Standing on the Edge, Looking into the Future
The Future of Tiny ML on the Edge
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The transcript of a talk I gave as part of the tinyML Talks series in December 2021 during which I talked about machine learning, edge computing, and privacy.
We’ve spent the last decade building large scale infrastructure in the cloud to manage big data. However, over the last couple of years, it has become very evident that we’ve probably made a mistake. The arrival of hardware designed to run machine learning models at vastly increased speeds, and inside a relatively low power envelope, without needing a connection to the cloud, means that edge computing has become a viable replacement to the big data architectures of the last decade. But ten years of big data, ten years of attempted technological fixes rather than cultural ones, has proven our industry is perhaps uniquely ill-suited to self-regulated. That has worrying implications for the future of both machine learning, and edge computing.
More than ten years ago now Mark Zuckerberg stood up and famously stated that that the age of privacy was over, that privacy could no longer be considered a “social norm.”
Zuckerberg was proved correct, and this became the mantra of the Big Data age, and for the last ten years, Silicon Valley has pursued it with vigour. But the companies that we have entrusted with our data, in exchange for our free services, have not been careful with it.
This from Alan Kay,
who, in 1972, anticipated the black rectangle of glass and brushed aluminium that lives in all of our pockets today, along with the ubiquity of ad-blocking software we need to make the mobile web even a little bit useable.
Yet, while Kay’s prediction of the existence of the smartphone was almost prophetic it was also, in a way, naive. Because Kay lived in a simpler…
