Topic

Data Science

Cutting through the laptop installation jungle: How our MacBook setup repository helps our Data Scientists to organise their tools in only half a day

For me, one of the most frustrating parts of learning to program in earnest was that I always had some tooling problem before I could start. When I found a cool Python package and was eager to wrap my head around how it could help me to solve a problem, for instance, I often lacked the right version of Python to just install the package version I wanted and dive right in.

idalab seminar #14: Academia to Industry: Looking back on a decade of machine learning

Machine Learning is one of that areas that has seen a rapid transformation from a purely academic topic to becoming a driving technology in the industry these days. Mikio has seen both sides of the coin and will share his experience. What is the difference between academic research and bringing a ML driven product live? What does it take to productionize ML? And finally, how close are we to true AI?

Hassle-free travelling: idalab teams up with DB Systel, DFKI and Door2Door for research project SIM3S

Using public transport can be a challenge. In case of disruptions, loudspeaker announcements are the critical source of information – but they can be hard to understand, even for native speakers. English translations aren’t always provided, except in bigger cities. Matters get even worse for handicapped or elderly people, because information about accessibility on the alternative routes is usually not available at all.

idalab seminar #13: Exploring Chemical Space with Deep Learning

What if we could build batteries for electric cars that would take us further than a full tank of gasoline? If we could grow affordable, tasty and nutritious meat in the laboratory instead of occupying one third of the land on our planet with animal farming? What if we could easily identify promising targets in the human body for new cancer drugs?

Die Sehnsucht nach Transparenz ist eine Sehnsucht nach Begründung. Warum Algorithmen lernen sollten, eine Geschichte zu erzählen.

Basis aller heutigen Künstlichen Intelligenz sind bekanntlich Algorithmen. Diese sind in den letzten Jahren in zunehmendem Ausmaß in den Fokus der öffentlichen Wahrnehmung gerückt. Dabei schwankt der Grad an thematischer Souveränität und Güte der einzelnen Wortmeldungen aus Presse, Politik und Gesellschaft teils erheblich.

idalab goes PyData

This year’s PyData Berlin conference, taking place from July 6th to 8th at the Charité-Campus (Virchow), was a huge success and with almost 600 participants the biggest PyData conference all over Europe. Concerned with providing a forum for python users and developers in the field of data analysis, a wide range of topics was covered in four simultaneous tracks, going from deep learning and scalability over data privacy and best practices to putting machine learning into production.

idalab seminar #12: The data-privacy dilemma: How full homomorphic encryption could bring healthcare into the digital era

Imagine this: The key to better cancer treatments is within reach, based on patterns from data that is scattered across various locations all over the world. This data could be digitalised, labelled, collected, stored and interpreted. However, this data belongs to a countless number of individuals – and their right to data privacy weighs just as much as the dream of curing a lethal disease.

What do we mean by “data”?

Some technical terms are so ubiquitous and (apparently) unambigious, that they almost become a transparent fluid: always used but never much reflected upon. Interestingly enough, the word “data” 1 is such a term. It is an abstract, weightless and unidentified mass of numbers (mostly digitally encoded), with a potent influence on our lives. It is also considered a rich source of insight that is worth being tapped. But what are the origins of the word “data” – and what are its implications?