Dr. Fei Fei Li: preference

Transcript context

…Okay, so let me ask you this question. It feels like we're always on this precipice of AGI, this kind of vague term people throw around, AGI is coming, it's going to take over everything. What's your take on how far you think we might be from AGI? Do you think we're going to get there on the current trajectory we're on? Do you think we need more breakthroughs? Do you think the current approach will get us there? Yeah, this is a very interesting term, Lenny. I don't know if anyone has ever defined AGI. There are many different definitions, including some kind of superpower for machines all the way to machines can become economically viable agent in the society. In other words, making salaries to live. Is that the definition of AGI? As a scientist, I take science very seriously and I enter the field because I was inspired by this audacious question of, can machines think and do things in the way that humans can do? For me, that's always the north star of AI. And from that point of view, I don't know what's the difference between AI and AGI. I think we've done very well in achieving parts of the goal, including conversational AI, but I don't think we have completely conquered all the goals of AI. And I think our founding fathers, Alan Turing, I wonder if Alan Turing is around today and you ask him to contrast AI versus AGI, he might just shrugged and said, "Well, I asked the same question back in 1940s," so I don't want to get onto a rabbit hole of defining AI versus AGI. I feel AGI is more a marketing term than a scientific term as a scientist than technologist. AI is my north star, is my field's north star, and I'm happy people call it whatever name they want to call it. So let me ask you maybe this way, like you described, there's kind of these components that from ImageNet and AlexNet took us to where we're today, GPUs essentially, data, label data, just like the algorithm of the model. There's also just the transformer feels like an important step in that trajectory. Do you feel like those are the same components that'll get us to, I don't know, 10 times smarter model, something that's like life-changing for the entire world? Or do you think we need more breakthroughs? I know we're going to talk about world models, which I think is a component of this, but is there anything else that you think is like, oh, this will plateau, or okay, this will take us just need more data, more compute, more GPUs?…

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