WEBVTT

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SAM RANSBOTHAM: Most of
us have used LinkedIn

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to search for a job or to make
new professional connections,

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but how can AI help
facilitate all the many ways

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users engage with LinkedIn?

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Find out today,
when we talk with Ya

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Xu, LinkedIn's head of data.

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Welcome to Me, Myself,
and AI, a podcast

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on artificial
intelligence in business.

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Each episode, we introduce you
to someone innovating with AI.

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I'm Sam Ransbotham, professor
of information systems

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at Boston College.

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I'm also the guest editor for
the artificial intelligence

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and business strategy Big Idea
program at MIT Sloan Management

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Review.

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SHERVIN KHODABANDEH: And
I'm Shervin Khodabandeh,

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senior partner with BCG, and I
also colead BCG's AI practice

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in North America.

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Together, MIT SMR
and BCG have been

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researching AI for six years
now, interviewing hundreds

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of practitioners and
surveying thousands

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of companies on what it takes
to build and deploy and scale

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AI capabilities across
the organization

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and really transform the
way organizations operate.

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SAM RANSBOTHAM: Shervin
and I are excited today

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to be talking with Ya Xu,
head of data at LinkedIn.

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Ya, thanks for taking
the time to join us.

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Welcome.

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YA XU: Thank you for having me.

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SAM RANSBOTHAM: Let's start
with your current role.

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What do you do for LinkedIn?

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YA XU: I am part of the
engineering organization.

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I lead a team that's
called "data."

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I know it's very confusing
to people outside LinkedIn

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to realize what "data" really
means, but, essentially,

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if you think about all
the data science, AI,

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and privacy
engineering, it's all

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happening in my organization.

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So we [work] all the
way from research

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to production, so it's a pretty
big organization that is really

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helping the company realize
our ambition in data and AI.

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SHERVIN KHODABANDEH: And
what is that ambition?

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YA XU: It is really
using AI, using data,

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to help create
economic opportunity

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for the global workforce.

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SHERVIN KHODABANDEH: Comment
a little bit more about that.

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I like, by the way,
how you said, "Look,

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it's all about
creating opportunity

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and economic value,"
and you didn't

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say, "It's about bringing the
most advanced technologies

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and building
sophisticated algorithms,"

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which I'm sure is all part
and parcel of what you do,

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but you started with a very high
meta framing and positioning.

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It's really encouraging
to hear that.

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But, just generally comment
on what that is for LinkedIn

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and what role AI plays in that.

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YA XU: First of all, I mean,
I think that creating advanced

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technology -- to
me, that is "how."

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The end goal is not just
creating technology,

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but really, like I mentioned,
creating opportunities.

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Maybe I would break
it down a little bit.

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I don't know how well
the audience understands

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LinkedIn in general.

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We like to think of ourselves as
having three key marketplaces.

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We have our knowledge
marketplace where people [are]

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creating content on LinkedIn and
people [are] consuming content

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and getting informed
so that they

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can advance in their career
and make the right connections

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to the right people.

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That's what we call the
knowledge marketplace.

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We also have the
talent marketplace.

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This is when job seekers
come to LinkedIn --

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and also employers and companies
post jobs and recruiters find

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the right talent for companies.

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So that's our
talent marketplace,

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which also includes
our learning --

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thinking about individuals
or professionals who are

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reskilling themselves so that
they can continue to advance

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in their career or
find new opportunities.

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The third marketplace is
really the product and services

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marketplace.

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This is where marketers come to
the LinkedIn platform and then

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really try to identify the
buyers for their products

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and services -- sales
individuals come to LinkedIn

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and really try to identify
the buyers as well for them.

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So if you're thinking
about the role

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that AI plays, hopefully
with that context,

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it's very clear that obviously
in the knowledge marketplace,

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we are really trying
to match the content

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creators with the right
content consumers.

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In the talent
marketplace, we really

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just try to match
the job seekers

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with the right companies and
with the right opportunities.

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And then, for the product
and services marketplace,

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we're just trying to match
buyers and sellers, etc.

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When you put it
in that context, I

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hope it is very straightforward
to see that obviously data

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and AI play such
an essential role,

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because how do you match them?

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It's really through
advanced technologies

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that we have in data and AI.

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SHERVIN KHODABANDEH: So
"How do you match them?"

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then takes us to a variety
of use cases and experiences,

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and I'm just curious, how
do you come up with those?

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What is the process where
you've got the business side

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and you've got the technology
and engineering and data side

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working together?

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SAM RANSBOTHAM: Take
us through the recipe.

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SHERVIN KHODABANDEH: Yeah.

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YA XU: [Laughs.] I would
actually maybe start getting

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to the tactical
aspects of things.

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I think LinkedIn is a very
unique place, because the way

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that I describe LinkedIn as
three different marketplaces

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and the way that data and
AI play a role, that's

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how our CEO describes LinkedIn.

