(soft music)
Sam: Are algorithms getting less important?
As algorithms become commoditized,
it may be less about the algorithm
and more about the application.
In our first episode of season two of "Me, Myself, and AI",
we'll talk with Craig Martel,
Head of Machine Learning at Lyft
about how Lyft uses artificial intelligence
to improve its business.
Welcome to "Me, Myself, and AI",
a podcast on artificial intelligence in business.
Each episode, we introduce you
to someone innovating with AI.
I'm Sam Ramsbotham, Professor of Information Systems
at Boston College.
I'm also the guest editor for the AI
and business strategy big idea program,
at MIT Sloan Management Review.
Shervin: And I'm Shervin Khodabandeh,
Senior Partner with BCG
and I co-lead BCG's AI practice in North America.
And together MIT SMR and BCG
have been researching AI for five years,
interviewing hundreds of practitioners
and surveying thousands of companies
on what it takes to build
and to deploy and scale AI capabilities across
the organization and really transform
the way organizations operate.
Sam: Today we're talking with Craig Martel.
Craig is the Head of Machine Learning for Lyft.
Thanks for joining us today, Craig.
Craig: Thanks Sam, I'm really happy to be here,
these are pretty exciting topics.
Sam: So Craig, Head of Machine Learning at Lyft,
what exactly does that mean and how did you get there?
Craig: So let me start by saying
I'm pretty sure I won the lottery in life
and here's why.
I started off doing political theory academically
and I have this misspent youth
where I gathered a collection
of master's degrees along the way
to figuring out what I want to do.
So I did philosophy, political science,
political theory, logic,
and I ended up doing a PhD
in computer science at Penn.
And you know, I sort of thought
I was going to do testable philosophy,
and so the closest to that was doing AI.
So I just did this out of love.
Like I just find the entire process
and the goals, and the techniques, steps really fascinating.
Sam: All part of your master plan, it all came together.
Craig: Not at all! I just fell into it, I fell into it.
Sam: So how did you end up then at Lyft?
Craig: So I was at LinkedIn for about six years
and then my wife got this phenomenal job
at Amazon and I wanted to stay married
so I followed her to Seattle.
I worked for a year here at Dropbox
and then Lyft contacted me
and I essentially jumped at the chance because
the space is so fascinating.
I love cars in general
which means I love transportation in general
and the idea of transforming
how we do transportation is just a fascinating space.
And then in my prior life
I was a tenured computer science professor,
which is still a big love of mine,
and so I'm an adjunct professor at Northeastern
just to make sure I keep my teaching skills up.
Shervie: Craig, your strong humanities background
in philosophy, political science,
you mentioned logic, all of that.
How did that play for you in your overall journey?
Craig: So that's really interesting.
When I think about what AI is,
I find the algorithms mathematically fascinating
but I find the use of the algorithms
far more fascinating because
from a technical perspective
we're finding correlations
in extremely high dimensional non-linear spaces.
It's statistics at scale in some sense, right?
We're finding these correlations between A and B
and those algorithms are really interesting
and I'm still teaching those now and they're fun.
But what's more interesting to me
is what do those correlations mean for the people?
I think every AI model launched
is a cognitive science test.
Like we're trying to model the way humans behave.
Now, for automated driving
we're modeling the way cars behave in some sense,
but it's really we're modeling
the right human behavior
given these other cars driven by humans.
So for me, I think the goals of AI,
I look at them much more from humanity's perspective,
although I can nerd out on the technical side as well.
Sam: Can you say a bit more about
how Lyft organizes AI and ML teams?
Craig: We have model builders
throughout the whole company.
We have a very large science org.
We also have what we call ML-SWES,
so ML software engineers.
I run a team called Lyft ML
and it consists of two major teams.
One is called applied ML
where we leverage expertise in machine learning
to tackle some really tough problems.
And also the ML platform,
which drives my big interest
in operational excellence on getting ML
to make sure it's effectively hitting business metrics.
Shervin: What do you think, because I think Craig,
you're still teaching, right?
Craig: Yeah, I adjunct teach at Northeastern University
here in Seattle.
Shervin: So, what do you think your students,
sort of, should be asking that they're not,
or maybe to state it in another way,
what would they be most surprised by
when they enter the workforce
and actually do AI in the real world?
