Dr. Fei-Fei Li

I do believe technology is a net positive for humanity, but I think every technology is a double-edged sword. If we're not doing the right thing as a society, as individuals, we can screw this up as well.

Human learning as well as evolution is actually a big data learning process. Humans learn with so much experience constantly. In the evolution, if you look at time, animals evolve with just experiencing the world.

Today, you take a model and run it through a video of a couple of office rooms and ask the model to count the number of chairs. And this is something a toddler could do or maybe an elementary school kid could do, and AI could not do that.

I think my students and I conjectured that a very critically-overlooked ingredient of bringing AI to life is big data.

A "world model" is an AI system that can generate interactive 3D environments from text or image prompts, beyond just text-based chatbots.

World model is a foundation that you can use to reason, to interact, and to create worlds.

There's nothing artificial about AI. It's inspired by people. It's created by people, and most importantly, it impacts people.

I definitely think we need more innovations. There's not a single deeply scientific discipline in human history that has arrived at a place that says we're done, we're done innovating.

Sometimes I do want to encourage young people to focus on what's important because I find myself constantly in mentoring mode when I see an incredible young talent who is over-focusing on every minute dimension and aspect of considering a job.

I do find many of the young people today think about every single aspect of an equation when they decide on jobs. At some point, maybe that's the way they want to do it, but sometimes I do want to encourage young people to focus on what's important because... maybe the most important thing is where's your passion? Do you align with the mission? Do you believe and have faith in this team?

I believe that whatever AI does, currently or in the future, is up to us. It's up to the people.

"Scaling loss" appears to be a transcription error - likely meant to say "scaling laws," which refer to predictable patterns in AI model performance as you increase data, compute, and model size.

I definitely think we need more innovations. I think scaling loss of more data, more GPUs, and bigger current model architecture is there's still a lot to be done there, but I absolutely think we need to innovate more.

I'm an intellectually very fearless person, and I have to say when I hire young people, I look for that because I think that's a very important quality if one wants to make a difference.