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Last year, Stanford University offered three online courses, which anyone in the world could enroll in and take for free. Students were expected to submit homeworks, meet deadlines, and were awarded a "Statement of Accomplishment" only if they met our high grading bar. Together, these three courses had enrollments of around 350,000 students, making this one of the largest experiments in online education ever performed. In the past few months, we have transitioned this effort into a new venture, Coursera, a social entrepeneurship company that partners with top universities to provide high-quality content to everyone around the world for free. In this talk, I'll report on this new experiment in education, and why we believe this model can provide both an improved classroom experience for our on-campus students, via a flipped classroom model, as well as a meaningful learning experience for the millions of students around the world who would otherwise never have access to education of this quality. I'll describe the pedagogical foundations for this type of teaching, and the key technological ideas that support them, including easy-to-create video chunks, a scalable online Q&A forum where students can get their questions answered quickly, sophisticated autograded homeworks, and a carefully designed peer grading pipeline that supports the at-scale grading of more open-ended homeworks, such as essay questions, derivations, or business plans.
Whereas technology and automation have made almost all segments of our economy---such as agriculture, energy, manufacturing, transportation---vastly more efficient, education today isn't much different than it was 300 years ago. Given also the rising costs of higher education, the hyper-competitive nature of college admissions, and the lack of access to a high quality education, we think there is a huge opportunity to use modern internet and AI technology to inexpensively offer a high quality education online. Through such technology, we envision millions of people gaining access to the world-leading education that has so far been available only to a tiny few, and using this education to improve their lives, the lives of their families, and the communities they live in.
Professor Andrew Ng's research is in the areas of machine learning and artificial intelligence.He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI.Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. Using this approach, Ng's group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. As part of this work, Ng's group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles.