Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
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.
AI in 2019: what actually moved and what stayed a promise
A year-end assessment for people making adoption decisions. Without hype - what became a production norm, what is still on the way.
NLP in production: the gap between a demo and a working system
Language models in 2019 deliver impressive demonstrations. Why the road from demo to a real working product is much longer than it looks.
Russia's national AI strategy: what it means for industry adoption
In October 2019 Russia approved a National AI Development Strategy through 2030. I look at what is practically meaningful for companies thinking about adoption right now.
GPT-2 and language models: what the signal means for business right now
After GPT-2, the conversation about text generation shifted. I look at what actually changes for companies today and what is still in the lab.
AutoML: what it is and what a manager should not expect from it
How AutoML tools lower the barrier to machine learning - and where they still require expertise and management decisions.