- [Werner Vogels] The only real constant is change.
No place knows that better than Singapore.
In 1965, the people we now know as Singaporians
found themselves separated from Malaysia
the nation they thought they belonged to.
It was a heartbreaking time for Singapore
as it became a sovereign nation overnight
with nothing but a sea port to work with.
The city had no natural resources,
no military, and a looming homelessness
and an employment problem.
Its people needed to have faith
that the future held a place for them.
In a single generation,
Singapore went from a poor newly formed city-state
to a global contender in finance, trade and technology
with the third highest GDP per capita in the world.
- [Werner Vogels] Our planet and our civilizations are changing
faster than ever before.
Join me as I travel the globe talking to startup founders,
using technologies to make our world more interesting,
accessible, and livable.
These are the entrepreneurs
that are creating the future we will live in.
This is Now Go Build.
(energetic music)
Since those tough early days,
Singapore has become a place of shiny towers,
beautiful temples, huge public gardens full of art,
and a public transportation system that is the envy of Asia.
On top of all of that,
Singapore is now home
to one of the most exciting start-up communities in Asia.
I wanted to find out
what makes Singapore so attractive to start-ups.
I decided to talk to Kuo-Yi Lim about it.
He's a Singapore native, a computer scientist,
and an entrepreneur turned venture capitalist.
If anyone knows what's going on with the start-up culture,
of Singapore, it is Kuo-Yi.
Singapore is quite unique,
definitely in the region
as being such a very different city or country.
From your perspective, what makes Singapore unique?
- [Kuo-Yi Lim] I think you just said it there,
a city and a country at the same time.
It has its advantages in terms of size, and scale, and speed,
and ability to do things fast
and obviously in the last 30 to 40 years
the political system gives it the ability to be a bit more
foresighted in how they implement policies and all that.
So I think the confluence of that
has led to a lot of interesting stuff happening
in the last 10, 15 years
because it's a city and a country at the same time.
Folks who come here, they want to start businesses
to get that growing relatively quickly.
There are a bunch of service providers around you
lawyers, accountants, etc.
And the very visible stuff are the grants
and support for entrepreneurs to set up companies
and that's been a very active intervention
in terms of making the system go faster.
- [Werner] What is it that you look for in a founder?
- As a VC, when we invest in a company,
we're looking for great founders.
Technical team because software at some level,
but founders are hard to create.
- [Werner] Founders, the often obsessed visionaries
that Kuo-Yi is looking for.
Pranoti Nagarkar and Rishi Israni founded one of
Singapores energetic new start-ups, Zimplistic.
Two engineers, husband and wife.
Pranoti is a mechanical engineer
and Rishi a software engineer.
The perfect team to work
in the realm of the internet of fix.
They have invented the Rotimatic,
a data driven, automated, machine learning roti maker.
It is just the first step in their vision
of a fully realized intelligent kitchen.
Meeting Pranoti and Rishi seemed like a good excuse as any
to visit one of Singapore's famous Hawker Centres.
- [Rishi] I came to Singapore when I was 19
and I've been here for 18 years.
- [Pranoti] I came here when I was 16, 17
I don't want to tell you how many years I've been here
then you'll figure out my age.
We came here to do our education
because there's a lot of scholarships given in Singapore
to bring foreign talent in.
And then I went on to a university
to earn my mechanical engineering.
That's where I met Rishi in fact.
We would always sit and discuss
one of the big problems in the world
and we would have a lot of arguments
on the way of thinking and approaching things
we are still very happily married. (laughter)
So when we came here, we really missed our home food
because you know the culture
is all about sitting together eating
and it's all around food
so you're done with your breakfast,
the discussion is okay what shall we make for lunch now.
So, I think rotis,
it's part of every meal
but it's extremely tedious
and it needs a lot of skill to make.
- So how much work goes into making a roti?
- [Rishi] Every milla can take you 20 to 30 minutes.
- [Werner] Why is it so hard to make?
- [Pranoti] Any flatbread actually requires a certain amount of skill
- [Rishi] Human intelligence plays a huge role
in making rotis which is the sensing
or the intuition that we talked about
of knowing when it is right.
- [Werner] When the dough is right,
then the dough is right.
- That is very crucial because that makes all the difference
to the end result of the rotis.
A puffed roti means it's a perfect roti
and getting every roti to puff is not easy.
I'm thinking we are going to challenge you tomorrow
in our office to make a roti and then you'll know.
- So you guys set out to solve this problem.
