OK, I really like that. But I am going to need something
round in the room. And I did see a table that I
liked again, you know, I'm using examples outside of my price
range or I just want to find something like this fast.
OK, OK, so I've gone ahead and I've I've uploaded the image.
Do you have a table like this? No, I'm not asking coffee table
like this nesting coffee table like this marble top coffee
table like this. So I think as you may have
talked to you about about the ability to search on images and
not only did it give me the coffee table, but it also gave
me some recommendations for chairs right as a part of this.
So love it. It came really close to what I'm
looking for so. And there we go.
All right, I love it. That gives me some of the
roundness, you know, for the room.
And you can imagine going further.
So let me go ahead. I think I talked about doing a
multiple query search. Let me show you what that was
like. Because now that I got my couch
and my table, right, maybe I want floor lamps, right?
And alternative chairs or something of that nature, right?
Now, more than likely in this scenario, it's going to give me
similar chairs back, but let's see what it let's see what it
does. OK, I came back with three floor
lamps as different examples that would work in this environment,
as well as one chair that it really highly recommended.
I'm not so crazy about the floor lamps.
I'm not so sure about the chair, but let me go ahead and explore
it, right. This is something that's you're
putting control in the hands of the consumer to explore and
visualise, to then say, oh, yeah, I do love it, or yeah,
that didn't work out. So I hope this is giving you a
sense of just the power of generative AI and AI agents for
retail shopping assistance. So I'm sure the next question
is, you know, how do I find out more?
So you come to nvidia.com and there's a full page on retail
shopping assistance. So we create an AI blueprint
that includes an AI agent. It includes Nemo components for
guardrails, a pointer to Llama 3.370, BA large language model,
being able to rank, being able to understand imagery, a an
accelerated database, vector database.
All of this is available in the blueprint for people to explore.
Try it out, Import your own product, catalogue information
and imagery, play with it, Start to change out components that
make sense for you, and then ultimately get to the point
where you might want to deploy it for yourself.
Where do people? Start a good question.
The place to start is sign up to be notified.
We have an upcoming package, Rev launchable blueprint, so it'll
be available for your teams to click a button.
It'll be unpacked and put on a clouds environment for them to
start starting with immediately. They don't need to know how to
download and install it. We're making it as simple as
possible for for your teams to get started.
You can also work with a number of our partners as well.