The paradox with artificial intelligence (AI) and machine learning (ML) is that despite (or because of) the hype, it’s still hard to find practitioners who understand how to use those approaches. Perhaps the best way to characterize the AI paradox is “so near yet so far.”
AI is tantalizingly accessible for a variety of reasons.
First, there’s the data. With the explosion of big data, there’s ample to go around enabling machines to do the learning. Unlike the old days, when data was sparse and storage expensive, we don’t have to rely on static rules devised by human experts.
