In a world where artificial intelligence is reshaping our future, one woman stands out as a trailblazer—and she’s not afraid to embrace her uniqueness. Meet Professor Fei-Fei Li, the 'godmother' of AI, who boldly declares, 'I'm proud to be different.' But here's where it gets intriguing: as the only woman among seven pioneers receiving the prestigious 2025 Queen Elizabeth Prize for Engineering from the King, her journey is both inspiring and thought-provoking. And this is the part most people miss—her acceptance of this title wasn’t just about personal recognition; it was a strategic move to spotlight women in STEM. Let’s dive in.
Today, at St James's Palace, Prof. Li joins six other luminaries—Prof. Yoshua Bengio, Dr. Bill Dally, Dr. Geoffrey Hinton, Prof. John Hopfield, Nvidia founder Jensen Huang, and Meta's Chief AI Scientist Dr. Yann LeCun—in being honored for their groundbreaking contributions to modern machine learning. While Dr. Hinton, Prof. Bengio, and Dr. LeCun are famously dubbed the 'Godfathers of AI' after their 2018 Turing Award, Prof. Li’s role as the sole 'godmother' is a testament to her unique impact. But is this title truly fitting? She initially hesitated, stating, 'I would not call myself godmother of anything.' Yet, she realized rejecting it would mean missing an opportunity to celebrate women in a male-dominated field. 'For all the young women I work with and future generations, I’m okay now accepting this title,' she shared.
Born in China and later excelling in computer science in the U.S., Prof. Li’s work on ImageNet revolutionized computer vision. Her creation of large-scale image recognition datasets laid the foundation for much of today’s AI technology, essentially teaching computers how to 'see.' But here’s the controversial part: while some, like Dr. Hinton, warn of AI’s 'extinction-level threat,' others, like Dr. LeCun, dismiss such claims as overblown. Prof. Li? She takes a 'pragmatic approach,' advocating for grounded, science-based communication over extreme rhetoric. 'Healthy debate is essential,' she notes, urging a more moderated discourse.
As these seven laureates gather for the first time, their differing views spark a critical question: How should we balance innovation with caution in AI’s rapid advancement? Prof. Li’s stance is clear—let’s focus on facts and education. But what do you think? Is her approach the right way forward, or does AI demand more radical caution? Share your thoughts in the comments—this conversation is far from over.
