Some of the things that one should consider when evaluating
AI strategy first is the cost versus return on investment.
What are you actually hoping to achieve with AI?
There has to be a business need.
There has to be a business outcome.
It's not just that we're doing it better.
There's brand new types of applications that we've never been
able to do before.
I'm Monica Livingston and I lead the AI Center of
Excellence at Intel.
So big question in AI is,
do you build from the ground up?
Do you have sufficient skill set and data and need
to build an application from the ground up?
Or do you purchase off the shelf?
Or you could even customize an off the shelf application
And the cost of that model or of that application
needs to be such that you have a return on
investment. The other part of getting started with AI all
together is understanding your data.
What data do you have?
Because in order to train AI models you need to
have your own data sets,
or you need to have access to data sets,
or you need to license data sets.
But one way or another,
you have to get data that is usable.
When you get to the point to where you understand
what your workload is,
whether it's building house or developed externally,
you know what workload you want to run,
Then you start looking at,
OK, infrastructure, what do I run this on For these
smaller models for running inference,
we recommend the 4th generation Intel Xeon Scalable Processor.
Most data centers have Xeon processors in their install base,
so they're already there, as well as our core processors
which are for client devices like notebooks and desktops.
So our role in making AI accessible is to add
AI functionality in these product lines.
So being able to run your AI applications on general
purpose infrastructure is incredibly important because then your cost for
additional infrastructure is reduced
and then you look at responsible AI.
And actually responsible AI is a huge growing area.
You should have a vendor checklist for responsible AI specifically
to be able to vet that your vendor is using
AI responsibly,
that they have processes in place to correct for
any sort of issues that might potentially come up.
So those are a couple of things that someone should
consider if once they're building their AI models and and
really infusing AI into their applications rather than just saying
I'm building AI and I'm building it from the ground
up.
For AI generally you have a business outcome,
you have an application, and the question is,
should I start using some form of AI in that
application?