Cathy Cheung | Searcharxiv

arXiv2026

Improving Entrepreneurial Skills by Practicing with AI

Durable skills like critical thinking, creativity, and collaboration are essential for innovation, yet teaching them remains difficult. In this work, we focus on the entrepreneurial domain, where these skills and others are required, and ask whether AI can be used as a tool for improving entrepreneurial proficiency. As a first step towards this challenge, we present an extension of Vantage, a platform developed at Google Research, adapted to serve as an interactive practice environment for entrepreneurship. Vantage pairs students with simulated AI peers engineered to navigate realistic business scenarios and challenge learners to exercise key skills. In a randomized controlled trial (RCT) embedded in summer entrepreneurship programs run by The Network for Teaching Entrepreneurship (NFTE), students using Vantage scored significantly higher on their Capstone Business Plans than control students with access to standard AI tools. These preliminary findings suggest that orchestrated AI peers can meaningfully support short-term performance on tasks requiring complex, durable skills.

cs.CY↗

arXiv2024

Capabilities of Gemini Models in Medicine

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.

cs.AI↗