A productive creative

I recently reflected on my productivity and discovered, to my horror, that I’m doing pretty badly! I used to be very organized and do lots of creative,

More pre-trained models, please

Every classification task is linearly separable in the right feature space. This statement is a clue to building ML solutions that scale to many different use cases. Here’

It's rocket science

“Founders at Work [https://www.goodreads.com/book/show/98233.Founders_at_Work]” is a unique book by Jessica Livingston, one of the founders of YCombinator. It contains

Datasets: the source code of Software 2.0

Datasets carve the terrain of AI

Lately, Twitter has been full of the 2020 US election, which has displaced everything interesting. That means it’s a good time to blog. I’ve recently been

Creating AI is Curating Examples

A few years ago at Starship, I contributed to the Data is the Specification [http://dataisspec.github.io/] manifesto. The core idea is that it's better

How do AI companies earn money?

Which business models benefit from AI? This is an audience question I recently got at a webinar aimed at early-stage startup founders. The answer is almost like enumerating

Two meanings of "AI"

What do people mean by “artificial intelligence”? When I say “AI” I mean one of two things. First it could be the experience of a product feeling intelligent.

Your AI team needs DataOps

A startup’s AI work often starts from a developer hacking algorithms on the side. When a generalist engineer with no data science background works on prediction problems,

How to build your AI startup