>> Group, yeah, exactly.
And there was some other small work for face detection and
things like this in France, and in various other places, but it was very small.
I discovered actually recently that a couple groups that came
up with ideas that are essentially very similar to convolutional nets, but
never quite published it the same way for medical image analysis.
And those were mostly in the context of commercial systems.
And so it never quite made it to the population.
I mean, it was after our first work on convolutional nets, and they were
not really aware of it, but it sort of developed in parallel a little bit.
So several people got kind of similar ideas several years interval.
But then I was really surprised by how fast
interest picked up after the ImageNet- >> 2012
>> In 2012, so it's the end of 2012.
It was kind of a very interesting event at ECCV,
in Florence, where there was a workshop on ImageNet.
And they already knew that had won by a large margin.
And so everybody was waiting for talk.
And most people in the computer vision community had no idea what a convolutional
net was.
I mean, they heard me talk about it.
I actually had an invited talk at CVPR in 2000 where I talked about it,
but most people had not paid much attention to it.
Senior people did, they knew what it was, but
the more junior people in the community were really, had no idea what it was.
And so just gives his talk, and he doesn't explain what a convolutional net is
because he assumes everybody knows, right?
because he comes from a so he says, here's how everything is connected,
and how we transform the data and what results we get.
Again, assuming that everybody knows what it is.
And a lot of people are incredibly surprised.
And you could see the opinion of people changing as he was kind of giving
his talk, very senior people in the field. >> So you think that workshop was
a defining moment that swayed a lot of the computer vision community.
>> Yeah, definitely.
>> That's right, yeah.
>> That's the way it happened, yeah,
right there. >> So today, you retain a faculty position
at NYU, and you also lead FAIR, Facebook AI Research.
I know you have a pretty unique point of view on how corporate research should
be done.
Do you want to share your thoughts on that?
>> Yeah, so I mean, one of the beautiful
things that I managed to do at Facebook in the last four years is that I was given
a lot of freedom to setup FAIR the way I thought was the most appropriate,
because this was the first research organization within Facebook.
Facebook is a sort of engineering-centric company.
And so far was really focused on sort of survival or short-term things.
And Facebook was about to turn ten years old, had a successful IPO.
And was basically thinking about the next ten years, right?
I mean, Mark Zuckerberg was thinking, what is going to be important for
the next ten years?
And the survival of the company was not in question anymore.
So this is the kind of transition where a large company can start to think, or
it was not such a large company at the time.
Facebook had 5,000 employees or so, but it had the luxury to
think about the next ten years and what would be important in technology.
And Mark and his team decided that AI was going to be a crucial
piece of technology for connecting people, which is the mission of Facebook.
And so they explored several ways to kind of build an effort in AI.
They had a small internal group, engineering group,
experimenting with convolutional nets and stuff that were getting really good
results in face recognition and various other things, which peaked their interest.
And they explored the idea of hiring a bunch of young researchers, or
acquiring a company, or things like this.
And they settled on the idea of hiring someone senior in the field, and
then kind of setting up a research organization.