These are synthetic IDs, as good as a genuine ID.

Didn't understand the phrase.

He took it literally.

Oh, it even read my handwriting.

Is this what people mean when they say that AI is going to take our jobs?

But what does a day in one of these roles actually look like?

From fighting scams and fraud, developing large-language models from scratch, to designing chatbots, they're all working with one technology, AI.

This is Most Wanted.

I'm going to show you some cool stuff, Nisa.

Yeah, that's absolutely cool.

That's a mask of your face.

Yeah, it's an exact replica of how I look like.

Rajat Maheshwari is part of the Cyber and Intelligence Solutions team at Mastercard.

In his role, his team develops tools to manage customer risk and prevent scams.

Part of his role involves getting in the mind of potential fraudsters by creating fake identities.

So these are synthetic IDs, as good as a genuine ID.

You can have these identities, you can have the face mask, you can have the fingerprint, and then you're essentially replicating someone else.

That person can do anything with these.

This was done by a Japanese artist who did the mask for the James Cameron movie Avatar.

Sometimes we have to think like bad actors to come up with the solution which can stop these things.

We have seen that the world has evolved from these masks and now the deepfakes are coming in.

The intent was not to break the technology, but the intent was to help the solution providers to enhance the level so that they can stand against these kinds of attacks as well.

Wow, what do we have here?

Yes, please, please, please have a seat.

Over at Amazon Web Services, Joel Garcia and his team have made a game out of simulating real-world security conditions as well, which they hope will help clients improve their incident response processes.

What we have here is a project called Chaos Kitty.

Why kitty?

Well, I have cats at home who always destroy my furniture.

Oh yes, agents of chaos, for sure.

What exactly is chaos engineering?

We're going to intentionally inject some failure so that we can learn.

We can see that there's many colourful lights and all these bricks that we use to represent what we have in our AWS cloud.

When it's red, something is wrong with the security configuration and when it's green, it's all good, all compliant.

We added on a Gen AI assistant here.

What we have here is a typical company security policy.

Typically, without a Gen AI assistant, they would have to look through and study this policy document very deeply.

We have fed this document along with the Gen AI assistant so it knows all this information.

I could go in there and ask questions.

It's going to give me some best practices.

Alexa, fix Chaos Kitty.

Remediating Chaos Kitty environment.

You'd be able to leverage AI and Gen AI to actually help fix the challenges and then they'd be more focused on the other areas that could be improved.

To be able to converse naturally with an AI chatbot, a large-language model, or LLM, is needed.

LLMs are AI models, pre-trained on vast amounts of data, which can understand and generate human language responses.

Popular models include ChatGPT-4 and Gemini.

The generative AI market is expected to grow over a trillion dollars in the next decade.

The team in Singapore is developing a large-language model that's catering to Southeast Asian languages.

Leong Wei Tee speaks 14 languages fluently and he's an AI engineer and linguist involved in developing the Sea Lion model.

We're going to look at an example about informal Indonesian.

We're essentially asking the model, our friend is sort of, because of his work, he's sort of panicking, and he's working very hard every day.

How can we help him best manage his work?

Yeah, but in that context, they're saying, they're using an idiom, right?

And they're saying that his beard is always on fire. So that idiom might not be so understandable to certain models, right?

So let's see how they deal with this.

For this demo, we have four panels, and each of them corresponds to one language model.

So on the left, we have Sea Lion, our model, and we have three other models on the right.

We can see that it didn't understand the phrase.

It took it literally, basically.

Like it's thinking about setting someone on fire.

Oh no, it's also in the same tone. It's casual, informal, it's still colloquial, whereas this one is still sticking to like a more formal kind of response.

And that's really what we want to achieve as well.

Now we are seeing that some of these models out there, they are not able to sort of handle multicultural contexts.

And that's sort of understandable because they're building those models for a particular audience, right?

For us in Southeast Asia, we need to operate within this region, handling our languages and cultures.

So this is why we decided to build Sea Lion.

Vincent Oh works as a Senior Specialist Solutions Architect at Amazon Web Services, and his projects involve leveraging generative AI based on human prompts to create personalized experiences.

StoryGen was a project that we did with the National Library Board of the entire Singapore, where we wanted to reinvent the future of the libraries.

