Information Age Education Blog
TED Talk about Computer Vision by Fei-Fei Li
TED is a nonprofit devoted to spreading ideas, usually in the form of short, powerful talks of 18 minutes or less. TED began in 1984 as a conference where Technology, Entertainment and Design converged, and today covers almost all topics—from science to business to global issues —in more than 100 languages (About TED, 2015).
What began as a quite exclusive and high-priced conference for a limited number of people has spread throughout the world. There are now more than 1,900 TED Talks available on the Web. Data presented by Hochman (3/7/2014) indicate that the TED Talks videos have had about 2 billion views.
The recent talk by Fei-Fei Li, Director of Stanford’s Artificial Intelligence Lab and Vision Lab, provides a prime example of why I enjoy watching TED Talks—especially ones focusing on Science, Technology, Engineering, and Mathematics (STEM). She has very good credentials, is a world leader in her field, and is a good presenter who makes effective use of visuals. In “How We're Teaching Computers to Understand Pictures,” Li organized and presented quite complex information in a form that I could understand. Quoting from her talk:
So for 15 years now, starting from my Ph.D. at Caltech and then leading Stanford's Vision Lab, I've been working with my mentors, collaborators and students to teach computers to see. Our research field is called computer vision and machine learning. It's part of the general field of artificial intelligence. So ultimately, we want to teach the machines to see just like we do: naming objects, identifying people, inferring 3D geometry of things, understanding relations, emotions, actions and intentions. You and I weave together entire stories of people, places and things the moment we lay our gaze on them.
The current efforts to bring human-like vision to computers illustrates how powerful and capable our human vision system and brain actually are. The best of current computer vision systems are not as good as that of an average three-year-old human.
Notice the word “understand” in the title of Li’s talk. By age three, a human child has considerable understanding of the world, the objects in it, and relationships among0 these various objects. Artificial intelligence is still a long way from achieving this level of human understanding. Quoting again from Li:
We have prototyped cars that can drive by themselves. But without smart vision, they cannot really tell the difference between a crumpled paper bag on the road, which can be run over, and a rock that size, which should be avoided. We have made fabulous megapixel cameras, but we have not delivered sight to the blind. Drones can fly over massive land, but don't have enough vision technology to help us to track the changes of the rainforests. Security cameras are everywhere, but they do not alert us when a child is drowning in a swimming pool. Photos and videos are becoming an integral part of global life. They're being generated at a pace that's far beyond what any human, or teams of humans, could hope to view, and you and I are contributing to that at this TED. Yet our most advanced software is still struggling at understanding and managing this enormous content. So in other words, collectively as a society, we're very much blind, because our smartest machines are still blind.
What You Can Do
Consider the five senses: vision, hearing, smell, touch, and taste. Computer scientists and engineers have been working for many years to provide computers with the sensing capabilities that a child develops seemingly with little or no effort. Do the computer systems you routinely use have “senses” that in any way compare with your own capabilities? Think about this the next time you become annoyed at your computer’s stupidity and call it a “dumb machine.”
Help your students to understand differences between capabilities of the human mind/body and capabilities of computers. An important part of a modern education is to help students appreciate and learn to make effective of their inborn natural capabilities that far exceed those of current computers.
About TED (2015). Retrieved 3/29/2015 from https://www.ted.com/about/our-organization.
Hochman, D. (3/7/2014). No, his name is not Ted. Chris Anderson, curator of TED Talks, builds his brand. The New York Times. Retrieved 3/29/2015 from http://www.nytimes.com/2014/03/09/fashion/Chris-Anderson-Curator-of-TED-Talks-Builds-his-Brand.html?_r=0.
Li, F. (March, 2015). How we're teaching computers to understand pictures. TED Talks. Retrieved video and transcript 3/27/2015 from https://www.ted.com/talks/fei_fei_li_how_Fewe_re_teaching_computers_to_understand_pictures.
Suggested Readings from IAE
Moursund, D., & Sylwester, R., eds. (4/12/2015). Education for students’ futures. Eugene, OR: Information Age Education. Available for free downloads: PDF file from http://i-a-e.org/downloads/free-ebooks-by-dave-moursund/269-education-forstudents-futures-1.html. Microsoft Word file from http://i-a-e.org/downloads/free-ebooks-by-dave-moursund/268-education-forstudents-futures.html.
Moursund, D. (4/11/2015). Viewing “now” from past forecasts. IAE Blog. Retrieved 4/13/2015 from http://i-a-e.org/iae-blog/entry/viewing-now-from-past-forecasts.html.
Moursund, D. (2/2/2015). Are high schools seriously misleading our students? Update. IAE Blog. Retrieved 4/13/2015 from http://i-a-e.org/iae-blog/entry/are-high-schools-seriously-misleading-our-students-update.html.
Moursund, D. (11/23/2014). Big data: A new facet of science research. IAE Blog. Retrieved 4/13/2015 from http://i-a-e.org/iae-blog/entry/big-data-a-new-facet-of-science-research.html.
Moursund, D. (2014). Brain science. IAE-pedia. Retrieved 5/13/2015 from http://iae-pedia.org/Brain_Science.