Today we are joined by Siddhika Nevrekar, an experienced product leader passionate about solving complex problems in ML by bringing people and products together in an environment of trust. We unpack the state of free computing, the challenges of training AI models for edge, what Siddhika hopes to achieve in her role at Qualcomm, and her methods for solving common industry problems that developers face.
Key Points From This Episode:
- Siddhika Nevrekar walks us through her career pivot from cloud to edge computing.
- Why she’s passionate about overcoming her fears and achieving the impossible.
- Increasing compute on edge devices versus developing more efficient AI models.
- Siddhika explains what makes Apple a truly unique company.
- The original inspirations for edge computing and how the conversation has evolved.
- Unpacking the current state of free computing and what may happen in the near future.
- The challenges of training AI models for edge.
- Exploring Siddhika’s role at Qualcomm and what she hopes to achieve.
- Diving deeper into her process for achieving her goals.
- Common industry challenges that developers are facing and her methods for solving them
Quotes:
“Ultimately, we are constrained with the size of the device. It’s all physics. How much can you compress a small little chip to do what hundreds and thousands of chips can do which you can stack up in a cloud? Can you actually replicate that experience on the device?” — @siddhika_
“By the time I left Apple, we had 1000-plus [AI] models running on devices and 10,000 applications that were powered by AI on the device, exclusively on the device. Which means the model is entirely on the device and is not going into the cloud. To me, that was the realization that now the moment has arrived where something magical is going to start happening with AI and ML.” — @siddhika_
Links Mentioned in Today’s Episode:
Siddhika Nevrekar on LinkedIn
Siddhika Nevrekar on X
Qualcomm AI Hub
How AI Happens
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Informações
- Podcast
- FrequênciaQuinzenal
- Publicado16 de dezembro de 2024 20:14 UTC
- Duração33min
- Episódio112
- ClassificaçãoLivre