Today on The Pinpoint Asia Podcast, we’re joined by Kevin Tian, a Machine Learning Engineer at TikTok with a deep background in AI, computer vision, and medical imaging.From cutting-edge research to Big Tech and startup innovation, Kevin has been at the forefront of AI development. He has also worked in Facebook (now Meta) a Research Scientist.On today's podcast, we’ll be asking Kevin questions about Machine Learning, Deep Learning, and AI—breaking it down in a way that anyone can understand.
Introduction

1. Could you please introduce yourself and give us an overview of your journey in machine learning and AI? What got you interested AI?
2. You moved from academia into the corporate world by working for Facebook. Why did you make this transition and what was the transition like?

Understanding Machine Learning

1. For our listeners who might not be familiar, how would you define machine learning?
2. What is the difference between machine learning and artificial intelligence?
3. What is deep learning?

Work Experience and Projects

1. Can you tell us about your role as a Machine Learning Engineer at TikTok? Are you able to talk about some of the key projects you have worked on?
2. Your work at Shenzhen University involved a 3D image segmentation framework. Can you talk about medical imaging, why it is important and what 3D image segmentation means?
3. What is an ASR model and how complex is it?
4. You have experience with self-supervised pretraining in ASR models at Facebook. Could you talk more about this?

Technical Insights

1. How do you approach the challenge of developing algorithms for video understanding?
2. In your opinion, what are the most important skills for someone looking to excel in machine learning?

Machine Learning in Practice

1. What are some common misconceptions about machine learning that you’ve encountered?
2. How do you manage to keep up with the fast-paced developments in AI and machine learning?
3. Can you share any insights on how machine learning can be applied to improve real-world business processes, particularly in the context of TikTok or your past roles?

Challenges and Future Prospects

1. What are some of the biggest challenges you’ve faced in your machine learning projects, and how did you overcome them?
2. What future advancements in AI and machine learning are you most excited about?
3. How do you see machine learning evolving over the next few years?

Closing Thoughts

1. If you could give one piece of advice to aspiring machine learning engineers, what would it be?
2. How do you see your career evolving in the next 5-10 years in the field of machine learning?