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  • [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?

[Dylan Ng Terntzer] With our deep learning,

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How Singapore Is Putting A New Face on AI

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    林宜悉 posted on 2020/11/09
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