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Jamie Allan on Nvidia's technological innovations for advertising | Variety Studio presented by Canva
Variety
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10/9/2024
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00:00
I think there's two really exciting areas right now.
00:02
One is to look at the traditional, you know,
00:06
machine learning and data science that's been happening
00:09
in ad tech and personalization and recommendation
00:12
for a long time.
00:13
And look at how you can bring
00:14
accelerated computing to that.
00:17
What that does for the industry
00:19
is increases the efficiency of the computing,
00:22
which ultimately can bring down costs,
00:23
bring down energy consumption,
00:25
and all the things that can grow exponentially
00:29
if not looked at and innovated around.
00:31
And, you know, we've done this in lots of other industries,
00:34
and we've been working really hard
00:35
to develop the tools needed
00:37
to move that traditional data science workflow
00:40
to NVIDIA's accelerated computing platform.
00:44
That also brings with it the ability
00:47
to have faster data flywheels.
00:50
And the concept of the data flywheel isn't a new one,
00:53
and I think the advertising industry
00:54
is one of the biggest data flywheels of any sector, really.
00:58
You can also look at financial services
01:00
and healthcare as similar, you know,
01:01
volumes of cyclical data information.
01:05
As we increase the efficiency
01:07
and the speed of processing data,
01:10
you can create more intelligence from that data faster.
01:13
And this is where we come into the world of generative AI
01:15
and the tools that we're building
01:18
to enable enterprises,
01:20
both the end users of advertising,
01:22
so the CMO groups in the industry,
01:25
as well as the agencies and the ad tech companies,
01:29
to be able to derive huge amounts of value
01:31
from that data faster.
01:34
That will then enable the ability
01:36
to create things like fine-tuned language models
01:39
much, much quicker with high levels of accuracy.
01:42
That can then drive faster recommendation,
01:44
really good consumer interaction.
01:47
You know, NVIDIA's been building these toolkits
01:50
and SDKs and platforms for a long time.
01:53
Our AI enterprise stack is like one of the most,
01:57
shall we say, like enterprise-hardened ways
02:00
to deploy your own AI journey and your own AI tools.
02:03
One of the really important things
02:05
in that group of technologies,
02:06
especially in this industry,
02:08
is the ability to guardrail
02:10
those language model interactions,
02:12
especially when you're dealing with either consumers
02:14
or your own internal teams,
02:15
building co-pilots for them or AI agents,
02:18
because you need those tools to stay on track, right?
02:22
So within that guardrailing toolkit,
02:24
you can be really specific about what they'll talk about,
02:27
what they'll interact with, and what they'll do.
02:29
♪♪
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