What we see and what we value: AI with a human perspective-Fei-Fei Li (Stanford University)
ฝัง
- เผยแพร่เมื่อ 19 ม.ค. 2024
- Allen School Distinguished Lecture Series
Title: What we see and what we value: AI with a human perspective
Speaker: Fei-Fei Li (Stanford University)
Host: Ranjay Krishna
Date: January 18, 2024
Abstract: One of the most ancient sensory functions, vision emerged in prehistoric animals more than 540 million years ago. Since then animals, empowered first by the ability to perceive the world, and then to move around and change the world, developed more and more sophisticated intelligence systems, culminating in human intelligence. Throughout this process, visual intelligence has been a cornerstone of animal intelligence. Enabling machines to see is hence a critical step toward building intelligent machines. In this talk, I will explore a series of projects with my students and collaborators, all aiming to develop intelligent visual machines using machine learning and deep learning methods. I begin by explaining how neuroscience and cognitive science inspired the development of algorithms that enabled computers to see what humans see. Then I discuss intriguing limitations of human visual attention and how we can develop computer algorithms and applications to help, in effect allowing computers to see what humans don't see. Yet this leads to important social and ethical considerations about what we do not want to see or do not want to be seen, inspiring work on privacy computing in computer vision, as well as the importance of addressing data bias in vision algorithms. Finally I address the tremendous potential and opportunity to develop smart cameras and robots that help people see or do what we want machines’ help seeing or doing, shifting the narrative from AI’s potential to replace people to AI's opportunity to help people. We present our work in ambient intelligence in healthcare as well as household robots as examples of AI's potential to augment human capabilities. Last but not least, the cumulative observations of developing AI from a human-centered perspective has led to the establishment of Stanford's Institute for Human-centered AI (HAI). I will showcase a small sample of interdisciplinary projects supported by HAI.
Bio: Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018. And during her sabbatical from Stanford from January 2017 to September 2018, Dr. Li was Vice President at Google and served as Chief Scientist of AI/ML at Google Cloud. Since then she has served as a Board member or advisor in various public or private companies.
Dr. Fei-Fei Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She also holds a Doctorate Degree (Honorary) from Harvey Mudd College.
Dr. Fei-Fei Li’s current research interests include cognitively inspired AI, machine learning, deep learning, computer vision, robotic learning, and AI+healthcare especially ambient intelligent systems for healthcare delivery. In the past she has also worked on cognitive and computational neuroscience. Dr. Li has published more than 300 scientific articles in top-tier journals and conferences in science, engineering and computer science. Dr. Li is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI. In addition to her technical contributions, she is a national leading voice for advocating diversity in STEM and AI. She is co-founder and chairperson of the national non-profit AI4ALL aimed at increasing inclusion and diversity in AI education.
Dr. Li is an elected Member of the National Academy of Engineering (NAE), the National Academy of Medicine (NAM) and American Academy of Arts and Sciences (AAAS). She is also a Fellow of ACM, a member of the Council on Foreign Relations (CFR), a recipient of the Intel Lifetime Achievements Award in 2023, a recipient of the 2022 IEEE PAMI Thomas Huang Memorial Prize, 2019 IEEE PAMI Longuet-Higgins Prize, 2019 National Geographic Society Further Award, IAPR 2016 J.K. Aggarwal Prize, the 2016 IEEE PAMI Mark Everingham Award, the 2016 nVidia Pioneer in AI Award, 2014 IBM Faculty Fellow Award, 2011 Alfred Sloan Faculty Award, 2009 NSF CAREER award, the 2006 Microsoft Research New Faculty Fellowship, among others.
Dr. Fei-Fei Li is the author of the book "The Worlds I See: Curiosity, Exploration and Discovery at the Dawn of AI", published by Macmillan Publishers in 2023.
This video is closed captioned.
![The Possibilities of AI [Entire Talk] - Sam Altman (OpenAI)](http://i.ytimg.com/vi/GLKoDkbS1Cg/mqdefault.jpg)
This video is a good supplement to reading her 'Worlds I See.' Thank you.
Still have to buy her book, will do!
Do you have the soft copy
EXCELLENT presentation! THANK YOU so much for sharing such amazing ideas!
A brilliant talk ❤
This is such a wonderful talk!
Very insightful contents. Thank you for this!
29:52 thank you for the introducing real world application for computer vision.
Thanks a Lot🎉 excellent work
Seit ihr schön am lernen... Find ich super 👍☺️
great talk !
really liked the class.
There is a huge place for this in emergency rescue work. If a fire or similar disaster occurs, people in wheelchairs, people with sensory disabilities, can be trapped without the same escape capacities as everyone else. If a system can tell emergency services that a wheelchair user has been identified on the east wing of the 3rd floor at the time of disaster, they can be informed as to where to focus efforts.
This is even before the embodied robotics capabilities are ready.
I am in the process of appying for a job. Aparently ADP is now using AI to vet candidates qualities for a job. Now instead of impressing a human being, I first have to impress an algorithm. Is this what they mean when they talk about better living through technology. AI will wind up being as bad or worse than social media.
انا ابحث في computer vision
The key word is "category," or "categorization." No categories, no math. No math, no neural net and no AI. But humans are beyond categories. Do you disagree? I challenge you then to have your preferred super-duper AI write a 60,000 word novel, that a human reader cannot put down once begun reading, and cannot forget once finished. A simple task that no AI can do, and most likely could not, even in a 100 years. You think your AI could? Have it write a novel then, and without any human editorial input, get it published by a regular publisher. I'd love to read it. But I am sure it would not be readable...
If she is so good why has she not got the Turin Award or even the ACM Prize in Computing??
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You know as well as I know Because America is an evil empire and a racist country, and she is NOT WHITE. Especially she is Chinese; therefore. There are a lot of China 🇨🇳 hater in America, and biases against her 😃👍!!!
You know as well as I know Because America is an evil empire and a racist country, and she is NOT WHITE. Especially she is Chinese; therefore. There are a lot of China 🇨🇳 hater in America, and biases against her 😃👍!!!
You know as well as I know Because America is an evil empire and a racist country, and she is NOT WHITE. Especially she is Chinese; therefore. There are a lot of China 🇨🇳 hater in America, and biases against her 😃👍!!!
You know as well as I know Because America is an evil empire and a R.A.C.1.ST country, and she is NOT WHITE. Especially she is Chinese; therefore. There are a lot of China 🇨🇳 hater in America, and biases against her 😃👍!!!
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Isn't what you call bias simply an undesirable generalization? how do you know your bias is not making the bias? Please, stop with all these wokeism. it's one thing to be kind, it's another thing to create new harm. new harm is what DEI creates.
She definitely supports CCP.
他能夠被斯坦福大學聘用，主要是因為他來自中國共產黨的貴族家庭。這可能意味著斯坦福的高層可能受到中共的賄賂或其他形式的誘惑。