Juan Manuel Ortiz de Zarate

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Join date: May 16, 2024

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I hold a Bachelor's degree in Computer Science from the University of Buenos Aires (UBA) and am about to defend my doctoral thesis focused on AI. I have taught Artificial Intelligence at the Faculty of Engineering of UBA and am the Co-Founder and CTO of Data Voices, a company dedicated to the development of AI technologies. Additionally, I worked alongside Peguy Luyindula, a former player of the French national team, in developing an innovative AI application for football contract negotiations.

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AI Expert
Worked as a Data Scientist/AI Expert and/or is an AI Researcher

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Location

Buenos Aires, Argentina

Posts (49)

Aug 13, 2025 ∙ 10 min

Language-Driven Precision in the Operating Room

The Hierarchical Surgical Robot Transformer (SRT-H) brings step-level autonomy to surgery by combining a language-driven high-level planner with a vision-guided low-level executor. Trained on over 16,000 demonstrations, it completed the clipping-and-cutting phase of gallbladder removal with 100% success in ex-vivo trials, adapting to variations and self-correcting without human intervention—marking a milestone toward clinically viable autonomous surgery.

Aug 7, 2025 ∙ 10 min

The Carbon Cost of Conversation

This article explores the environmental impact of large language models (LLMs), based on Dauner and Socher’s 2025 study. By analyzing 14 models across reasoning tasks, it reveals a trade-off between accuracy and CO₂ emissions. Larger models and reasoning modes achieve higher performance but drastically increase energy use due to verbose outputs. The findings highlight the urgent need for optimizing reasoning efficiency and integrating sustainability into AI development.

Aug 2, 2025 ∙ 11 min

When AI Slows You Down

This article analyzes a 2025 randomized controlled trial that challenges common assumptions about AI-enhanced software development. Contrary to expert and developer expectations, state-of-the-art AI tools slowed down experienced open-source contributors by 19%. Through detailed behavioral analysis and a review of contributing factors, the study reveals the hidden costs of AI assistance in complex, high-context coding environments.