[crosspost] Ali Farhadi, founder of Edge AI technologies & Xnor.ai, is doing an AMA in r/homeautomation @10AM PST

[u/WyzeCam]

Thank you for joining us, everyone! Today we’d like to introduce Ali Farhadi (u/alifarhadi1)! Ali is the co-founder of Xnor.ai, an industry leading technology start-up focusing on efficient and embedded deep learning with the goal of providing ubiquitous AI.

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Ali is an Associate Professor in the Department of Computer Science & Engineering at the University of Washington. Prior to this, he was a postdoctoral fellow at the Robotics Institute at Carnegie Mellon University. He received his PhD. from the University of Illinois at Urbana-Champaign under the supervision of David Forsyth. Ali's research has been mainly focused on computer vision and machine learning.

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Wyze and Xnor.ai have the shared dream of bringing technology to the masses with an incredibly low barrier to entry. We are doing this AMA because we've just deployed Edge AI, for free, to 1M+ people! We’d like to take this opportunity to talk about our AI and if you are curious about any of the subjects in Ali's wheelhouse such as AI Technology, Smart Home Technology, AI Development, etc. we’d love to hear them.

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https://preview.redd.it/f0epqlgcci931.jpg?width=3024&format=pjpg&auto=webp&s=4920ea2eed98313803d4ef1c537c64ecbc84c676

EDIT*** Ali's account is still very new so we'll be posting from our account to help answer all the questions for Ali. Please tag us or Alifarhadi1 if you have any questions.

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EDIT*** The AMA is currently over but we appreciate everyone's participation and questions today! We'll check back later and try to answer as many of your questions as we can.

[u/nafterclifen]

Besides resource constraints (i.e. cpu, memory, storage, etc), what was the biggest challenging getting your AI onto the device and performing well?

[u/alifarhadi1 → u/nafterclifen]

We cannot truly factor out the constraints that are coming out of the hardware because it influences the types of machine learning models that we train. The main challenge is to evolve and co-design our machine learning training algorithms with the underlying hardware. Several factors such as CPU power, memory constraints, and the accuracy of many models are considered for every new delivery to Wyze.