SAM RANSBOTHAM: Many people have already
invited artificial intelligence into their homes
with voice assistants like Siri and Alexa, but how can
we individually benefit from computer vision?
Today we talk with Sanjay Nichani, vice president of AI
and computer vision at Peloton Interactive,
about a new product that incorporates
AI for fitness coaching.
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 Ransbotham, 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 KHODABANDEH: And I'm Shervin Khodabandeh,
senior partner with BCG, and I colead BCG's AI practice
in North America.
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 and really transform
the way organizations operate.
SAM RANSBOTHAM: Today, Shervin and I
are talking with Sanjay Nichani, vice president
of artificial intelligence and computer vision
at Peloton Interactive.
Sanjay, thanks for joining us.
Welcome.
SANJAY NICHANI: Thank you for having me.
SAM RANSBOTHAM: Sanjay, can you tell us
about your current role at Peloton?
SANJAY NICHANI: I lead the AI and computer vision team
at Peloton.
Peloton's mission is to use technology and design
to connect the world through fitness, empowering people
to be the best version of themselves
anywhere and anytime.
Most people recognize this via our bikes and treadmills
that we sell.
We have world-class instructors that teach some really awesome
content related to cardio and strength and yoga
and meditation, and this gets streamed not only
to your bikes, treadmills, but also onto your digital apps --
whether it's iPhone or Android --
and also it's available on TV, as an app.
We're here to make people happy and healthy
and change their lives.
SAM RANSBOTHAM: Tell us a bit about your education
and background.
I know you studied at Babson, which
is down the street from us at MIT in Cambridge, actually.
Take us on your path to get to Peloton
and what got you interested in artificial intelligence.
SANJAY NICHANI: I would say that my career is sort of divided
into four phases.
The first one was more around using
computer vision for manufacturing and factory
automation.
And then my second phase was more around security and access
control; that's where a lot of 3D computer vision applications
are used for finding people in revolving doors
and retail stores and so on.
And then I did a little bit of a stint
in identity verification and document forensics.
So now I'm in my fourth stint, working in the fitness space,
which I'm really excited about.
With all this experience I've had
in computer vision over the years,
just bringing it into the space is exciting.
SAM RANSBOTHAM: I think we all are, obviously,
as you said, very familiar with Peloton and the bike.
What other things are going on that we may not be aware of?
What kinds of uses of artificial intelligence
are you using there that maybe we can't see?
SANJAY NICHANI: Peloton Guide -- this is something that we
recently announced.
This is our first strength product,
and it's also the first that uses AI technology that
actually runs on a physical device
in the form of a platform.
We're quite excited about it.
It basically connects to any TV to transform
the TV into sort of an interactive personal training
studio.
Peloton instructors lead a wide range of fun classes,
but quite intense, that actually use dumbbells and body weights.
And so where we bring computer vision technology and AI
in there is, we have something called the movement tracker,
which allows you to track members --
allows you to recognize their activity --
so that as you follow along [with] the instructors,
[it] makes sure that you're actually completing these moves
as you go through the class.
And this real-time feedback and metrics-driven accountability
is very appealing to our members,
because now they have a goal to work to,
especially when you don't have a coach at home.
SHERVIN KHODABANDEH: And that's a device
that they put in the room?
SANJAY NICHANI: Exactly.
It connects to your TV.
The other really nice thing about the device
is that it has what is called smart-frame technology;
it basically gives you the freedom to go around the room,
and it automatically pans and zooms where you are,
and then you can see yourself on TV,
so you're reflected on TV so that you can see your form.
We're really excited about this product
because a lot of the technologies, all the way
from finding people and figuring out
what activity they're doing, all that
is driven by computer vision.
SHERVIN KHODABANDEH: So as I'm listening to you
about the setup here, my mind goes
to just the amount of real-time data
that's coming from many, many thousands of people
at the same time.
Tell us a bit about how you're processing all that
and how much of that is really real time versus pre-packaged.
SANJAY NICHANI: Yeah, definitely.
Let me talk a little bit about the production aspects.
This device over here is completely self-contained.
There is really nothing that's going out of the device
into the cloud as far as image data's concerned
or any other type of data is concerned.
It's mostly the content that's coming through that's
streaming in that is displayed to the user for them to follow.
It makes it a very secure system.
One of the big things for AI is keeping [systems]
secure and private, and so we respect that.
From a training perspective, you have to bootstrap your AI,
and that's where you need the data.
As we're aware, we need a fair amount
of data that fuels the AI.
This is where we have spent a fair amount of time sourcing
data, annotating data.
We'll talk a little bit, probably more, about some
of the other aspects about having diversity of this data
that's very vital for you to bootstrap your system,
and once you have the data and label the data,
then you build your AI systems from that.
There is a separation of what happens during training
and what happens during production.
During training, there's a bootstrapping process
that we use.
Basically, the feedback [to] the person
is, "Are you following along to that particular exercise
or not?"
And you can imagine how powerful that is, right?
So, for example, if you get your feedback saying that,
"Hey, last week, you did x number of moves,
but now you did x y number of moves,"
that's very motivating for the user.
