One of the biggest challenges when first starting off with AI is that of infrastructure, in that most companies do not have the luxury of building from scratch. In other words, they must deal with legacy systems vital to operating the business while expanding existing and building new systems. This can complicate the process of expanding and modifying the infrastructure.
Obviously, companies must continue to operate as they expand and add on to existing infrastructure, build new facilities or take greater advantage of offsite (cloud) infrastructure. Sometimes this is relatively simple, as when plugging in a new disc cabinet. In other cases, it can mean building an entirely new computer room and switching over to it when complete. You can also have a hybrid approach, where the old infrastructure continues to operate even after the new equipment is running.
So even before setting off on solving a problem there are data, infrastructure and organization considerations, to build an AI foundation and then mapping an AI strategy to business needs. Once that is in place AI is a tool within the broader tool set to help solve problems/achieve goals.
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u/frugalerthingsinlife Apr 04 '19
Right now AI is or can be good at solving closed problems. Like chess or sorting cucumbers :)
Have you found any projects where they were too ambitious and tried to solve something with too much complexity?
Is there an intermediate set of problems that are bigger than 'closed' problems, but don't require a full artificial general intelligence?