Bio-Tech•
AI Model Successfully Designs Functional Biological Viruses
Stanford University researchers used an AI model to design and create functional biological viruses that successfully killed bacteria in a lab setting.
Sarah Jenkins
Sarah Jenkins is a science and technology correspondent for Neurozzio, specializing in artificial intelligence research, machine learning interpretability, and their applications in biomedical science. She reports on breakthroughs that enhance the understanding and reliability of complex AI systems.
7 Articles
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Joined September 2025
Languages
English
Areas of Expertise
Artificial IntelligenceMachine LearningBiomedical ScienceData Interpretability
Latest Articles by Sarah Jenkins
AI•
ChatGPT Shows Human-Like Flaws in Math Study
A study by Hebrew University and Cambridge researchers found ChatGPT exhibits unpredictable, learner-like behavior and makes human-like errors when solving math problems.
AI•
ChatGPT's Error on Ancient Math Problem Reveals Learner-Like Reasoning
A new study reveals that ChatGPT's error on a 2,400-year-old math problem suggests it improvises solutions in a manner similar to a human learner.
Investment•
Nscale Secures $1.1 Billion for AI Infrastructure Expansion
Cloud computing startup Nscale has raised $1.1 billion in a new funding round led by Aker, valuing the company at approximately $3 billion.
Health Tech•
MIT AI Tool Speeds Up Medical Image Analysis for Researchers
MIT researchers have developed an AI tool called MultiverSeg that dramatically speeds up the annotation of medical images, a crucial but time-consuming task.
Health Tech•
AI-Powered Smart Bandage Speeds Up Wound Healing by 25%
Engineers have developed a smart bandage using AI and a tiny camera to monitor wounds and deliver personalized treatments, speeding up healing by 25% in early tests.
AI•
Diamond Method Boosts Machine Learning Interpretability
A new method called Diamond is enhancing machine learning interpretability by reliably identifying complex feature interactions while controlling for errors, crucial for scientific discovery.