Who started with the idea?
- I remember we were chatting one night
and I said Rishi, I'm done with this project
I'm ready to start Rotimatic.
- And you know it was a time of our lives,
we'd just gotten married and we had no money
because I was doing my start-up almost on no salary,
she was deciding to quit her job
and it was a chat that we had in the evening.
Next morning she quit.
- I put in my resignation.
- You have to have a certain vision as a founder
'cause otherwise it's very hard
to keep doing what you're doing.
- Really very fun actually.
You're reminding me again – transports me back.
- Cheers, guys.
- Cheers.
- [Werner] So what's happening here?
- [Pranoti] It is making the dough ball right now.
And this is where the AI is in motion.
It's actually detecting
if the dough ball is correct in consistency.
If it is not,
then it'll keep adding a little bit flour or water.
- Okay.
- And it'll keep adjusting it
till it's a perfect dough ball.
- So what kind of sensors are in there
to sense whether the ball is correct or not?
- [Pranoti] There are multiple sensors there.
If it's a hard dough ball there's more resistance
so you measure the dock.
And all that is fed to the algorithm
which has a particular statistically and analyzed graph
which tells you is it in the good band or bad band
and accordingly it'll adjust.
- [Rishi] So what is happening here
is that dough ball was made
and it got moved to the pressing section.
- And so pressing happens here at a lower temperature
because when you're rolling a roti it's a cold press.
- Okay
- So the same thing happens,
it retains the moisture of the roti
and then it sort of kicks the roti forward
which it's doing now.
Yeah, and so there.
- [Rishi] So there you have a roti.
- [Werner] Okay.
- [Pranoti] Now you eat.
- Great roti, thank you.
- So Pranoti always had this joke
she says, 'You know Rishi,
whatever you do you can not eat software'. (laughter)
So for the kitchen to evolve, of course,
it has to get a little bit more robotic
but we think you can build devices
that have a narrow set of capability but fully autonomous.
And you can put together some of these devices to
change the way we cook.
- I belong to the generation of women
who have been the providers in the past
who have to nourish the family
but at the same time you have to balance a career
and you have to balance you being an engineer
I want something fulfilling out of that.
- So amazing device.
I understand you worked on it for quite a number of years
before actually software was introduced.
- Yes
- So why did you move from let's say,
hardware only device
to also having a major software component?
- You know our first priority was to get the product right.
Just before, we were almost ready with the product
and then we realized that we'd need to service it
and when we got into the details of servicing
we realized how big a challenge it would be.
And then we started exploring the AWS IoT Systems
and the more and more we implemented and explored
the more benefits we reaped,
not just in the first attempt of getting the service right
but also in collecting further data to improve our models
but it turned out to be
that's the core, central backbone of our operations.
- It's also a product requirement as we as the users
you don't want us to be locked into
using a particular type of flour.
So that means if you're making a tortilla
you're using a cornmeal flour.
If you're making a roti, you're using a whole wheat flour.
And you want it to actually extend
into making any kind of flatbread.
So to extend it into a platform,
I think the software was very, very crucial.
- So here's our IoT dashboard,
where we collect all the data from all the various machines
to help improve the product,
to help improve customer experience and after sale service.
This one is showing different countries
and what sort of number of rotis that have been made,
how many machines are online,
how many users are happy with Rotimatic performance.
We also track all the errors that happen on the machine.
This informs us and helps us prioritize what to fix first
and where to put our engineering resources.
So you can see every roti making,
every dough ball that we make,
we collect tons of sensor data
which tells us how much water was used,
what's the hardness, what are the errors that were faced,
what was the size of the dough ball
and of course which former version or not.
So all of this helps us improve the machine over time.
- How do you go from that data to improving your model,
to sort of become better?
- So we built a closed loop mechanism
where you rate your roti experience
at the end of every cycle.
And as we improve our product, release after release
you can see how different software versions
improve customer experience.
So we have data-driven,
data-backed understanding of what's actually going on
with all the efforts that we are putting in.
- I remember we would have these conversations
on how to design a connected system
and in the beginning when IoT wasn't the buzz word yet
we were discussing can we put a sim card inside Rotimatic.
Then can it talk to us, somehow tell us that it's alive.
And then IoT came and the WiFi model
it just changed the whole way it now is,
if you have any issue with your machine
you just log into our app,
there's a chat window you chat with our agents.
They have a dashboard which appears on their screens
which tell you the held stat of your machine.
So they already know what's wrong with the machine
and they send you a software patch.