We used generative AI and AWS technology to create an experience whereby young children and adults, they can put in a series of inputs, a selection, and a brand new book will be created on the spot for them.

What you call the prompts, right, that you're actually sending back to the large language model.

This is just the very beginning of the art of the possible, and it's going to be amazing how people will leverage gen-AI to unleash their extended level of creativity.

Part of those new jobs that are created as part of AI is a role or a skill set which is actually prompt engineering, which didn't exist before.

And when you look at prompt engineering, you don't need to be technical, you just need to understand how to put those prompts as you would, for example, like a detailed search, to maximize and leverage the power of a large language model.

There is a growing demand for AI specialists, and here at AI Singapore, the rest of Wei Qi's team, hailing from all over Southeast Asia, including Vietnam, the Philippines and Thailand, are working on making Sea Lion more inclusive.

You are the one who brought everyone into this team.

More or less, more or less.

My dream is eventually we have everybody, you know, from all the countries in Southeast Asia being represented here.

AI Singapore's apprenticeship program aims to grow the pool locally, even those considering mid-career switches.

In fact, some of the Sea Lion team graduated as AI apprentices after switching disciplines, such as Tai, who studied finance, and Wei Qi, who was previously a pharmacist.

We're not really alone in trying to tackle low-resource languages.

Ultimately, all the stuff that we do is fully open-source as well, and it's really shared with the public in general so that everyone can benefit.

Everyone is working on different aspects of the large language model.

We get to learn about it with each other.

It does feel a bit like mini ASEAN.

Mini ASEAN, yeah.

There is so much to do in this field.

Yeah, and AI, you know, is one of those industries where you can make a mid-career switch.

Absolutely, absolutely.

And I'm a good example, Nisa.

Rajat started his career in the mobile and semiconductor industry and made the switch over to AI in 2014.

I have seen that journey in the past 20 years or so where AI has really flourished and it's becoming an inherent part of our lives.

When you ask people to use the digital ecosystem, there are side effects.

According to the Global Anti-Scam Alliance, more than $1 trillion are lost to scams every year, affecting 2 billion victims.

Rajat's work at Mastercard includes working with banks and governments to apply intelligent AI systems to predict whether scams are taking place.

Let's take the example that you giving me $100, Nisa, looks a perfectly legitimate transaction.

But 1,000 more people giving me $100 in a day, something is suspicious.

There were sound alarm bells.

Exactly.

We train the models to detect the behavior, to detect the patterns, and then do the risk assessment and save you being sending money to me.

Welcome to the Mastercard Experience Center.

Whenever a person taps the card, our AI models kicks in, then we give a risk assessment to the issuing institution that what do we feel about this transaction.

You can see the screen here showing that the transactions are getting declined.

Where do you see the state of AI in the next couple of years?

The integration with the LLMs.

So that's, LLM is essentially the large language models.

Large language models form a big part of the user experience solutions that Joel works on for AWS.

Take reading invoices, for example.

If you want to introduce something else into your business, a new form, a new process, then you'll have to reevaluate the technology or refactor that in.

With Gen AI, because of the large language model, it can actually parse these.

Parsing refers to the process of breaking down a user's input into smaller pieces and analyzing each piece to determine its meaning.

I'd like to know if there is a proper signatory.

Oh, it even read my handwriting, so it knows that it's my terrible handwriting, that it's Michael Garcia.

Did you see yourself ending up where you are today?

Even at a young age, I knew that in some form, way, or that I was going to be involved with technology.

What did you start out studying?

I graduated computer science.

I went through a variety of jobs.

I even founded a company before startups were fashionable.

Today, with AI, when you're thinking about redefining experiences, you're not going to get to it on the first.

This is like super long-shot ambition, right?

You know that Southeast Asia is not 10 languages, only there are like hundreds of dialects, Balinese, Javanese, Visayan.

So, we hope to get those represented as well, eventually.

How soon can we get there?

Our hope is that it will trigger similar movements across the region.

Outside of Southeast Asia, the interest is also very strong, right?

We have similar situation in India, we have similar situation in Africa, where there's a bit of under-representation.

If I'm able to save someone's lifelong savings, I mean, it's itself that makes my day.

Is this what people mean when they say that AI is going to take our jobs?

It's going to make something more efficient, so that people can actually concentrate on what matters more, either for the business or for customers.