ML ✕ GLAM

Museums + AI Network

Philo van Kemenade

@phivk

June 2019

Cognitive Computing

Computational Studio Arts

🔮

G

L

A

M

alleries

ibraries

rchives

useums

vast public cultural collections

✨

Collections as

Data

Collections as Data

Pieter de Hooch - The Courtyard of a House in Delft

Collections as Data

Collections as Data

too much data?

Machine Learning

Data feeds

interfaces?

generative

adaptive

responsible

Similarity in N Dimensions

Bengler - Principle Components (t-SNE)

Generative Models

The research examines how our custom developed AI, utilising the largest recent advancement in computer vision and cognition, operates when trained on datasets consisting of thousands of 3D scans of classical sculptures from renowned international museum collections (i.e., British Museum, Metropolitan, National Roman Museum etc.).

Egor Kraft

Egor Kraft - Content Aware Studies (GAN)

Classification

ImageNet via Imagenet Roulette

duchess

ancient

Classification

ImageNet via Imagenet Roulette

homeless, homeless person

Black, Black person, blackamoor, Negro, Negroid

Responsibility

#openGLAM 4 #creativeAI ?

ML

GLAM

slides.com/phivk/mplusai

Philo van Kemenade

@phivk

Thank you 💛

lab.SNG - Web umenia online collection platform
Mitchell Whitelaw - Towards Generous Interfaces for Archival Collections
Thomas Padilla et al. - The Santa Barbara Statement on Collections as Data

Pieter de Hooch - The Courtyard of a House in Delft
Bengler, Audun Mathias Øygard & Norwegian National Museum - Principle Components
Egor Kraft - Content Aware Studies
Stanford - ImageNet
Princeton - WorldNet
Leif Ryge for Trevor Paglen Studio - ImageNet Roulette
Cristóbal Valenzuela - Machine Learning En Plein Air: Building accessible tools for artists
Gene Kogan et al. - Machine Learning for Artists

Dave Gershgorn - A Privacy Dustup at Microsoft Exposes Major Problems for A.I.

MegaPixels - Brainwash Dataset

slides.com/phivk/mplusai

Philo van Kemenade

@phivk