Not magic- reading about the SpiNNaker supercomputer today just reinforced this.
"Even with a million processors we can only approach 1% of the scale of the human brain and that's with a list of simplifying assumptions"
Steve Furber Professor Computer Engineering statement via University of Manchester.
So AI is important but only as part of an overall strategy. See "Artificial Intelligence Is Not A Technology" if you are relying on AI to gain competitive advantage. There are almost certainly more important priorities and tasks to achieve before you can exploit technology.
"AI is hip again. It’s in vogue. The money is flowing. Companies are becoming “AI first” as if the previous stigma never existed. Interest in AI continues to heat up and shows no signs of slowing. And yet, with all the billions invested and thousands of the world’s best thinkers on the case, we still have not cracked the nut of artificial general intelligence. " Kathleen Walch Contributor Forbes November 1st 2018
Kathleen goes on to say- "They don’t ask whether something they implemented is or isn’t AI. Instead they ask what transformative effect that technology has. They ask what benefit they can realize from machines that can think and act as humans do. They ask how AI will represent opportunities to dramatically increase efficiency, reduce expenses, increase customer satisfaction, improve existing products and services, and create new business opportunities. Because at the end of the day, an organization itself is not about its technology, but its overall mission and objective. And just like those organizations, AI is not defined by technology, but by the overall objective."
AI- "Analogue Fools rush in where Digital Angels fear to tread".
At the end of the day, the deep learning systems of today are less “AI” than fancy pattern extractors. Like any machine learning system, they are able to blindly identify the underlying patterns in their training data and apply those patterns as-is to future data. They cannot reason about their input data or generalize to higher order abstractions that would allow them to more completely and robustly understand their data. In short, while they can perform impressive feats, deep learning systems are still extraordinarily limited, with brittleness that can manifest in highly unexpected ways. After all, the “AI” of today’s deep learning revolution is still just machine learning, not magic.