[Dylan Ng Terntzer] With our deep learning,
we'll see an object in front of us.
We need to tell whether she is a human,
a pile of bricks, or a chair.
And even if you tell it is a human,
we must think, 'What is a human going to do next?'
Are you going to turn left?
Are you gonna turn right?
Going to jump in front of us?
[Robot] Hi, so sorry, but you're in my way.
Could you please move?
LionsBot, we make professional cleaning robots
so the cleaners don't have to work so hard.
Singapore is the Lion City,
so the lion is the emblem of Singapore.
Hence, our robots are LionsBot,
and at the heart of every robot,
there is one grain of sand from Singapore,
and it brings the love and the technology of Singapore
to the rest of the world.
We have multiple sensors, each feeding in information
multiple times a second.
In robots, anyone can put in a lot of sonars,
a lot of sensors, but it is how we use them,
how we make intelligent decisions
with that information that counts.
[Laurence Liew] Where you are is our AI Singapore office.
Singapore has a long history of willing to spend money
to get its citizens to re-skill, deep-skill,
or upgrade their skills.
Our mission, really, is to promote the use of AI,
get more researchers to embark on a career in AI,
to do AI research.
We have one very popular program.
We call the AI for everyone,
and the intent is to demystify AI for the man in the street,
for everyone in that sense.
When the audience walked out of the auditorium,
they say, 'Ah, OK, AI is not so scary.'
AI is actually nothing more than just another piece of code,
obviously very sophisticated code,
but it is just another IT system or infrastructure.
[Annabelle Kwok] Hi, I'm Annabelle.
I founded NeuralBay, which is a software AI company
that looks into image and video processing.
So I was very lucky to be in Singapore
where the hackathon scene was slowly starting,
and it was still kind of ahead of its time.
So when this whole field of image processing came up,
I think that opened a lot of doors for opportunities
to not just analyze still photos
but also to look at real-life events.
So for example, in traffic flow management in crowded areas,
you can help to better direct human traffic.
So we're in our office, and we have a lot
of people walking around.
So what we can do with this software is that we can count
the number of people in this area,
as well as to track their movements.
So in recognizing people, it's a very tough problem
because when they look away,
can you still recognize that it's the same person?
So Zeldon, if you can just turn around very gracefully.
So you can see that in this software,
it still tracks that Zeldon is the same person.
[Laurence] I think when we design AI systems
or any smart city technology,
ultimately the question to ask is
how will it affect the citizen in the country?
We do have several healthcare related AI projects
that are undergoing, and I think there's lots
of interesting areas where AI could be used in education.
When we launched AI for Everyone,
the original target was 10,000 Singaporeans
to be trained in three years.
1-1/2 years down the road, we are already at 7,000.
I told my team can we do 100,000?
Let's go from 10 to 100, all right?
Training the people, the apprentice,
they again, at eight or nine months,
they will go out to the industry.
There is an economic implication in that.
[Dylan] Singapore has a wide pool of talented engineers.
The government has spent a lot of money developing
and training these engineers,
so with such a big latency pool of people
that we can tap on, why not build in Singapore?
[Annabelle] In terms of the software,
I think the next step would be accessibility to more data
and also the diversity of data available.
Most of the open-source data is from the West
and not necessarily from Southeast Asia
because Southeast Asian countries may not necessarily
have the infrastructure to capture that data.
So recognizing a woman of color within Southeast Asia,
the confidence interval might not be as high
as recognizing one from the West.
By doing it and making it available
for the small enterprises, hopefully that might help correct
some of the cultural bias in technology.
We don't always have to give back
in terms of time and money,
but we can also give back in terms of knowledge and skills.
As for myself, I'm good at building things,
so why not build things to help people?