AI

EN: Found on heise.de. After all coding assistants are also coming for Databricks: Databricks Blog anouncement: Introducing Genie Code Press release: Databricks Launches Genie Code: Bringing Agentic Engineering to Data Work Overview intro video Get to know video with pipelines: Get to know

Found on google.com after search for "What are the different RAG methods to minimize confabulation?" Before model context protocol MCP and other agentic context enhancement protocols retrieval augmented generation or RAG was the most important tool to add and extend context on order to minimize

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 bias (stereotyping, recognition, denigration,

Found on theregister.com A feature regarding Geoff Huntley, his invention of the Ralph Wiggum Loop and the state of coding and agile: Developers, he argues, should now spend more time thinking about writing loops that drive coding assistants to produce better output, rather than persisting with code reviews.

The AI world clocks by Brian Moore is a good demonstration on how good AI is on creating graphical elements right now. Every minute, there are 9 new clocks displayed, which are generated by nine different AI models. Each model is allowed to use 2000 tokens to generate its clock. Here his prompt:

Is the AI bubble soon ready to burst? EN: Found on theregister.com - "Big money is nervous about AI hype, but not ready to call it a bubble" AI hype train may jump the tracks over $2T infrastructure bill, warns Bain Ars Live: Is the AI bubble about to pop? A live chat with Ed Zitron Moody's raises

Found on theregister.com The intro "Remember when AI was supposed to make us more productive, not hate each other?" gives the questions around the term workslop the perfect framing: AI-generated garbage, dubbed "workslop" is, essentially, when

Gefunden auf heise.de. Irgendwie kann ich mich nicht dem Eindruck erwehren, dass Prof. Dr. Michael Stal diesen Sommer einiges zum Thema PyTorch gelesen hat... Zumindest hat er mit seiner Serie von Blog-Artikeln Künstliche neuronale Netze im Überblick eine lesenswerte Einführung in die Erstellung von künstliche neuronale Netze mit PyTorch gepostet:

Found on arstechnica.com How a big shift in training - from imitation learning towards reinforcement learning - led to a capability explosion of LLMs, especially with extended context and in agentic setups. It was very nice of arstechnica.com to reprint this original Timothy B. Lee posting in his

There are some big legal questions, which are affecting any content creator and/or content provider today: Is it legal for a internet robot so scrape all of the available content for the purpose of training a generative AI model? Is it legal for a AI company to train any generative AI model on the scraped content? Is it legal when a generative AI model reproduces licensed and/or copyrighted content while inferencing? Hmm... Let us apply some logical thinking and common sence when trying to answer this questions...