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A growing trend called TinyML squeezes machine-learning models onto chips so small and low-power they can run on a coin battery for months.
TinyML runs trained machine-learning models directly on tiny, low-power microcontroller chips rather than sending data to a distant cloud server. Engineers shrink and simplify models to need only a fraction of typical memory and processing power, letting a device recognize a wake word or gesture using mere milliwatts, sometimes running months on a coin battery.
Building the world's largest data center is the opposite extreme, massive centralized facilities rather than tiny embedded chips. Replacing every programmer is an unrelated claim about AI's societal impact, not this specific engineering technique.
Because processing stays on the device rather than a server, TinyML offers real privacy benefits, since sensitive sound or image data near the sensor never has to leave it.
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