Craig: The algorithms themselves
are becoming less important.
I'm hesitant to use the word commoditized,
but to some degree they're being commoditized, right?
You could pick one of five, one of seven,
you could try them all model families
for a particular problem.
But what's really happening,
or what I think is the exciting thing happening,
is how those models fit
into a much larger engineering pipeline
that allow you to measure and guarantee
that you're being effective against a business goal.
And that has to do with the cleanliness of the data,
making sure the data is there in a timely way,
classic engineering things.
Like are you returning your features at the right latency?
So the actual model itself
has shrunk from say, 85% of the problem
to 15% of the problem.
And now 85% of the problem is the engineering
and the operational excellence surrounding it.
And I think we're at a point of inflection there.
Shervin: So do you believe with the advent
of auto ML and these packaged tools
and your point about over time,
it's less about the algo,
more about the data and how you use it?
Do you think the curricula, and the training,
and just the overall orientation
of data scientists ten years from now,
would be dramatically different?
Like, should we teach them different things,
different scales? Because it used to be
a lot is focused on creating the algorithms,
trying different things,
and I think you're making the point that
that sort of plateauing, what does that mean
in terms of the workforce of the future?
Craig: Yeah, I think that's great.
I'm going to say some controversial things here
and I hope not to offend anybody.
Shervin: That's why I asked so I hope that you will!
Craig: So if you look just five or ten years ago
in order to deliver the kind of value
that tech companies wanted to deliver
you needed a fleet of PhDs, right?
The technical ability to build those algorithms
was extremely important.
I think the point of inflection there
was probably TensorFlow, 2013 ish,
where it wasn't commoditized.
You still needed to think very hard
about the algorithm but the actual getting the algorithm
out the door became a lot easier.
Now there's plenty of frameworks for that.
Sam: I wonder -- this is a real wonder --
I wonder the degree to which
we're going to need specialized machine learning,
AI data science training going forward.
I think CS undergrads or engineering undergrads in general
are all going to graduate with two or three AI classes
and those two or three AI classes
with the right infrastructure in the company,
the right way to gather features,
the right way to specify your label data.
If we have that ML platform in place,
people with two or three strong classes
are going to be able to deliver 70%
of the models a company might need.
Now that 30%,
I think you're still going to need experts for awhile.
Craig: I do, I just don't think you can need it
like you used to need it,
where almost every expert had to have a PhD.
Shervin: Yeah, I actually resonate with that Sam
in an interesting way, it sort of corroborates
what we've been saying about what it takes
to actually get impact at scale
which is like, the technical stuff gets you only so far
but ultimately you have to change the way it's consumed,
and you have to change the way people work,
and the different modes of interaction between humans and
AI, I guess that's a lot of the humanities,
and the philosophy, and the political science,
and the how, sort of, the human works
more so than what the algo does.
Sam: Well that's a good redirection too,
because if we're not careful,
then that conversation slips us
into the curriculum being DevOps more.
And so what Shervin is pointing out is that
maybe that's a component, of course too,
but there's process change
and more, let's say business-oriented initiatives.
So what other kinds of things
are you trying to teach people?
Or what other kinds of things
do you think executives should know?
I mean, we can't have the ...
everybody can't have to know everything.
Then it would be a bit overwhelming.
I mean that perhaps that's ideal
if everyone knows everything,
but what exactly do different levels
of managers need to know?
Craig: I think the top decision maker
needs to understand dangers of a model going awry
and they need to understand the overall process
that you really need label data.
Like there's no magic here.
That they have to understand there's not magic, right?
So they have to understand
that label data is expensive,
that getting the labels right
and sampling the distribution
of the world that you want correctly
is extremely important.
I believe they also have to understand
the life cycle in general,
which is different than, you know, two week sprints.
We're going to close these JIRA tickets, right?
That data gathering is extremely important
and that could take a quarter or two.
And that the first model you ship
probably isn't going to be very good,
you know, because it was from a small label dataset
and now you're gathering data in the wild.
So there's a life cycle piece
that they need to understand.
And they need to understand that,
unfortunately in a lot of ways,
maybe not for car driving but for recommendations,
the first couple that you ship get iteratively better.
