[MUSIC] So how did you get into machine learning? >> It's interesting. So as I talked about
earlier in the module, I started because I really
cared about robots. I really wanted to build robots,
and I thought okay, the way to build robots is to
do mechanical engineering. So I started mechanical engineering, and
then I realized the hard thing about building robots is the robot brains, and
that took me to computer science, and then I realized the hard part of
the AI was understanding data and they got me into machine learning so
I did my PhD in machine learning. So that's the journey from
thinking about robots, all the way to becoming somebody excited
about data and about machine learning. So how about you? >> Similar but different journey. I wasn't sitting there reading Issac
Asimov making these lifetime aspirations to build robots, but eventually I
actually was working with robots. But the thing that really got me
into machine learning is I was looking on this target tracking
application where they're trying to track these really highly
evasive vehicles like a plane. So a plane is flying and make some
maneuver to try and avoid being tracked. But the issue is that
the systems were really, really highly engineered to assuming that
you knew the physics of this airplane, that you knew that it was gonna do some
kind of turn right maneuver and turn left. And this is how the physics are when
the plane is flying straight or landing or things like this. And so you would build these models that
were known based on physics of the system. All these different modes but what would happen is the thing would do
something that we hadn't seen before and the system would try to accommodate for
it by saying, oh well, it's just rapidly switching between turn
right, turn left, turn right, turn left. But, of course, that didn't actually
describe what was going on, so I thought there must be some better
way to actually learn the parameters of the models that we are trying
to deploy And somehow capture uncertainty about how much we actually
believe in the model that we specified, to be a bit more robust to
these types of situations. So, that's what got me into
machine learning and statistics. >> Right, so as you can see,
I came from mechanical engineering and got into data and to machine learning. Came from electrical engineering and
tracking planes and got into machine learning. But today, it's really exciting because
we see people from all sorts of fields get in to machine learning. We see physicists excited about data. We see people in biology. >> Social sciences, philosophy. >> All sorts of place, math,
we do self statistics, we do science, all the
>> Art. You always like to mention art. >> Oh, yeah.
When I talked at Carnegie Mellon there were some art students that
took my machine learning class. And did really cool art installations
that use machine learning inside them, which is really quite amazing. We even taught a class
there with my friend on building art installations
that use machine language. So we see a variety of people with
different backgrounds, staging the lives, thinking about data science,
thinking about machine learning and building intelligent applications and
they all have a passion for data. Which is cool. >> It is cool, that's how we're
gonna end every one of these chats. That's cool. It is cool. >> It is cool. [LAUGH]
>> See you next chat. >> Cool [LAUGH] [MUSIC]