You might be good with bicep curls but not as good
with planks or pushups.
We provide that feedback so you can work on it.
Or it could also look at another dimension.
For example, you could say, "Hey, you
worked on these muscle groups; how
about focusing on some other muscle groups,
and how about taking these classes to focus
on the other muscle groups?"
So really having that feedback come back to the user
and really guiding the user in their fitness journey,
basically.
So that really is the purpose of Guide.
SAM RANSBOTHAM: That seems pretty fascinating,
because you talked about recommending classes,
but some of the appeal here might
be that class doesn't have to be packaged anymore.
It could be, "Well, Sam he's a slacker on planks,
so he needs lots of ab work or core work,
whereas he's awesome at doing pushups,"
or something, because of my massive build.
Fortunately, this is on audio so no one can
verify that that's not true.
[Laughs.] But I can see then that you could actually somehow
generate these classes, maybe in real time,
so they don't necessarily have to be packaged;
they could be adaptive.
Is that some of the goal, or is that thinking too far ahead?
SANJAY NICHANI: Yes, absolutely.
To your point that we do know exactly what the class
plans are, what we're working on is recommending
classes that might be more appropriate or personalized
to a person's fitness journey.
Absolutely.
SAM RANSBOTHAM: That's kind of interesting,
because when I hear about education in general,
we're kind of shifting from talking about a fitness class.
We're almost talking about more of an education product
here, in that it's adapting to what you need,
and so much of what I hear talks about individualized training.
But the production part you're mentioning,
and the packaging and the overall scope,
is still important too.
SANJAY NICHANI: Absolutely, yeah.
And to me, it's a combination of things.
Education is great, because you're
providing insights and metrics that help
you improve your performance.
I think one of the big things about Guide
is the accountability.
You have nobody looking over your shoulder;
[you just] have a machine looking at you,
and it holds you accountable, and it brings out
the competitiveness in you.
It's that accountability that's important.
I think there are other things that we are striving
for in terms of making the experience gamified
in the sense that you want your whole workout routine to be fun
and engaging, right?
You don't want to keep looking at your watch
and saying, "Ugh, are 30 minutes done yet?"
And that is what I think is fantastic about all Peloton
products, but particularly even this Guide product,
is that we really strive for making
it a fun process in addition to all the other advantages
I mentioned.
SHERVIN KHODABANDEH: Sanjay, it is a great example
of AI enabling an experience in a different setting,
in the privacy of your own home, and it's
a great example of AI creating something that it's not
possible to do without it.
What are some other uses of AI for Peloton as a company?
SANJAY NICHANI: There are already
AI initiatives that are going on,
and we're making them better.
Another area is voice -- the convenience of using voice
for hands-free operation.
Especially for a product like Guide,
where if you're holding dumbbells,
or if you're on the floor, you're
prone and trying to do exercises,
you can't hold the remote.
So we have an AI team that's focused on voice.
It is also going to be making its debut in the Guide.
There's a fair amount of AI in that
too, other than computer vision.
SAM RANSBOTHAM: Sanjay, also, this is a big deal too.
You guys are putting a product with artificial intelligence
in lots of people's homes as a consumer product.
There's just not a lot of that going on.
SHERVIN KHODABANDEH: Sanjay, one question I wanted to ask you
is, if you help peel back the onion for our audience in terms
of what it actually takes to design and scale a solution
like you were talking about -- either with Guide or with voice
-- that goes beyond the technical aspects
and the algorithms and the technical aspects
of the product.
Where my mind is going is just the user experience itself.
Talk a little bit about the process of the product
design itself and how you bring that aspect into it.
SANJAY NICHANI: Let me start off answering
the question with what really kicks off things at Peloton.
One of the real core values of Peloton is "put members first."
We are obsessive about customer experience,
and everything is centered around that.
We listen to our members, get feedback from our members,
and a lot of product work really starts with that.
We have a cross-functional team that
looks into many of those things, but it really
starts from the member experience
and how do we make our members happy and healthy,
and there's a fair amount of work
that's done also by user research teams,
maybe building prototypes and putting them in front
of users, sometimes experts.
In this particular case, we've interviewed coaches.
What I really like about AI -- or generally machine learning
development -- is that it's fundamentally iterative,
and it sort of intersects with the whole agile philosophy
of software development.
You basically say, "All right, now;
we have a hypothesis right now, so we build a prototype."
And the way ML works is, what you
need to do first is deploy a minimal system, see where
your errors are, and that decides, "Oh,
do you need more data?
Do you need to improve your models?
Is the problem the quality of the data that you already had?"
Then, when you actually put it out,
it really does give you the intended benefit.
I feel that ML forces you to be agile.
That's how things get started.
It's more of an iterative process.
That's really something that we as an organization --
and all of the people developing AI products -- have to realize:
that it is something that gets better over time.
As people use it, it gets more and more accurate,
and that's fundamentally because you are always
looking at errors, looking at feedback,
and that drives the whole process
of continuous improvement.