So I think that's extremely important for the top.
I think for a couple of levels down,
they need to understand
like the precision-recall trade-off,
the kinds of errors your model can make.
Your model can either be making false negative errors
or false positive errors
and I think it's extremely important as a product person
that you own that choice.
So if we're doing document search,
I think you care a lot more about false positives.
You care a lot more about precision.
You want the things that come to the top to be relevant.
And for most search problems,
you don't have to get all the relevant things.
You just have to get enough of the relevant things.
So if some relevant things are called non-relevant
you're okay with that, right?
But for other problems,
you need to get everything.
Sam: Document search, that's fine.
But yeah, Lyft as well, like put it in the context
of one of these companies where you've had a precision
and recall trade-off, false positive, false negative.
Craig: I think luckily at Lyft
we have nice human escape hatches
which I think is extremely important.
Like all these recommendations
ideally should have a human escape hatch.
So if I recommend a destination for you
and that destination is wrong,
no harm, no foul, you just type the destination in.
So I think for Lyft as a product
I think we're pretty lucky
because most of our recommendations,
which are trying to lower friction
to get you to take a ride,
it's okay if we don't get them exactly right.
There's no real danger there.
Self-driving cars, that one's tough
because you want to get them both.
You want to know that's a pedestrian and you
also want to make sure you don't miss any pedestrians.
Sam: And the idea of putting a human in the loop
there is much more problematic than just saying,
all right, here's some destinations,
which one do you like?
Shervin: Craig, earlier you talked about,
you know, how AI in real life
is like a bunch of cognitive science experiments
because it's ultimately about-
Craig: For me at least.
Shervin: Yeah, and it brought up the idea of
unconscious bias. And so like we as humans have become
a lot more aware about our unconscious biases
across everything, right?
Because they've been ingrained
through generations and stereotypes, et cetera.
Craig: And just our past experience, right?
Like a biased world creates a biased experience
even if you have the best possible intentions.
Shervin: Exactly, right?
And so I guess my question is,
clearly there is unintended bias in AI, has to be.
What do you think we need to think about now
so that 10, 20 years from now
that bias hasn't become so ingrained
in how AI works that it would be so hard
to then course correct?
Craig: (laughs) It already has.
So the question is how do we course correct?
So let me start by saying I was on a panel
for Northeastern about this movie "Coded Bias."
So if you haven't seen the movie "Coded Bias"
you should absolutely see it.
It's about this MIT media lab undergraduate black woman
who tried to do a project
that didn't work because facial recognition
just simply didn't work for black females.
It's just an absolutely fascinating social study.
The dataset that was used
to train the machine learning,
so the facial recognition algorithm,
was gathered by the researchers at the time,
and the researchers at the time
were a bunch of white males.
And this is a known issue, right?
There's a skew in the way the dataset is gathered.
Look, there's a similar skew in all psychological studies.
Psychological studies don't apply to me.
I'm 56, psychological studies apply to college students
because that's the readily available subjects, right?
So these were the readily available people
because of the biased world
and so that's how the dataset came about.
So even if no ill intention
the world was skewed,
the world was biased,
data was biased,
it didn't work for a great number of people.
And not a lot of females were part of the training set
and then the darker your skin, the worse it got.
And there's all kinds of technical reasons
why darker skin has less contrast, blah, blah, blah,
but that's not the issue.
The issue is, should we have gathered the data that way?
What is the goal of the dataset?
Who are our customers?
Who do we want to serve?
And let's sample the data
in such a way that it's serving our customers.
We talked about this earlier about the undergrads,
I think that's really important.
One way to get out of that
is diversity in the workplace.
I believe this so strongly.
And you ask everybody, all of these diverse groups,
to test the system
and to see if the system works for them.
When we did image search at Dropbox
we asked all of the employee research groups,
please search for things
that in the past have been problematic for you
and see if we got them right.
And if we found some that were wrong,
we would go back and regather data
to mitigate against those issues.
So look, your system is going to be biased
by the data that's gathered, fact, just a fact.
It's going to be biased by the data that's gathered.
You want to do your best to gather it correctly.
You're probably not going to gather it correctly
because you have your own unconscious bias
as you point out.
