『Kelvin Chan: From Math to Google AI, Nano Banana, How It’s Built & Where It’s Headed – E657』のカバーアート

Kelvin Chan: From Math to Google AI, Nano Banana, How It’s Built & Where It’s Headed – E657

Kelvin Chan: From Math to Google AI, Nano Banana, How It’s Built & Where It’s Headed – E657

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 Kelvin Chan, an AI researcher at Google, joins Jeremy Au to unpack his unconventional path from mathematics in Hong Kong to applied AI research across Singapore and the United States. They explore how AI research differs from traditional academic work, why iteration and results often matter more than theory, and how scale has transformed research culture from small experiments to highly collaborative, compute-heavy systems. The conversation covers the rapid evolution of image and video models including Google’s Nano Banana model, the push toward world modeling and embodied AI, and how AI tools are reshaping daily productivity for engineers. Kelvin also reflects on choosing AI in 2018 before it was mainstream, and why he believes the long-term future lies in AI as a trusted partner that augments human work rather than replaces it. 03:18 Image processing redirected Kelvin away from finance: Hands-on work with visual data revealed a stronger pull toward applied problem solving than abstract financial paths. 06:00 AI research prioritizes iteration over proofs: Progress comes from training models, debugging failures, and refining results rather than deriving formal guarantees. 09:16 Nano Banana reflects Google’s applied AI approach: Large-scale models are used to speed up coding, debugging, documentation, and internal productivity. 11:00 Results matter more than explanations in applied AI: Kelvin focuses on whether models work in practice, not on fully understanding internal neural mechanisms. 16:12 Scaling models reshaped research culture: Moving from millions to billions of parameters forced deeper collaboration and reduced solo experimentation. 20:05 World modeling targets physical understanding: Researchers aim to teach AI how gravity, motion, and real-world constraints actually behave. 26:25 Choosing AI before it was mainstream required risk: Kelvin’s decision to pursue AI in 2018 became the most defining and courageous move of his career. Watch, listen or read the full insight at https://www.bravesea.com/blog/kelvin-chan-inside-google-ai WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter: https://twitter.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea Spotify English: https://open.spotify.com/show/4TnqkaWpTT181lMA8xNu0T Bahasa Indonesia: https://open.spotify.com/show/2Vs8t6qPo0eFb4o6zOmiVZ Chinese: https://open.spotify.com/show/20AGbzHhzFDWyRTbHTVDJR Vietnamese: https://open.spotify.com/show/0yqd3Jj0I19NhN0h8lWrK1 YouTube English: https://www.youtube.com/@JeremyAu?sub_confirmation=1 Apple Podcast English: https://podcasts.apple.com/sg/podcast/brave-southeast-asia-tech-singapore-indonesia-vietnam/id1506890464 #GoogleAI #ArtificialIntelligence #AIResearch #FutureOfAI #TechCareers #MachineLearning #DeepLearning #AITrends #AIatScale #BRAVEpodcast
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