Arvind Sanjeev shows how private user data leaks into machine learning data — from medical records and smart home photos to real faces.

Jakob Nielsen believes AI is introducing the third user-interface paradigm in computing history, shifting to a new interaction mechanism where users tell the computer what they want, not how to do it — thus reversing the locus of control.

Fawzi Ammache explores solutions and challenges of paying artists for their contributions to train AI models.

Yet another sad story shows that futuristic AI-based products are backed by sweat & pain of low-waged human workers.

Artists can search machine learning databases for links to their work and flag them for removal. It's a part of a bigger Spawning initiative — they're building tools for artist ownership of their training data, allowing them to opt into or opt out of the training of large AI models, set permissions on how their style and likeness is used, and offer their own models to the public.

A fantastic article by Amy Goodchild about the nature of generative art. She digs into three key pillars: randomness, rules, and natural systems.

Brilliant thinking by Alexander Wales on how tools like DALL-E and Midjourney influence professional illustrators and artists. It'll kick the economy of these professions in the stomach for sure. However, art as self-expression will stay for sure. Erik Hoel has a similar take.

Aaron Hertzmann draws interesting parallels between today's algorithm-driven design tool boom and other branches of arts and culture for past centuries. He thinks current state is just interim and shows on-spot analogies.

This experiment shows how words like "assertive" and "gentle" are mapped to stereotypes and biases in models like Stable Diffusion and DALL-E 2 (review). Bloomberg has a good long-read article about this problem.

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