Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
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.
Text analysis becomes practical: what it means for business
Natural language processing tools have reached the point where they can be used without a research lab. What to do with that.
Machine learning for mid-size business: what is real, what is not
An honest look at which problems machine learning actually solves for companies without research labs, and which ones remain academic.
ML in fraud detection: where AI saves money and where it only complicates the investigation
A look at the decision loop and the explainability problem in machine-learning-based anti-fraud systems.