medium.com/womenintechn...

Writing is thinking

"On the value of human-generated scientific writing in the age of large-language models."

www.nature.com/articles/s44...

Do You Smell That? Hidden Technical Debt in AI Development| Towards Data Science Why speed without standards creates fragile AI products

Feeling like your team's code quality is inconsistent because it relies on people remembering standards? This article shows how automated guardrails like linters and tests can enforce standards and stop prototype issues from becoming production bugs.
medium.com/@erikapatg/d...

Why should we bother with Quantum Computing in ML?| Towards Data Science Quantum Machine Learning principles

Learn why the limits of Moore's Law are pushing machine learning toward quantum computing. This article explains the computational bottlenecks that quantum methods aim to solve.
medium.com/@erikapatg/w...

No Peeking Ahead: Time-Aware Graph Fraud Detection| Towards Data Science Leak-free Graph Fraud detection

Many AI models promise high accuracy but fail in real-world scenarios. Often data leakage is to blame. In this article the author explains how adopting a time-aware graph approach can prevent future-peeking and ensure reliable fraud detection.
medium.com/@erikapatg/n...

Leadership in the Labyrinth: Reprogramming (AI/True) Gender Expectations This is about transformation, not mere representation.

Bias in AI isn't just about representation. Models learn deeper, intrinsic societal traits, making hidden bias harder to detect but critical to address for building fair and responsible AI. Here's an interesting article to explore:
medium.com/womenintechn...

Thalia: la herramienta con IA que evalúa los anuncios Macarena Estévez, experta en analítica, lanza Thalia, la primera herramienta en España con IA que analiza la creatividad de los anuncios.

Thalia, creada por Macarena Estévez y desarrollada por CIRENTIS, tiene como objetivo principal ofrecer a marcas y agencias un diagnóstico rápido y riguroso sobre ... Si te gusto esta noticia descúbrenos en https://f.mtr.cool/yfuijdnjgk

www.nature.com/articles/s41... "AI paradox" in science: helps individual scientists publish more and get cited faster, but it narrows the collective focus of science.

Instead of exploring riskier/more uncertain new avenues AI encourages researchers to crowd around established, data-rich topics.

Do You Smell That? Hidden Technical Debt in AI Development | Towards Data Science Why speed without standards creates fragile AI products

Feeling like your team's code quality is inconsistent because it relies on people remembering standards? Erika Gomes-Gonçalves shows how automated guardrails like linters and tests can enforce standards and stop prototype issues from becoming production bugs.

Leadership in the Labyrinth: Reprogramming (AI/True) Gender Expectations This is about transformation, not mere representation.

Bias in AI isn't just about representation. Models learn deeper, intrinsic societal traits, making hidden bias harder to detect but critical to address for building fair and responsible AI. Here's an interesting article to explore:
medium.com/womenintechn...

Inside Boston AI Week — 2025 What mattered: human-centered AI, skills, and scale

Inside Boston AI Week 2025: human-centered AI, skills, and scaling from a region packed with universities & startups. I caught part of it—here are the takeaways and why these events still matter (even if you can’t attend every session): medium.com/womenintechn..., #BostonAIWeek

AI Operations Under the Hood: Challenges and Best Practices | Towards Data Science Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation, and systematic validation

LLMs don’t fail in the lab... they fail in production. This new article by Erika G. Gonçalves walks you through what really breaks in AI apps: silent retrieval failures, prompt drift, hallucinations, biased outputs + more.

No Peeking Ahead: Time-Aware Graph Fraud Detection | Towards Data Science How to implement leak-free graph fraud detection

Many AI models promise high accuracy but fail in real-world scenarios. Often data leakage is to blame. Erika G. Gonçalves explains how adopting a time-aware graph approach can prevent future-peeking and ensure reliable fraud detection.