Media
Why AI music will let human artists be less like machines
We believe in the power of music to inspire"... so proclaims the new Apple Music for Business landing page. As a concept, music for business is booming, but when brands talk about'music' can we take them at face value? Is it really'music', stripped of all context, that allows brands to build emotional connections and affinity - or is it the people who make that music? As a lover and creator of music myself, I see musicians as creatively equal to filmmakers. Their purpose is not only to be synced to film or video.
Sonos Move review: brilliant sound now portable
Sonos has finally made a portable wifi and Bluetooth speaker that sounds great – but it's not quite what most will have imagined. For years Sonos has made some of the very best wifi speakers, recently adding optional voice assistants from Google and Amazon. But they have never been truly wireless, needing to be plugged in and on your home wifi network. The Move changes that, essentially taking the excellent Sonos One and adding a battery to the bottom. But it comes at a hefty £399 price tag, although some retailers already have it at £329. The Move is slightly larger than the One.
r/MachineLearning - [1912.10389] Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
Abstract: A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy.