The extent to which foundation models can disclose novel chemical, biological, radiation, and nuclear (CBRN) threats to expert users is unclear due to a lack of test cases. I leveraged the unique opportunity presented by an upcoming publication describing a novel catastrophic biothreat - "Technical Report on Mirror Bacteria: Feasibility and Risks" - to conduct a small controlled study before it became public. Graduate-trained biologists tasked with predicting the consequences of releasing mirror E. coli showed no significant differences in rubric-graded accuracy using Claude Sonnet 3.5 new (n=10) or web search only (n=2); both groups scored comparably to a web baseline (28 and 43 versus 36). However, Sonnet reasoned correctly when prompted by a report author, but a smaller model, Haiku 3.5, failed even with author guidance (80 versus 5). These results suggest distinct stages of model capability: Haiku is unable to reason about mirror life even with threat-aware expert guidance (Stage 1), while Sonnet correctly reasons only with threat-aware prompting (Stage 2). Continued advances may allow future models to disclose novel CBRN threats to naive experts (Stage 3) or unskilled users (Stage 4). While mirror life represents only one case study, monitoring new models' ability to reason about privately known threats may allow protective measures to be implemented before widespread disclosure.

Bangalore: Artificial Intelligence (AI) has emerged as the most frequently mentioned theme in discussions among the key disruptive technologies during the first quarter (Q1) of 2018 on Twitter, according to GlobalData study. An analysis from GlobalData's influencer platform revealed that AI was way ahead of other disruptive technologies with more than one-fourth share of overall discussions, followed by Internet of Things (IoT), blockchain and augmented reality. Robotics and analytics were also among the leading themes that were discussed across disruptive portfolio. "AI's domination among influencer mentions is primarily driven by significant discussions related to machine learning. Technologies such as Insurtech, big data and deep learning too have helped AI in witnessing highest discussions on Twitter," said Vaibhav Mathur, Influencer Research Head - GlobalData.