Two stories of getting AI functionality to production, which demonstrate the risks inherent in custom development versus starting with no-code approach.
Getting ChatGPT Plus to elaborate on possible book content and produce a PDF cheatsheet, with the goal of learning about its capabilities.
Reflecting on an interview with Jason Warner, CEO of poolside.
Shifting focus to ‘Data & AI for Impact’, with more startup-related content, increased posting frequency, and deeper audience engagement.
Replacing ‘artificial intelligence’ with ‘automation’ is a useful trick for cutting through the hype.
Despite the excitement around large language models, building with machine learning remains an engineering problem with established best practices.
Yes, data science projects have suffered from classic software engineering mistakes, but the field is maturing with the rise of new engineering roles.
Exploring the hackability of speed-based coding tests, using CodeSignal’s Industry Coding Framework as a case study.
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).
My perspective after a week of using ChatGPT: This is a step change in finding distilled information, and it’s only the beginning.
Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.
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.
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.
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.
An overview of my PhD in data science / artificial intelligence. Thesis title: Text Mining and Rating Prediction with Topical User Models.