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

A fantastic initiative — it shows source images in the training data of models like Stable Diffusion that led to an image you've just generated.

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

Josh W Comeau thinks about increasingly-impressive demos from tools like GPT-4, and thinks if front-end developers should worry that by the time they're fluent in HTML/CSS/JS, there won't be any jobs left for them. He disagrees with that and believes it's similar to no-code website builders which exist since 1996. New algorithm-driven tools will make developmers more productive. Christian Heilmann has similar thoughts.

Dan Mall confirms the common logic of an algorithm-driven design process and shows how modern tools help here.

Why too many logos of algorithm-driven tools are based on the swirling hexagon. The same thing happens to sparkle icons.

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.

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

AI-powered UX research insight generation tools have many problems: lack of context, extremely vague summaries and recommendations, inability to analyze image & video content, lack of citation and validation.

A creepy story of Hollie Mengert — her commercial illustrations were used to train a neural network and let anyone to literally clone the style. Andy Bayo contacted code author Ogbogu Kalu. Comments are disgusting — people just don't respect Hollie's work.