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What I've described
is not so much like,

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"Oh, this is our data
or AI view of LinkedIn";

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this is LinkedIn as a company's
overarching view of LinkedIn.

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So hopefully that sets the stage
for how integrated data and AI

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are for the mission and the
vision of the company, for how

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the company operates, for the
collaboration, which we can

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talk more about,
for how different

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product development processes
work and collaboration happens

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across different
team boundaries.

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That's where I wanted to start,
because it's just one word:

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It's very integrated.

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SHERVIN KHODABANDEH: Yeah.

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YA XU: Tactically maybe [I'll]
just talk about little bit

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of how we are organized.

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LinkedIn is very
functionally organized.

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So if you're looking at who
sits at our CEO's table,

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you have, essentially,
all the functional

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heads: head of engineering,
head of product, head of sales,

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head of marketing,
head of legal,

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you know, HR, head of,
obviously, finance.

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We have a very strong
muscle because of that

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to work cross-functionally.

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And so even though that all the
data science and AI functions

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are in my team, we have the
muscle and the structure

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to enable really strong
collaboration between all

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the other functions that
we work with, in order

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to bring the AI solutions
to production, to life,

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and to deliver that value to
our members and customers.

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So concretely, my team --

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I have leaders who are
focused on different areas.

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And as an example,
I have a leader

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who is focusing on our
consumer experience

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in particular, so the
way that he would operate

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is that he would
work very closely,

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in a very embedded fashion, with
other cross-functional leaders

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who focus on
consumer experience.

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So, what does that mean?

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They have many,
many touch points,

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all the way from
quarterly planning --

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"How do you set OKRs?

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How do you do reviews?"

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When it comes to a
particular initiative,

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then you've got functional heads
all coming together and then

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strategizing what
needs to happen,

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who [should] work on them
-- planning our road maps.

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All [of this is]
happening very seamlessly

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as a cross-functional team.

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And, as I said, because we
as a company have always,

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ever since the existence of
LinkedIn, operated in this way;

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everyone has that muscle.

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SHERVIN KHODABANDEH:
It's brilliant,

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because Sam and I have
done a lot of research

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here and talked with
companies across sectors,

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and it's really
interesting: You said

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the three key [things
that] have really

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been challenges
for most who aren't

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getting any value from AI.

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You said "strategy and mission"
-- that's where you started.

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You said "integrated
cross-functional teams,"

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and then you said
"collaboration."

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And, and in fact, when you look
at the data of 90% of companies

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[that] aren't really
getting as much value

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for their investments
in AI [compared with]

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those 10% who are
getting it, it comes down

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to these very three points.

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And it's interesting that
LinkedIn, since its inception,

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has been that kind of a
place, where it's been "data

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first" in an integrated way.

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[This] completely resonates,
and [it is] also very refreshing

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that in your role as the
head of engineering and data,

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that's also where you first
go, before you go and you

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talk about everything else,
which is all important,

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but it's the "how";
it's not the "what."

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YA XU: Yeah,
absolutely, and I always

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look to the leaders
on my team as well.

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They are not just like, "Hey,
I'm just a leader of AI;

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I'm just a leader of data."

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They really need to understand
where the end goal is, right?

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The end goal is
never just creating

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maybe the largest model or
the best state of the art.

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It's really about
delivering the value.

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When you understand
that, and then

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when you've got the
cross-functional team all

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having the same shared
goal and purpose,

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then that also brings
that collaboration to life

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really easily.

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SHERVIN KHODABANDEH: How hard
was that to put that in place

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and align the incentive and
the organization and keep --

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you said "different muscles"?

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How hard was that to
get to that place?

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Was it always there?

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Were there some very hard
decisions that had to be made?

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YA XU: I have been at LinkedIn
for close to nine years now.

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I want to say it's
pretty much always there,

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because this is how we've
always been organized.

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The culture is so
ingrained that I

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think the new employees will
just very quickly assimilate

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into "This is how we operate."

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SAM RANSBOTHAM: You
mentioned getting better

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in collaboration, and I'm
kind of thinking back on what

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you were saying about matching.

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You used the word matching in
all three of your scenarios.

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What risk is there
that you get down

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this path of a
bunch of engineers

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ever-improving the existing
matching algorithms

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and perhaps missing
out on a fourth area

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that you need to be focusing on?

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Is there a tension
that you're feeling

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between ever-improving that
matching process through better

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and better algorithms and
data, and figuring out

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where to apply it in some
of your newer products

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like your I don't
know newsletter,

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LinkedIn Lives, or some of
those kinds of new things?

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Where's that tension?

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YA XU: There are
nonstop new areas

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that pop up regarding
how we can innovate

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and should be innovating,
and even just starting

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with matching, it's
very simplistic to say,

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"Hey, we just can try
to do better matching."

