The negotiation technique that enables deep conversations

The single biggest thing that made me a better friend, partner, colleague, and mentor came from an unexpected place. Two years ago, I was working through a breakup and rethinking how I approach all relationships. At a conference in Berlin, I happened to participate in a Circling workshop. It'

Stock options are hard

As an employee, startup stock options are hard. I'm feeling pretty confident by now, but only because I've seen my friends get burned and been burned myself. What's so hard? 1. At one company, my option contract was to be signed "at a

Talks & videos

Commitment

I'm both a millennial and a startup founder and both of these groups are often considered flaky perhaps, unable to commit. They seem like they're, flip-flopping between startup ideas or between jobs, between relationships. And I think all of this comes from different expectations to commitment.

Cause variance

How do you end up with an outlier success? I guess the common advice would be to "leave your comfort zone". That's hard to do. In the comfort zone, everything makes sense. Things work. Clients are using the product. You might even be growing slightly. It

Digital dopamine-seeking

My nervous system is in a civil war over my actions. The dopamine-seeking side keeps winning. Dopamine itself is crucial: it's a neurotransmitter driving you to achieve external goals. The bad part is being driven to the easiest, least meaningful ones. I try not to feed the enemy.

School-career mashup

There's an abrupt life transition almost everyone goes through: school to job. You might go from middle school to manual labour, or PhD to teacher. But regardless of details, a more gradual shift would be better. Most entry-level jobs could be apprenticeships that mix education and work. Why

Courage in the face of variance

Startups have high variance: the top few % reap most of the rewards. The same is true for artists of all kind, and to a lesser extent, software engineers: some Waymo engineers earn 100x the salary of the lowest-paid programmers. Risk is usually taken to be bad. But it might not

The office

Who needs an office these days? It’s a needless expense. I currently work alone, and when I meet others I do it via video call or on a walk outside. Plus, private offices are expensive. A desk in a co-working space is 150€ — and not an improvement over my

To kill a mock-up, first

I ran my first design sprint last week. According [https://www.gv.com/sprint/] to the creators: > The sprint is a five-day process for answering critical business questions through design, prototyping, and testing ideas with customers. The starting point of a sprint could be a problem like “website visitors

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, important work consistently every day for months on end. Somehow, I had lost that in the past 3-5 years. Doing what comes

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’s a visual explanation. Say you have data points in a given two-dimensional vector space. If you can use a straight line

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 un-narrated, almost unedited interviews with founders of successful startups like Paypal, Hotmail, Apple, and 27 others. I’m only through about 10%

Talks & videos

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

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

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

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. Or I could refer to what it looks like technically: that the system uses machine learning, deep neural networks, code where you

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

Talks & videos

How to build your AI startup

Timid New World

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.

Talks & videos

ML at Veriff: What is possible in a year

devclub.lv meetup 07.11.2019ML at Veriff: What is possible in a year?Google Docs

Talks & videos

Future of e-Estonia - a young engineer’s view

Data loops are the bottleneck in applied AI

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

Building automation-heavy products

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

The two loops of building algorithmic products

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