Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
GPT-3 one year on: what actually changed for business
A year after GPT-3's release is a good moment to separate real shifts from noise and understand where language models already work.
GitHub Copilot and the start of mass AI-assisted development
What the GitHub Copilot launch means for companies that hire developers and manage engineering teams.
Transformer moves beyond NLP: what it means
The transformer architecture that reshaped text processing is beginning to work with images and structured data. What this means for business.
MLOps: the gap between experiment and production
Why most ML experiments never reach production, and what to do about it at the organisational level.
CLIP and multimodality: the arrival of zero-shot behaviour
What OpenAI's CLIP release means for companies thinking about practical AI beyond text.
AlphaFold: the moment AI starts changing science, not just back-office
In November 2020, DeepMind announced AlphaFold 2 results at CASP. Why this matters more than most AI news - and what it changes about how we understand the technology.
Narrow AI in operations: what works, what stalls
Companies launch AI pilots, but most never reach production. What distinguishes working cases from stalled ones.
GPT-3: what a founder should actually think about it
In summer 2020, OpenAI released GPT-3 - the largest language model at the time. What it actually means for business right now.
GPT-3 and the new baseline for language models
What the release of GPT-3 means for search, support, text analytics and product UX - a view for founders and directors.
Predictive analytics in supply chains: what the crisis revealed
How the pandemic tested investments in demand forecasting and inventory management - and what to take away from it.
Computer vision for quality control: what is realistic in 2020
A plain assessment of where computer vision actually delivers in factory quality control right now - and what the common misconceptions are about cost and scope.
Narrow AI in manufacturing: what actually works in 2020
An honest review of the tasks where AI in manufacturing already delivers measurable results - and where it does not yet.