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But what does "better
matching" even mean, right?

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Let's take our data
marketplace as an example.

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Obviously, we want to match
the best qualified candidate

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with the best company.

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But how do you even define that?

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SAM RANSBOTHAM: What is "best"?

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YA XU: What is "best," not
only for the job seeker,

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but also, what's the
best for the other side

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of the marketplace,
which is the companies?

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If I were trying
to hire somebody,

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my ultimate ideal
state is, I really only

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talk to one candidate, and
that is the dream candidate

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I wanted to hire.

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And then, same thing, let's say,
in the knowledge marketplace,

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as we try to connect to
the content creators,

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to people who are interested
in reading their articles:

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What does that even mean by
"the right matching" as well?

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Is it that we want to maximize
the engagement that people

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have on a particular post?

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How do we think about
distribution of those

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engagements across ?

265
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How do we think about maybe
I'm a new creator, first time

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writing a post on LinkedIn,
and I didn't get any response.

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I will be so discouraged
and never post again.

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How do we think about that
shorter-term trade-off

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and the long-term trade-off?

270
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There's so much more, even
just in this very simplistic

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framework of "We're
just doing matching."

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So now, coming out of the
matching aspect of it,

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there's again way more,
like thinking about,

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how do we help people
do content discovery?

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And when advertisers come
to us, how do we actually

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help them pace their budget?

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How do we help them utilize
their budget better?

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SHERVIN KHODABANDEH: Given
the progressive culture

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and the highly collaborative,
integrated functional culture

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of LinkedIn, what makes a
good candidate for your team?

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What are you looking
for, in addition to

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the hard skills of technology
and data and AI and data

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science?

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What do you think is
the secret sauce there?

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YA XU: I would say,
number one, someone who --

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I think Satya [Nadella]
was the one who

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said that quote,
"Would you want to hire

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00:13:22.050 --> 00:13:25.500
a learn-it-all or
a know-it-all?"

290
00:13:25.500 --> 00:13:31.070
I am a strong believer
in a learn-it-all,

291
00:13:31.070 --> 00:13:34.075
and what made a candidate
successful in the past

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00:13:34.075 --> 00:13:35.700
doesn't necessarily
mean that they will

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00:13:35.700 --> 00:13:37.160
be successful in the future.

294
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But that attitude of "I'm going
to learn; I'm going to adapt"

295
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--

296
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I think that's so important.

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So I would say learning --
somebody who is really a big

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learner.

299
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And the second one is
someone who is just curious.

300
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Because when you are
curious, you have that drive,

301
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you have that "I'm going to
get to the bottom of it."

302
00:13:59.480 --> 00:14:05.250
And so much of the amazing
progress we've made

303
00:14:05.250 --> 00:14:07.330
is because someone is
like, "You know what?

304
00:14:07.330 --> 00:14:10.720
I'm not here for all
the fluffy things.

305
00:14:10.720 --> 00:14:13.980
I'm just going to be here to
really focus on this problem

306
00:14:13.980 --> 00:14:16.722
that I saw that I'm just trying
to figure out how to solve."

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SHERVIN KHODABANDEH: You
know, it reminds me --

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00:14:18.680 --> 00:14:21.900
I had a mentor who
used to tell me,

309
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"There are no boring
projects, only boring people."

310
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And so every time
I was like, "Adam,

311
00:14:28.010 --> 00:14:29.900
I'm not sure I'm crazy
about this project,"

312
00:14:29.900 --> 00:14:31.620
he said, "You could
make it interesting.

313
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You have the ability to
learn; you have the ability

314
00:14:34.340 --> 00:14:36.050
to complain constructively."

315
00:14:36.050 --> 00:14:39.230
So now you're saying that;
it brought back to my memory,

316
00:14:39.230 --> 00:14:41.750
there's only boring
people, no boring projects.

317
00:14:41.750 --> 00:14:42.740
YA XU: Absolutely.

318
00:14:42.740 --> 00:14:43.330
I love that.

319
00:14:43.330 --> 00:14:44.900
I might start
quoting that as well.

320
00:14:44.900 --> 00:14:45.400


321
00:14:45.400 --> 00:14:46.870
SAM RANSBOTHAM:
What's interesting,

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00:14:46.870 --> 00:14:48.950
and I think that
ties into you're

323
00:14:48.950 --> 00:14:51.128
a bit of a hero in the
academic community.

324
00:14:51.128 --> 00:14:53.170
I think maybe you're
attracted to those people --

325
00:14:53.170 --> 00:14:55.770
academics complain a lot;
maybe you're attracted to those

326
00:14:55.770 --> 00:14:57.200
academics that complain a lot.

327
00:14:57.200 --> 00:14:59.800
But I wanted to tie
that to another way

328
00:14:59.800 --> 00:15:03.272
that you're creating economic
value through LinkedIn.

