What is a conference? Common definitions provide only a vague sketch: “a meeting of two or more persons for discussing matters of common concern” (Merriam Webster a); “a usually formal interchange of views” (Merriam-Webster b); “a formal meeting for discussion” (Google a).
What qualifies as a meeting? Are all congregations of people in all places conferences? How formal must it be? Must the borders be agreed upon? Does it require a designated name? What counts as a discussion? How many discussions can fit in one conference? Does a sufficiently formal meeting held within the allotted times and assigned premises of a larger, longer conference constitute a sub-conference?
Absent context, the word verges on vacuous. And yet in professional contexts, e.g., among computer science academics, culture endows precise meaning. Google also offers a more colloquial definitions that cuts closer:
[This article originally appeared on the Deep Safety blog.]
This year’s NIPS gave me a general sense that near-term AI safety is now mainstream and long-term safety is slowly going mainstream. On the near-term side, I particularly enjoyed Kate Crawford’s keynote on neglected problems in AI fairness, the ML security workshops, and the Interpretable ML symposium debate that addressed the “do we even need interpretability?” question in a somewhat sloppy but entertaining way. There was a lot of great content on the long-term side, including several oral / spotlight presentations and the Aligned AI workshop.
In a shocking tweet, organizers of the 35th International Conference on Machine Learning (ICML 2018) announced today, through an official Twitter account, that this year’s conference has sold out. The announcement came as a surprise owing to the timing. Slated to occur in July, 2018, the conference has historically been attended by professors and graduate student authors, who attend primarily to present their research to audience of peers. With the submission deadline set for February 9th and registrations already closed, it remains unclear if and how authors of accepted papers might attend.
Conversations about the social impact of AI often are very abstract, focusing on broad generalizations about technology rather than talking about the specific state of the research field. That makes it challenging to have a full conversation about what good public policy regarding AI would be like. In the interest of helping to bridge that gap, Jack Clark and I have been playing around with doing recaps that’ll take a selection of papers from a recent conference and talk about the longer term policy implications of the work. This one covers papers that appeared at NIPS 2016.
If it’s helpful to the community, we’ll plan to roll out similar recaps throughout 2017 — with the next one being ICLR in April.