If there's a hardware issue,
we send our logistics partners like FedEx or UPS
and they just give you a replacement.
I remember there was one particular phase
when you know we tried to make it an embedded system
where you know you have a fixed program that can run it
and hopefully figure out
the right proportion of flour and water
but that wasn't enough.
You had to build machine learning in
because every time you make a dough ball
you learn something more about it
and you use it for the next dough ball.
And it just kept making the machine better and better.
- We've seen machine learning mostly in computer systems
where things are not moving as much.
Here with life there's degradation
and all the tolerances and all the different parts
so the performance of parts keeps shifting over time.
- That's aging, that's aging of the mechanical components
of the parts and things.
- Which happens only through software systems so
so that was also taken into account across the life
of the machine where we're collecting data.
- [Werner] How much of this
did you already know as a software engineer?
- It was a new world for me actually.
I was completely taken aback by the complexity and hardware
especially such complex robotic hardware
is a magnitude harder.
- And every single time it needs to give you that output
with so many moving parts,
with the manufacturing complexity how do you ensure,
at a certain price point with commodity motors
and commodity sensors, and linear systems
you give the same precision every time?
So this is whole wheat flour,
its got all the fiber, all the nutrition in it.
And you mix it with the right amount of water,
it's easier for you.
- Next.
- Next up, you're to take like one spoon of oil
make a small middle
and then you start mixing while you get the water.
- Okay
- But don't put too much
because suddenly it will be too much
- I know so I'm just going to go for it.
- Put very little yeah.
So guys imagine this is for lunch and dinner
and sometimes even breakfast.
And you know sometimes there are times when you know.
(laughing)
- I think it's been a long time since,
- Since you did it.
- I am realizing.
- So you see, if you had a little bit more water
it would have perhaps been easier
but see now the time has passed.
Now you can't add water to it.
Because the gluten won't (laughs).
And now you start rolling this
- [Werner] Okay.
- And do a round shape
and evenly thin.
Just enough pressure.
When you see it's thick here and thin there
then it won't puff.
- Whaddya say, whaddya say!
- Not bad Rishi.
- Oh nice.
- [Dr. Vogels] Oh look at mine.
- Yes, yes it is is.
Press a little bit here
so the air sort of gets pushed under but very gently.
- [Rishi] No, it's too late.
- [Pranoti] Too late.
- [Rishi] There's a puffing momentum, you lose it
and it's gone.
- Well you show how hard it is to actually do it then.
- [Pranoti] And this roti is perhaps
you will not serve it for your family
but you'll keep it for yourself.
- Okay
- From a software guy's perspective,
somebody who only enjoy to eat food
but never go out into the realm
of figuring out how to make food
I realized a few big problems with our world.
70% of illnesses are lifestyle disease related.
And they are primarily related
to the type of food you consume.
One third of the world's food is wasted
so on the one hand you have people who aren't healthy
and maybe eating a little bit more than they need
and on the other hand you have people who are dying
because of lack of food.
And you find that these two problems
are actually prime problems
to be solved by the kitchen of the future.
Again, a software guy with a software hat
I think the big challenge is that the data
that is required to solve these problems is in silos.
And if we could just connect some system,
the data could just come out
we'd be able to solve this problem.
If you look at what's happening in the world today,
our generation,
we are forgetting how to cook,
we are forgetting the basics of nutrition.
As young parents,
we worry about
the right nutritional framework
to teach our kid.
How should you look at food?
What's good, what's not good?
So this is again a data problem.
It's again an information data problem.
I mean we already know how to fix the knowledge gaps
but you can also fix the execution gaps
by building machines that are fully connected
and that exchange information with each other
because of our cooking experience.
- And also sees all of those devices as a platform
not only as a single function device.
- Correct, that's the add offs you know it's
- Software eats the world.
- But you can't eat software.
- Can't eat software. (laughter)
- [Werner] The long look, the visionaries,
the ones who can see a future
that the rest of us may not see.
As we race into the future,
enabled by the Cloud,
assisted by machine learning,
driven by faster and more powerful computers,
it will take intention to hold on to age-old traditions.
And yet, it is that very technology
that gives us the tools to build better,
more efficient ways of holding on to things
that we never want to live without.
Things that we want to take with us
into this promising future.
It makes all the sense in the world
that optimistic visionaries like Pranoti and Rishi
have made their home here in Singapore,
a place where the past is never forgotten
and that long optimistic look towards the future
has not faltered.