329
00:15:03.272 --> 00:15:05.230
And I think one thing
that's really fascinating

330
00:15:05.230 --> 00:15:07.600
here is that you've
got a platform that

331
00:15:07.600 --> 00:15:11.040
has insight into what's
going on into the economics

332
00:15:11.040 --> 00:15:15.610
of the invisible hand, making
the invisible hand visible.

333
00:15:15.610 --> 00:15:17.435
You've got unparalleled insight.

334
00:15:17.435 --> 00:15:19.810
I was hoping you could talk
a little bit about the things

335
00:15:19.810 --> 00:15:20.310
that you --

336
00:15:20.310 --> 00:15:22.893
I mean, I'm aware of some of the
things you've done with code,

337
00:15:22.893 --> 00:15:24.580
and with your LinkedIn
graphs projects

338
00:15:24.580 --> 00:15:25.920
with the academic world.

339
00:15:25.920 --> 00:15:28.150
What are you doing
in that aspect

340
00:15:28.150 --> 00:15:30.040
to get insights
into those things

341
00:15:30.040 --> 00:15:32.300
that we've just never
had insight into before?

342
00:15:32.300 --> 00:15:33.530


343
00:15:33.530 --> 00:15:35.610
YA XU: Really,
really good point.

344
00:15:35.610 --> 00:15:39.920
Because of the volume that
we have on the platform

345
00:15:39.920 --> 00:15:42.630
and how much economic
opportunity activity that

346
00:15:42.630 --> 00:15:46.740
happens on the platform,
we particularly

347
00:15:46.740 --> 00:15:50.330
have that insight into
future of work, what skills

348
00:15:50.330 --> 00:15:53.330
are in demand, how different
companies are hiring,

349
00:15:53.330 --> 00:15:58.060
which industry is hiring more
and hiring less, and even just

350
00:15:58.060 --> 00:16:03.340
thinking about the equity
aspect of it as well: Do women

351
00:16:03.340 --> 00:16:06.520
[or] men have a different
rate of job changing,

352
00:16:06.520 --> 00:16:08.280
etc., [or] advancements
in their careers?

353
00:16:08.280 --> 00:16:09.080
All that.

354
00:16:09.080 --> 00:16:12.620
So tons of insights that
we have on our platform.

355
00:16:12.620 --> 00:16:16.440
So, what we have done, and, Sam,
you kind of alluded to, is we

356
00:16:16.440 --> 00:16:18.900
-- by the way, we call
all these activities,

357
00:16:18.900 --> 00:16:21.440
this vibrant activity
on our graph,

358
00:16:21.440 --> 00:16:25.050
what we call economic graph
-- and we have started

359
00:16:25.050 --> 00:16:28.090
a particular effort
on economic graph,

360
00:16:28.090 --> 00:16:32.660
probably seven at
least six years ago,

361
00:16:32.660 --> 00:16:36.030
where we essentially stand up
a team that includes a bunch

362
00:16:36.030 --> 00:16:39.100
of folks on my team
with our policy teams,

363
00:16:39.100 --> 00:16:42.300
with our comms teams,
with our editorial teams,

364
00:16:42.300 --> 00:16:45.830
that really try to share
and bring some of the data

365
00:16:45.830 --> 00:16:48.770
and insights to the
external communities.

366
00:16:48.770 --> 00:16:50.807
And we have been
very successful,

367
00:16:50.807 --> 00:16:51.640
as a matter of fact.

368
00:16:51.640 --> 00:17:00.640
For example, last year we sent
a report to most of the [U.S.]

369
00:17:00.640 --> 00:17:05.780
congressmen/women on what the
labor marketplace looks like

370
00:17:05.780 --> 00:17:07.690
for their region.

371
00:17:07.690 --> 00:17:10.280
We have a collaboration
partnership, for example,

372
00:17:10.280 --> 00:17:13.490
with Singapore's
education department

373
00:17:13.490 --> 00:17:18.020
to help them figure out
what skills [are] in demand

374
00:17:18.020 --> 00:17:20.230
and [are] lacking
so that they can

375
00:17:20.230 --> 00:17:23.484
change their educational
curriculum to help.

376
00:17:23.484 --> 00:17:24.859
SAM RANSBOTHAM:
That's just huge.

377
00:17:24.859 --> 00:17:28.390
YA XU: And then we have
worked with obviously a lot

378
00:17:28.390 --> 00:17:31.460
of other institutions,
either much more directly

379
00:17:31.460 --> 00:17:33.550
with a particular
local government

380
00:17:33.550 --> 00:17:37.770
or with some, like, the World
Economic Forum, the G-20;

381
00:17:37.770 --> 00:17:39.860
we share a lot of
our reports with them

382
00:17:39.860 --> 00:17:43.270
to help influence some of
the policies they have.

