What to build, how to prototype and ship it, whether it works, and what users experience.
I hacked a bit on an AI experiment: idea generator. In general, working on these AI apps makes you think a lot about the work process and unique value add of Knowledge workers in general, and myself specifically. How predictable is it? Which parts are easy to automate; which are easy to assist? How much Context -- and what exactly -- does
You might imagine all great startups started from a clear mission. Sometimes they were, but usually, that story is woven in hindsight to cover up the gnarly pivots of the early days. In PR-optimised founding stories, the narrative usually goes like this. Our visionary founders found an apparent $PROBLEM in the world; through hard work and over many different
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. Or I could refer to what it looks like technically: that the system uses machine learning, deep neural networks, code where you optimise parameters against a dataset. The not explicitly agreed but
Pretty much every presentation I give about AI includes the following slide, in the context of automatically authenticating users using face recognition. The idea is that even automation-reliant businesses don’t need to go 100% automatic to start serving customers: you can start with a basic model that outputs a confident “yes” (green region), a confident “no” (red region)
It’s often assumed you need a number of PhDs and double the number of developers to create useful machine learning (ML) solutions. This is only sometimes true. If you’re creating leading-edge products — meaning you’re developing brand new machine learning methods — then you will certainly need a team of highly skilled and quite expensive talent. However, most
