Speaker
Description
A long, long time ago, in a far away land, some smart people thought about how to connect hardware and machine learning to make ML and hardware more resource-aware. At this time, the term "resource-aware ML" came along. While our roots go back some 10-15 years now, the term "resource-aware ML" only partially reflects the current trend in ML research. In fact, most new projects and ideas typically somehow incorporate the term "sustainability" or "green". And indeed, only a portion of our research directly connects to hardware, but there is also broader research concerning science communication, societal impact, management of resources (beyond a single machine) as well as ML workflow management.
In this open discussion round, I want to ask you:
What do you think about the term "resource-aware" ML? Is this still fitting? Do we need to "rebrand" ourselves? Do we want to move to other ideas?
Note: This is an open discussion. Input from everyone is welcome. No need to formally prepare anything. We are flexible with the time in our slot :-)