383
00:17:43.270 --> 00:17:48.630
Another simple example is, we
are really helping the broader

384
00:17:48.630 --> 00:17:51.740
community know what the
green skills [are] --

385
00:17:51.740 --> 00:17:55.150
people who are either
hiring for green skills,

386
00:17:55.150 --> 00:17:58.440
people who are like, "Where
is that talent going?"

387
00:17:58.440 --> 00:18:02.840
so that as we invest
more in green energy,

388
00:18:02.840 --> 00:18:05.790
both the governments
and those industries

389
00:18:05.790 --> 00:18:08.313
can be more guided in that
from a talent perspective.

390
00:18:08.313 --> 00:18:09.480
SAM RANSBOTHAM: That's huge.

391
00:18:09.480 --> 00:18:10.495


392
00:18:10.495 --> 00:18:12.870
SHERVIN KHODABANDEH: You've
been featured in Fortune's 40

393
00:18:12.870 --> 00:18:17.370
Under 40, you've written a book,
you've given numerous speeches,

394
00:18:17.370 --> 00:18:20.220
you're a very successful
practitioner in a very

395
00:18:20.220 --> 00:18:21.370
successful company.

396
00:18:21.370 --> 00:18:27.480
I'm curious, what would
your advice be to your peers

397
00:18:27.480 --> 00:18:32.540
in other organizations that
are leveraging AI as the

398
00:18:32.540 --> 00:18:37.440
how to achieve whatever the
what of their company is?

399
00:18:37.440 --> 00:18:40.140
What would be the two
things that you think

400
00:18:40.140 --> 00:18:41.863
might not be obvious to others?

401
00:18:41.863 --> 00:18:42.770


402
00:18:42.770 --> 00:18:44.610
YA XU: The first thing
that came to my mind

403
00:18:44.610 --> 00:18:47.430
is maybe not so provocative.

404
00:18:47.430 --> 00:18:50.990
It's really just,
have the best talent.

405
00:18:50.990 --> 00:18:52.730
It's so important.

406
00:18:52.730 --> 00:18:58.070
And I am such a strong believer
that when you bring the best

407
00:18:58.070 --> 00:19:01.950
people, all you need to do
is to get out of the way,

408
00:19:01.950 --> 00:19:03.760
and then help them
to be successful,

409
00:19:03.760 --> 00:19:05.840
and then wonder just happens.

410
00:19:05.840 --> 00:19:08.410
Many of folks on my team
can do their job way

411
00:19:08.410 --> 00:19:11.570
better than I can do
their job, especially

412
00:19:11.570 --> 00:19:16.350
in a field that is
constantly innovating.

413
00:19:16.350 --> 00:19:22.410
I always joke about how the pace
that this field, this domain,

414
00:19:22.410 --> 00:19:28.660
is changing is like 300, 400,
500 miles per hour; it's crazy.

415
00:19:28.660 --> 00:19:31.780
I mean, I got my
Ph.D. in this domain,

416
00:19:31.780 --> 00:19:35.860
and I was in this workshop that
was talking about graph neural

417
00:19:35.860 --> 00:19:38.320
networks and just graph
learning in general,

418
00:19:38.320 --> 00:19:43.920
and what the practitioners are
doing today versus when I was

419
00:19:43.920 --> 00:19:48.050
doing my thesis 10 years ago
-- it's entirely different.

420
00:19:48.050 --> 00:19:51.880
And what was state-of-the-art
10 years ago is nowhere to be

421
00:19:51.880 --> 00:19:54.490
found in today's practice.

422
00:19:54.490 --> 00:19:56.990
So I think that's, again,
just really emphasizing

423
00:19:56.990 --> 00:19:59.560
how important it is
to bring the best

424
00:19:59.560 --> 00:20:01.080
people and the
talent, especially

425
00:20:01.080 --> 00:20:03.160
in a very innovative space.

426
00:20:03.160 --> 00:20:05.990
And the second thing
I want to say is --

427
00:20:05.990 --> 00:20:08.210
maybe this is a
little less obvious --

428
00:20:08.210 --> 00:20:13.590
is to make data and AI
work in our company,

429
00:20:13.590 --> 00:20:20.690
the way to do it is not to build
a wall between folks who know

430
00:20:20.690 --> 00:20:24.280
data and AI and people who
don't know data and AI.

431
00:20:24.280 --> 00:20:28.340
And, by the way, this
is a general pitfall,

432
00:20:28.340 --> 00:20:33.040
either in the mindset of people
or how companies are organized.

433
00:20:33.040 --> 00:20:37.350
Let's say you've got
an expert team who

434
00:20:37.350 --> 00:20:41.010
is world-class in
data and AI, and then

435
00:20:41.010 --> 00:20:43.020
you just expect,
"Hey, you know what?

436
00:20:43.020 --> 00:20:46.555
The rest of the company knows
nothing about data and AI."

