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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]
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