SAM RANSBOTHAM: But something seems different about this
to me because you're talking about the culture within
Peloton that may understand this need to iterate and improve
and be agile, but when you're talking about delivering this
as a product to consumers, I feel like they might have
a different expectation of how ...
well, first, I'm not even sure if they're
going to know that there's artificial intelligence/machine
learning involved in the product.
Maybe they will or maybe they won't, and you
could comment on that.
But how do you get that culture about iteratively improving
and "don't expect it to be perfect"?
I think consumers expect things to be perfect initially.
SANJAY NICHANI: Yeah, that's a great point.
And I wasn't trying to say that we
should be deploying stuff that's not
perfect or close to perfect.
The point I was making was that, for example,
when we are going to be launching the Guide,
we have gone through a lot of trials, field trials,
and we're really trying to identify,
what are the operating conditions for Guide?
What makes it perfect?
There are a lot of things -- some things that we absolutely
cannot compromise on: safety, reliability,
making sure it works for everyone.
SAM RANSBOTHAM: Those are areas you can improve over time.
SANJAY NICHANI: Those are areas we can improve over time.
So it's a question of, we start with the operating parameters
we are very confident in.
And that's what good companies do, is figure out,
"OK, it is a space that's big enough to provide value
to the customer, but at the same time,
we have to be absolutely certain that it does very, very
well in that," or near perfection, as you're
talking about.
And then you kind of expand on it from there,
maybe adding more features or perhaps being able to handle
more occlusion of body parts -- things like that.
SHERVIN KHODABANDEH: I wanted to switch gears a bit
and talk about talent.
You guys are doing really cool stuff with AI
at the core of the product and the customer experience.
What are your thoughts on the kind of talent you
and companies that are aspiring to do similar things need?
There is a talent war out there.
What do you think it takes for the right talent to join
and want to stay?
SANJAY NICHANI: I'll sort of the answer
this question in two parts.
I have strong opinions about it.
The first one is more around the talent itself.
I feel like that is one of the biggest challenges.
It's not just the challenge of the scarcity of talent,
but also the type of talent.
I feel like there is a lot of research and researchers in AI,
and a lot of work being done in AI.
But the focus is more on competitions, on papers --
topics such as architectures or optimization techniques --
but there are clearly not enough people focused on practical
aspects of deployment.
I just talked about what it takes to build an AI product,
right?
And to me, that's where I think that having people focus more
on deployment and on production is very, very vital.
And this requires people all the way
from "How do I source the right type of data?
Do I have the right data quality?
How would I mix it with synthetic data?
How do I build data pipelines, and how do I version it?"
All that becomes important.
Also, finding people with experience
around just deployment of these models.
SHERVIN KHODABANDEH: Yup.
SANJAY NICHANI: Finding people in that area
is where I feel like there are the biggest scarcities.
SAM RANSBOTHAM: That's something that Shervin and I come back
to a lot.
So much emphasis is on these algorithms,
and what we frame as consumption is really where
a lot of the bottleneck is.
SANJAY NICHANI: Coming back to what really keeps talent
at Peloton, I think it's the mission.
I think that Peloton's mission is just to empower people to be
the best versions of themselves and have them feel good about
themselves -- be happy, be healthy.
It is a very noble mission that, every time that I ask people,
"Why do you want to join Peloton?"
that's that first thing that comes out: "I have a bike.
I know someone who has a bike," and, you know,
how it's changed their life.
You have to be in line with that mission.
That, to me, is the primary driver.
There are other things, from a culture perspective,
like the way we operate as teams.
One more thing that actually is fairly important
is the impact that you can make.
All of the people working on Guide
are going to be making an impact on millions of members,
and this is just one of the products.
So I think that having that sort of impact also
drives people a lot.
I would say those are the three reasons, really.
SHERVIN KHODABANDEH: Great.
SAM RANSBOTHAM: One of the things you described was a very
edge-oriented approach to ML -- that it's within the box,
it's within the home, the data doesn't leave.
But some of the things you're describing now
seem like they would benefit from aggregation.
It would really help collectively
if we understood better how to get fit to
or how to improve our health.
Health information is something that we tend to keep private,
and we tend to want it to be private,
but I can't help but wonder, would we in aggregate
benefit if we were a bit more open with that data?
What are your thoughts on that?
SANJAY NICHANI: I think that's trust, right?
From an industry standpoint, AI needs to get there.
Once you get to that point, maybe it's possible.
We've all seen what has happened with facial recognition
systems and other examples.
So definitely, there are trade-offs.
There is a move more toward trying
to anonymize it in some way.
Can you achieve both objectives?
SAM RANSBOTHAM: Sanjay, great talking with you.
I think most people are familiar with the physical Peloton bike,
but this is a product that you're
talking about putting artificial intelligence
in real time in people's lives.
And there's just not a lot of examples of that going on
that people are used to.
We've really enjoyed talking with you.
Thank you.
SHERVIN KHODABANDEH: Yeah, thank you so much.
SANJAY NICHANI: Thank you; I really appreciate your
having me.
SAM RANSBOTHAM: Thanks for listening.
Tune in next time when we talk with Katia Walsh, Levi Strauss
& Co.'s chief global strategy and AI officer.
ALLISON RYDER: Thanks for listening
to Me, Myself, and AI.
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