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 reading James C. Scott’s Seeing Like a State, which manages to combine forestry, agriculture, and land/city planning into a history

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 to solve problems directly against a collection of examples, as opposed to trying to generalise the problem first and then solving the

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 all business models because AI is like Javascript. Which businesses could benefit from Javascript? It can create value at almost every company.

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

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, they often don’t use a dataset at all, or at best a small one. For example, in the early days of

As the coronavirus pandemic escalated, I was travelling around the world. The timing was unfortunate, but the endless hours of flying also presented an opportunity for reflecting on the virus. Most people correctly focus on the immediate effects of the virus: survival is required for anything interesting in the future.

You can quickly iterate code, but not data. This is one of the major bottlenecks in companies trying to automate human activities, and solving it generally would change the way every machine learning team works. To understand how easy things could be, consider a simple web application. To make a

This post is based on my talk at North Star AI 2019 [https://aiconf.tech/]. When I speak about automation in general, and the Automation team at Veriff, I mean it in a specific sense of the word. Building software to perform typically-human tasks… whose solutions usually cannot be perfectly

This is a talk I gave at a Zen of Data Teams [https://www.meetup.com/Machine-Learning-Estonia/events/260081030/] event in Tallinn. The full slides are here [https://github.com/taivop/talks/blob/master/files/loops_of_building_algorithmic_products.pdf] . My talk starts at 50:53. The two loops

Now the title might be a bit clickbaity but it’s true: when I joined Veriff I had received an offer with a higher salary from a larger company. I decided to write about my choice because, year after year, I’ve kept moving towards riskier ventures in earlier and

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

It’s common to see job descriptions along the lines of the following. -------------------------------------------------------------------------------- Looking for a data scientist. Responsibilities: 1. Building big data databases, data warehouses, data lakes, and pipelines. 2. Training and deploying deep learning models and neural networks for prediction. 3. Creating dashboards, analyses, visualisationgs and reports.

As the popularity of machine learning grows, so does the amount of academic papers, blog posts and software released. As an industry practicioner I need to be up-to-date with useful ones without spending hours each week combing through new developments to find the few important updates. The obvious solution to

There’s a specific type of person I’ve noticed applying to jobs, participating in ML communities, or writing to me online: software engineers with an interest in machine learning. Their ML background typically consists of a course or two from Andrew Ng [https://www.coursera.org/courses?query=machine%

Ask them if AI will kill all humans. Actually, anything along these lines will work. When will we achieve 100% automation? When are we going to build a machine that is better than humans at every kind of work? These are questions I often get at talks, interviews, or one-on-one

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

I’ve often needed to enter a domain where I previously had no expertise: e.g. when I started my blog, when I decided to consciously work on my social skills, or when I decided I want to understand business better. Getting an initial understanding of the new domain is

I’m reading a book on happiness and meditation which contains the following story. In Chinese history, during the reign of Emperor Shun (who lived sometime around 2200 BCE), what was then known as China was plagued by frequent destructive floods along the Yellow River. The emperor ordered a nobleman