Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
Feature engineering is a business decision in disguise
The variables you feed into a machine learning model are not a purely technical choice. They encode assumptions about your business that deserve explicit review.
Chatbots: between the hype and the first practical use
In 2016 everyone is talking about chatbots. Here is where they actually work and where they are a marketing promise.
TensorFlow and open source: what actually changed for companies
Why Google opening its ML framework shifts the conversation from 'we can't afford it' to 'we need data and an engineer'.
AlphaGo and the shift in what we expect from AI
AlphaGo's win over Lee Sedol is not just a technical result. It is the moment when the AI conversation stops being only about recognition.
TensorFlow goes open source: what changes for non-researchers
Google opened TensorFlow in November 2015. I look at what this means for companies that are not in the business of academic research.
TensorFlow and the shift of machine learning from research to engineering
What Google's open release of TensorFlow changes for companies: pipeline, reproducibility, and deployment become the central question, not algorithms.
NLP text classification as a practical enterprise baseline
Before the deep learning wave reshaped NLP, classical text classification already solved real problems. What it does well, where it stops, and how to start.
Gradient boosting: the machine learning that already works in production
Why ensemble methods - random forests and gradient boosting - became the first real ML for business, and how a manager should think about them.
Machine translation is improving, but the enterprise gap remains
Why impressive results in neural translation do not mean a company can remove translators from its workflows.
Deep learning: what is behind the hype and what is not ready yet
What the current wave of interest in neural networks means for companies that do not have a research lab.
Recommendation systems: what they need before they work
What a recommendation system actually requires to function, and why most projects stumble before they ever reach the algorithm.
Neural translation is entering product territory
What changed in machine translation in 2014 and why it matters for companies dealing with large volumes of text.