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Instead, experts say, it’s best to be patient and particular about beginning on AI or machine learning projects. It’s better to first understand what the technology can do, understand what problems you have to solve, and what it takes to make it happen. The first two parts are covered here, but what about the third aspect, which is implementation?
“The first thing you have to do is figure out what AI can actually do for you as a company,” said AI/ML consultant Adam Geitgey, who helps companies develop software and blogs extensively about it.
Presently, Geitgey told TechRepublic, where AI/ML software works best is in automating repetitive human tasks that require a small amount of judgment. “What you want to look for are places where you have a lot of people making decisions over and over… find something that is labor intensive that you do a lot of,” he explained.
Reviewing civil discovery documents in lawsuits, image classification, and transcribing audio are some of the examples. Examples for internal IT functions include tuning/optimizing your data center operations, configuration management, and systems patching/updating, analyst Henry Baltazar of 451 Research added. Second, for all of these examples, “You need a lot of data to train AI to do that… if you don’t have that data you’re not going to be able to build an AI system,” he noted. You can buy off-the-shelf applications from Amazon, Google, and IBM, but if you need something custom you will have to assemble a team to build it.
“A lot of people hire specialists right now, but enough mid-level software developers are getting interested,” Geitgey observed. “It’s pretty immature first-generation stuff. You can imagine a couple of years out, these kinds of tools will be much more available and standardized, and you probably won’t get them from your hardware vendor.” For now, “If it’s the first thing your company has ever done, it might be helpful to have guidance.”
“The third step then is actually creating the solution and testing the effectiveness,” Geitgey added. It’s common to read about hyperscale companies using AI for their internal computer maintenance operations, but that’s probably not efficient for normal-sized companies, he said. Common mistakes include wanting to use AI/ML simply because it’s popular, and jumping into software development before understanding the problem you need to solve, Geitgey said.
In order to start collecting enough data to understand the difficulties and to develop smart enough software, “What I always advise is to tell CEOs and decision-makers that the data itself is an asset to your company… especially if it’s something no one else has,” he said. After building or buying AI/ML software, you also have to understand how to measure whether it delivers on the promises or not, 451’s Baltazar said. Currently less than half of developers understand how to do this, he said.
The article first appeared on TechRepublic
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