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EN: Found on theregister.com The central bank for central banks is concerned about the eye-watering sums being invested into AI, and it's raising the specter of a global recession should the bubble burst. In its annual report for 2026, the Bank for International Settlements compared the current

Ich habe auf arstechnica.com in deren Überblick über interessante wissenschaftliche Ergebnisse einen bedeutsamen Beitrag zum Thema Kunst gefunden: Geschmack ist in der Kunst sehr subjektiv und zu verstehen, warum einige Werke mehr als andere mit der Öffentlichkeit resonieren, fasziniert Forscher seit langem. Mathematiker denken, dass sie eine versteckte "goldene Regel" der abstrakten Kunst identifiziert haben, die erklären könnte, warum wir uns zu einem Jackson Pollock hingezogen fühlen, während weniger bekannte Werke uns kalt lassen. Die Zusammenfassung der Autoren in der verlinkten Publikation PLOS Computational Biology "Art’s hidden topology: A window into human perception" sagt folgendes:

EN: Found on tomshardware.com I think it is a good idea to activate a safety car phase on AI development and slightly interrupt the AI race in order to remove dangerous things from the racing track. Why not to establish some kind of FIA (Fédération Internationale de l’Automobile) which sets the rules for that and oversights the whole thing? And I think, this is a good metaphor for Anthropic's suggestion to halt frontier model development and to take a deep breathe.

I found a very interesting posting about art in arstechnica.com roundup of cool science stories: Taste in art is highly subjective, and understanding why some works resonate with the public more than others has long fascinated researchers. Mathematicians think they have identified a hidden “golden rule” of abstract art that might account for why we gravitate toward a Jackson Pollock while lesser works leave us cold Author Summary in the referenced paper PLOS Computational Biology "Art’s hidden topology: A window into human perception" states following:

Do you remember the Lippitt-Knoster model for managing complex change? This framework, which identifies critical elements needed for successful change, might help you in your AI transition and/or automation project! The framework identifies six critical elements required for successful change:

There are two postings recently published on golem.de which highlight the risks of AI usage in the first place / at the first step and in the further processing of AI produced artifacts: Excessive transscription of every (business) conversation and/or meeting may lead into a situation, where each

A couple of days ago I added an ASCII Art diagram to my posting about AI assistants and test driven development but then noticed, it was rendering wrong on android devices. After searching around for a while I found a good explanation about the underlying problem and the proper solution on stackoverflow.com. In total, three things are required:

Found on Decoder podcast from The Verge Initially found on simonwillison.net Any business process that looks like code talking to a database in a repetitive way is up for grabs. It’s everywhere: the absolute cutting edge of advertising and marketing is automation with AI. It’s not being a creative.

Update on the 19th of April 2026 regarding DuckLake specification 1.0, found on thegerister.com Initially found on thegerister.com. DuckDB proposed DuckLake as a standard for metadata and catalogs, claims it simplifies lakehouses by using a standard SQL database for all metadata, instead of complex file-based systems, while still storing data in open formats like Parquet. It will be seen, if this makes them more reliable, faster, and easier to manage. The corresponding blog post is worth reading, because it gives a systematic view on the high level data architectures of DuckLake, Databricks, IceBerg, BigQuery and Snowflake.

Update on 19th of April 2026, found on theregister.com Initially found on google.com after search for "What are the different sources of bias in AI models?" What are the different sources of bias in AI models? Training data bias Historical data --> historical bias Sampling --> representation