AI News Archive: May 16, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
- Malta offers free ChatGPT Plus access to its citizens through a national AI program
Citizens and residents registered with Malta’s online identity system can apply to get access to ChatGPT Plus after completing a free online course.
- OpenAI seals deal in Malta to give all Maltese access to ChatGPT Plus
OpenAI seals deal in Malta to give all Maltese access to ChatGPT Plus Reuters
- Stripe’s John Collison says agentic commerce will completely transform online shopping
John Collison thinks keyword search is a “ridiculous” way to find things to buy. The Stripe co-founder told Bloomberg that agentic commerce, in which AI agents shop on behalf of consumers, will completely transform the online shopping experience, reshaping not just how people purchase but how retailers sell. The argument is structural. For more than […] This story continues at The Next Web
- Odd Lots: Stripe’s John Collison on Agentic Commerce (Podcast)
The internet is made for shopping. For years, the main inputs for e-commerce transactions involved targeted ads, algorithmic recommendations, SEO, and lots of mindless scrolling. But agentic commerce might represent a sea change for e-commerce: With the rise of AI agents doing shopping on behalf of consumers, how are retailers going to adapt? John Collison, co-founder of the financial services and payment processing company Stripe, has first-hand experience with all the ways e-commerce has chang
- Stripe's John Collison on How Agentic Commerce Will Reshape the Internet
Adapting to a world where AI agents are doing the shopping.
- Pope Leo launches Vatican group to study impact of AI on ‘human dignity’
Comes as the Pontiff prepares his inaugural encyclical underlining need for ethics-driven approach to AI
- Easy to use and low cost leaf disease quantification workflow using Ilastik
Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) w