Today’s data watchers estimate that we create at least 2.7 million terabytes of data per day — enough to double the digital universe every two years. Our digital, sharing economy has given rise to a universe of apps, all generating bytes and bytes of data that, for the most part, no one looks at or uses. How will we begin to meaningfully utilize this overwhelming amount of data?
The consensus answer is with artificial intelligence; AI will draw sense from this data junkyard, thereby enabling groundbreaking progress in applications from self-driving cars and medical diagnoses to national security and scientific discoveries. But the reality is that, beyond some specialized exceptions, AI as a broad discipline has been largely stagnant for decades. Absent a few systems, the AI available today seems to be powered by the same machine learning I studied in college as a computer engineering major a little more than 20 years ago, when the entire Internet could be surfed in a couple of hours.
Given this state, the way forward needs to be an accelerated catch-up game that enrolls business and IT leaders, technologists, educators and policy-makers alike. As a society we have ambitious and noble dreams for AI, and we all need to invest to deliver on them. Here are the three efforts we need to target to bring AI to fruition.
1. Cultivating and engaging young talent for AI
From an education perspective, we need to nurture a younger generation that is interested in an AI career path instead of one that just involves coding. There’s not enough brainpower working on AI today, period, and we’re going to need a much more expert talent pool. AI’s core is hard, high-level math, the kind of deep expertise that is the domain of Ph.D.s, not new grads who start coding after a 6-week bootcamp.
Read the source article at informationweek.com.