437
00:20:46.555 --> 00:20:47.930
SHERVIN KHODABANDEH:
I think what

438
00:20:47.930 --> 00:20:50.510
you said about talent is
probably not obvious to many,

439
00:20:50.510 --> 00:20:53.930
and I also think it really
corroborates your earlier point

440
00:20:53.930 --> 00:20:55.940
about curiosity and learning.

441
00:20:55.940 --> 00:20:58.400
I mean, if those
are the ingredients,

442
00:20:58.400 --> 00:21:00.590
then of course
talent matters a lot.

443
00:21:00.590 --> 00:21:01.763
So thank you for that.

444
00:21:01.763 --> 00:21:03.930
SAM RANSBOTHAM: We're just
seeing that over and over

445
00:21:03.930 --> 00:21:05.860
again, or maybe it's
just the kinds of people

446
00:21:05.860 --> 00:21:08.150
that we're, perhaps,
attracted to on the show.

447
00:21:08.150 --> 00:21:10.990
But it does seem to
be showing up a lot.

448
00:21:10.990 --> 00:21:12.128
YA XU: Absolutely.

449
00:21:12.128 --> 00:21:12.920
SAM RANSBOTHAM: OK.

450
00:21:12.920 --> 00:21:13.320
So

451
00:21:13.320 --> 00:21:15.430
SHERVIN KHODABANDEH: Is this
time for the five questions?

452
00:21:15.430 --> 00:21:16.140
Should we do that, or --

453
00:21:16.140 --> 00:21:16.500
SAM RANSBOTHAM: Sure.

454
00:21:16.500 --> 00:21:16.560
Yeah.

455
00:21:16.560 --> 00:21:16.970
Do that.

456
00:21:16.970 --> 00:21:18.195
SHERVIN KHODABANDEH:
Do you know about this?

457
00:21:18.195 --> 00:21:18.422


458
00:21:18.422 --> 00:21:20.380
YA XU: I do not know
about this, but fire away.

459
00:21:20.380 --> 00:21:21.550
SHERVIN KHODABANDEH:
Oh, we didn't tell you?

460
00:21:21.550 --> 00:21:22.050
All right.

461
00:21:22.050 --> 00:21:23.092
SAM RANSBOTHAM: Surprise!

462
00:21:23.092 --> 00:21:24.195
YA XU: I like surprises.

463
00:21:24.195 --> 00:21:25.820
SHERVIN KHODABANDEH:
We have this thing

464
00:21:25.820 --> 00:21:27.620
where we have five questions.

465
00:21:27.620 --> 00:21:29.620
You could just riff,
give an answer.

466
00:21:29.620 --> 00:21:32.232
So, what's your
proudest AI moment?

467
00:21:32.232 --> 00:21:34.040


468
00:21:34.040 --> 00:21:36.420
YA XU: I would probably
go back all the way

469
00:21:36.420 --> 00:21:41.660
to when I was in grad school,
and I was taking this class,

470
00:21:41.660 --> 00:21:46.280
and it was probably the
very first time I really saw

471
00:21:46.280 --> 00:21:49.730
AI in application in the
way that you can feel

472
00:21:49.730 --> 00:21:50.550
and you can touch.

473
00:21:50.550 --> 00:21:53.710
I was taking this class
with Andrew Ng where

474
00:21:53.710 --> 00:21:56.810
we were supposedly
building an algorithm that

475
00:21:56.810 --> 00:21:59.740
was able to, given
a stream of video,

476
00:21:59.740 --> 00:22:02.460
identify objects in the video.

477
00:22:02.460 --> 00:22:06.160
I worked really hard
with a classmate of mine

478
00:22:06.160 --> 00:22:07.810
and a close friend of mine.

479
00:22:07.810 --> 00:22:10.750
At the end of the day,
they had this competition

480
00:22:10.750 --> 00:22:14.090
of the accuracy and
precision recall, etc.

481
00:22:14.090 --> 00:22:17.590
In a pretty large class,
we won second place --

482
00:22:17.590 --> 00:22:20.540
so not the first place, so
still room for improvement --

483
00:22:20.540 --> 00:22:23.780
but [I'm] very proud that,
especially [since it was the]

484
00:22:23.780 --> 00:22:26.350
second year in my Ph.D.
and before that a lot

485
00:22:26.350 --> 00:22:29.383
of my experience was a little
more contrived examples.

486
00:22:29.383 --> 00:22:30.178


487
00:22:30.178 --> 00:22:31.470
SHERVIN KHODABANDEH: Very good.

488
00:22:31.470 --> 00:22:33.410
What worries you about AI?

489
00:22:33.410 --> 00:22:36.210
YA XU: On one hand,
you know, obviously

490
00:22:36.210 --> 00:22:38.020
I'm super excited
for the potential,

491
00:22:38.020 --> 00:22:42.480
but what worries me about AI
would be in the responsible AI

492
00:22:42.480 --> 00:22:43.950
space in particular.

