Here's an interview with Adam Grzywaczewski, the senior deep learning architect at NVIDIA. In this interview, we talk about his role, and the interview process to get into such a role, and we learn more about NVIDIA and more interesting insights from Adam.
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Adam's GTC events:
The Possibilities for Natural Language Processing and Large Language Models in Finance: Insights from Deutsche Bank [S51160]: https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1666078863989001bkfy
Connect with the Experts: Deep Learning, Machine Learning, and Data Science [CWES52118]: https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1670255843552001iaMr
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Chapters:
0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.
00:58 Academic background
07:55 You were trying to scale models, but the hardware didn’t allow you?
09:25 Did you really want a Ph.D. or was it just to work on the project you had in mind?
10:14 Did you already have a goal in mind? Like getting a good job
12:41 How are you assessing the candidate’s capabilities before and during the interview?
14:43 If you have a lot of resumes, are there any projects or degrees that are more interesting than others?
17:41 What is the shape and format of the interview?
20:08 What is a deep learning architect?
21:55 Other than scaling. In what areas are you working on?
23:36 Are you part of a team that supports companies using Nvidia’s products?
24:40 Could you go over the details of a specific project you’ve had?
26:41 Which complicated challenges require your help?
28:15 How do people that work with you deal with large models or data sets?
30:47 So the current challenge is mainly to find which tool to use and how to do it in a cost-effective way?
32:30 Will you talk in GTC about how to scale and deploy NLP models?
33:30 What is your day-to-day like at Nvidia?
37:20 Would you say that AI technology is now more insane than it was in 2017?
38:10 How do you keep up with this fast rate of progress?
39:08 As the field is maturing, would you say that you have to be more specific on what you’re doing compared to 5-6 years ago?
40:06 Is the need for specific knowledge more challenging than when you had to have broader knowledge?
42:54 What is your favorite tool to use?
43:04 What internal tools are you using?
47:00 Are you surprised by the fact that open-source technologies are so powerful?
50:16 What is the biggest challenge in just deploying models?
53:33 So the main challenges come with the complexity of the solution and the randomness?
