In 2006, the U.S. patent office received a filing for “an automatically generated display that contains information relevant to a user about another user of a social network.”
Rather than forcing people to search through disorganized” content for items of interest, the system would seek to generate a list of “relevant” information in a “preferred order.” The listed authors were “Zuckerberg et al.” and the product was the News Feed.
The platform’s recommendation systems were still in their infancy, and as an algorithm, it wasn’t much. By 2010, the company was looking beyond the crude system to recommend content based on machine learning and user behavior.
There was no question that the computer science was dazzling and the gains concrete. But the speed, breadth, and scale of Facebook’s adoption of machine learning came at the cost of comprehensibility. Read the full extract.
AI is at an inflection point, Fei-Fei Li says
Fei-Fei Li is one of the most prominent computer science researchers of our time. The co-director of Stanford’s Human-Centered AI Institute is best known for creating ImageNet, a popular image data set that was pivotal in allowing researchers to train modern AI systems.
In her newly published memoir, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI, Li recounts how she went from an immigrant living in poverty to the AI heavyweight she is today. It’s a touching look into the sacrifices immigrants have to make to achieve their dreams, and an insider’s telling of how artificial-intelligence research rose to prominence.
Li recently spoke to Melissa Heikkilä, our senior AI reporter, about the future of AI and the hard problems that lie ahead for the field. Read the full story.