AI Daily — May 1, 2026
2026-05-01
deepmind.google
Enabling a new model for healthcare with AI co-clinician
Google DeepMind has published research on an AI co-clinician system designed to augment clinical care workflows. The work outlines a path toward AI systems that collaborate with healthcare professionals rather than replacing them, integrating into diagnostic and treatment decision pipelines. This represents a concrete step from DeepMind toward deployed medical AI beyond benchmark demonstrations.
www.technologyreview.com
This startup's new mechanistic interpretability tool lets you debug LLMs
Goodfire released Silico, a mechanistic interpretability tool that allows researchers and engineers to inspect and adjust model parameters during training rather than only post-hoc. The tool claims to offer more granular control over model behavior than existing fine-tuning approaches, enabling real-time intervention at the feature level. If the claims hold up, this could meaningfully advance the practical utility of mechanistic interpretability beyond academic research.
rss.arxiv.org
End-to-end autonomous scientific discovery on a real optical platform
Researchers introduce the Qiushi Discovery Engine, an LLM-based agentic system that autonomously conducts end-to-end scientific experiments on a real physical optical platform — not a simulation. The system combines nonlinear research phases, a Meta-Trace memory mechanism, and a dual-layer architecture to maintain coherent long-horizon research trajectories. This is a notable result because it demonstrates nontrivial autonomous discovery with experimental validation on physical hardware, a bar few prior systems have cleared.
openai.com
Introducing Advanced Account Security
OpenAI is rolling out Advanced Account Security featuring phishing-resistant login (likely FIDO2/passkey-based), stronger account recovery flows, and enhanced protections against account takeover. The move is notable given the sensitivity of data stored in ChatGPT and API accounts, including system prompts, conversation history, and fine-tuning datasets. This is a product-level hardening effort rather than a research announcement.