00:00 Some of the things that one should consider when evaluating AI strategy, first is the
00:07 cost versus return on investment.
00:09 What are you actually hoping to achieve with AI?
00:13 There has to be a business need.
00:14 There has to be a business outcome.
00:16 It's not just that we're doing it better.
00:19 There's brand new types of applications that we've never been able to do before.
00:28 I'm Monica Livingston and I lead the AI Center of Excellence at Intel.
00:32 So big question in AI is, do you build from the ground up?
00:36 Do you have sufficient skill set and data and need to build an application from the
00:42 ground up?
00:43 Or do you purchase off the shelf or you could even customize an off the shelf application?
00:48 And the cost of that model or of that application needs to be such that you have a return on
00:54 investment.
00:56 The other part of getting started with AI altogether is understanding your data.
01:02 What data do you have?
01:04 Because in order to train AI models, you need to have your own data sets or you need to
01:08 have access to data sets or you need to license data sets.
01:11 But one way or another, you have to get data that is usable.
01:17 When you get to the point to where you understand what your workload is, whether it's built
01:23 in-house or developed externally, you know what workload you want to run.
01:28 Then you start looking at, okay, infrastructure, what do I run this on?
01:32 For these smaller models, for running inference, we recommend the fourth generation Intel Xeon
01:37 Scalable Processor.
01:39 Most data centers have Xeon processors in their install base, so they're already there,
01:44 as well as our core processors, which are for client devices like notebooks and desktops.
01:49 So our role in making AI accessible is to add AI functionality in these product lines.
01:56 So being able to run your AI applications on general purpose infrastructure is incredibly
02:03 important because then your cost for additional infrastructure is reduced.
02:09 And then you look at responsible AI.
02:12 Actually responsible AI is a huge growing area.
02:15 You should have a vendor checklist for responsible AI, specifically to be able to vet that your
02:21 vendor is using AI responsibly, that they have processes in place to correct for any
02:26 sort of issues that might potentially come up.
02:29 So those are a couple of things that someone should consider once they're building their
02:33 AI models and really infusing AI into their applications rather than just saying, "I'm
02:39 building AI and I'm building it from the ground up for AI."
02:43 So you have a business outcome, you have an application, and the question is, "Should
02:46 I start using some form of AI in that application?"
02:50 [Music]
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