Machine learning can run on tiny, low-power chips

Re:Machine learning can run on tiny, low-power chips by oulipo on Monday June 11, 2018 @03:58AM (#17282580)

At Snips we are running all our Voice AI models on embedded devices (like a Raspberry Pi 3) and we can also work on MCUs, and we believe that embedded ML will be the preferred way to solve privacy and efficiency challenges in the future (disclaimer: I'm a co-founder)

If you are interested, you can start building your own Voice AI for free and make it run on embedded devices in under an hour: https://snips.ai

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Re:Machine learning can run on tiny, low-power chips by adamdrake on Monday June 11, 2018 @07:23AM (#17283361)

Fully agreed. I think the privacy angle is particularly compelling, and doing on-device analytics using models that have low memory requirements and acceptable (although not academically impressive) accuracy will be the norm.

I wrote about the privacy perspective a bit:

https://adamdrake.com/scalable-machine-learning-with-fully-a...

and I recently gave a lecture more focused on the performance aspect:

https://adamdrake.com/big-data-small-machine.html

The case for doing centralized data collection and model training seems to be increasingly related to corporate greed and moat-building rather than actually providing a good experience for users.

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Re:Machine learning can run on tiny, low-power chips by endymi0n on Monday June 11, 2018 @08:46AM (#17283747)

Technically, on-device processing is clearly the way forward (it's interesting how Apple is currently pioneering the field in a way).

The pessimist in me already sees how three letter agencies worldwide will welcome this change in order to push down their selectors to the device as well. Recording only the one percent of potentially relevant conversations will make backdoors exponentially easier to hide in the background traffic as well as being much lighter to process.

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