493
00:22:43.950 --> 00:22:46.840
Obviously, I'm really
glad the attention

494
00:22:46.840 --> 00:22:49.800
that responsible AI is
able to get in the public

495
00:22:49.800 --> 00:22:52.720
and in the research community,
and in industry as well.

496
00:22:52.720 --> 00:22:55.790
But at the same time,
it's not just a buzzword.

497
00:22:55.790 --> 00:22:58.122
We've got to really
put it in practice

498
00:22:58.122 --> 00:22:59.830
and make sure that we
are [continuing to]

499
00:22:59.830 --> 00:23:03.510
research how we are
able to identify biases

500
00:23:03.510 --> 00:23:07.730
that AI systems can bring, and
it's a super challenging space.

501
00:23:07.730 --> 00:23:11.430
I've been working in this
space for, I want to say, like,

502
00:23:11.430 --> 00:23:13.660
you know, extensively,
a couple years

503
00:23:13.660 --> 00:23:16.060
now, and just know how
challenging this is,

504
00:23:16.060 --> 00:23:18.370
so my call to action
to your audience

505
00:23:18.370 --> 00:23:22.620
is to definitely lean in
the space and research.

506
00:23:22.620 --> 00:23:24.877
Continue to push the
boundaries on what's possible.

507
00:23:24.877 --> 00:23:25.550


508
00:23:25.550 --> 00:23:27.050
SHERVIN KHODABANDEH:
Very well said.

509
00:23:27.050 --> 00:23:29.080
What is your favorite
activity that

510
00:23:29.080 --> 00:23:31.250
does not require technology?

511
00:23:31.250 --> 00:23:31.750


512
00:23:31.750 --> 00:23:32.570
YA XU: That's easy.

513
00:23:32.570 --> 00:23:33.070
It's skiing.

514
00:23:33.070 --> 00:23:33.570


515
00:23:33.570 --> 00:23:35.490
SHERVIN KHODABANDEH:
That requires technology.

516
00:23:35.490 --> 00:23:35.508


517
00:23:35.508 --> 00:23:36.550
SAM RANSBOTHAM: [Laughs.]

518
00:23:36.550 --> 00:23:38.180
YA XU: It does not
require technology!

519
00:23:38.180 --> 00:23:39.690
SHERVIN KHODABANDEH: The ski.

520
00:23:39.690 --> 00:23:42.160
SAM RANSBOTHAM: Oh, oh, come on.

521
00:23:42.160 --> 00:23:42.660


522
00:23:42.660 --> 00:23:44.840
YA XU: By that argument

523
00:23:44.840 --> 00:23:47.410
SAM RANSBOTHAM: You can expand
that to anything; come on!

524
00:23:47.410 --> 00:23:48.150


525
00:23:48.150 --> 00:23:51.130
YA XU: About four years ago,
I really got into skiing.

526
00:23:51.130 --> 00:23:56.100
And the reason I love skiing
is it's a forced meditation.

527
00:23:56.100 --> 00:24:00.020
It's like, you go down
the hill, and I'm not

528
00:24:00.020 --> 00:24:02.330
nearly a good skier,
but all I think

529
00:24:02.330 --> 00:24:03.830
about is, how can
I get down safely?

530
00:24:03.830 --> 00:24:04.872
SHERVIN KHODABANDEH: Yes.

531
00:24:04.872 --> 00:24:04.880


532
00:24:04.880 --> 00:24:06.060
YA XU: Nothing
else comes to mind.

533
00:24:06.060 --> 00:24:06.650
So that --

534
00:24:06.650 --> 00:24:08.220
SAM RANSBOTHAM: I was
sure you were going

535
00:24:08.220 --> 00:24:09.350
to say "gradient descent."

536
00:24:09.350 --> 00:24:10.580


537
00:24:10.580 --> 00:24:13.920
YA XU: [Laughs.] That's funny.

538
00:24:13.920 --> 00:24:17.150
But I also like that every
time when you go out,

539
00:24:17.150 --> 00:24:21.500
it's different, because the
snow conditions, the weather,

540
00:24:21.500 --> 00:24:26.010
the slope all add variability
to how you actually

541
00:24:26.010 --> 00:24:27.675
do when you're skiing
that day, and I

542
00:24:27.675 --> 00:24:30.050
love that, just because, you
know, it's never status quo.

543
00:24:30.050 --> 00:24:33.430
SHERVIN KHODABANDEH: What was
the first career you wanted,

544
00:24:33.430 --> 00:24:34.940
like in your childhood?

545
00:24:34.940 --> 00:24:37.160
What did you want to
be when you grew up?

546
00:24:37.160 --> 00:24:40.300
YA XU: Well, if
you say way back,

547
00:24:40.300 --> 00:24:42.610
I joke about it still today.

