ExplainableAI
AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and
AI
AI "digital twins" to watch over ICU patients Meet DT-ICU: a system that builds a live, data-driven "digital twin" of each person in intensive care. As new vitals, labs, and other observations arrive, it updates risk estimates — helping clinicians spot trouble early and track recovery. * Fuses
AI
Sometimes, vision-language AIs see what the prompt says, not what is in the picture. New research maps how that happens—and how to dial it down. Rudman and colleagues tested object counting with prompts that intentionally overstate what is in an image (for example, "describe four waterlilies" when
AI
Personalized AI you can read, edit, and take anywhere Most AI systems hide your preferences in opaque vectors. This work makes them plain English instead. Your "profile" becomes a short text summary the model can use across apps and tasks. * Interpretable: you can see and revise what the
AI
Decision trees (and ensembles like random forests and gradient boosting) make powerful predictions—but their reasoning can be hard to follow, which is risky in healthcare, finance, and other safety-critical settings. This paper shows how to turn those predictions into clear, formal explanations using Answer Set Programming (ASP), a logic-based
AI
Deep neural networks are powerful—but often inscrutable. xDNN(ASP), a new method by Ly Ly Trieu and Tran Cao Son, turns a trained network into a human-readable set of logical rules using Answer Set Programming (a logic-based AI method). Unlike many explainability tools that only highlight which inputs mattered
AI
Can AI write like Dickens with a single word? What if one token could cue an AI to channel Austen, Twain, or Melville? This study fine-tunes large language models so a minimal, single-token prompt triggers 19th-century novelist styles. * How they checked it: An AI detector trained on authentic sentences judged
AI
AI + Designers: A Better Way to Co-Create AI is entering graphic design studios, not to replace designers, but to collaborate with them. A new review by Joana Rovira Martins, Pedro Martins, and Ana Boavida scans 872 papers and zooms in on 68 AI systems used in graphic design. The result:
AI
Smarter, faster explanations for what you’re recommended Big AI models can explain why you’re shown a product or movie—but wiring them directly into recommendation engines often slows everything down and muddles goals. "Prism" takes a cleaner route: it splits the system into two jobs. * Oracle:
AI
Can AI essay graders be fooled? New research shows many automatic essay scoring (AES) systems reward “word soups” over real writing. Using interpretability tools, the authors found that a few keywords drive most of the score, while flow, grammar, and coherence matter far less. The wild part: because these models
UrbanPlanning
Cities need AI that can explain itself Most planning AI spots patterns in data. The next leap is AI that helps choose sites, allocate budgets, and balance trade-offs—while showing its work. This paper introduces the Agentic Urban Planning AI Framework built on new reasoning methods like Chain-of-Thought, ReAct, and
AI
Interpreting what large language models “think” is slow and expensive. Each new model often needs custom tools and lots of manual labeling. Atlas-Alignment offers a shortcut. Instead of rebuilding everything, it lines up a new model’s hidden activity with a shared, human-labeled Concept Atlas using only overlapping inputs and