The research project IDOVIR strives for making results in the field of digital architectural reconstruction available in a comprehensible, permanent, and open-access form and to facilitate scientific discussion of the research results.
Source: Home – IDOVIR
This document outlines the intended behavior and values of MAI models, the models developed by Microsoft AI. It summarizes our approach to training and operating them, following a set of design principles we call Humanist AI. This document, and our approach more generally, is still under development so we are not using it to train our models today. Instead, we’re sharing it broadly for public consultation. We’ll take feedback, iterate on it, and publish a revised version toward the end of the year, which we’ll use to guide our model development in 2027 and beyond.
Source: Humanist AI Code of Conduct | Microsoft AI
h/t D’Arcy Norman
LrLexicon is a free, open-source Lightroom Classic plugin that automatically generates photo keywords using AI. Select photos, run one command, and get AI-suggested keywords covering subject, setting, mood, and photographic technique — reviewed and editable before anything is written to your catalog.
Here we introduce a genomic pretrained network with species tree and alignment representations (GPN-Star), which is a biologically grounded genomic language model featuring a phylogeny-aware architecture that leverages whole-genome alignments and species trees to model evolutionary relationships explicitly.
Source: Predicting genome-wide functional constraints with GPN-Star | Nature
Source: Remote Laboratories
You can just prove things, apparently.
Source: How I Vibed a Proof of Conway’s Conjecture — overreacted
Advancements in Image Processing: The integration of AI, machine learning (ML), and deep learning (DL) has revolutionized image processing and analysis, enabling high-resolution, non-destructive imaging for failure analysis (FA) in electronics and semiconductors. AI-Powered Image Segmentation: AI-driven segmentation techniques improve the precision and scalability of defect detection, addressing challenges posed by complex and high-dimensional image datasets. Future of FA with AI and Data Management: The adoption of cloud-based training, multimodal pipelines, and robust data management systems is paving the way for automated, efficient, and predictive failure analysis workflows.
Source: Artificial Intelligence in Multimodal Microscopy Workflows for Failure Analysis | ZEISS
Extracted system prompts from Anthropic – Claude Fable 5.1, Opus 5, Claude Design, Claude Code. OpenAI – ChatGPT GPT-6-Astra, Codex. Google – Gemini 3.8 Flash, 3.1 Pro, Antigravity. xAI – Grok, Grok Bot, Cursor, Kimi and more! Updated regularly.
Mathematician Tristan Buckmaster of New York University and Levent Alpöge, a researcher affiliated with Anthropic, one of OpenAI’s major competitors, had made significant progress solving the problem over the past month using AI tools adapted from OpenAI and Anthropic. But before they could publicize their findings, OpenAI mounted its own colossal effort—pouring millions of dollars of computational resources into the problem. OpenAI says it didn’t knowingly tap into Buckmaster and Alpöge’s latest work, but Buckmaster has released public statements questioning that position.
Source: How an AI math breakthrough ignited a controversy | Science | AAAS
Their goal is a scientific superintelligence wired directly into the wet lab. They are all in on the bitter lesson, and the thesis follows from it: a lab is an infinite token generator. Produce data at scale, and the synergies give you a general reasoner that can tackle any scientific problem. They are committing hard. Biology, chemistry, drug discovery, and materials science, all at the same time. Time will tell if it works, but it is an exciting hypothesis.