So you have to ask all the people
who are going to be your customers
to try it, to bang on it,
to make sure it's doing the right thing.
And when it's not, go back and gather
the data necessary to fix it.
So I think the short answer is diversity in the workplace.
Sam: Craig, thanks for taking the time
to talk with us today.
Lots of interesting things.
Craig: My pleasure, these are really fun conversations.
I'm pretty nerdy about this so I enjoyed it very much.
Sam: Your enthusiasm shows.
Shervin: Really insightful stuff,
thank you. Craig: Thank you guys.
Sam: Well Shervin, Craig says
he won the lottery in his career
but I think we won the lottery
in getting him as a guest
for our first episode of season two.
Let's recap.
Shervin: I mean, he made a lot of good points.
Clearly the commoditization of algorithms over time,
and how it's more and more going to be about
tying it with strategy going back to key business metrics,
making change happen, the usage.
I really liked his point on
what it takes to get the bias out of the system
and how bias is already in the system.
Sam: The commoditization is particularly important.
I think it resonates with us
because we were talking about this
from a business perspective.
And so what he's saying is that,
a lot of this is going to become
increasingly a business problem.
When it's a business problem it's not a technical problem.
I don't want to discount the technical aspects of it
and certainly, you know,
he brings plenty of technical chops to the table,
but he really reinforced the
this is a business problem now aspect.
Shervin: Yeah, I mean in five minutes
he basically provided such a cogent argument
for our last two reports, right?
The 2019, 2020, it's about strategy
and process change and process redesign and reengineering
and it's about human and AI interaction and adoption.
Sam: And what's also a business problem too
is the managerial choice.
I mean, he came back to that as well.
He was talking about some of these things
are not clear-cut decisions.
There's a choice between which way you make a mistake.
That's a management problem not a technical problem.
Shervin: And it also requires managers
to know what they're talking about,
which means they need to really, really understand
what AI is saying and what it could be saying
and what's its limitations,
and what's the art of the possible.
And I also really liked the point that
as you get closer to the developers
and the builders of AI,
you have to really, really understand the math
and the code because otherwise you can't guide them.
Sam: Although don't you worry
that we're just running into this thing
where everyone has to understand everything?
I feel like that's a tough sell.
Like if the managers have to understand
the business and how to make money
and they have to understand the code.
I mean, having everyone understand everything is obviously-
Shervin: Well I guess the question is
how much do you have to understand everything?
I mean, a good business executive
already understands everything
to the level that he or she should,
to the point of asking the right questions.
I think you're right.
But I think, isn't this like what Einstein said that
you don't really understand something
unless you can describe it to a five year old.
You know, you can describe gravity to a five year old
and to a 20 year old and to a grad student
in different ways and they will all understand it.
The question is, at least you understand it
rather than just say, I have no idea
there is such a thing as gravity.
Sam: So basically teaching and academics
are really important.
Is that what Shervin has just gone on the record as saying?
Shervin: I think the idea that managers
and senior executives need to understand AI,
itself is not a slam dunk
because you're raising the right question.
What is the right level of understanding?
And so what is the right level of synthesis
and articulation that allows you
to make the right decisions
without having to know everything,
but isn't that what a successful business executive
does with every business problem?
And I think that's what we're saying,
that with AI, you need to know enough to be able to probe
but suffice it to say, it's not a black box.
Like a lot of the technology implementations
have been a black box in the past.
Sam: And that helps get back
to the whole learning more,
and where to draw the line,
and help to understand that balance.
I guess after the discussion of gravity
each one of those people would understand
more about gravity than they did before.
And so it's a matter of moving from current state
to next state.
Shervin: Yeah.
Sam: Craig made some important points
about diversity in the workplace.
If the team gathering data
isn't hyper aware of the inherent biases
in their data sets,
algorithms are destined to produce a biased result.
He refers to the movie "Coded Bias"
and the MIT media lab researcher, Joy Buolamwini.
Joy is the founder of the Algorithmic Justice League.
We'll provide some links in the show notes
where you can read more about Joy and her research.
Thanks for joining us today.
We're looking forward to the next episode
when we'll talk with Will Grannis
who has a unique challenge of building
the CTO function at Google Cloud.
Until next time.
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