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.
Schedule
Timezone: PST
Standing at the Edge, Looking into the Future
Alasdair ALLAN, Head of Documentation
Raspberry Pi
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.
Alasdair ALLAN, Head of Documentation
Raspberry Pi
Alasdair Allan is a scientist, author, hacker, maker, and journalist. He currently works for Raspberry Pi, and is responsible for their documentation. However in the past he has worked as a consultant and journalist, focusing on open hardware, machine learning, big data, and emerging technologies — with expertise in programming, electronics, and especially wireless devices and distributed sensor networks. A former astronomer, he built a peer-to-peer network of telescopes that, acting autonomously, reactively scheduled observations of time-critical events. Notable successes included contributing to the detection of what—at the time—was the most distant object yet discovered.
Schedule subject to change without notice.