While the history of artificial intelligence and its profound impact on society is still being written, a watershed moment from the 2012 ImageNet competition has staked a firm claim on the opening chapters of modern AI’s rise to prominence. ImageNet was the brainchild of Stanford Computer Science Professor Fei-Fei Li. Fifteen years ago, Li built a dataset of millions of labeled images which evolved into an annual competition to see which algorithms could identify objects with the lowest error rate. In the latest episode of the Radical Talks podcast, Li recounts the announcement of the 2012 ImageNet winner, when a University of Toronto team, led by Professor Geoffrey Hinton, used a deep learning model to deliver groundbreaking results that would change the course of computer vision and pave the way for a Cambrian Explosion of AI innovation and application building.
Today, Fei-Fei Li is widely regarded as a pioneer in the field of AI and computer vision. As WIRED magazine puts it, Li is “one of a tiny group of scientists—a group perhaps small enough to fit around a kitchen table—who are responsible for AI’s recent remarkable advances.” Building on those earliest breakthroughs, Li’s work now focuses on how AI will revolutionize the healthcare industry. In particular, she’s exploring how the application of ambient intelligence from sensors will provide doctors and caregivers with clinical insights that may change the nature of how healthcare is delivered. Li is a co-founder of Dawnlight, a Radical portfolio company building technologies in this space.
In this episode of Radical Talks, Li also discusses how we should be thinking about the human values of AI systems and why the diversity within today’s AI research community needs to evolve alongside the technology. It is a timely discussion with an AI luminary who has helped shape the course of modern AI innovation. We hope you enjoy the conversation as much as we did.
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