ImageNet: Where have we been? Where are we going?


#1

Title: ImageNet: Where have we been? Where are we going?
Date: 9/21/17
Time: 1:00 PM ET
Duration: 1 Hour

Speaker:
Fei-Fei Li
Chief Scientist of AI/ML at Google Cloud; Associate Professor at Stanford, Director of Stanford A.I. Lab

Resources:
Registration Link


#2

I have heard AI/neural network visual image recognition described in almost human terms, e.g. finding features on a face, matching nose shape, etc. But given adversarial images, it is clear AI does nothing similar to human image recognition. Can we expect AI image recognition to ever exhibit common sense?


#4

Professor Li,

Since Krizhevsky et al.'s entry in the ImageNet competition, deep learning has since exploded and has been a keynote in a good majority of AI papers since. As someone who has been in both academic and industry, has deep learning replaced more traditional machine learning techniques (e.g. searching, logic, knowledge bases, Bayes nets, inference) or traditional techniques in computer vision/geometry? Is a student’s time better spent on investing into learning more about deep learning or are more traditional and model based approaches still important for a student’s foundation? Which texts would you recommend for students looking to gain a solid foundations (theoretical and applied) and begin doing research in computer vision, 3D geometry, and localization?

Thank you in advance for your seminar and for your work in computer vision.


#5

I will be broadcasting this webinar at AI students Ghana Technology University College


#6

Professor Li,

Recently AI has been one of the most popular field of research in which scientists devoted a lot. And AI can be divided into different specifications such as unmanned vehicle, NLP, and image recognition. Which area do you think will be mostly focused and developed? What is the motivation for it?

Thank you!


#7

My country’s educational system doesn’t require a 15-year-old high school student (like me) to know more than how to create a simple spreadsheet on Microsoft Excel; however, I believe that physical barriers are only for the flesh, whereas mind (and so imagination) are free to go further than the edge of real to magical, from where we pull a better reality.
Though I could use online available open courseware to learn some quite advanced Mathematics, and computer science, but I’m struggling to know what to do next!?
The last research I’ve worked on was a Neural Network that analyzes EEG data to predict motor data based on its patterns, and it worked well, it’s actually won three international awards! But what’s next??!
Unfortunately, for the past few years, I could not find more than a couple of people who share me my interests in my country, however, I hope to find more of these people who have enough imagination to imagine an imagination and have curiosity to ask how to make next in the university and research life coming ahead!
What’s next?


#8

Do not feel that life is owning us, but we do not do enough.