Google’s on-device AI is very important – reasons

#News(IoTStack) [ via IoTGroup ]

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Apparently, Google thinks so too, because Sundar Pichai made the announcement himself, describing the complexity of turning speech to text and how his company has managed to leverage advances in deep learning to bring the power of Google’s AI to the device that fits in your pocket.
That’s why AI assistants and smart speakers need a connection to the cloud to process voice commands.
He likens running the machine learning algorithms on mobile devices to “putting the power of a Google data center in your pocket, an incredibly challenging computer science problem.”
Every time you want to use AI to process an audio file or image or video, you must upload the data to the cloud where the deep learning models reside.
For the Google Assistant, the delay can make the experience clunky and slow, forcing users to wait a few seconds every time they utter a command to the AI assistant.
With Google’s on-device machine learning technology, the Assistant becomes ten times faster, and users can give consecutive commands to it in real-time.
As Scott Huffman, VP of Engineering at Google Assistant, described, the AI’s on-device voice-processing was so fast that “tapping to operate your phone would almost seem slow.”
Every email you compose in Gmail is being processed by the AI algorithms in Google’s cloud.
What this means is that, from a power consumption perspective, performing AI tasks at the edge will become more and more efficient as companies continue to develop specialized hardware for running deep learning algorithms.
Second, even though I mentioned power consumption as one of the pros of edge AI, I’m curious to see how Google’s on-device machine learning capabilities will affect the battery life of devices.
For instance, when you’re giving five to ten voice commands to Google Assistant every minute, you would expect it to be smarter than an AI assistant you summon a few times per day

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