Google's Rules of Machine Learning still apply in the age of large language models
Despite the excitement around large language models, building with machine learning remains an engineering problem with established best practices.
artificial intelligence
Was data science a failure mode of software engineering?
Yes, data science projects have suffered from classic software engineering mistakes, but the field is maturing with the rise of new engineering roles.
How hackable are automated coding assessments?
Exploring the hackability of speed-based coding tests, using CodeSignal’s Industry Coding Framework as a case study.
Remaining relevant as a small language model
Bing Chat recently quipped that humans are small language models. Here are some of my thoughts on how we small language models can remain relevant (for now).
ChatGPT is transformative AI
My perspective after a week of using ChatGPT: This is a step change in finding distilled information, and it’s only the beginning.
Causal Machine Learning is off to a good start, despite some issues
Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.
Building useful machine learning tools keeps getting easier: A fish ID case study
Lessons learned building a fish ID web app with fast.ai and Streamlit, in an attempt to reduce my fear of missing out on the latest deep learning developments.
Use your human brain to avoid artificial intelligence disasters
Overview of a talk I gave at a deep learning course, focusing on AI ethics as the need for humans to think on the context and consequences of applying AI.
Defining data science in 2018
Updating my definition of data science to match changes in the field. It is now broader than before, but its ultimate goal is still to support decisions.
My PhD work
An overview of my PhD in data science / artificial intelligence. Thesis title: Text Mining and Rating Prediction with Topical User Models.