Today I gave a short talk at Digital Agenda for Estonia 2021+, the Ministry of Economic Affairs and Communications event where the country's next ten years of digital policy are being planned. I work at Veriff and have no policy experience whatsoever. It's twelve minutes long and the argument is that the state should build APIs
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 small change to the site – e.g. changing the way
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 described in code. An example of an automation task
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 of building algorithmic productsThe two loops of building
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 earlier stages, and in hindsight, I always used to be
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 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. 4. Supporting marketing and other teams with A/B test analyses,
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 this is curation: having people or algorithms digest
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%20learning%20andrew%20ng], and perhaps a few other MOOCs. This
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 conversations. Others associating with AI in their work