548
00:24:42.610 --> 00:24:44.820
Like I told my mom, I
wanted to be president then.

549
00:24:44.820 --> 00:24:46.487
SAM RANSBOTHAM: Oh,
I will vote for you.

550
00:24:46.487 --> 00:24:48.230
SHERVIN KHODABANDEH:
What's your greatest

551
00:24:48.230 --> 00:24:50.123
wish for AI in the future?

552
00:24:50.123 --> 00:24:50.830


553
00:24:50.830 --> 00:24:52.980
YA XU: It's just really help.

554
00:24:52.980 --> 00:24:57.387
AI should serve the
people and help everything

555
00:24:57.387 --> 00:24:59.470
that we are doing to be
more efficient and better.

556
00:24:59.470 --> 00:25:02.470
And I would say that in
general about technology.

557
00:25:02.470 --> 00:25:05.510
So many things that we
were not able to do,

558
00:25:05.510 --> 00:25:08.400
now we can do because
of AI and technology,

559
00:25:08.400 --> 00:25:12.870
so I continue to be
very bullish about that,

560
00:25:12.870 --> 00:25:15.420
and I'm very excited
that I can be part of it.

561
00:25:15.420 --> 00:25:16.790
SHERVIN KHODABANDEH: Thank you.

562
00:25:16.790 --> 00:25:17.070


563
00:25:17.070 --> 00:25:18.220
SAM RANSBOTHAM: Ya, it
is absolutely wonderful

564
00:25:18.220 --> 00:25:18.750
talking to you.

565
00:25:18.750 --> 00:25:20.330
You know, I was
thinking back to when

566
00:25:20.330 --> 00:25:22.413
you were talking about
muscles, and when you first

567
00:25:22.413 --> 00:25:24.740
said "muscles," I have to
say, I was thinking biceps.

568
00:25:24.740 --> 00:25:26.450
You know, I'm thinking
about big muscles.

569
00:25:26.450 --> 00:25:30.490
But as we've talked, I'm now
thinking more like eye muscles

570
00:25:30.490 --> 00:25:33.270
-- like focus, like
getting the granularity --

571
00:25:33.270 --> 00:25:35.900
because what you're talking
about is the data that's going

572
00:25:35.900 --> 00:25:39.490
to let us see things that are
happening in the world that we

573
00:25:39.490 --> 00:25:43.203
just have not been able to see
before, and it's fascinating,

574
00:25:43.203 --> 00:25:45.120
and we've really enjoyed
talking to you today.

575
00:25:45.120 --> 00:25:45.910
Thank you so much.

576
00:25:45.910 --> 00:25:46.272


577
00:25:46.272 --> 00:25:46.980
YA XU: Thank you.

578
00:25:46.980 --> 00:25:48.120
Thank you for having me.

579
00:25:48.120 --> 00:25:50.450
SHERVIN KHODABANDEH: It's
been truly, truly insightful.

580
00:25:50.450 --> 00:25:52.370
And thank you for making time.

581
00:25:52.370 --> 00:25:53.690
YA XU: Of course.

582
00:25:53.690 --> 00:25:56.330
SAM RANSBOTHAM: Thanks
for joining us today.

583
00:25:56.330 --> 00:25:58.470
On our next episode,
Shervin and I

584
00:25:58.470 --> 00:26:02.380
talk with Nitzan Mekel-Bobrov,
eBay's chief AI officer.

585
00:26:02.380 --> 00:26:04.120
Hope you can join us then.

586
00:26:04.120 --> 00:26:05.680
ALLISON RYDER:
Thanks for listening

587
00:26:05.680 --> 00:26:07.180
to Me, Myself, and AI.

588
00:26:07.180 --> 00:26:09.630
We believe, like you,
that the conversation

589
00:26:09.630 --> 00:26:11.860
about AI implementation
doesn't start and stop

590
00:26:11.860 --> 00:26:13.040
with this podcast.

591
00:26:13.040 --> 00:26:15.530
That's why we've created a
group on LinkedIn, specifically

592
00:26:15.530 --> 00:26:16.640
for leaders like you.

593
00:26:16.640 --> 00:26:19.390
It's called AI for Leaders,
and if you join us,

594
00:26:19.390 --> 00:26:21.410
you can chat with show
creators and hosts,

595
00:26:21.410 --> 00:26:25.040
ask your own questions, share
insights, and gain access

596
00:26:25.040 --> 00:26:27.520
to valuable resources
about AI implementation

597
00:26:27.520 --> 00:26:29.620
from MIT SMR and BCG.

598
00:26:29.620 --> 00:26:34.740
You can access it by visiting
mitsmr.com/AIforLeaders.

599
00:26:34.740 --> 00:26:37.460
We'll put that link
in the show notes,

600
00:26:37.460 --> 00:26:39.890
and we hope to see you there.

601
00:26:39.890 --> 00:26:45.000