The “all” in the title of Davey Alba’s Wired article is italicized, for emphasis.
The first picture flashes on the screen. “A man is standing next to an elephant,” a robotic voice intones. Another picture appears. “A person sitting at a table with a cake.”
Those descriptions are obvious enough to a person. What makes them remarkable is that a human is not supplying the descriptions at all. Instead, the tech behind this system is cutting-edge artificial intelligence: a computer that can “see” pictures.
Fei-Fei Li, director of the Stanford Artificial Intelligence Lab, is standing on a lit stage in a dark auditorium showing off the advanced object-recognition system she and her fellow researchers built. But as impressive as the system is, Li grows more critical as her presentation unfolds. She says that even if the computer is technically accurate, it could do more. The computer may be able to describe in simple, literal terms what it “sees” in the pictures. But it can’t describe the stories behind the pictures. The person sitting at the table, for instance, is actually a young boy—Li’s son, Leo. Li explains that he is wearing his favorite T-shirt. It’s Easter, and we non-computers can all see how happy he is.
“I think of Leo constantly and the future world he will live in,” Li tells the audience at TED in a video that’s been viewed more than 1.2 million times. In Li’s ideal future, where machines can see, they won’t just be built for maximum efficiency. They’ll be built for empathetic purposes. Artificial eyes, for instance, could help doctors diagnose and take care of patients. If robot cars had empathy, they could run smarter and safer on roads. (Imagine if the builders of self-driving cars used algorithms that didn’t account for the safety of pedestrians and passengers.) Robots, Li says, could brave disaster zones to save victims.
Li is one of the world’s foremost experts on computer vision. She was involved in building two seminal databases, Caltech 101 and ImageNet, that are still widely used by AI researchers to teach machines how to categorize different objects. Given her stature in the field, it’s hard to overstate the importance of her humanitarian take on artificial intelligence. That’s because AI is finally entering the mainstream.