Uploaded November 2022 | Updated September 2026, 2 weeks ago
Speaker: Nic Lane, Associate Professor, Department of Computer Science and Technology, University of Cambridge; and Laboratory Director, Samsung AI in Cambridge
Host: Fred Jiang, Associate Professor of Electrical Engineering, The Fu Foundation School of Engineering and Applied Science; and Co-Chair of the DSI Smart Cities Center
Machine Learning and the Data Center: A Dangerous Dead End
Abstract: The vast majority of machine learning (ML) occurs today in a data center. But there is a very real possibility that in the (near?) future, we will view this situation similarly to how we now view lead paint, fossil fuels and asbestos: a technological means to an end, that was used for a time because, at that stage, we did not have viable alternatives – and we did not fully appreciate the negative externalities that were being caused. Awareness of the unwanted side effects of the current ML data center centric paradigm is building. It couples to ML an alarming carbon footprint, a reliance to biased close-world datasets, serious risks to user privacy – and promotes centralized control by large organizations due to the assumed extreme compute resources. In this talk, I will offer a sketch of preliminary thoughts regarding how a data center free future for ML might come about, and also describe how some of our recent research results and system solutions (including the Flower framework — http://flower.dev) might offer a foundation along this path.
Speaker: Nic Lane, Associate Professor, Department of Computer Science and Technology, University of Cambridge; and Laboratory Director, Samsung AI in Cambridge
Host: Fred Jiang, Associate Professor of Electrical Engineering, The Fu Foundation School of Engineering and Applied Science; and Co-Chair of the DSI Smart Cities Center
Machine Learning and the Data Center: A Dangerous Dead End
Abstract: The vast majority of machine learning (ML) occurs today in a data center. But there is a very real possibility that in the (near?) future, we will view this situation similarly to how we now view lead paint, fossil fuels and asbestos: a technological means to an end, that was used for a time because, at that stage, we did not have viable alternatives – and we did not fully appreciate the negative externalities that were being caused. Awareness of the unwanted side effects of the current ML data center centric paradigm is building. It couples to ML an alarming carbon footprint, a reliance to biased close-world datasets, serious risks to user privacy – and promotes centralized control by large organizations due to the assumed extreme compute resources. In this talk, I will offer a sketch of preliminary thoughts regarding how a data center free future for ML might come about, and also describe how some of our recent research results and system solutions (including the Flower framework — http://flower.dev) might offer a foundation along this path.
