Galleries are using AI to measure the ‘quality’ of art… SET ME AFLAME

The system analyzes interactions with artworks

Data crunching art

Data analytics have influenced art for centuries, from counting footfall at theaters to projecting album sales.

In more recent years, the Relativity Media studio has been using predictive algorithms to select movies to produce.

“I’m not in this for the art,”saidRelativity founder Ryan Kavanaugh in 2012.

The company has since filed for bankruptcy twice.

In galleries,AIcan help improve accessibility and make exhibitions more interactive. But it’s a horribly reductive measurement of artistic value.

Our attention is often drawn to the controversial or bizarre before the subtle and thoughtful. Brilliant works could be overlooked because they don’t generate sufficient “engagement.”

Furthermore, our expressions are, at best, an unreliable measurement of our feelings. We all show our emotions differently and algorithms often fail to discern them — particularly when they’re applied tominority groups.

The ShareArt system is currently focused on gaze analysis, but with rules on masks easing, it could soon move on to facial gestures. That sounds like another good reason to wear a face covering — even if COVID disappears.

Greetings Humanoids! Did you know we have a newsletter all about AI? You can subscribe to itright here.

Story byThomas Macaulay

Thomas is a senior reporter at TNW. He covers European tech, with a focus on AI, cybersecurity, and government policy.Thomas is a senior reporter at TNW. He covers European tech, with a focus on AI, cybersecurity, and government policy.

Get the TNW newsletter

Get the most important tech news in your inbox each week.

Also tagged with

More TNW

About TNW

Europe has opened a door to a universal wallet. The web’s inventor wants to enter

AI is changing science: Google DeepMind duo win Nobel Prize in Chemistry

Discover TNW All Access

Battle of the programming languages: Kotlin vs Java in the wake of AI

Tech bosses think nuclear fusion is the solution to AI’s energy demands – here’s what they’re missing