Many of us have visited cultural heritage sites while traveling or have explored museums filled with artifacts and artwork. But have you ever wondered about the work involved to preserve these historical treasures?
The long and expensive process takes many hours, expert knowledge, and tireless hands to get each piece preserved, cataloged and to a place where you can enjoy it and the story behind it.
But now, in the digital age, is it no longer enough to have each item cataloged for internal use and available to in-person visitors. Plus, even with great care, some items face natural deterioration over time. We’re faced with preserving these collections digitally, at scale, on a limited budget, with as little bias in interpretation as possible. A steep ask for these organizations.
Can AI help?
We invited Lora Aroya, a research scientist at Google NY, in for an AI Deep Dive to discuss how she’s been using machine learning and crowdsourcing to overcome this challenge. In “Data Science For Smart Culture: Telling Stories With Humans and Machines at Scale” on Tuesday, February 23, Lora shared how she’s accomplishing this feat.
Lora discussed how crowdsourcing with platforms like CrowdTruth can obtain descriptions of artwork from many perspectives, limiting bias. A challenge when art is inherently thought-provoking and subject to a wide variety of interpretations. She’ll also explained how machine learning can be used to scale this process as well as help with metadata enrichment, and, the impact it’s making on heritage collections around the globe.