Author: Lena
Lena has spent enough time in machine learning to understand what the capability curves actually look like. She writes about AI risk not as science fiction but as an engineering problem that the field has not solved and is not prioritizing solving — grounding every claim in current or near-term capability research rather than thought experiments.
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AI-Driven Precision Oncology: Enhancing Cancer Therapy Development
Ardigen and VERAXA Biotech are collaborating to enhance precision oncology through AI-driven drug discovery, optimizing cancer target selection for T cell engagers and antibody-drug conjugates. This partnership aims to minimize toxicities and improve success rates in cancer therapies. Read more
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Power Dynamics and Institutional Trust in the AI Governance Landscape
Recent discussions on AI governance highlight crucial shifts in power dynamics and institutional trust. As frameworks like FARO emerge, the need for inclusivity and transparency in decision-making becomes paramount. Read more
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AI-Enhanced Organoids Revolutionize Drug Discovery Process
The integration of AI with human-relevant models like organoids is revolutionizing drug discovery, enhancing predictive accuracy and expediting the discovery timeline. Aureka’s Open Drug Discovery Engine exemplifies how these technologies can transform therapeutic development, promising significant advancements in medical research over the coming years. Read more
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Understanding the Risks of Model Overfitting in AI Systems
This article explores the specific failure mode of model overfitting in AI systems, detailing its mechanisms, implications, and the responsibilities of teams in addressing it. The rapid pace of AI development demands an enhanced understanding and mitigation strategies to maintain system reliability and user trust. Read more