One discussion I find myself in more often recently is people asking me whether something is "really AI" or not. Often, what people seem to mean with that is whether someone is already using deep learning, or still "just" machine learning. I mentioned this to a friend in the industry and he just rolled his … Continue reading But is it AI?
Once upon a time, Paul Adams and I were standing next to our teams' desks, discussing something as we noticed a helicopter hovering over nearby buildings. Easily distracted as I am, we started to observe it and wondering where it was going. The chopper slowly drifted to the side and it seemed as if it … Continue reading Love Being A Lead
People often ask me how I make my figures. Here is a recent example from a talk given at O'Reilly's Strata conference in London: So the short version is, I use an iPad Pro with an Apple Pencil and the Paper by Fifty Three app. The app is pretty minimalistic, but the pens just look … Continue reading How I Make My Figures
Ever since I join Zalando as a lead I was interested in understanding what leadership is, with the additional challenge of figuring out how to do data science and machine learning at an industrial level. The latter might be a topic for another post, but here are three quotes on leadership I discovered along my … Continue reading 3 Quotes on Leadership
Two and a half-years ago I jumped off the AI bandwagon, left a permanent position in academia to joined "the industry," namely Zalando, a big European fashion ecommerce retailer. On the other hand, ever since I left, AI really did explode, with NIPS 2017 selling out in 15 days, and absurdities happening like Intel AI … Continue reading That Post-Academia Thing I Needed to Write.
(Repost from 2014) I don’t know whether this word exists, but mainstreamification is what’s happening to data analysis right now. Projects like Pandas or scikit-learn are open source, free, and allow anyone with some Python skills do lift some serious data analysis. Projects like MLbase or Apache Mahout work to make data analysis scalable such … Continue reading Data Analysis: The Hard Parts
(Repost) In case you haven’t heard yet, Data Science is all the craze. Courses, posts, and schools are springing up everywhere. However, every time I take a look at one of those offerings, I see that a lot of emphasis is put on specific learning algorithms. Of course, understanding how logistic regression or deep learning … Continue reading Three Things About Data Science You Won’t Find in the Books
Nowadays Python is probably the programming language of choice (besides R) for data scientists for prototyping, visualization, and running data analyses on small and medium sized data sets. And rightly so, I think, given the large number of available tools (just look at the list at the top of this article). However, it wasn’t always … Continue reading How Python Became the Language of Choice for Data Science
When I enrolled in Computer Science in 1995, Data Science didn’t exist yet, but a lot of the algorithms we are still using already did. And this is not just because of the return of the neural networks, but also because probably not that much has fundamentally changed since back then. At least it feels … Continue reading AI’s Road to the Mainstream – 20 Years of Machine Learning
Honestly, smartphone cameras have come a long way... . And Berlin is beautiful by night.
