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Okay, given your great historical perspective of machine learning-
>> Are you calling me old?
>> I am calling you old.
>> It's always like this.
>> [LAUGH] I was wondering if you could give me a little perspective on
the future.
>> Well, let's talk about the present.
>> Okay.
>> [LAUGH] >> I'll accept that,
we can talk about the present.
He always makes things really long and really detailed.
So just talk about the present.
We'll get to the future eventually, don't worry.
>> [LAUGH] >> Here we go, the present.
What is the state of machine learning, Carlos?
>> [CROSSTALK] I'm really excited about machine learning today, honestly.
Being old, that you work in machine learning for a long time.
But today, every time I open a web app, it uses machine learning inside it.
And that's pretty cool.
And I pull out my phone, where's my phone?
You always have your phone with you.
>> I actually don't have my phone right now.
>> She's always on her phone.
Anyway, when you pull out your phone, every time you use it, there's
a system that uses machine learning right inside the cloud that supports it.
Which is really pretty cool.
And now with devices, check this out.
I got this watch.
>> Pretty spiffy. >> It's a wonderful watch that
tracks my activity, knows when I'm running, when I'm biking-
>> Knows when he's sleeping or
not sleeping.
Always this debate, who got less sleep with the baby?
[LAUGH] >> [LAUGH] So, yeah,
that's a wonderful watch.
And underneath these things, it's all using machine learning.
So we see the power of machine learning just kind of, again and
again, being used today.
But I think we've only scratched the surface of what machine learning can do.
Where do you see machine learning going in the future?
You tell me.
>> Where do I see it?
Well, I know probably a lot of ways in which it would be useful to Carlos.
One being the fact that he hates driving.
So of course, there is this smart car idea that this self-driving, not smart car.
>> They're also smart.
>> Are they smart?
They're pretty smart, I guess.
It's a self-driving car.
A smart car is a brand.
That's not what I mean, I don't mean those little cars.
I mean normal cars [LAUGH] that drive themselves.
>> That would be really useful for me, because I don't like driving.
I only like biking.
And so I need some- >> Self-driving bikes.
>> No, no, I like driving my bike.
>> [CROSSTALK] That's for when you get really, really old.
[LAUGH] >> [CROSSTALK] So
I'm gonna talk about that.
But when I think about me getting old, I think about how machine learning can
really have impact with your personalized medicine.
And the question here is, why is it when I go to a doctor for
certain disease or certain condition,
the treatment that Emily gets is exactly the same as the treatment that I get.
I mean, we're different people with different lifestyles,
with different body types, as you can see.
And still, with medicine, we're getting exactly the same treatment.
That makes no sense.
The treatment needs to be personalized to who I am, what I do,
how much of a couch potato I am, what my DNA looks like.
And with machine learning, we're going to be able to do that all the time.
It's really super cool, I'm pretty excited.
>> That was just so optimistic, which is good.
It's really nice, but it's hard!
It's really, really hard to do this.
The data is really noisy, cuz there's some doctor who's there taking these records,
making notes about Carlos' health conditions compared to mine.
And then there's lots of missing data, because
maybe Carlos skipped his last checkup or the doctor forgot to enter something.
Or maybe the doctor put something under a different entry than they did for me.
And so somehow our machine learning algorithms are gonna have to be able to
parse this really complex and
noisy data, in order to do this type of personalized medicine.
Which I think is a possibility.
I think it's really cool, but- >> It is hard.
All these other things we're talking about, from self-driving
cars which we're seeing as a reality today, to personalized medicine.
It's all hard stuff.
But in the future, and this is the optimistic in me,
the world is going to be personalized to me.
>> [LAUGH] >> You know what I mean?
>> The grand goal of everything for Carlos.
[LAUGH] >> [LAUGH] For me and for Emily, too.
She can have her own world.
So the idea that today when we go to the Web and
we buy things, we have to go through very similar experiences as others.
But as the world becomes smarter, as more data is collected, as we come up with this
algorithms which are robust to all this kind of noise in the data,
we're gonna have experiences that are really unique to us, idiosyncratic.
And we're not gonna suffer as much from technology.
Technology's gonna really adapt to us, which is really super cool.
>> I agree, I think that is a really, really exciting goal.
And I think that is one of the directions that machine learning is going in.
But I think something else that's really cool is even doing the things we can
do right now.
But doing them as the data's coming in, in real time, with really complicated
different types of data sets, and putting all this information together.
And maybe it's just even still recommending products,
something we've done before in the past, but with different types of data.
And again, in this really fast, rapid, online type of setting, and
getting feedback and incorporating this to make these types of prediction, yeah.
>> Yeah, dealing with temporal data, which is the topic here,
as well as the spatial data like location data.
All this different kind of data sets, images, video, audio, sensing data,
combining all these different data sources in real time is one of the big challenges.
And what I'm excited to see is that there's a lot more machine learning work
and a lot more algorithms being developed that try to address this confusion of
different data types.
And that's a really cool topic to work on right now.
>> For sure, so.
>> Of course.
>> We hope that's really exciting to you, in addition to us.
>> Yeah, it is exciting.
And of course, the methods that we develop have to scale to these
massive data sets that we're talking about, different kinds of data.
Be able to be really useful and robust as you are analyzing the data.
And honestly, it shouldn't require
a PhD in machine learning to be able to access the systems and be able to use it.
So part of the challenge that we have to do is be able to make
these algorithms robust and these methods robust.
So that a wide range of people, even those that don't
have the most theoretical background are able to build the smartest,
coolest applications using machine learning.
That's exciting to me as well.
>> Absolutely.
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