When you picture AI, what do you see? A humanoid robot? When you think about a real-world application of AI, what comes to mind? Probably autonomous driving. When you think about the technical details of AI, what approach do you name? I’m willing to bet it’s deep learning.
In reality AI comes in many shapes and forms. AI machines go far beyond humanoid robots; they range from software detecting bullying on social media to wearable devices monitoring personal health risk factors to robotic arms learning to feed paralyzed people to autonomous robots exploring other planets. The potential applications of AI are limitless: personalized education, elderly assistance, wildlife behavior analysis, medical-record mining, and much more.
Our failure to appreciate this spectrum threatens to hold back the field. When we collectively picture AI as one type of thing—whether it’s humanoid robots or self-driving cars or deep learning—we’re encouraging the next generation of researchers to be excited exclusively about those narrow things. If students are presented with a homogeneous pool of AI research role models, then it’s a self-fulfilling prophecy that only students who “fit in” will remain in the field.