Stephanie KirmerinTowards Data ScienceThe Meaning of Explainability for AIDo we still care about how our machine learning does what it does?Jun 42Jun 42
Stephanie KirmerinTowards Data ScienceThe Importance of Collaboration in DataAsking for feedback is a secretly powerful tool in data work. Let’s talk about why, and how to do it well.May 17May 17
Stephanie KirmerinTowards Data ScienceEnvironmental Implications of the AI BoomThe digital world can’t exist without the natural resources to run it. What are the costs of the tech we’re using to build and run AI?May 25May 25
Stephanie KirmerinTowards Data ScienceHow Do We Know if AI Is Smoke and Mirrors?Musings on whether the “AI Revolution” is more like the printing press or crypto. (Spoiler: it’s neither.)Apr 1711Apr 1711
Stephanie KirmerinTowards Data ScienceThe Coming Copyright Reckoning for Generative AICourts are preparing to decide whether generative AI violates copyright-let’s talk about what that really meansApr 16Apr 16
Stephanie KirmerinTowards Data ScienceUncovering the EU AI ActThe EU has moved to regulate machine learning. What does this new law mean for data scientists?Mar 143Mar 143
Stephanie KirmerinTowards Data ScienceSeeing Our Reflection in LLMsWhen LLMs give us outputs that reveal flaws in human society, can we choose to listen to what they tell us?Mar 22Mar 22
Stephanie KirmerArt and AIThinking about the intersection of people and technology in the creative process in the AI eraFeb 17Feb 17
Stephanie KirmerinTowards Data ScienceUsing Poetry and Docker to Package Your Model for AWS LambdaAn accessible tutorial for one way to put a model into production, with focus on hiccups you might encounter along the wayJan 291Jan 291
Stephanie KirmerinTowards Data ScienceClosing the Gap Between Machine Learning and BusinessWhat would you say it is you do here?Jan 132Jan 132