Kelvin Chan: From Math to Google AI, Nano Banana, How It’s Built & Where It’s Headed – E657
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What is the main topic discussed in this episode?
I don't even know how myself work, but it works, right? I enjoy the weight of getting the result instead of knowing what happened. Maybe I'm not that very pure researcher, but I'm more applied way.
How did Kelvin Chan transition from finance to AI research?
I mean, I don't care if I don't understand it. I'm happy as long as I know how to use it. So maybe I'm more like a practical person.
What is the significance of iteration in AI research?
Welcome to Brave. Learn from Southeast Asia's best tech leaders. Build the future, learn from our past, and stay human in between.
How does Google's Nano Banana model enhance productivity?
No BS on success. I'm Jeremy Au, venture capitalist, Cira founder, Harvard MBA, science fiction nerd, and dad of two daughters.
Why are results prioritized over explanations in applied AI?
Every week, we debate startup news, interview changemakers, answer listener questions, and share personal insights. Join our movement of over 40,000 members and get transcripts, resources, and community at www.bravesea.com. Stay well and stay brave. Hey, Kelvin, good to see you. Yeah, thank you so much for inviting me.
How has scaling models transformed AI research culture?
Yeah, I think it's such a credible journey from Hong Kong to Singapore for your PhD to America working with AI and R&D. So lots to learn. Could you introduce yourself? Yeah, so long story short, I was born in Hong Kong and then I studied mostly mathematics in my bachelor and master degree.
What is the focus of world modeling in AI?
And then after my master degree, I went to Singapore for my PhD degree in computer science. And then during my PhD, I did an internship at Google in the United States. And then after that, I returned to Google as a full-time. And then I worked in Google since then and until now. Yeah, amazing. What was it like growing up in Hong Kong?
What challenges did Kelvin face in choosing AI before it was mainstream?
And how did you decide that you're going to work on mathematics? Yeah, I didn't have a very comprehensive plan back at the moment. I liked mathematics back in my high school. And many people told me that because Hong Kong is a very financial city, a lot of financial institutions in Hong Kong. And back in the time when I need to choose what I want to study in the university. And then many people told me, if you want to work on the financial institution, you need to have mathematical skills. Yeah, and then I listened to them. And then back in Hong Kong, many people think, Working in finance is the way to make money. Yeah. And then I had the same feeling and then I just think, okay, then I need to do mathematics.
Yeah. And then I know that I knew that back in that time, there is a double degree program, which is mathematics and information engineering. And therefore I chose that program. And then I got to CUHA, the Chinese University of Hong Kong, and then I studied mathematics. And then I remember back in that time, I hate coding. Yeah. And then I focused more on the mathematics side. And then I think I like mathematics. And then I continued working in applied mathematics. I'm more on the applied side. And then in my master, I tried to use mathematics to solve some image processing problems. And then, yeah, this is the whole journey why I chose mathematics. At some point, I feel like I don't like finance that much, information engineering.
And then my final year project, I touch base some of the image processing program. And then I feel like I want to do something like image processing more than finance. And then therefore, I do apply mathematics in my master degree. And then, yeah, this is the whole journey until my master. And growing up in Hong Kong, I think it's a very crowded city and it's a very beautiful city. The pace is extremely fast. And yeah, I feel very happy growing up in Hong Kong because it's a multicultural city. I learned a lot of Chinese and Western culture. But at the time, I feel like I want to explore something new. And therefore, I considered doing my PhD somewhere else. First, of course, US is the place I applied.
And this is another coincidence because remember the second degree in my bachelor, I studied information engineering. And then my final year project supervisor is actually my PhD supervisor. I knew him back in the FIP. And then I do master in mathematics. And then we didn't have a lot of contact back there. And then when I tried to apply for PhD, he gave me the chance to study, to follow him as a PhD student.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–0:12
2
How did Kelvin Chan transition from finance to AI research?
0:12–0:19
3
What is the significance of iteration in AI research?
0:19–0:30
4
How does Google's Nano Banana model enhance productivity?
0:30–0:40
5
Why are results prioritized over explanations in applied AI?
0:40–1:04
6
How has scaling models transformed AI research culture?
1:04–1:24
7
What is the focus of world modeling in AI?
1:24–1:44
8
What challenges did Kelvin face in choosing AI before it was mainstream?
1:44–30:02
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