The evolution of AI has been a rich tale of exploration since its origins in the 1950’s , with the last decade providing an especially dramatic chapter of breakthrough innovations.
The evolution of AI has been a rich tale of exploration since its origins in the 1950’s , with the last decade providing an especially dramatic chapter of breakthrough innovations. But I believe the real story is what comes next — when the disruption stabilizes and machine learning transitions from a staple of Silicon Valley headlines to an everyday technology. It’ll be a far longer chapter — perhaps decades — in which developers all over the world use a mature set of tools to transform their industries.
In 2019, we find ourselves at the start of this new chapter. AI has undergone a remarkable refinement in recent years, as barriers to entry have fallen and a wide range of products, services, resources, and best practices have emerged. As our focus shifts — finally — from AI itself to the impact that AI can have on your business, the question is no longer how this technology works, but what it can do for you .
In other words, we’re entering the age of deployed AI. Deployed AI is about more than engineering — it’s about a shared vision. Engineering expertise will always play a role in AI. But in the age of deployed AI, our most important asset will be the vision that guides that expertise. What problems can AI solve, and what kind of data might the solution require? By what metrics will success be measured? And how can the result be integrated most effectively with the people and processes already in place in any given business? These are broad, organizational questions, and their answers won’t come from any single stakeholder. Every voice can contribute to deployed AI — technical and non-technical alike — and it’s vital that businesses establish workflows that empower everyone to play a role.
One of my favorite recent examples of this shift in possibilities comes from Carnegie Mellon University (CMU), where I formerly served as dean of the computer science department. While I was there, a student was considering her options for an upcoming artificial intelligence project, and thought of her sister, who happens to be deaf. She wanted to make it easier for her friends to learn the basics of American Sign Language, so she developed an AI-powered tool that tracked their movements and provided automatic feedback as they learned new signs. And here’s the best part: she wasn’t a computer science postdoc or even a grad student — she was a history major, taking an introductory class for fun.
It’s hard to imagine a better example of how accessible and powerful deployed AI can be — or a better indication that this technology is ready to solve problems for every business, in every industry, today.[…]