Fei Fei Li

for linguistics. So WordLab had nothing to do with AI. It had nothing to do with vision. But what happened for my own North Star is that I was obsessed with the problem of making computers recognize millions of objects in the world. Why I was obsessing with it. I was not satisfied because my field was using extremely contrived data sets, like data set of four objects or 20 objects.

for linguistics. So WordLab had nothing to do with AI. It had nothing to do with vision. But what happened for my own North Star is that I was obsessed with the problem of making computers recognize millions of objects in the world. Why I was obsessing with it. I was not satisfied because my field was using extremely contrived data sets, like data set of four objects or 20 objects.

for linguistics. So WordLab had nothing to do with AI. It had nothing to do with vision. But what happened for my own North Star is that I was obsessed with the problem of making computers recognize millions of objects in the world. Why I was obsessing with it. I was not satisfied because my field was using extremely contrived data sets, like data set of four objects or 20 objects.

I was really struggling with this discrepancy because my hypothesis was that we need to learn the much more complex world. We need to solve that deeper problem than focusing on a very handful of objects. But I couldn't really wrap my head around that.

I was really struggling with this discrepancy because my hypothesis was that we need to learn the much more complex world. We need to solve that deeper problem than focusing on a very handful of objects. But I couldn't really wrap my head around that.

I was really struggling with this discrepancy because my hypothesis was that we need to learn the much more complex world. We need to solve that deeper problem than focusing on a very handful of objects. But I couldn't really wrap my head around that.

And then again, Southern California, I remember that Biederman number in my book is that I read a psychologist paper, Irv Biederman, who was up till two years ago, a professor at University of Southern California. He conjectured that humans can recognize tens of thousands of object categories. So we can recognize millions of objects, but categories are a little more abstract.

And then again, Southern California, I remember that Biederman number in my book is that I read a psychologist paper, Irv Biederman, who was up till two years ago, a professor at University of Southern California. He conjectured that humans can recognize tens of thousands of object categories. So we can recognize millions of objects, but categories are a little more abstract.

And then again, Southern California, I remember that Biederman number in my book is that I read a psychologist paper, Irv Biederman, who was up till two years ago, a professor at University of Southern California. He conjectured that humans can recognize tens of thousands of object categories. So we can recognize millions of objects, but categories are a little more abstract.

Yeah, sedan, fighter jet, and all that. So he conjectured that, but that conjecture didn't go anywhere. It was just buried in one of his papers, and I dug it out, and I was very fascinated. I called it the Biedermann number because I thought that number was meaningful, but I don't know how to translate that into anything actionable because...

Yeah, sedan, fighter jet, and all that. So he conjectured that, but that conjecture didn't go anywhere. It was just buried in one of his papers, and I dug it out, and I was very fascinated. I called it the Biedermann number because I thought that number was meaningful, but I don't know how to translate that into anything actionable because...

Yeah, sedan, fighter jet, and all that. So he conjectured that, but that conjecture didn't go anywhere. It was just buried in one of his papers, and I dug it out, and I was very fascinated. I called it the Biedermann number because I thought that number was meaningful, but I don't know how to translate that into anything actionable because...

As a computer scientist, we're all using data sets of 20 objects. That's it. And then I stumbled upon WordNet. What WordNet was, was a completely independent study from the world of linguistics. It was George Miller, a linguist in Princeton.

As a computer scientist, we're all using data sets of 20 objects. That's it. And then I stumbled upon WordNet. What WordNet was, was a completely independent study from the world of linguistics. It was George Miller, a linguist in Princeton.

As a computer scientist, we're all using data sets of 20 objects. That's it. And then I stumbled upon WordNet. What WordNet was, was a completely independent study from the world of linguistics. It was George Miller, a linguist in Princeton.

He was trying to organize taxonomy of concepts, and he feels alphabetically organized dictionary was unsatisfactory because in dictionary, an apple and an appliance would be close to each other, but that apple should be closer to a pear. Oh, I see. Then appliance. So how do you organize that? How do you regroup concepts?

He was trying to organize taxonomy of concepts, and he feels alphabetically organized dictionary was unsatisfactory because in dictionary, an apple and an appliance would be close to each other, but that apple should be closer to a pear. Oh, I see. Then appliance. So how do you organize that? How do you regroup concepts?

He was trying to organize taxonomy of concepts, and he feels alphabetically organized dictionary was unsatisfactory because in dictionary, an apple and an appliance would be close to each other, but that apple should be closer to a pear. Oh, I see. Then appliance. So how do you organize that? How do you regroup concepts?

So he created WordNet, which hierarchically organized concept according to meaning and similarity rather than alphabetical ordering.

So he created WordNet, which hierarchically organized concept according to meaning and similarity rather than alphabetical ordering.